The wireless sensor network ( WSN ) refers to a group of spatially dispersed and special sensors to monitor and record the physical state of the environment and manage the data collected in a central location. WSN measures environmental conditions such as temperature, noise, pollution level, humidity, wind, and so on.
This is similar to wireless ad hoc networks in the sense that they depend on wireless connectivity and spontaneous network formation so that sensor data can be transported wirelessly. WSN is a spatially distributed autonomous sensor for monitoring physical or environmental conditions, such as temperature, sound, pressure, etc. And to cooperatively provide their data through the network to the main location. The more modern network is bi-directional, it also allows control the sensor activity. The development of wireless sensor networks is motivated by military applications such as battlefield surveillance; Currently such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
WSN is built from "nodes" - from a few to several hundred or even thousands, where each node is connected to one (or sometimes multiple) sensors. Each such sensor network node usually has several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit to interact with sensors and energy sources, usually batteries or embedded forms of energy harvesters.. A sensor node may vary in size from shoebox to grain size dust, although functioning "motes" of the original microscopic dimension has not been made. The cost of sensor nodes also varies, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. The size and cost limitations on the sensor nodes produce appropriate constraints on resources such as energy, memory, computing speed and communication bandwidth. The WSN topology can vary from a simple star network to a sophisticated multi-hop wireless mesh network. The propagation technique between network hops can be routing or flooding.
In computer science and telecommunications, wireless sensor networks are active research areas with workshops and conferences organized annually, for example IPSN, SenSys, and EWSN.
Video Wireless sensor network
Apps
Area monitoring
Area monitoring is a general application of WSN. In area monitoring, WSN is placed in areas where some phenomena must be monitored. A military example is the use of sensors to detect enemy intrusions; Civilian example is fencing geo-fence gas or oil pipeline.
Healthcare monitoring
Sensor networks for medical applications can be of several types: planted, worn, and embedded in the environment. Implant medical devices are tools that are inserted into the human body. The wearable device is used on the surface of the human body or just near the user. The embedded environment system uses the sensors contained in the environment. Possible applications include measurement of body position, location of the person, monitoring of all sick patients in the hospital and at home. Embedded devices in the environment trace the physical state of a person for a sustained health diagnosis, using as input data from depth camera networks, sensing floors, or other similar devices. Body area tissues can gather information about one's health, fitness, and energy expenditure. In health care applications, the privacy and authenticity of user data is essential. Mainly because the integration of sensor networks, with IoT, user authentication becomes more challenging; however, the solution is presented in the latest work.
Environmental/Earth Sensing
There are many applications in monitoring environmental parameters, the examples given below. They share additional challenges from harsh environments and reduce power supplies.
Monitoring air pollution
Wireless sensor networks have been deployed in several cities (Stockholm, London and Brisbane) to monitor the concentration of harmful gases for residents. This can take advantage of ad hoc wireless links rather than wiring, which also makes them more mobile for testing readings in different areas.
Forest fire detection
The Node Sensor Network can be installed in the forest to detect when a fire has started. Knots can be equipped with sensors to measure temperature, humidity and gas produced by fire in trees or vegetation. Early detection is essential for successful action of firefighters; thanks to Wireless Sensor Networks, the fire department will be able to know when the fire started and how it spread.
Avalanche detection
The landslide detection system utilizes wireless sensor networks to detect slight ground movements and changes in various parameters that may occur before or during landslides. Through the data collected it is possible to know the landslide events that will occur long before it actually happened.
Water quality monitoring
Water quality monitoring involves the analysis of water properties in dams, rivers, lakes and oceans, as well as underground water reserves. The use of multiple wireless distributed sensors enables the creation of more accurate water status maps, and allows for permanent placement of monitoring stations in difficult access locations, without the need for manual data retrieval.
Natural disaster prevention
Wireless sensor networks can act effectively to prevent the consequences of natural disasters, such as floods. Wireless nodes have been successfully placed in streams where changes in water levels should be monitored in real time.
Industrial monitoring
Machine health monitoring
Wireless sensor networks have been developed for machine-based maintenance (CBM) because they offer significant cost savings and enable new functionality.
Wireless sensors can be placed in locations that are difficult or impossible to reach with cable systems, such as spinning engines and unplugged vehicles.
Data center monitoring
Due to the high server rack density in the data center, wiring and IP addresses are often a problem. To solve that problem more and more shelves are equipped with wireless temperature sensors to monitor intake and take shelf temperature. Because ASHRAE recommends up to 6 temperature sensors per rack, the integrated wireless temperature technology provides benefits compared to traditional cable sensors.
Data recording
Wireless sensor networks are also used for data collection for monitoring of environmental information, this can be as simple as monitoring the temperature in the refrigerator to the water level in the overflow tank at a nuclear power plant. The statistical information can then be used to show how the system has worked. The advantage of WSN over conventional loggers is a possible "direct" data feed.
Water/wastewater monitoring
Water quality and water quality monitoring includes many activities such as checking the quality of underground water or surface water and ensuring the water infrastructure of a country for the benefit of humans and animals. This can be used to protect the wastage of water.
