Distributed sensor networks
Sensors and received device
Wireless sensor networks are built from nodes called motes (mote) - small stand-alone devices powered by batteries and microchips with radio communication at a frequency - for example, 2.4 GHz. Special software allows mots to organize themselves into distributed networks, communicate with each other, poll and exchange data with the nearest nodes, the distance to which usually does not exceed 100 meters.
In English-language literature, such a network is called wireless sensor network(WSN) is a wireless network composed of geographically distributed autonomous devices using sensors to jointly monitor physical or environmental conditions in different areas.
They can measure parameters such as temperature, sound, vibration, pressure, movement of objects or air. The development of wireless sensor networks was originally motivated by military tasks, such as monitoring the battlefield. Wireless sensor networks are now increasingly used in many areas of civilian life, including industrial and environmental monitoring, healthcare, and object movement control. The field of application is becoming wider.
3-level network diagram. 1st level of sensors and gateway. 2nd level of the server. 3rd level thin client
Each node on the network: mot equipped with a radio transceiver or other wireless communication device, a small microcontroller and a power source, usually a battery. Use of solar panels or other alternative energy sources is possible
Data from distant elements is transmitted over the network between those nearest from node to node, via a radio channel. As a result, the data packet is transmitted from the nearest mobile to the gateway. The gateway is connected, as a rule, with a USB cable to the server. On the server - the collected data is processed, stored and can be accessed through the WEB shell to a wide number of users.
The cost of a sensor node varies from hundreds of dollars to a few cents, depending on the size of the sensor network and its complexity.
Gateway (2pcs), connected to a laptop with a USB cable. The laptop is connected to the Internet via UTP and acts as a server
Sensors with radio antenna
Wireless host hardware and host-to-host networking protocols are power optimized to ensure a long system life with stand-alone power supplies. Depending on the operating mode, the lifetime of the node can reach several years.
A number of standards are currently either ratified or under development for wireless sensor networks. ZigBee is a standard designed for the use of things like industrial control, embedded sensing, medical data collection, building automation. The development of Zigbee is facilitated by a large consortium of industrial companies.
WirelessHART, ISA100, ZigBee, and they are all based on the same standard: IEEE 802.15.4 - 2005.
Operating systems for wireless sensor networks are less complex than generic operating systems due to limited resources in sensor network hardware. Because of this, the operating system does not need to enable user interface support.
Wireless sensor network hardware does not differ from traditional embedded systems, and therefore an embedded operating system can be used for sensor networks
Measurement results visualization and report generation software MoteView v1.1
Data from wireless sensor networks is usually stored as digital data in a central base station. There are many standard programs, such as TosGUI MonSense, GNS, to make it easier to view these large amounts of data. In addition, the Open Consortium (OGC) specifies standards for encoding metadata interoperability and interoperability, which will allow anyone to monitor or control a wireless sensor network in real time via the Web Browser.
To work with the data coming from the nodes of the wireless sensor network, programs are used to facilitate viewing and evaluating the data. One of these programs is MoteView. This program allows you to view data in real time and analyze it, build all sorts of graphs, issue reports in various sections.
Each sensor is the size of a beer cap (but could be reduced by a factor of hundreds in the future) and contains a processor, memory, and radio transmitter. Such covers can be scattered on any territory, and they themselves will establish communication with each other, form a single wireless network and begin to transmit data to the nearest computer.
Connected in a wireless network, sensors can track environmental parameters: movement, light, temperature, pressure, humidity, etc. Monitoring can be carried out over a very large area, because sensors transmit information along a chain from neighbor to neighbor. The technology allows them to work for years (even decades) without changing batteries. Sensor networks are universal senses for a computer, and all physical objects in the world that are equipped with sensors can be recognized by a computer. In the future, each of the billions of sensors will receive an IP address, and they may even form something like a Global Sensor Network. So far, only the military and industry are interested in the capabilities of sensor networks. The market is booming this year, according to the latest report from ON World, which specializes in researching the sensor networking market. Another highlight this year was the launch of the world's first single-chip ZigBee system (manufactured by Ember). Among large US industrial companies surveyed by ON World, about 29% are already using sensor networks, and another 40% plan to deploy them within 18 months. More than a hundred commercial firms have sprung up in America to create and maintain sensor networks.
By the end of this year, the number of sensors on the planet will exceed 1 million. Now not only the number of networks is growing, but also their size. For the first time, several networks of more than 1000 nodes were created and successfully operated, including one for 25 thousand nodes.
Source: Web PLANET
The applications of WSN are many and varied. They are used in commercial and industrial systems for monitoring data that is difficult or expensive to monitor using wired sensors. WSNs can be used in hard-to-reach areas where they can remain for many years (environmental monitoring) without the need to replace power supplies. They can control the actions of violators of the protected object
WSN is also used for monitoring, tracking and control. Here are some applications:
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monitoring of temperature, air consumption, presence of people and control of equipment for maintaining the microclimate; |
| lighting control; | |
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power supply management; |
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collection of readings from apartment meters for gas, water, electricity, etc .; |
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security and fire alarm; |
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monitoring of the condition of the bearing structures of buildings and structures. |
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remote control and diagnostics of industrial equipment; |
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maintenance of equipment according to the current state (forecasting the safety margin); |
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monitoring of production processes; |
Wireless sensor networks: an overview
Akuldiz I.F.
Translated from English: Levzhinsky A.S.
annotation
The article describes the concepts of sensor networks, the implementation of which became possible as a result of the combination of microelectro-mechanical systems, wireless communication and digital electronics. The tasks and potential of sensor networks are studied, an overview of the facts influencing their development is made. The architecture of building sensor networks, the developed algorithms and protocols for each layer of the architecture are also considered. The article explores the issues of the implementation of sensor networks.
1. Introduction
Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications and digital electronics have made it possible to create low-cost, low-power, multifunctional motes (nodes) that are small and communicate directly with each other. Sensor networks based on the joint work of a large number of tiny nodes, which consist of data acquisition and processing modules, a transmitter. Such a network has significant advantages over a set of traditional sensors. There are two key features of traditional sensors: Sensors can be located far from the observed phenomenon. This approach requires many sensors that use some sophisticated techniques to isolate targets from the noise.
You can deploy multiple sensors that only collect data. Carefully design sensor positions and topology. They will transmit observations to central nodes, where data collection and processing will be performed.
The sensory network consists of a large number of nodes (motes) that are densely located close to the observed phenomenon. The position of the motes does not need to be calculated in advance. This allows them to be randomly placed in hard-to-reach areas or used for relief operations that require a quick response. On the other hand, this means that the network protocols and algorithms for the operation of the motes must be self-organizing. Another unique feature of sensor networks is the collaboration of individual nodes. Mots are equipped with a processor. Therefore, instead of transferring the original data, they can process it by performing simple calculations and transfer further only the necessary and partially processed data. The features described above provide a wide range of applications for sensor networks. Such networks can be used in health care, military needs and security. For example, the physiological data of a patient can be monitored remotely by a physician. This is convenient for both the patient and allows the doctor to understand his current condition. Sensor networks can be used to detect foreign chemical agents in air and water. They can help determine the type, concentration and location of contaminants. In essence, sensor networks allow for a better understanding of the environment. We envision that in the future, wireless sensor networks will be an integral part of our lives, more so than today's personal computers. These and other projects requiring the use of wireless sensor networks require special techniques. Many protocols and algorithms have been developed for traditional wireless peer-to-peer networks, so they are not well suited to the unique features and requirements of sensor networks. Here are the differences between sensor and peer-to-peer networks: The number of nodes in a sensor network can be several orders of magnitude higher than nodes in a peer-to-peer network.
The nodes are tightly spaced.
Nodes are prone to failure.
Sensor network topology can change frequently
Nodes mainly use broadcast messages, while most peer-to-peer networks are based on point-to-point communication.
Nodes are limited in power, processing power, and memory.
Nodes cannot have a global identification number (GID) due to the large amount of overhead and the large number of sensors.
