Wireless sensor network

A wireless sensor network (WSN) (sometimes called a wireless sensor and actor network [1] (WSAN)^[1]) are spatially distributed autonomous sensors to monitor physical or environmental conditions,^[2] such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.

The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.^[3]^[4]

In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year, for example IPSN, SenSys, and EWSN.

Application

Process Management

it's not working for cables or wires only sensors in management

Area monitoring

Area monitoring is a common application of WSNs. In area monitoring, the WSN is deployed over a region where some phenomenon is to be monitored. A military example is the use of sensors detect enemy intrusion; a civilian example is the geo-fencing of gas or oil pipelines.

Health care monitoring

The medical applications can be of two types: wearable and implanted. Wearable devices are used on the body surface of a human or just at close proximity of the user.^[5] The implantable medical devices are those that are inserted inside human body. There are many other applications too e.g. body position measurement and location of the person, overall monitoring of ill patients in hospitals and at homes. Body-area networks can collect information about an individual's health, fitness, and energy expenditure.^[6]

Environmental/Earth sensing

There are many applications in monitoring environmental parameters,^[7] examples of which are given below. They share the extra challenges of harsh environments and reduced power supply.

Air pollution monitoring

Wireless sensor networks have been deployed in several cities (Stockholm^{[citation needed]}, London^{[citation needed]} and Brisbane^{[citation needed]}) to monitor the concentration of dangerous gases for citizens. These can take advantage of the ad hoc wireless links rather than wired installations, which also make them more mobile for testing readings in different areas.

Forest fire detection

A network of Sensor Nodes can be installed in a forest to detect when a fire has started. The nodes can be equipped with sensors to measure temperature, humidity and gases which are produced by fire in the trees or vegetation. The early detection is crucial for a successful action of the firefighters; thanks to Wireless Sensor Networks, the fire brigade will be able to know when a fire is started and how it is spreading.

Landslide detection

A landslide detection system makes use of a wireless sensor network to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. Through the data gathered it may be possible to know the occurrence of landslides long before it actually happens.

Water quality monitoring

Water quality monitoring involves analyzing water properties in dams, rivers, lakes & oceans, as well as underground water reserves. The use of many wireless distributed sensors enables the creation of a more accurate map of the water status, and allows the permanent deployment of monitoring stations in locations of difficult access, without the need of manual data retrieval.^[8]

Natural disaster prevention

Wireless sensor networks can effectively act to prevent the consequences of natural disasters, like floods. Wireless nodes have successfully been deployed in rivers where changes of the water levels have to be monitored in real time.

Industrial monitoring

Machine health monitoring

Wireless sensor networks have been developed for machinery condition-based maintenance (CBM) as they offer significant cost savings and enable new functionality.^[9]

Wireless sensors can be placed in locations difficult or impossible to reach with a wired system, such as rotating machinery and untethered vehicles.

Data logging

Wireless sensor networks are also used for the collection of data for monitoring of environmental information, this can be as simple as the monitoring of the temperature in a fridge to the level of water in overflow tanks in nuclear power plants. The statistical information can then be used to show how systems have been working. The advantage of WSNs over conventional loggers is the "live" data feed that is possible.

Water/Waste water monitoring

Monitoring the quality and level of water includes many activities such as checking the quality of underground or surface water and ensuring a country’s water infrastructure for the benefit of both human and animal.It may 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 logging, using appropriately interfaced sensors.

Characteristics

The main characteristics of a WSN include:

Power consumption constraints for nodes using batteries or energy harvesting
Ability to cope with node failures (resilience)
Mobility of nodes
Heterogeneity of nodes
Scalability to large scale of deployment
Ability to withstand harsh environmental conditions
Ease of use
Cross-layer design

Cross-layer is becoming an important studying area for wireless communications. In addition, the traditional layered approach presents three main problems:

Traditional layered approach cannot share different information among different layers ， which leads to each layer not having complete information. The traditional layered approach cannot guarantee the optimization of the entire network.
The traditional layered approach does not have the ability to adapt to the environmental change.
Because of the interference between the different users, access conflicts, fading, and the change of environment in the wireless sensor networks, traditional layered approach for wired networks is not applicable to wireless networks.

