Possible applications include body position measurement, location of persons, overall monitoring of ill patients in hospitals and at home.
Especially due to the integration of sensor networks, with IoT, the user authentication becomes more challenging; however, a solution is presented in recent work.
[11] Wireless sensor networks have been used to monitor various species and habitats, beginning with the Great Duck Island Deployment, including marmots, cane toads in Australia and zebras in Kenya.
For research purposes, wireless sensor networks have been deployed to monitor the concentration of dangerous gases for citizens (e.g., in London).
[15] Moreover, the quality of data is currently insufficient for trustworthy decision-making, as field calibration leads to unreliable measurement results, and frequent recalibration might be required.
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.
Wireless sensor networks have been developed for machinery condition-based maintenance (CBM) as they offer significant cost savings and enable new functionality.
[19] Wireless sensors can be placed in locations difficult or impossible to reach with a wired system, such as rotating machinery and untethered vehicles.
[23][obsolete source] The data fusion process occurs within the sensor network rather than at a centralized computer and is performed by a specially developed algorithm based on Bayesian statistics.
[24] WATS would not use a centralized computer for analysis because researchers found that factors such as latency and available bandwidth tended to create significant bottlenecks.
[24] An important factor in WATS development is ease of deployment, since more sensors both improves the detection rate and reduces false alarms.
[24] The development of improved sensors is a major component of current research at the Nonproliferation, Arms Control, and International Security (NAI) Directorate at LLNL.
WATS was profiled to the U.S. House of Representatives' Military Research and Development Subcommittee on October 1, 1997, during a hearing on nuclear terrorism and countermeasures.
[23] On August 4, 1998, in a subsequent meeting of that subcommittee, Chairman Curt Weldon stated that research funding for WATS had been cut by the Clinton administration to a subsistence level and that the program had been poorly re-organized.
[25] There are studies that show that using sensors for incident monitoring improve in a great way the response of firefighters and police to an unexpected situation.
[33] In addition, the traditional layered approach presents three main problems: So the cross-layer can be used to make the optimal modulation to improve the transmission performance, such as data rate, energy efficiency, quality of service (QoS), etc.
There are an increasing number of small companies producing WSN hardware and the commercial situation can be compared to home computing in the 1970s.
WSNs may be deployed in large numbers in various environments, including remote and hostile regions, where ad hoc communications are a key component.
Recently, it has been observed that by periodically turning on and off the sensing and communication capabilities of sensor nodes, we can significantly reduce the active time and thus prolong network lifetime.
[44][45] However, this duty cycling may result in high network latency, routing overhead, and neighbor discovery delays due to asynchronous sleep and wake-up scheduling.
[clarification needed] Simulation experiments demonstrated the validity of this novel approach in minimizing routing information stored at each sensor.
Performance improvements of up to 12-fold and 11-fold are observed in terms of routing traffic load reduction and energy efficiency, respectively, as compared to existing schemes.
LiteOS is a newly developed OS for wireless sensor networks, which provides UNIX-like abstraction and support for the C programming language.
PreonVM[47] is an OS for wireless sensor networks, which provides 6LoWPAN based on Contiki and support for the Java programming language.
The architecture of the Wikisensing system[48] 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.
Network localization refers to the problem of estimating the location of wireless sensor nodes during deployments and in dynamic settings.
In 2000, Nirupama Bulusu, John Heidemann and Deborah Estrin first motivated and proposed a radio connectivity based system for localization of wireless sensor networks.
Researchers studying wireless sensor networks hypothesize that much more information can be extracted from hundreds of unreliable measurements spread across a field of interest than from a smaller number of high-quality, high-reliability instruments with the same total cost.
Different reprogramming protocols exist that provide different levels of speed of operation, reliability, energy expenditure, requirement of code resident on the nodes, suitability to different wireless environments, resistance to DoS, etc.
This was used this to develop a primitive called "local monitoring"[51] which was used for detection of sophisticated attacks, like blackhole or wormhole, which degrade the throughput of large networks to close-to-zero.