Since the nodes in the network are densely located, neighboring nodes can be very close to each other. Consequently, multi-hop connections in sensor networks will consume less power than direct connections. In addition, the low power of the data signal can be used, which is useful in covert surveillance. Multi-hop communications can effectively overcome some of the difficulties in long-distance wireless communications. One of the most important limitations for nodes is low power consumption. Mots have limited energy sources. So, while traditional networks are aimed at achieving high signal quality, mot network protocols should focus mainly on energy conservation. They must have mechanisms that allow the user to extend the lifespan of the mote by either decreasing the bandwidth or increasing the latency of the data transfer. Many researchers are currently involved in the design of circuits that fulfill these requirements. In this article, we will provide an overview of the protocols and algorithms currently available for sensor networks. Our goal is to provide a better understanding of current research issues in this area. We will also try to explore design constraints and identify tools that can be used to solve design problems. The article is organized as follows: in the second section, we describe the potential and usefulness of sensor networks. In Section 3, we discuss the factors that influence the design of such a network. We will consider a detailed study of existing techniques in this area in Section 4. And summarize in Section 5.
2. Application of wireless sensor networks
Sensor networks can be comprised of various types of sensors, such as seismic, magnetic, thermal, infrared, acoustic, that are capable of a wide variety of environmental measurements. For example, such as:
temperature,
humidity,
car traffic,
lightning condition,
pressure,
soil composition,
noise level,
the presence or absence of some objects,
mechanical load
dynamic characteristics such as speed, direction and size of an object.
Motes can be used for continuous sensing, detection and identification of events. The micro sensing concept and wireless connectivity promise many new applications for such networks. We have categorized them according to the main areas: military applications, environmental research, health care, use in homes and other commercial areas. But you can expand this classification and add more categories, such as space exploration, chemical processing and disaster relief.
2.1. Military use
Wireless sensor networks can be an integral part of military command, communications, intelligence, surveillance and orientation systems (C4ISRT). Rapid deployment, self-organization and resiliency are characteristics of sensor networks that make them a promising tool for solving the assigned tasks. Since sensor networks can be based on a dense deployment of disposable and cheap nodes, the destruction of some of them during hostilities will not affect the military operation in the same way as the destruction of traditional sensors. Therefore, the use of sensor networks is better suited for battles. Let's list some more ways of using such networks: monitoring weapons and ammunition of friendly forces, observing the battle; orientation on the ground; assessment of damage from battles; detection of nuclear, biological and chemical attacks. Monitoring friendly forces, weapons and ammunition: Leaders and commanders can constantly monitor the state of their troops, the condition and availability of equipment and ammunition on the battlefield using sensor networks. Sensors can be attached to each vehicle, equipment and critical ammunition that report their status. This data is collected together at key nodes and sent to executives. Data can also be redirected to the upper levels of the command hierarchy for merging with data from other parts. Combat Observations: Critical sections, routes, routes and straits can be quickly covered with sensor networks to study the activities of enemy forces. During operations or after new plans are developed, sensor networks can be deployed at any time to monitor the battle. Reconnaissance of enemy forces and terrain: Sensor networks can be deployed in critical areas, and valuable, detailed and timely data on enemy forces and terrain can be collected within minutes before the enemy can intercept them. Targeting: Sensor networks can be used in smart ammunition targeting systems. Post-Combat Damage Assessment: Immediately before or after an attack, sensor networks can be deployed to a target area to collect damage assessment data. Detection of nuclear, biological and chemical attacks: When using chemical or biological weapons, the use of which is close to zero, it is important to have timely and accurate identification of chemical agents. Sensor networks can be used as systems for preventing chemical or biological attacks and the data collected in a short time will help to dramatically reduce the number of victims. You can also use sensor networks for detailed reconnaissance, after detecting such attacks. For example, it is possible to carry out reconnaissance in the event of radiation contamination without exposing people to radiation.
2.2. Environmental application
Some of the areas in ecology where sensor networks are used: tracking the movement of birds, small animals and insects; monitoring the state of the environment in order to identify its impact on crops and livestock; irrigation; large-scale earth monitoring and planetary exploration; chemical / biological detection; detection of forest fires; meteorological or geophysical research; flood detection; and pollution research. Wildfire Detection: Since motes can be strategically and densely deployed in the forest, they can relay the exact origin of the fire before the fire becomes uncontrollable. Millions of gauges can be deployed permanently. They can be equipped with solar panels, as the nodes can be left unattended for months or even years. The motes will work together to perform distributed sensing tasks and overcome obstacles such as trees and rocks that block wired sensors. Mapping the bio-state of the environment: requires complex approaches to integrating information on time and space scales. Advances in remote sensing technology and automated data collection have significantly reduced research costs. The advantage of these networks is that the nodes can be connected to the Internet, which allows remote users to control, monitor and observe the environment. While satellite and airborne sensors are useful in observing a wide variety of species, such as the spatial complexity of dominant plant species, they do not allow observation of the small elements that make up most of the ecosystem. As a result, there is a need to deploy wireless sensor network nodes in the field. One example of an application is the compilation of a biological map of the environment in a nature reserve in Southern California. Three areas are covered with a network, each of which has 25-100 nodes, which are used for constant monitoring of the state of the environment. Flood detection: An example of flood detection is the US warning system. Several types of sensors located in the alert system detect rainfall, water level and weather. Research projects such as the COUGAR Device Database Project at Cornell University and the DataSpace Project at Rutgers University are exploring different approaches to interacting with individual nodes on the network to generate snapshots and long-term data collection. Agriculture: The advantage of sensor networks is also the ability to monitor pesticide levels in water, soil erosion and air pollution levels in real time.
2.3. Application in medicine
One of the medical uses is devices for the disabled; patient monitoring; diagnostics; monitoring the use of medicines in hospitals; collection of human physiological data; and monitoring doctors and patients in hospitals. Human physiological monitoring: physiological data collected by sensor networks can be stored for a long period of time and can be used for medical research. Installed nodes can also track the movements of the elderly and, for example, prevent falls. These nodes are small and provide the patient with greater freedom of movement, at the same time, allow doctors to identify the symptoms of the disease in advance. In addition, they contribute to a more comfortable life for patients compared to hospital treatment. To test the possibility of such a system, the “Healthy smart House"". ... Monitoring doctors and patients in the hospital: each patient has a small and lightweight network node. Each node has its own specific task. For example, one can monitor the heart rate while the other takes a blood pressure reading. Doctors can also have a knot so that other doctors can find them in the hospital. Monitoring of medicines in hospitals: Nodes can be attached to medicines, then the chances of dispensing the wrong medicine can be minimized. So, patients will have nodes that determine their allergies and the necessary medications. Computerized systems as described in have shown that they can help minimize side effects from misdispensed drugs.
2.4. Home use
Home Automation: Smart Nodes can be integrated into household appliances such as vacuum cleaners, microwave ovens, refrigerators, and VCRs. They can interact with each other and with the external network via the Internet or satellite. This will allow end users to easily manage devices at home, both locally and remotely. Smart environment: Smart environment design can take two different approaches, i.e. human-centered or technology-centered. In the case of the first approach, the smart environment must adapt to the needs of end users in terms of interaction with them. For technology-centered systems, new hardware technologies, networking solutions, and middleware applications must be developed. Examples of how nodes can be used to create smart environments are described in. Nodes can be built into furniture and appliances, they can communicate with each other and the room server. The room server can also communicate with other room servers to learn about the services they have to offer, such as printing, scanning, and faxing. These servers and sensor nodes can be integrated into existing embedded devices and constitute self-organizing, self-regulating and adaptive systems based on the control theory model, as described in the paper.
3. Factors influencing the development of sensor network models.
The design of sensor networks depends on many factors, which include fault tolerance, scalability, production costs, type of operating environment, sensor network topology, hardware constraints, communication model, and energy consumption. These factors are considered by many researchers. However, none of these studies fully takes into account all the factors that influence the design of networks. They are important because they serve as a guideline for developing a protocol or algorithms for sensor networks. Moreover, these factors can be used to compare different models.