So the cross-layer can be used to make the optimal modulation to improve the transmission performance, such as data rate, energy efficiency, QoS (Quality of Service), etc.. Sensor nodes can be imagined as small computers which are extremely basic in terms of their interfaces and their components. They usually consist of a processing unit with limited computational power and limited memory, sensors or MEMS (including specific conditioning circuitry), a communication device (usually radio transceivers or alternatively optical), and a power source usually in the form of a battery. Other possible inclusions are energy harvesting modules,^[10] secondary ASICs, and possibly secondary communication interface (e.g. RS-232 or USB).

The base stations are one or more components of the WSN with much more computational, energy and communication resources. They act as a gateway between sensor nodes and the end user as they typically forward data from the WSN on to a server. Other special components in routing based networks are routers, designed to compute, calculate and distribute the routing tables.

Platforms

Hardware

One major challenge in a WSN is to produce low cost and tiny sensor nodes. There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the 1970s. Many of the nodes are still in the research and development stage, particularly their software. Also inherent to sensor network adoption is the use of very low power methods for radio communication and data acquisition.

In many applications, a WSN communicates with a Local Area Network or Wide Area Network through a gateway. The Gateway acts as a bridge between the WSN and the other network. This enables data to be stored and processed by devices with more resources, for example, in a remotely located server.

Software

Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. WSNs may be deployed in large numbers in various environments, including remote and hostile regions, where ad hoc communications are a key component. For this reason, algorithms and protocols need to address the following issues:

Increased lifespan
Robustness and fault tolerance
Self-configuration

Lifetime maximization: Energy/Power Consumption of the sensing device should be minimized and sensor nodes should be energy efficient since their limited energy resource determines their lifetime. To conserve power, wireless sensor nodes normally power off both the radio transmitter and the radio receiver when not in use.

Operating systems

Operating systems for wireless sensor network nodes are typically less complex than general-purpose operating systems. They more strongly resemble embedded systems, for two reasons. First, wireless sensor networks are typically deployed with a particular application in mind, rather than as a general platform. Second, a need for low costs and low power leads most wireless sensor nodes to have low-power microcontrollers ensuring that mechanisms such as virtual memory are either unnecessary 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 perhaps the first^[11] operating system specifically designed for wireless sensor networks. TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed of event handlers and tasks with run-to-completion semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS signals the appropriate event handler to handle the event. Event handlers can post tasks that are scheduled by the TinyOS kernel some time later.

LiteOS is a newly developed OS for wireless sensor networks, which provides UNIX-like abstraction and support for the C programming language.

Contiki is an OS which uses a simpler programming style in C while providing advances such as 6LoWPAN and Protothreads.

Online collaborative sensor data management platforms

Online collaborative sensor data management platforms are on-line database services that allow sensor owners to register and connect their devices to feed data into an online database for storage and also allow developers to connect to the database and build their own applications based on that data. Examples include Xively and the Wikisensing platform. Such platforms simplify online collaboration between users over diverse data sets ranging from energy and environment data to that collected from transport services. Other services include allowing developers to embed real-time graphs & widgets in websites; analyse and process historical data pulled from the data feeds; send real-time alerts from any datastream to control scripts, devices and environments.

The architecture of the Wikisensing system^[12] describes the key components of such systems to include APIs and interfaces for online collaborators, a middleware containing the business logic needed for the sensor data management and processing and a storage model suitable for the efficient storage and retrieval of large volumes of data. 0525162632

Simulation of WSNs

At present, agent-based modeling and simulation is the only paradigm which allows the simulation of complex behavior in the environments of wireless sensors (such as flocking).^[13] Agent-based simulation of wireless sensor and ad hoc networks is a relatively new paradigm. Agent-based modelling was originally based on social simulation.

Network simulators like OPNET, NetSim and NS2 can be used to simulate a wireless sensor network.