Structural health monitoring
Wireless sensor networks can be used to monitor the condition of civil infrastructure and related geo-physical processes close to real time, and over long periods through data recording, using precisely connected sensors.
Wine production
Wireless sensor networks are used to monitor wine production, both on the ground and in the cellar.
Maps Wireless sensor network
Characteristics
The main characteristics of WSN include
- The limit of energy consumption for nodes uses batteries or energy harvesting. Examples of suppliers are ReVibe Energy and Hunger
- Ability to resolve node failure (resistance)
- Some mobility nodes (for very mobile nodes see MWSN)
- Heterogeneity of vertices
- Homogeneity of nodes
- Scalability for large-scale implementation
- Ability to withstand harsh environmental conditions
- Ease of use
- Cross-layer design
Cross-layer becomes an important learning area for wireless communication. In addition, the traditional layered approach presents three major issues:
- Traditional layered approaches can not share different information between different layers, which causes each layer to have incomplete information. The traditional layered approach can not guarantee the optimization of the entire network.
- Traditional layered approaches lack the ability to adapt to environmental changes.
- Due to interference between different users, access conflicts, fades, and environmental changes in wireless sensor networks, the traditional layered approach to wired networks does not apply to wireless networks.
So cross-layers can be used to make optimal modulation to improve transmission performance, such as data rate, energy efficiency, QoS (Quality of Service), etc. Sensor nodes can be imagined as small computers that are very basic in terms of their interface and components. They usually consist of processing units with limited computing power and limited memory, sensors or MEMS (including certain conditioning circuit), communication devices (usually transceivers radio or alternative optics), and resources are usually in the form of batteries. Other possible inclusions are energy harvesting modules, secondary ASICs, and possibly secondary communication interfaces (eg RS-232 or USB).
BTS is one or more components of WSN with much more computing, energy and communication resources. They act as gateways between sensor nodes and end users as they usually pass data from WSN to the server. Another special component in network-based routing is routers, designed to calculate, calculate, and distribute routing tables.
Platform
Hardware
One of the major challenges in WSN is to generate low cost and low sensor nodes. There is an increasing number of small companies producing WSN hardware and commercial situations comparable to home computing in the 1970s. Many nodes are still in the research and development stage, especially their software. Also attached to the adoption of sensor networks is the use of very low power methods for radio communications and data acquisition.
In many applications, WSN communicates with a Local Area Network or Wide Area Network through a gateway. Gateway serves as a bridge between WSN and other networks. This allows data to be stored and processed by devices with more resources, for example, on remote servers. A wide area wireless network used primarily for low-power devices is known as Low Power Area Network (LPWAN).
Wireless
There are several wireless standards and solutions for sensor connectivity. Thread and ZigBee can connect sensors that operate at 2.4 GHz with data rates of 250kbit/s. Z-wave operates at 915 MHz and has a greater radio range but with lower data rates. The IEEE 802.15.4 working group provides a standard for low-power device connectivity and generally smart sensors and gauges using one of these connectivity standards. With the advent of the Internet of Things, many other proposals have been made to provide sensor connectivity. LORA is a form of LPWAN that provides low-power wireless connectivity for various devices, which have been used in smart meters. Wi-SUN connects device at home. NarrowBand IOT and LTE-M can connect up to millions of sensors and devices using cellular technology.
Software
Energy is the rarest resource of the WSN node, and determines the life of WSN. WSN can be deployed in large numbers in various environments, including remote and hostile areas, where ad hoc communication is a key component. For this reason, algorithms and protocols need to address the following issues:
- Increasing age
- Resilience and fault tolerance
- Self-configure
Maximum lifetime: Energy/Power Consumption of sensing devices should be minimized and node sensors must be energy efficient because their limited energy resources determine their lifetime. To save power, wireless sensor nodes usually turn off radio transmitters and radio receivers when not in use.
Operating system
Operating systems for wireless sensor network nodes are usually less complex than general-purpose operating systems. They are more like embedded systems, for two reasons. First, wireless sensor networks are usually deployed with specific applications in mind, not as a common platform. Secondly, the need for low cost and low power causes most wireless sensor nodes to have a low power microcontroller that ensures that mechanisms such as virtual memory are not required or too expensive to implement.
It is therefore possible to use embedded operating systems such as eCos or uC/OS for sensor networks. However, such operating systems are often designed with real-time properties.
TinyOS is probably the first operating system designed specifically for wireless sensor networks. TinyOS is based on an event-driven programming model, not multithreading. The TinyOS program consists of event handlers and assignments with run-to-completion semantics. When an external event occurs, such as incoming data packets or sensor readings, TinyOS provides the appropriate event handler signal to handle the event. Event handlers can post tasks scheduled by the TinyOS kernel sometime later.
LiteOS is a newly developed OS for wireless sensor networks, which provides UNIX abstraction and support for C programming languages.
Contiki is an OS that uses a simpler programming style in C while providing progress like 6LoWPAN and Protothreads.
PreonVM is an OS for wireless sensor networks, which provides 6LoWPAN based on Contiki and support for Java programming languages.