3.1. fault tolerance
Some components may fail due to lack of energy, physical damage or third-party intervention. The failure of the node should not affect the operation of the sensor network. This is a matter of reliability and resiliency. Fault tolerance - the ability to maintain the functionality of the sensor network without failures in the event of a node failure. Reliability Rk (t) or fault tolerance of a node is modeled using the Poisson distribution to determine the probability of no node failure in the time period (0; t) It should be noted that protocols and algorithms can be focused on the level of fault tolerance required for building sensor networks ... If the environment in which the nodes are located is less prone to tampering, then the protocols can be less tamper-resistant. For example, if nodes are embedded in a house to monitor humidity and temperature levels, the requirements for fault tolerance can be low, since such sensor networks cannot fail and the "noise" of the environment does not affect their operation. On the other hand, if nodes are used on the battlefield for surveillance, then the resiliency must be high, since surveillance is critical and the nodes can be destroyed during hostilities. As a result, the level of resiliency depends on the application of sensor networks and models must be designed with this in mind.
3.2. Scalability
The number of nodes deployed to study the phenomenon can be on the order of hundreds or thousands. Depending on the application, the number can reach extreme values (millions). New models should be able to handle this number of nodes. They must also use a high density of sensor networks, which can range from a few nodes to several hundred in an area that can be less than 10 m in diameter. Density can be calculated according to,
3.3. Production costs
Since sensor networks consist of a large number of nodes, the cost per node must be such as to justify the total cost of the network. If the cost of the network is higher than deploying traditional sensors, then it is not economically viable. As a result, the cost of each node should be low. Now the cost of a node using a Bluetooth transmitter is less than $ 10. The price for PicoNode is around $ 1. Consequently, the cost of a sensor network node must be much less than $ 1 for the economic justification of their use. The cost of a Bluetooth node, which is considered a cheap device, is 10 times higher than the average prices for sensor network nodes. Please note that the node also has some additional modules, such as a data acquisition module and a data processing module (described in section 3.4.) In addition, they can be equipped with a positioning system or a power generator depending on the application of the sensor networks. As a result, node cost is a tricky issue given the amount of functionality even at less than $ 1.
3.4. Hardware Features
The sensor network node consists of four main components, as shown in Fig. 1: data acquisition unit, processing unit, transmitter and power supply. The availability of additional modules depends on the network application, such as location modules, power generator and mobilizer (MAC). The data acquisition module usually consists of two parts: sensors and analog-to-digital converters (ADC). The analog signal generated by the sensor based on the observed phenomenon is converted into a digital signal using an ADC and then fed to the processing unit. The processing module, which uses the integrated memory, manages the procedures that allow, in conjunction with other nodes, to perform the assigned monitoring tasks. The transmitter unit (transceiver) connects the node to the network. One of the most important components of the assembly is the power supply. The power supply can be rechargeable, for example using solar panels.
Most nodes transmitting and collecting data need to know their location with high accuracy. Therefore, in general scheme location module enabled. Sometimes you may need a mobilizer, which, if necessary, moves the unit when it is necessary to complete the assigned tasks. All of these modules may need to be housed in a matchbox-sized enclosure. The knot size can be less than a cubic centimeter and light enough to stay in the air. Besides the size, there are some other hard restrictions on the nodes. They must :
consume very little energy,
work with a large number of nodes at short distances,
have a low production cost
be autonomous and work unattended,
adapt to the environment.
Since the nodes can become inaccessible, the life of the sensor network depends on the power of the individual nodes. Power supply is a limited resource and due to size limitations. For example, the total energy supply of a smart node is about 1 J. For Wireless Integrated Sensor Network (WINS), the average charge level should be less than 30 LA to ensure long operating time. It is possible to extend the life of sensor networks by using rechargeable batteries, for example, by drawing energy from the environment. Solar panels are a prime example of the use of recharging. The node data transmission module can be a passive or active optical device, as in a smart node or a radio frequency (RF) transmitter. RF transmission requires a modulation module that uses a certain bandwidth, a filtering module, demodulation, which makes them more complex and expensive. In addition, data transmission losses between two nodes are possible due to the fact that the antennas are located close to the ground. However, radio communication is preferred in most existing sensor network projects, as the data transmission frequencies are low (typically less than 1 Hz) and the transmission cycle rates are high due to the short distances. These characteristics allow the use of low radio frequencies. However, the design of energy efficient and low frequency radio transmitters is still a technically challenging task, and the existing technologies used in the production of Bluetooth devices are not efficient enough for sensor networks because they consume a lot of energy. Although today processors are constantly decreasing in size and increasing in power, processing and storing data is still a node. weak point... For example, a smart node processing module consists of a 4 MHz Atmel AVR8535 processor, a microcontroller with 8 KB for instructions, flash memory, 512 bytes of RAM, and 512 bytes of EEPROM. This module, which has 3500 bytes for the OS and 4500 bytes of free memory for the code, uses the TinyOS operating system. The processing module of another prototype lAMPS node has an SA-1110 processor with a frequency of 59-206 MHz. The IAMPS hosts use the multi-threaded L-OS operating system. Most data collection tasks require knowledge of the position of the node. Since nodes are generally randomly and unattended, they must cooperate using a positioning system. Location determination is used in many sensor routing protocols (see Section 4 for details). Some suggest that each node has a global positioning system (GPS) module that operates to within 5 meters. The paper argues that equipping all GPS nodes is not necessary for sensor networks to function. There is an alternative approach, where only some nodes use GPS and help other nodes determine their position on the ground.
3.5. Network topology
The fact that nodes can become unavailable and prone to frequent failures make network maintenance challenging. From hundreds to several thousand nodes can be located on the territory of the sensor network. They deploy ten meters apart. The density of the nodes can be higher than 20 nodes per cubic meter. The dense arrangement of many nodes requires careful network maintenance. We will cover issues related to maintenance and network topology changes in three stages:
3.5.1. Predeployment and the actual deployment of nodes can consist of mass scattering of nodes or installing each one individually. They can be deployed:
From an airplane scatter
by placing in a rocket or projectile
thrown by means of a catapult (for example, from a ship, etc.),
placement at the factory
each node is placed individually by a human or a robot.
While the sheer number of sensors and their automatic deployment usually precludes placement according to a carefully crafted plan, the schematics for initial deployment should:
reduce installation costs,
eliminate the need for any preliminary organization and preliminary planning,
increase the flexibility of placement,
promote self-organization and resiliency.
3.5.2. Post-network phase
After the network is deployed, a change in its topology is associated with a change in the characteristics of the nodes. Let's list them:
position,
accessibility (due to interference, noise, moving obstacles, etc.),
battery charge,
malfunctions
change of assigned tasks.
Nodes can be expanded statically. However, device failure is common due to battery drain or destruction. Sensor networks with high node mobility are possible. In addition, nodes and networks perform different tasks and may be subject to deliberate interference. Thus, the structure of the sensor network is prone to frequent changes after deployment.
3.5.3. Deployment Phase of Additional Nodes
Additional nodes can be added at any time to replace faulty nodes or in connection with changing tasks. The addition of new nodes creates the need to reorganize the network. Dealing with frequent changes in the topology of a peer-to-peer network, which contains many nodes and has very tight power consumption limits, requires special routing protocols. This issue is discussed in more detail in Section 4.
3.6. Environment
The nodes are densely packed very close to or directly within the observed phenomenon. Thus, they work unattended in remote geographic areas. They can work
at busy intersections,
inside big cars,
at the bottom of the ocean
inside a tornado,
on the ocean surface during a tornado,
in biologically and chemically contaminated areas
in the battlefield,
in a house or a large building,
in a large warehouse,
attached to animals
attached to fast moving vehicles
in a sewer or river along with a stream of water.
This list gives an idea of the conditions under which the nodes can operate. They can work under high pressure at the bottom of the ocean, in harsh environments, in debris or on a battlefield, in extreme temperatures, such as in the nozzle of an aircraft engine or in arctic regions, in very noisy places where there is a lot of interference.