Event Detection

Since energy costs are a major constraint, it is sometimes desirable to only send data to the gateway when an event is triggered at the node. Sensors will then only open communication during a probable event, saving on communication costs. Fields interested in this type of network include surveillance, home automation, disaster relief, traffic control, health care and more.

Energy savings come from the fact that nodes only communicate back to the gateway when there is an event to report. This may be difficult in dynamic environments where much of the data collected may not be of interest. To overcome this issue constraints on what defines an event are relaxed and usually modeled as a set of thresholds or probabilities. Event detection falls under three categories: threshold based, supervised and unsupervised^[14].

Threshold Based Detection

Threshold based detection is a simple way events may be detected through the use of various parameters that can hint as to whether an event has occurred or not. Data collected at a sensor node, also called the data-event, is analyzed to see if it has reached a certain threshold point to be considered an event. For example a light detector may only report back back to they gateway if it detects light above a certain level. The data-event can be also sum of multiple thresholds from different types of sensors that either collaborate at the gateway or through small hop peer-to-peer communication. Two important parameters that affect the effectiveness of the detection is sampling rate and event area^[15]. The sampling rate will depend on whether events occur over long periods of time like tides in the ocean or over short periods of time such as cars passing a building. The event area will determine how many sensors need to be report values above a threshold value for the likelihood of an event to be high enough to be considered.

If sensors collect multiple data points at a given time then another method of using threshold based detection is to measure the variance of the data and only send data to a gateway node if the variance reaches a certain threshold. Events may occur in only a small portion of the data set. In this case it is important to break the data into smaller segments and take into account the variance of each individual segment. Consider the general variance for an N-dimensional data set.

$V_{1}=\sum _{\mathbf {s} }|I_{(\mathbf {s} )}-Iref_{(\mathbf {s} )}|$ , where $0\leq s_{i}\leq {\frac {m}{2}}$ , where $\mathbf {s}$ is the N-dimensional sampled area in the region of support ranging from 0 to $m$ in each dimension, $I_{(s)}$ is the set of sampled points at point $\mathbf {s}$ , and $Iref_{(s)}$ is the reference set of data points.

The variance above is one quadrant of the total space sampled and represents the variance of a subset of the data. By collecting all the variances for the different quadrants it is more likely to detect the occurrence of an event even if the event only affects a portion of the total region of support^[16].

Supervised Detection

Supervised detection is when events are known ahead of time and can be described by patterns that can be compared to live data. It requires some information to be know about the events in question and the environment a priori. This is usually done by taking sample measurements, or training vectors, across the network during an event and creating a signature of what the data looks like.

Classifying an event can be generally described as the probability that an observed measurement x was a result of event ω. Given a set of N-dimensional vectors $\{\mathbf {x} \}$ and a predetermined set of events $\{\mathbf {\omega } \}$ , the goal is to achieve the minimum error of misclassification, or Bayes error. The sensor needs to decide if $\mathbf {x}$ belongs to $\omega _{i}$ , by ensuring that $p(\omega _{i}|\mathbf {x} )>p(\omega _{j}|\mathbf {x} )$ for $j\neq i$ ^[17]. Once the most probable event has been determined and the probability of $\mathbf {x}$ belonging to an event is calculated it can be checked against a threshold. If the probability of an event is large enough then the data is forwarded to the gateway node. Below are a few examples of algorithms that solve the optimal Bayes classifier. They all require a set of training vectors to simulate or re-enact events of interest.

k-Nearest Neighbor (k-NN) Classifier

The k-NN algorithm is a well known pattern recognition algorithm where a set of predetermined prototypes {p_k} are used during the sample, or testing phase, of an supposed event. The prototypes model the events that are of interest in the application. The distance between each test vector and each prototype is calculated and the nearest k nearest test vectors closest to the prototype vectors are taken as the most likely classification or group of classifications. From there the probability that x belongs to the prototype event can be calculated. This approach, however, requires much memory and processing power as the number of prototypes increases and thus it is not a very practical choice for WSNs. It does however act as a good baseline to gauge performance of other classifiers since it is well known and that probability of misclassification when k=1 approaches twice the optimal Bayes error^[18].