Collaborative collaborative data collection platform online
The online collaborative sensor data management platform is an on-line database service that allows sensor owners to register and connect their devices to enter data into online databases for storage and also allows developers to connect to databases and build their own applications based on that data. Examples include Xively and the Wikisensing platform. The platform simplifies online collaboration among users across a wide array of data ranging from energy and environmental data to collected from transport services. Other services including allowing developers to embed real time graphics & amp; widget on the website; analyze and process historical data taken from data feeds; send real-time alerts from any datastream to control scripts, devices, and environments.
The Wikisensing system architecture explains the key components of the system to include APIs and interfaces for online collaborators, middleware that contains the business logic required for the management and processing of sensor data and storage models suitable for storing and storing large volumes of data efficiently.
Simulation
Currently, agent-based modeling and simulation is the only paradigm that allows simulation of complex behaviors in wireless sensor environments (such as clusters). The agent-based simulation of wireless sensors and ad hoc networks is a relatively new paradigm. Agent-based modeling was originally based on social simulation.
Network simulators like Opnet, Tetcos NetSim, and NS can be used to simulate wireless sensor networks.
Other concepts
Security
Infrastructures are lacking (ie no gateways are inserted, etc.) and inherent requirements (ie unattended work environment, etc.) of the WSN may inflict some weak points that attract the enemy. Therefore, security is a big issue when WSN is deployed for specific applications like military and health care. Due to their unique characteristics, traditional computer network security methods will become useless (or less effective) for WSN. Therefore, the lack of security mechanisms will lead to the intrusion of these networks. This intrusion needs to be detected and mitigation methods should be applied. More interested readers will refer to Butun et al. ' s papers on intrusion detection systems designed for WSNs.
Distributed sensor network
If the centralized architecture is used in the sensor network and the central node fails, then the entire network will collapse, but the reliability of the sensor network can be increased by using a distributed control architecture. Distributed controls are used in WSN for the following reasons:
- The sensor node is vulnerable to failure,
- For better data collection,
- To provide a backup node in case of a central node failure.
Nor is there a centralized body to allocate resources and they must be self-regulated.
Data integration and web sensor
Data collected from wireless sensor networks is usually stored in the form of numerical data in the central base station. In addition, the Open Geospatial Consortium (OGC) sets the standard for interoperability and metadata encoding interfaces that enable real time integration of heterogeneous sensor networks to the Internet, allowing each individual to monitor or control a wireless sensor network via a web browser.
Network processing
To reduce communication costs, some algorithms eliminate or reduce redundant sensor information nodes and avoid forwarding useless data. Because nodes can check the data they receive, they can measure average or directionality as an example of reading from other nodes. For example, in sensing and monitoring applications, generally the case of adjacent sensor nodes monitoring environmental features usually record the same value. Redundancy of data like this is because spatial correlations between sensor observations inspire techniques for data aggregation within the network and mining. Aggregation reduces the amount of network traffic that helps reduce energy consumption on sensor nodes. Recently, it has been found that network gateways also play an important role in increasing the energy efficiency of sensor nodes by scheduling more resources for nodes with more important energy efficiency requirements and sophisticated energy efficiency scheduling algorithms need to be implemented on network gateways for improvement. of the overall network energy efficiency.
Collect data securely
This is a form of processing in a network where the sensor node is assumed to be insecure with limited available energy, while the base station is assumed to be safe with unlimited available energy. Aggregation complicates the existing security challenges for wireless sensor networks and requires new security techniques designed specifically for this scenario. Providing security for collecting data in a wireless sensor network is known as secure data aggregation in WSN . are some of the first works that discuss techniques for securing data aggregation in wireless sensor networks.
The two major security challenges in securing data collection are data confidentiality and integrity. While encryption is traditionally used to provide end-to-end secrecy in wireless sensor networks, aggregators in secure data aggregation scenarios need to decrypt encrypted data to aggregate. It exposes the plaintext on the aggregator, making the data vulnerable to attacks from enemies. Likewise the aggregator can inject the false data into the aggregate and make the base station receive false data. Thus, while data aggregation improves network energy efficiency, it complicates the existing security challenges.
See also
- Autonomous system
- Bluetooth mesh network
- Embedded Networking Sensing Center
- List of ad hoc routing protocols
- Mobile wireless sensor network
- OpenWSN
- Optical wireless communication
- Robotic Mapping
- Smart and connected products
- Wireless ad hoc network
References
Further reading
- Kiran Maraiya, Kamal Kant, Nitin Gupta "Wireless Sensor Network: A Review of Data Aggregation" International Journal of Scientific & amp; Technical Research Volume 2 Issue 4, April 2011.
- Chalermek Intanagonwiwat, Deborah Estrin, Ramesh Govindan, John Heidemann, "Impact of Network Density on Data Aggregation in Wireless SensorNetworks," November 4, 2001.
External links
- IEEE 802.15.4 Standards Committee
- Secure Data Aggregation in Wireless Sensor Networks: A * Survey
- The list of aggregate proposals is safe for WSN
Source of the article : Wikipedia
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