3.7. Data transfer methods
In a multi-hop sensor network, nodes communicate wirelessly. Communication can be carried out via radio, infrared or optical media. In order to globally use these methods, the transmission medium must be available throughout the world. One of the radio communication options is the use of industrial, scientific and medical (ISM) bands, which are available without a license in most countries. Some of the types of frequencies that can be used are described in the International Frequency Table contained in Article S5 on the Radio Regulations (Volume 1). Some of these frequencies are already used in wireless telephony and wireless local area networks (WLAN). For sensor networks of small size and low cost, a signal amplifier is not required. According to the hardware limitations and the compromise between antenna efficiency and power consumption, certain limitations are imposed on the choice of the transmission frequency in the microwave range. They also offer 433 MHz ISM in Europe and 915 MHz ISM in North America. Possible transmitter models for these two zones are discussed in. The main advantages of using ISM radio frequencies are a wide range of frequencies and worldwide availability. They are not tied to a specific standard, thus giving more freedom to implement energy-saving strategies in sensor networks. On the other hand, there are different rules and restrictions, such as different laws and interference from existing applications. These frequency bands are also called uncontrolled frequencies. Most of the modern node equipment is based on the use of radio transmitters. The IAMPS wireless nodes, described in, use Bluetooth-compatible 2.4 GHz transmitters and have an integrated frequency synthesizer. The device of low-power nodes is described in the work, they use one radio transmission channel, which operates at a frequency of 916 MHz. The WINS architecture also uses radio communications. Another possible method of communication in sensor networks is the infrared port. Infrared communication is available without a license and is immune to interference electrical appliances... IR transmitters are cheaper and easier to manufacture. Many of today's laptops, PDAs, and mobile phones use infrared to transmit data. The main disadvantage of such communication is the requirement of line of sight between the sender and the receiver. This makes IR communication undesirable for use in sensor networks due to the transmission medium. An interesting transmission method is used by smart nodes, which are modules for automatic monitoring and data processing. They use optical media for transmission. There are two transmission schemes, passive using a corner-cube retroreflector (CCR) and active using a laser diode and controlled mirrors (discussed in). In the first case, an integrated light source is not required; a three-mirror configuration (CCR) is used to transmit the signal. The active method uses a laser diode and an active laser communication system to send light beams to the intended receiver. The unusual requirements for the application of sensor networks make the choice of transmission medium difficult. For example, marine applications require the use of an aquatic transmission medium. Here you need to use long-wavelength radiation that can penetrate the surface of the water. In difficult terrain or on the battlefield, errors and more interference can occur. In addition, it may turn out that the antennas of the nodes do not have the required height and radiation power for communication with other devices. Therefore, the choice of the transmission medium must be accompanied by reliable modulation and coding schemes, which depends on the characteristics of the transmission channel.
3.8. Power consumption
A wireless node, being a microelectronic device, can only be equipped with a limited power supply (
3.8.1. Connection
The node consumes maximum energy for communication, which involves both transmission and reception of data. We can say that for communication over short distances with low radiation power, transmission and reception require approximately the same amount of energy. Frequency synthesizers, voltage control oscillators, phase locking (PLL) and power amplifiers all require energy that is limited in resources. It is important that in this case we do not consider only the active power, we also consider the power consumption when starting the transmitters. The transmitter starts up in a fraction of a second, so it consumes negligible amounts of power. This value can be compared with the PLL lock time. However, as the transmitted packet decreases, the starting power begins to dominate the power consumption. As a result, it is ineffective to turn the transmitter on and off all the time. most of the energy will be spent on this. Currently, low power radio transmitters have standard Pt and Pr values of 20 dBm and Pout close to 0 dBm. Note that the PicoRadio aiming at Pc is -20 dBm. The design of small-sized, inexpensive transmitters is discussed in the source. Based on their results, the authors of this article, taking into account the budget and estimates of energy consumption, believe that the values of Pt and Pr should be at least an order of magnitude less than the values given above.
3.8.2. Data processing
The power consumption for data processing is significantly lower compared to data transmission. The example described in the work actually illustrates this discrepancy. Based on Rayleigh's theory that a quarter of the power is lost during transmission, it can be concluded that the power consumption for transmitting 1 KB over a distance of 100 m will be about the same as for executing 3 million instructions at a speed of 100 million instructions per second (MIPS ) / W by the processor. Consequently, local processing is critical to minimizing power consumption in a multi-hop sensor network. Therefore, nodes must have built-in computing capabilities and be able to interact with the environment. Cost and size constraints will lead us to choose semiconductors (CMOS) as the main technology for microprocessors. Unfortunately, they have limitations on energy efficiency. CMOS requires power every time it changes state. The energy required for a state change is proportional to the switching frequency, capacitance (area dependent) and voltage fluctuations. Therefore, reducing the supply voltage is an effective means of reducing active power consumption. The dynamic voltage scaling discussed in seeks to adapt the power supply and processor frequency to suit the workload. When the processing load on the microprocessor is reduced, simply reducing the frequency gives a linear decrease in power consumption, however, decreasing the operating voltage gives us a square-law decrease in power consumption. On the other hand, all possible processor performance will not be used. This will work when you take into account that peak performance is not always required and therefore the operating voltage and frequency of the processor can be dynamically adapted to the processing requirements. The authors propose workload prediction schemes based on adaptive processing of existing load profiles and on the analysis of several already created schemes. Other strategies for reducing processor power are discussed in. It should be noted that additional schemes may be used to encode and decode the data. Integrated circuits can also be used in some cases. In all these scenarios, the structure of the sensor network, algorithms of operation and protocols depend on the corresponding energy consumption.
4. Architecture of sensor networks
The nodes are usually located randomly throughout the observation area. Each of them can collect data and knows the route of data transmission back to the central node, to the end user. Data is transmitted using a multi-hop network architecture. The central site can communicate with the task manager via the Internet or satellite. The protocol stack used by the central node and all other nodes is shown in Fig. 3. The protocol stack includes information about power and route information, contains information about network protocols, helps to communicate efficiently through a wireless environment, and facilitates nodes to work together. The protocol stack consists of the application layer, transport layer, network layer, data link layer, physical layer, power management layer, mobility management layer, and task scheduling layer. Depending on the tasks of data collection, different types of applied software can be built at the application level. the transport layer helps to maintain the flow of data, if required. The network layer provides routing of data provided by the transport layer. Since the environment has extraneous noise and nodes can be moved, the MAC protocol should minimize the occurrence of collisions when transferring data between neighboring nodes. The physical layer is responsible for the ability to transfer information. These protocols help nodes complete tasks while conserving energy. The power management layer determines how the node should use energy. For example, a node can turn off the receiver after receiving a message from one of its neighbors. This will help you avoid getting a duplicate message. In addition, when a node has low battery power, it communicates to its neighbors that it cannot participate in message routing. It will use any remaining energy to collect data. The mobility management layer (MAC) determines and registers the movement of nodes, so there is always a route for transmitting data to the central node and the nodes can determine their neighbors. And knowing its neighbors, a node can balance power consumption by working with them. The task manager plans and schedules the collection of information for each region separately. Not all nodes in the same region are required to perform sounding tasks at the same time. As a result, some nodes perform more tasks than others, depending on their power. All these layers and modules are necessary in order for the nodes to work together and strive for maximum energy efficiency, optimize the data transmission route in the network, and also share each other's resources. Without them, each node will work individually. From the point of view of the entire sensor network, it is more efficient if the nodes work together with each other, which helps to extend the lifetime of the networks themselves. Before discussing the need to include modules and control layers in the protocol, we will consider three existing papers on the protocol stack, which is shown in Figure 3. The WINS model discussed in the source, in which the nodes are combined into a distributed network and have access to the Internet. Since a large number of WINS hosts are located close to each other, multi-hop communications keep power consumption to a minimum. The environmental information received by the node is sequentially routed to the central node or WINS gateway through other nodes, as shown in Figure 2 for nodes A, B, C, D, and E. The WINS gateway communicates with the user through common network protocols such as the Internet. The WINS network protocol stack consists of an application layer, a network layer, a MAC layer, and a physical layer. Smart nodes (or dust particles). These nodes can be attached to objects or even float in the air due to their small size and weight. They use MEMS technology for optical communication and data acquisition. The dust particles can have solar panels for recharging during the day. They require a line of sight to communicate with an optical transmitter, a base station or other speck of dust. Comparing the architecture of a network with dust particles with that shown in Figure 2, we can say that smart nodes, as a rule, communicate directly with a base station transmitter, but one-to-one communication is also possible. Another approach to the development of protocols and algorithms for sensor networks is driven by the requirements of the physical layer. Protocols and algorithms should be designed according to the choice of physical components such as the type of microprocessors and the type of receivers. This bottom-up approach is used in the IAMPS model and also considers the dependence of the application layer, network layer, MAC layer, and physical layer on the host hardware. The IAMPS nodes interact with the end user in exactly the same way as in the architecture shown in Figure 2. Different schemes, for example, time division multiplexing (TDMA) or frequency division multiplexing (FDMA) and binary modulation or M-modulation are compared at the source. A bottom-up approach means that the node's algorithms must know the hardware and use the capabilities of microprocessors and transmitters to minimize power consumption. This can lead to the development of various node designs. A various designs nodes will lead to different types of sensor networks. This, in turn, will lead to the development of various algorithms for their work.