Maximum Likelihood Classifier

The Maximum Likelihood Classifier models the distribution of training vectors from the same class as a mixture of Gaussian density functions. The probability of a given training vector x, given a class ω_i is

$p(\mathbf {x} |w_{i})=G(\mathbf {x} |\theta _{i})=\sum _{k}\alpha _{ik}|\mathbf {\Lambda } _{ik}|^{-N/2}exp(-1/2(\mathbf {x} -\mathbf {m} _{ik})^{T}\mathbf {\Lambda } _{ik}^{-1}(\mathbf {x} -\mathbf {m} _{ik})$

where $\theta _{i}=[\mathbf {\alpha } _{i1},...\mathbf {\alpha } _{iP},\mathbf {m} _{i1},...\mathbf {m} _{iP},\mathbf {\Lambda } _{i1},...\mathbf {\Lambda } _{iP}]$ are the mixture, mean, and co-variance matrix parameters of the P mixture densities corresponding to class ω_i. Identifying the matrix parameters can done through other algorithms for finding the mixture densities of the training vectors, such as the k-means algorithm or the expectation-maximization algorithm^[17].

Support Vector Machine Classifier

A support vector machine maps a set of linear transformations $\{\phi (\mathbf {x} )\}_{i=1}^{M}$ from the N-dimensional input vector to a higher M-dimensional feature space. A linear classifier can be described as a determinant function ${\mathfrak {g}}(\mathbf {x} )$ that satisfies ${\mathfrak {g}}_{i}(\mathbf {x} )>{\mathfrak {g}}_{j}(\mathbf {x} )$ , if $p(\omega _{i}|\mathbf {x} )>p(\omega _{j}|\mathbf {x} )$ for $j\neq i$ .

The linear classifier for this support vector machine classifier is,

${\mathfrak {g}}(\mathbf {x} )=\sum _{i=1}^{Q}\alpha _{i}K(\mathbf {x} ,\mathbf {x} _{i})+b$ ,where $K(\mathbf {x} ,\mathbf {x} _{i})=\sum _{j=1}^{M}\phi _{j}(\mathbf {x} )\phi _{i}(\mathbf {x} _{i})$ , and $b$ is the bias parameter and $\alpha _{i}$ is a constant associated with the weight of each dimension from the transform^[17].

The determinant function can be used to approximate the probabilities for each class.

Unsupervised Detection

When events of interest may be events that are unknown or have not been seen before then it is necessary to use unsupervised detection. This requires some machine learning algorithms that over time find anomalous events compared to normal happenings. This is an active area of research for event detection and other applications in WSNs.

Other concepts

Distributed sensor network

If a centralised architecture is used in a sensor network and the central node fails, then the entire network will collapse, however the reliability of the sensor network can be increased by using a distributed control architecture. Distributed control is used in WSNs for the following reasons:

Sensor nodes are prone to failure,
For better collection of data,
To provide nodes with backup in case of failure of the central node.

There is also no centralised body to allocate the resources and they have to be self organized.

Data integration and Sensor Web

The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. Additionally, the Open Geospatial Consortium (OGC) is specifying standards for interoperability interfaces and metadata encodings that enable real time integration of heterogeneous sensor webs into the Internet, allowing any individual to monitor or control Wireless Sensor Networks through a Web Browser.