Literature
The day is already near when hundreds of millions of semiconductor sensors will be integrated into everything that is possible, from a key fob to a baby stroller. And all of them will be able not only to act as smart sensors, but also to perform primary information processing, as well as interact with each other, forming a single wireless sensor network. At the same time, such sensors will practically not consume electricity, since the built-in miniature batteries will last for several years, that is, for the entire life of the sensors. It will be a conceptually new type of computer system operating using a wireless sensor network. This network is commonly called Ad-hoc Wireless Sensor Networks. The term Ad-hoc is borrowed from modern wireless networks, such as the IEEE 802.11b standard. These wireless networks have two communication modes: Infrastructure mode and Ad-hoc mode. In the Infrastructure mode, the nodes of the network do not interact with each other directly, but through the Access Point, which acts as a kind of hub in the wireless network (similar to how it happens in traditional cable networks). In Ad-hoc mode, also called Peer-to-Peer, stations communicate directly with each other. Accordingly, in wireless sensor networks, the Ad-hoc mode means that all sensors directly interact with each other, creating a kind of cellular network
Wireless sensor networks are a kind of step towards the transition to the next era - when computers will be directly connected to the physical world and will be able to guess the desires of users and also make decisions for them.
Let's dream a little about what such sensor networks will bring us in the future. Imagine cribs listening to babies breathing; bracelets that monitor the condition of patients in the clinic; smoke detectors, which can not only call firefighters if necessary, but also inform them in advance about the source of fire and the degree of complexity of the fire. Electronic devices will be able to recognize each other, power supplies will remind them that they need to "refresh".
Imagine hundreds of thousands of sensor sensors networked together in a forest. In such a forest it will simply be impossible to get lost, since the movement of a person will be recorded and analyzed by sensors. Another example is sensors in the field, tuned to monitor the condition of the soil and, depending on changing conditions, regulate irrigation and the amount of fertilizer applied.
Sensor networks on the roads will be just as useful. By communicating with each other, they will be able to regulate the flow of cars. This is the dream of any driver - roads without traffic jams! Such networks will be able to cope with this task much more efficiently than any agency. Control problem
offenses on the roads will be resolved by itself.
The use of sensor networks for power management will achieve incredible energy savings. Imagine such a control network in your apartment. By tracking your location, sensors will be able to turn off the light behind you and turn it on as needed. Well, if you use such networks to control the lighting of streets and roads, then the problem of lack of electricity will disappear by itself. In order for sensor networks to become a reality of tomorrow, research in this direction is already underway. And the leader in this area is Intel Corporation, which supports all the advanced computing technologies of the future. Particular attention is paid to the development of wireless multi-nodal sensor networks, capable of self-formation and automatic configuration as needed. The implementation of this technology will allow deploying a network of inexpensive, but at the same time very complex semiconductor sensor devices that will be able to independently establish communication with each other, reporting on certain changes in the environment. For example, the Mica sensor comes with 128 kilobytes of flash memory software, 256 kilobytes of flash memory for data storage, and a 900 MHz radio transmitter.
Some of these devices run an operating system
TinyOS, the code for this operating system is open source and consists of everything
8.5 Kb.
Such devices will find application in fundamentally new areas, for example, in the development of smart garments, connected blankets that will monitor the health of the newborn and report the most important indicators of his vital functions, smart farms in which semiconductor sensors installed in the soil will manage the irrigation
system and fertilization. Sensor networks research at Intel Corporation is
the famous Intel Berkeley Research laboratory located in California. Experimental sensor networks existing today only partially satisfy the above requirements. So, today networks consist of only hundreds of sensors with a limited coverage area and perform only well-defined tasks. They are capable of transmitting only a certain type of information from one sensor to another and only in a given bandwidth. Energy consumption is also not negligible.
- The battery only lasts for a few days. The existing sensor sensors are still quite inert, and there is no question of high reliability and invisibility in operation (at least because of the size). And, of course, such sensors are quite expensive, so a network of hundreds of sensors is not cheap. But we must remember that we are talking about experimental networks and the development of the technology of the future. At the same time, experimental sensor networks are already providing benefits. One such sensor network, created jointly by Intel Berkeley Research Laboratory, the Atlantic Institute and the University of California, operates on Great Duck Island in Maine.
The purpose of this network is to study the microenvironment of various biological organisms inhabiting the island.
Any human intervention (even for the purpose of learning) is sometimes unnecessary,
This is where sensor networks come to the rescue, allowing without direct human participation to collect all the necessary information.
The sensor network uses two boards as nodal elements. The first board contains a temperature sensor, humidity and barometric pressure sensors, and an infrared sensor. The second board contains a microprocessor (4 MHz), 1 KB RAM, flash memory for storing programs and data, a power supply (two AA batteries) and a radio transmitter /
a receiver operating at a frequency of 900 MHz. Sensors allow you to register all the necessary information and transfer it to the database of the host computer. All sensors are thoroughly tested beforehand - the board with sensors is immersed in water for two days and monitors its functionality. All sensor nodes form a single wireless network and are able to exchange information. In this case, the transfer of information from a remote network node to a gateway (Gateway Sensor) occurs along a chain, that is, from one network node to another, which allows you to create a large coverage area.
The information reaches the host computer through the gateway. The gateway uses a directional antenna, which makes it possible to increase the transmission distance up to 300 m. From the host computer, information is transmitted via satellite via the Internet to Research Center located in California.
The laboratory staff are no less actively working on precision biology and the creation of biochips. In addition to sensory perception of the world of solid things, the possibility of "feeling" liquid media and biological, developing objects is being investigated. Such research opens up tremendous prospects for medical and pharmaceutical development, the implementation of chemical processes and the manufacture of biological products. Since the main purpose of sensor networks is to perceive and transmit useful information, the specialists of the Intel laboratory in Berkeley are busy developing a method for combining sensors with objects that they are responsible for monitoring, and are also exploring the possibility of creating "actuators" - devices based on sensors that can influence situation, and not just register its state. Sensor networks are obviously useful for military applications, one of the possible variations of the networks was "combat" tested in Afghanistan, where the US military has deployed several hundred sensors in order to track the movements of enemy military equipment. However, on the introduction
It is too early to say real networks in our life, the network is vulnerable to fault tolerance. A Denial of Service (DoS) attack on a sensor network is any event that reduces or eliminates the network's ability to perform its intended function. The authors propose to base sensor network protocols on a layered architecture, which can damage the efficiency of the network, but increase its reliability. The types of DoS attacks that are typical for each layer and the acceptable methods of protection are discussed. Thus, even today, despite the imperfection and still quite a narrow range of use, sensor networks are being used in science, and later in life.
Materials from sites were used:
The invention relates to wireless sensor networks for automated monitoring systems. The technical result is to ensure efficient routing, extending the life of the network and increasing reliability. A method and system for distributed traffic balancing in a wireless sensor network based on a routing algorithm from a source node to a destination node is proposed, where the wireless sensor network is represented as a graph G (N, M), where N network nodes, and M faces, there are K routes, and information is generated at a rate of Q c and transmitted over the communication channel C at a rate of qc, and the i-th the node has an energy reserve E i, and each face ij has a weight / price e ij, which corresponds to the energy for transmitting one data packet from node i to j, and the lifetime T i of each node is defined as
At each node, a routing table is determined and a message transmission vector is set, route options are analyzed according to the most optimal total vectors, which are calculated from the routing table. For this, the lifetime of the entire network T sys = min i ∈ N T i (q c) is determined. Lifetime maximization is defined as maximize T sys, and to achieve the maximum lifetime of the entire network, routes are allocated where the route selection in the network is based on using the least costly transmissions at each node, and the most expensive ones are excluded. 2 n. and 9 p.p. f-ly, 4 dwg.
The technical field to which the invention relates
The invention relates to the field of wireless communication and can be used in automated monitoring systems operating both independently and as part of multi-level information and control systems, in particular in systems for monitoring environmental or industrial parameters in real time with nodes distributed over large areas and not having wired communication lines and power supply lines.
State of the art
Nowadays, sensor networks are increasingly taking their place in applications for monitoring various places and events. In connection with the development of wireless technology, it became possible to develop wireless distributed sensor networks (RCC). Distributed sensor networks differ from conventional networks in terms of limited energy resources, low computing power, the need for a denser arrangement and a low cost per node. These features from other networks (for example, cellular) determine new goals and objectives of their application. Wireless sensor networks are widely used in many areas of human activity, and therefore they are now receiving great attention.
The distributed sensor network consists of many cheap, self-contained, multifunctional nodes that are located in the monitoring area. Each node consists of a set of blocks, such as: a sensor used to receive data from the environment, a data receiving and transmitting unit, a microcontroller for processing and controlling signals, and a power source. The processor is powered by a self-contained battery with a finite energy resource, which leads to significant restrictions in power consumption. Sensor node maintenance, such as battery replacement, is costly, especially when the nodes are located in hard-to-reach places, so most sensor networks are maintenance-free and work until the battery runs out. This property of sensor networks is very important in the development of routing algorithms in the RCC, allowing to increase the efficiency of the network energy resource consumption.
So, there are many ways to save energy resources of nodes in the sensor network, and figure 1 shows their classification. The methods can be divided into three large groups - energy conservation through work cycles, based on the amount of information transmitted and on mobility.
Cycles include topology control and energy management. Topology control aims to use or reduce redundant links in the network in order to save resources. Consumption can be controlled using various energy-saving media access control (MAC) protocols and device operating modes. The second class of energy conservation methods is based on the amount of information transmitted, as well as on obtaining this information in economical ways. The energy spent on information processing is incomparably less than the energy required for its transmission, therefore, intra-network data processing, data compression or prediction is used. Repeaters are also used to save energy of sensor network nodes.
Routing methods can be divided into the following categories: direct, hierarchical, and based routing. geographic location.
Direct routing implies the transfer of messages from node to node in the network, where each node performs the same transmission and / or relay function, as opposed to hierarchical, where one or more nodes for collecting and processing information are allocated. The disadvantage of direct routing is that networks collecting information from some area will send a lot of redundant information, especially when the sensor network is dense. In order to avoid information redundancy, special algorithms are used to obtain information not from nodes, but from a specific area of the network. For example, the Sensor Protocols for Information via Negotiation (SPIN) algorithm is known, where the base station sends a request to a specific region of the sensor network. Upon receipt of the request, the domain nodes fulfill the request request, exchange data locally, and send back a generic response.
With hierarchical routing, collection and processing requires the use of nodes with a large reserve of energy, which, although it saves on the transmission of already processed data, a much smaller volume, is often unacceptable due to the homogeneity of the instruments used or other difficulties. In order not to use specialized nodes, there are several technologies. So, the Low-Energy Adaptive Clustering Hierarchy (LEACH) technology is known, when the collection function is taken in turn by several nodes of the sensor network, selected according to a certain algorithm, thereby distributing the load of the collection node.
Geo-based routing is also called geometrical routing because it uses the geometrical direction to the base station to find the route. There is also routing by virtual coordinates, which are built not only depending on the real position of the node, but also take into account the natural unevenness of the surface, obstacles, the level of the transmission channel, etc.
Also known is multithreaded routing, where the delivery of a message from one node is possible along several paths. Recently, much attention has been paid to on-demand routing at the base station, for example, based on finding the shortest path and maintaining it, taking into account a bad channel and failure of nodes. However, the nodes located at the shortest distance are quickly depleted, which leads to disconnections and a decrease in the network lifetime, which is often understood as the lifetime of the first node that fails. Therefore, there is a need to create a technology for maximizing the lifetime of the sensor network, which is solved by one or another method of linear programming.
Thus, patent RU 2439812 C1, published 2012-01-10, IPC H04W 36/00, is known as a technical solution close to its essence, where a self-configuring sensor network of a plurality of sensors and actuators based on routing depending on geographic location is disclosed. The sensor network consists of a central data processing unit (DPC) and N base stations (BS), located uniformly or chaotically along the boundaries of the network coverage area, where BS are spatially referenced to global positioning coordinates and contain memory for storing the value of the confidence coefficient, which is a number in the range of the specified minimum and maximum values. The BS confidence factor is set approximately equal to the maximum value. Inside the coverage area of the sensor network, M nodes are evenly or chaotically located, with M >> N. The nodes are equipped with memory for storing the coordinate values of the spatial reference, which is initialized random values in the production process, and to store the confidence factor, which is initialized to a value approximately equal to the minimum value of the confidence factor. Each node and BS establish a connection with no more than K neighboring nodes and BS, and the value of K depends on the characteristics of the communication channel throughput, performance characteristics and power consumption of the microprocessors included in their composition. After the connection is established, the nodes and the BS perform the operation of mutual determination of the values of spatial coordinates. For this, each node or BS cyclically transmits the values of its own memory for storing the values of the coordinates of the spatial reference and the memory for storing the value of the confidence coefficient. In each processing cycle, the node receives the values of coordinates and confidence factors from all neighboring devices with which a connection is established, and determines the calculated values of its own coordinates and its own confidence factor by the method of weighted averaging of the values of the eigen coordinates and coordinates of neighboring devices, using the confidence factors as weighting factors the device itself and neighboring devices. Thus, the nodes of the sensor network receive a spatial reference. To route a message from a data center to a node with coordinates (x, y, z), it transmits a message to one or more BSs closest to the required coordinates. These BSs transmit the message to the nearest nodes, and the nodes sequentially - to their nearest nodes in the direction of the vector directed to the required point (x, y, z). Nodes spatially linked to points located at a distance not exceeding the sensitivity radius of the sensor network r perceive the message as addressed to them. Further arbitration of nodes to select the final addressee of the message, as well as sending an acknowledgment of receipt of the message, is carried out as needed, based on the technical requirements for the operation of the network. To route a message from a node to a data center, the nodes are additionally equipped with memory for storing a list of coordinates of the nearest BS. To transmit a message to the data center, the node sends a message to one or more neighboring nodes in the direction of the vector directed to the point with the coordinates of the BS, when the message reaches the BS, it transmits the message directly to the data center and, if necessary, sends a confirmation message to the sending node.
The disadvantage of such a self-configuring sensor network and the method of its functioning is the complexity of the equipment used, associated with the need to set and use the coordinates of the spatial reference of nodes and base stations, and this solution does not provide a long lifetime of the entire network as a whole.
As the closest analogue - a prototype, one can propose a routing method with a maximum lifetime in a wireless Ad-hoc network, disclosed in the publication by Arvind Sankar and Zhen Liu, Maximum Lifetime Routing in Wireless Ad-hoc Networks, INFOCOM 2004, Twenty-third Annual Joint Conference of the IEEE, Computer and Communications Societies, vol. 2, pp1089-1097, where the problem of maximizing the lifetime of the sensor network is formulated, which is solved by the linear programming method, namely, an algorithm is proposed to minimize the sum of the potential functions of all queues.
The disadvantage of this method is low efficiency, since nodes located at the shortest distance are often quickly depleted, which leads to disconnections and a decrease in the network life.
Thus, there is a need to solve the above problems of the prior art.
The essence of the invention
The technical result to which the proposed invention is directed is, in particular: ensuring effective routing and extending the lifetime of a wireless sensor network for monitoring various objects and parameters in real time, where the information of each node is important, increasing functionality, reliability and reducing the cost of use systems for monitoring. The use of the proposed solution will improve the operation efficiency of the controlled object due to the longer service life of the autonomous power supply battery, which will allow registering and transmitting data on the parameters of the object and / or the environment for a longer time.