In-network processing

To reduce communication costs some algorithms remove or reduce nodes' redundant sensor information and avoid forwarding data that is of no use. As nodes can inspect the data they forward, they can measure averages or directionality for example of readings from other nodes. For example, in sensing and monitoring applications, it is generally the case that neighboring sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires techniques for in-network data aggregation and mining

References

^ .F. Akyildiz and I.H. Kasimoglu, "Wireless Sensor and Actor Networks: Research Challenges,"; Ad Hoc Networks, vol. 2, no. 4, pp. 351-367, Oct. 2004.
^ "Environmental and Temperature Monitoring", Centrak
^ Dargie, W. and Poellabauer, C., "Fundamentals of wireless sensor networks: theory and practice", John Wiley and Sons, 2010 ISBN 978-0-470-99765-9, pp. 168–183, 191–192
^ Sohraby, K., Minoli, D., Znati, T. "Wireless sensor networks: technology, protocols, and applications", John Wiley and Sons, 2007 ISBN 978-0-471-74300-2, pp. 203–209
^ "Patient Safety", Centrak
^ Peiris, V. (2013). "Highly integrated wireless sensing for body area network applications". SPIE Newsroom. doi:10.1117/2.1201312.005120.
^ J.K.Hart and K.Martinez, "Environmental Sensor Networks: A revolution in the earth system science?", Earth Science Reviews, 2006
^ Spie (2013). "Vassili Karanassios: Energy scavenging to power remote sensors". SPIE Newsroom. doi:10.1117/2.3201305.05.
^ Tiwari, Ankit et al., Energy-efficient wireless sensor network design and implementation for condition-based maintenance, ACM Transactions on Sensor Networks (TOSN), http://portal.acm.org/citation.cfm?id=1210670
^ Magno, M.; Boyle, D.; Brunelli, D.; O'Flynn, B.; Popovici, E.; Benini, L. (2014). "Extended Wireless Monitoring Through Intelligent Hybrid Energy Supply". IEEE Transactions on Industrial Electronics. 61 (4): 1871. doi:10.1109/TIE.2013.2267694.
^ TinyOS Programming, Philip Levis, Cambridge University Press, 2009
^ Silva, D.; Ghanem, M.; Guo, Y. (2012). "WikiSensing: An Online Collaborative Approach for Sensor Data Management". Sensors. 12 (12): 13295. doi:10.3390/s121013295.{{cite journal}}: CS1 maint: unflagged free DOI (link)
^ Muaz Niazi, Amir Hussain (2011). A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments. IEEE Sensors Journal, Vol.11 No. 2, 404–412. Paper
^ Bahrepour, M.; Meratnia, N.; Havinga, P.J.M. (2011-12-01). "Online unsupervised event detection in wireless sensor networks". 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP): 306–311. doi:10.1109/ISSNIP.2011.6146583.
^ Vairo, C.; Amato, G.; Chessa, S.; Valleri, P. (2010-10-01). "Modeling detection and tracking of complex events in wireless sensor networks". 2010 IEEE International Conference on Systems Man and Cybernetics (SMC): 235–242. doi:10.1109/ICSMC.2010.5642242.
^ Veeraraghavan, K.; Peng, Dongming; Sharif, H. (2005-05-01). "Energy efficient multi-resolution visual surveillance on wireless sensor networks". 2005 IEEE International Conference on Electro Information Technology: 6 pp.-6. doi:10.1109/EIT.2005.1626975.
^ ^a ^b ^c Li, Dan; Wong, K.D.; Hu, Yu Hen; Sayeed, A.M. (2002-03-01). "Detection, classification, and tracking of targets". IEEE Signal Processing Magazine. 19 (2): 17–29. doi:10.1109/79.985674. ISSN 1053-5888.
^ Duda, R.O.; Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: Wiley.

External links

IEEE 802.15.4 Standardization Committee

[1] .F. Akyildiz and I.H. Kasimoglu, "Wireless Sensor and Actor Networks: Research Challenges,"; Ad Hoc Networks, vol. 2, no. 4, pp. 351-367, Oct. 2004.

[2] "Environmental and Temperature Monitoring", Centrak

[3] Dargie, W. and Poellabauer, C., "Fundamentals of wireless sensor networks: theory and practice", John Wiley and Sons, 2010 ISBN 978-0-470-99765-9, pp. 168–183, 191–192

[4] Sohraby, K., Minoli, D., Znati, T. "Wireless sensor networks: technology, protocols, and applications", John Wiley and Sons, 2007 ISBN 978-0-471-74300-2, pp. 203–209

[5] "Patient Safety", Centrak

[6] Peiris, V. (2013). "Highly integrated wireless sensing for body area network applications". SPIE Newsroom. doi:10.1117/2.1201312.005120.