The essence of the proposed method for distributed traffic balancing in a wireless sensor network is to apply a new routing algorithm from a source node to a destination node. Communication between these nodes in the sensor network is carried out, for example, over the Zigbee protocol, or in the unlicensed radio frequency band, or over a mobile digital radio network, or over any other suitable wireless communication protocol. A distributed sensor network can be represented as a graph G (N, M), which defines a set of the mentioned nodes and connections between them, where N are network nodes, and M are faces, there are also K routes. Information is generated at a rate of Q c and is transmitted over the communication channel C at a rate of qc, and the i-th node has an energy reserve E i, and each face ij has a weight / price e ij, which corresponds to the energy for transmitting one data packet from node i to j, while the lifetime T i of each node is defined as
Next, a routing table is determined at each node and a message transmission vector is established, an analysis of possible route options is carried out according to the most optimal total vectors, which are calculated from the routing table, for this, the lifetime of the entire network T sys
Thus, maximizing the lifetime is defined as maximize T sys, and in order to achieve the maximum lifetime of the entire network, routes are allocated for the transmitted information, while the choice of a traffic route in the network is based on using the least costly transmissions at each node, and when building a route, the most expensive nodes based on its calculated T i.
At least one source node contains a self-powered sensor for measuring and monitoring physical parameters (quantities), which monitors in a given area of the network and sends messages (data packets) with measured parameters to at least one destination node.
Alternatively, in each node, to bring the monitoring data to a uniform form, they can perform primary processing of the physical parameters obtained from the sensors, for example, by accumulating them in memory, averaging, analog-to-digital conversion into the appropriate code. As measured parameters for monitoring, for example, the environment, various parameters are used, such as temperature, pressure, humidity, illumination, smoke, vibration level, etc.
Alternatively, the choice of a route when forming and / or updating the routing table is made in accordance with combinations of criteria such as the length of the route, measured by the number of routers through which it is necessary to go to the destination node; communication channel bandwidth; predicted total transmission time; the cost of the communication channel; the amount of residual energy at the node.
Alternatively, the method additionally updates the values of the lifetime T i of each node or the lifetime of the entire system T sys in accordance with the aforementioned combination of criteria, carried out when a message is sent from a source node to a destination node or when a disconnection is detected between nodes.
Alternatively, after building the routing table, the function of transmitting packets along the optimal paths (route) is implemented when sending a packet, each network node puts the address of the next node in the packet header at the level of access control to the transmission medium (MAC-level).
Also proposed is a distributed traffic balancing system in a distributed sensor network based on a routing algorithm from a source node to a destination node in a distributed sensor network according to the proposed method, comprising: a destination node connected by a wireless communication channel to a source node, which is a sensor module where the transceiver is located , a sensor of physical parameters, a microcontroller for processing and control and an autonomous source of their power supply, and the destination node contains a transceiver, means for storing received information and means for processing and displaying information received from sensor modules to build a model of the object or space under study.
Alternatively, the sensor modules can be divided into groups, and each group is wirelessly connected to the destination node through its own transceiver. Monitoring of environmental or industrial parameters in real time is carried out pointwise in a given area, where the first subset of the aforementioned set of source nodes performs monitoring functions, and the second subset of source nodes performs only the functions of transceiving data packets with measured physical parameters received from the first subset of source nodes.
These and other structural and functional features and advantages of the proposed invention will become apparent from the detailed description of its variants, which should be read in conjunction with the drawing.
Brief Description of Drawings
Figure 1 shows a known classification of methods for storing the energy of nodes in a sensor network.
Figure 2 shows an algorithm for constructing a sensor network based on polling.
Figure 3 shows a sensor network in the form of a graph G (N, M).
4 shows options for determining routes.
Detailed description of the invention
An algorithm is proposed on which the technology of automated data collection and transmission through the proposed RCC (network of autonomous wireless self-organizing mobile devices) is based on a single point for building a model of the investigated object or space. This model can mainly be used to build networks for monitoring environmental or industrial parameters in real time, monitoring the state in the life cycle of buildings and structures, in the design and construction of recreational areas and sanitary-resort construction facilities, as well as in various other areas of the automotive industry, for railway transport, road construction, medicine.
The proposed invention can significantly increase the functionality, reliability and reduce the cost of using such monitoring systems. Reducing the cost is inextricably linked with the constructive, functional and software unification of the parts from which the system is built, which implies a thorough analysis of the requirements and research on ways to build a universal software and hardware platform for creating environmental monitoring systems based on wireless sensor networks technology. For this, various parameters are investigated: temperature, pressure, humidity, illumination, smoke, vibration, which are collected by means of self-organizing sensor networks. RCC consists of end devices, intermediate routers, a network coordinator and a dedicated data collection point, sometimes such a point is called a network gateway, it serves to convert data from a radio channel to a network organized on optical or copper wires - Ethernet. Sensors for collecting physical parameters are attached to network nodes - end devices, which, through the network coordinator, are built into a single structure, for example, using the ZigBee protocol. This allows you to deploy a network for monitoring in a short period of time with minimal cost and sufficiently high reliability.
Each PCC node is equipped with an autonomous power source, which allows them to be installed in hard-to-reach places to take the required readings with minimal labor costs. A feature of the proposed invention is the creation of a unique scalable software and hardware, consisting of a set of modules required for implementation, which allows you to control devices for the maximum possible operating time, and at the same time form a reliable model of a spatial heterogeneous environment in an automatic mode. Communication between devices takes place over a radio channel in various communication standards, including the Zigbee protocol, in an unlicensed frequency range or over a mobile digital radio network. The data collected for processing makes it possible to use such a system to build an ecological 3D model of the investigated environment / space or the investigated object, significantly reducing the amount of time required for processing and obtaining information and financial resources. The essence of the proposed algorithm, called two ladder-logic, is to control the elements of the RCC, which allows balancing the load on the network nodes in such a way that the transmitted data is sent to the nearest network node not randomly, but to the one that has the greatest energy supply at the current time ... The used algorithm of the RCC functioning allows changing the load on the network nodes in such a way that the entire network remains operational for the longest possible time.
The use of RCC can provide significant advantages, both in technological and economic aspects, over traditional data collection and processing systems. The fundamental increase in the productivity of collection and processing of digital telemetry, achieved through the use of RCC, allows aggressive introduction into the market and the transition to technological solutions of a new generation, thereby becoming possible and easy to implement the emergence of new automated systems operating in real time based on cloud technologies. As technology advances, there should be a transition from interconnected local monitoring networks to large-scale monitoring, surveillance and prediction systems based on wireless RSS.
Figure 2 shows an example of polling-based routing and construction of a sensor network. RCC consists of many cheap, self-contained, multifunctional nodes that are located in the monitoring area. Each node consists of a set of blocks such as a sensor used to receive data from the environment, a data transmission and reception block, a microcontroller for signal processing and control, and a small-sized power source. The processor is powered by a self-contained battery with a finite energy resource, which leads to significant restrictions in power consumption. Maintenance of sensor nodes, such as battery replacement, is costly, especially when the nodes are located in hard-to-reach places, so that most sensor networks are maintenance-free and operate until the battery is depleted.
The routing algorithm allows you to build a route based on requests and responses. Network coordinator 1 sends a broadcast HELLO request and receives responses from router (router) 2. Each router also sends a broadcast request and receives responses from neighboring devices, these can be other routers or end devices 3. Based on the received responses (signal strength, response time and other parameters), the coordinator builds a routing table on each router. Further, the choice of the route is carried out in the standard algorithm by determining the weight graph with the minimum total value.
As a rule, sensor nodes are equipped with devices of the same type with a certain set of functions. After installation, during operation, the sensor nodes must organize themselves into a communication network, where each node uses only those functions that are necessary to solve the task at hand. Routing also occurs automatically. In addition to primary routing, regular network rebuilding is also required, because devices can lose the communication channel or fail for reasons related to external or internal factors.
The work of each sensor node is aimed at measuring various environmental parameters, such as temperature, pressure, illumination, humidity, smoke, vibration levels, etc. Such a variety of parameters entails various applications, such as data collection and environmental monitoring, monitoring of various production facilities, located both in a separate building and over a large area, oil and gas facilities, transport facilities, military applications, etc. Sensor networks perform various tasks, which can be roughly divided into two categories. The first category of tasks is related to the detection of events that occur very rarely, but require immediate notification and / or location detection. The second category (monitoring) includes the tasks of continuous measurement of any quantity over a long period of time. Here, the delay time can be equal to the characteristic time of change of the measured parameter. Monitoring can be carried out pointwise over any area, with point measurement, the main part of the nodes plays the role of transmitters, and only a small part of the nodes directly monitors.
A routing algorithm with balancing traffic in a distributed sensor network is proposed. For this, a distributed sensor network can be represented as a graph G (N, M) with N nodes and M edges, which represents a set of existing nodes and possible connections between them, as shown in Fig. 3. Each i-th node initially has an energy supply E i. Each face ij has a weight / price e ij, which corresponds to the energy for transmitting one data packet from node i to j. It is considered that there are K routes, and information is generated at a rate of Q c and transmitted over the communication channel C at a rate of q c.
The lifetime T i of each node will be equal in such a system
According to the algorithm used, the routing table is determined by the coordinator at each node. The vector of message transmission is being built. Further, the analysis of possible route options is carried out according to the most optimal total vectors, which are calculated using the routing table. Thus, the goal is to save the total energy spent in the entire network for the transmission of one packet. This is effective for data networks, where the lifetime of the network is determined by the time during which the network is able to transmit messages.
In networks where each node performs two functions simultaneously: measurement of some value and transmission of messages, that is, the sensor network performs the function of monitoring physical quantities in a given area, the value of each node is important for completeness of the picture.
Then the lifetime of the entire system T sys is defined as:
The problem of maximizing the lifetime will look like: maximize T sys, and in order to achieve the maximum lifetime of the entire system, it is necessary to distribute routes for the transmitted information. The essence of the proposed routing method with balancing traffic in the RCC is that the choice of the traffic route in the network is based on the use of the least costly transmissions at each node, which can be involved in data transmission. In other words, the most costly hops-hops (transit section or transition in the network between two network nodes through which traffic is transmitted) are excluded from the possible options for the route of the data packet movement, thereby saving energy at each node and reducing the probability of node failure, which eliminates the collapse of the entire measurement network due to the fact that one node has already stopped performing actual measurements.
The choice of the route option (shown in Fig. 4) during the formation and updating of the routing table is made in accordance with combinations of such criteria as: the length of the route, measured by the number of routers through which it is necessary to go to the destination; communication channel bandwidth; the predicted total transfer time; the cost of the communication channel; the amount of residual energy at the node.
After building the routing table, the algorithm implements the function of transmitting packets along optimal paths by the fact that when sending a packet through a router, each node of the local network puts the address of the next recipient in the packet header at the MAC level. Thus, in the example shown in Fig. 3, based on the minimum total costs (weight / price) at the nodes (Fig. 4), route 1 will be chosen, with the sum of the weight / price costs - 9 as the most minimal value. Thus, the passage of traffic through the nodes of route 1 will lead in the shortest possible time to the complete energy depletion of node 4, which will disable these nodes and exclude the possibility of collecting parameters at the necessary points of the study.
However, when using the proposed distributed traffic balancing algorithm based on weighting factors, route 2 will be chosen, which will allow the sensor network to exist an order of magnitude longer. This is possible due to the fact that the load on all nodes, in the case of the proposed algorithm, is distributed more systematically across all network nodes.
The proposed invention can be implemented using various functional and / or hardware, software, special-purpose processors, and / or combinations thereof. Preferably, the invention is implemented as a combination of hardware and software. The software is preferably implemented as an application program tangibly implemented on a program storage / reading device. An application program can be downloaded or executed by a computer containing any architecture and is implemented on a computing platform having hardware: one or more central processors, random access memory, and input / output interfaces. The above different options embodiments of the invention are presented for understanding and by way of example only and should not be limited to these examples.
1. A method of distributed traffic balancing based on the routing algorithm from a source node to a destination node in a distributed sensor network,
in this case, the distributed sensor network is represented as a graph G (N, M), which characterizes the set of the mentioned nodes and the connections between them, where N are network nodes, and M faces, there are K routes, and information is generated at a rate of Q c and transmitted over the communication channel С with a speed qc, and the i-th node has an energy reserve E i, and each face ij has a weight / price e ij, which corresponds to the energy for the transmission of one data packet from node i to j,
in this case, the lifetime T i of each node is defined as 
a routing table is determined at each node and a message transmission vector is set,
analysis of possible route options is carried out according to the most optimal total vectors, which are calculated from the routing table, for this, the lifetime of the entire network T sys 
in this case, maximizing the lifetime is defined as maximize T sys, and to achieve the maximum lifetime of the entire network, routes are allocated for the transmitted information, while the choice of a traffic route in the network is based on using the least costly transmissions at each node, and the most expensive ones are excluded when constructing a route.
2. The method according to claim 1, characterized in that at least one source node contains a self-powered sensor that measures and monitors physical parameters in a given area and transfers data packets with measured physical parameters to at least , one destination node.
3. The method according to claim 2, characterized in that sensors are used to measure physical parameters for monitoring the environment based on monitoring the following parameters: temperature, pressure, humidity, illumination, smoke, vibration level.
4. The method according to claim 3, characterized in that at least one source node performs primary processing of physical parameters obtained from said sensors, for example, accumulation, averaging, analog-to-digital conversion.
5. The method according to claim 1, characterized in that communication between nodes in the sensor network is performed using the Zigbee protocol, or in an unlicensed radio frequency range, or over a mobile digital radio network, or any other wireless communication protocol.
6. The method according to claim 1, characterized in that the communication channel between the source node and the destination node contains a router that interacts with these nodes.
7. The method according to claim 1, characterized in that the choice of the route when forming and / or updating the routing table is made in accordance with combinations of such criteria as the length of the route, measured by the number of routers through which it is necessary to pass to the destination node, the throughput of the communication channel , the predicted total transmission time, the amount of residual energy at the node, the cost of the communication channel.
8. The method according to claim 1, characterized in that after building the routing table, the function of transmitting packets along optimal routes is implemented when sending a packet, where each network node places the address of the next node in the packet header at the level of access control to the transmission medium (MAC-level) ...
9. The method according to any one of claims 1, 6, 7, characterized in that the method further includes the step of updating the values of the lifetime T i of each node or the lifetime of the entire system T sys in accordance with the above combination of criteria, carried out when sending a message from a source node to a destination node, or when a broken connection is detected between nodes.
10. A distributed traffic balancing system in a wireless sensor network for monitoring physical parameters according to the method according to any one of claims 1-9, comprising a plurality of source nodes connected to each other, and a destination node connected to at least one source node, which is a sensor module where a transceiver, a sensor of physical parameters, a microcontroller for processing and control and an autonomous power source are located, the sensor modules are divided into groups and each group is connected to the destination node through its own transceiver, while the destination node contains a transceiver, means of accumulating the received information and means for processing and displaying the information received from the sensor modules for building a model of the investigated object or space.
11. The system according to claim 10, characterized in that monitoring is carried out pointwise in a given area, where at least one subset of said plurality of source nodes performs monitoring functions by means of its physical parameters sensors, and another subset of source nodes performs through its transceivers only functions of receiving and transmitting data packets with measured physical parameters received from the mentioned subset of source nodes.
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Due to the lack of clear standardization in sensor networks, there are several different platforms. All platforms meet the basic basic requirements for sensor networks: low power consumption, long operating time, low-power transceivers and sensors. The main platforms include MicaZ, TelosB, Intel Mote 2.
MicaZ
The most common 3 years ago was to use the CC2420 chipset as a low power transceiver.
There are no software standards for sensor networks. There are several hundred different data processing and transmission protocols, as well as node control systems. The most common operating system is an open source system - TinyOs (while at Stanford University, I personally met one of the developers). Many developers (especially for commercial systems) write their own control system, often in the Java language.
The control program for the touch node under the control of the TinyOs operating system is written in the nesC language.
It should be noted that due to the high cost of equipment and the complexity of setting up sensor networks, various modeling systems have become widespread, in particular the TOSSIM system, specially designed to simulate the operation of nodes under the control of TinyOs.
I have been working with sensor networks for 6 years and I can tell a lot about these technologies. If the Habrasocommunity is interested and I have the opportunity, then I will gladly write a series of articles on this topic. I can touch on such things as: real work with the TmoteSky platform, peculiarities of programming for the TinyOs system in the nesC language, original research results obtained in our laboratory, impressions of 1.5 months of work at Stanford University, in a project on sensor networks.
Thank you all for your attention, I will be happy to answer your questions.