[7] J.K.Hart and K.Martinez, "Environmental Sensor Networks: A revolution in the earth system science?", Earth Science Reviews, 2006

[8] Spie (2013). "Vassili Karanassios: Energy scavenging to power remote sensors". SPIE Newsroom. doi:10.1117/2.3201305.05.

[9] Tiwari, Ankit et al., Energy-efficient wireless sensor network design and implementation for condition-based maintenance, ACM Transactions on Sensor Networks (TOSN), http://portal.acm.org/citation.cfm?id=1210670

[10] Magno, M.; Boyle, D.; Brunelli, D.; O'Flynn, B.; Popovici, E.; Benini, L. (2014). "Extended Wireless Monitoring Through Intelligent Hybrid Energy Supply". IEEE Transactions on Industrial Electronics. 61 (4): 1871. doi:10.1109/TIE.2013.2267694.

[11] TinyOS Programming, Philip Levis, Cambridge University Press, 2009

[12] Silva, D.; Ghanem, M.; Guo, Y. (2012). "WikiSensing: An Online Collaborative Approach for Sensor Data Management". Sensors. 12 (12): 13295. doi:10.3390/s121013295.{{cite journal}}: CS1 maint: unflagged free DOI (link)

[Paper-13] Muaz Niazi, Amir Hussain (2011). A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments. IEEE Sensors Journal, Vol.11 No. 2, 404–412. Paper

[14] Bahrepour, M.; Meratnia, N.; Havinga, P.J.M. (2011-12-01). "Online unsupervised event detection in wireless sensor networks". 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP): 306–311. doi:10.1109/ISSNIP.2011.6146583.

[15] Vairo, C.; Amato, G.; Chessa, S.; Valleri, P. (2010-10-01). "Modeling detection and tracking of complex events in wireless sensor networks". 2010 IEEE International Conference on Systems Man and Cybernetics (SMC): 235–242. doi:10.1109/ICSMC.2010.5642242.

[16] Veeraraghavan, K.; Peng, Dongming; Sharif, H. (2005-05-01). "Energy efficient multi-resolution visual surveillance on wireless sensor networks". 2005 IEEE International Conference on Electro Information Technology: 6 pp.-6. doi:10.1109/EIT.2005.1626975.

[:0-17] Li, Dan; Wong, K.D.; Hu, Yu Hen; Sayeed, A.M. (2002-03-01). "Detection, classification, and tracking of targets". IEEE Signal Processing Magazine. 19 (2): 17–29. doi:10.1109/79.985674. ISSN 1053-5888.

[18] Duda, R.O.; Hart, P.E. (1973). Pattern Classification and Scene Analysis. New York: Wiley.

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v t e Wireless sensor network
Operating systems	Contiki ERIKA Enterprise LiteOS NanoQplus Nano-RK OpenTag RIOT TinyOS OpenWSN
Communications protocols	6LoWPAN IEEE 802.15.4 ANT Bluetooth Bluetooth Low Energy (Wibree) DASH7 ISA100.11a MiWi Near-field communication OCARI One-Net OSIAN Sidewalk Thread TIBUMAC TSMP WirelessHART Zigbee Z-Wave
Programming languages	C LabVIEW
Hardware	Arduino Iris Mote Sun SPOT XBee
Software	NS-2 LinuxMCE OPNET TinyDB-TOSSIM
Applications	Key distribution Location estimation Sensor network query processor Sensor web Wireless powerline sensor Telemetry
Routing protocols	Ad hoc On-Demand Distance Vector Routing (AODV) Dynamic Source Routing (DSR)
Conferences, journals	European Conference on Wireless Sensor Networks (EWSN) International Conference on Information Processing in Sensor Networks (IPSN) Conference on Embedded Networked Sensor Systems (SenSys)