Energy management has been a consistent problem in wireless sensor network (WSN). According to MG Murshed a researcher at the University of Aberdeen, UK “One of the most significant challenges for Wireless Sensor Networks (WSN) is long-lived sensor nodes and minimization in overall power consumption, because these nodes are generally battery operated and have a cost-conscious energy budget. In WSN, sensor nodes collect data from network sites and send that to a Base Station (BS) or Gateway. By increasing the lifetime of the nodes, connectivity is maintained and the network lifetime is increased. As the nodes spend substantial energy in sending and receiving data, a robust and power-aware routing protocol can maximize the network lifetime. In direct transmission”
Energy efficient operation and conservation is still a major challenge hindering sensor network adoption. WSN are utilized in a large number of application domains for continual monitoring of environmental and physiological parameters.
The following applications requires that energy be efficient during deployment of WSN solutions
- Environmental or habitat monitoring
- Military systems
- Security and surveillance systems
- Emergency management
In another development towards complete WSN deployment, Monash researchers have discovered a way to prevent energy failure in wireless sensor networks. Their innovation was based on using fuzzy-based situation-aware adoption frameworks allowing sensor nodes to adapt to a task and conserving energy. The discovery is targeted to WSN deployment in places such as Forest, high mountains and ocean beds.
Dr Pari Delir Haghighi, one of the key researchers who developed the solution-aware approach, says that “the FSI (Fuzzy Situation Inference) is situation modelling and reasoning.
The expert who studies situation modelling and reasoning approaches and how to use the inference results for adaptating applications and power related parameters on power-constrained devices said by using FSI, the sensor would have the ability to compute an optimal sleeping time, for example, when the situation demands lesser frequency of data the sensor can spend longer sleeping and vice-versa when situations demands more frequent data.
Monash University intends to employ FSI on distributed sensor network to adapt sensor’s sensing, sending and sleeping operations depending on situation context.
The following links will help to understand this innovation.
Delir Haghighi, P., Zaslavsky, A., Krishnaswamy, S., Gaber, M.M., Loke, S.W., 2009, Context-aware adaptive data stream mining, Intelligent Data Analysis [P], vol 13, issue 3, IOS Press, Amsterdam Netherlands, pp. 423-434.
Burstein, F., Delir Haghighi, P., Zaslavsky, A., 2011, Context-aware mobile medical emergency management decision support system for safe transportation, in Decision Support: An Examination of the DSS Discipline, eds David Schuff, David Paradice, Frada Burstein, Daniel J. Power and Ramesh Sharda, Springer, New York NY USA, pp. 163-181.
Jayaraman et al (2010) “Intelligent processing of K-nearest neighbours queries using mobile data collectors in a location aware 3D wireless sensor network”. IEA/AIE 10 Proceedings of the 23rd International Conference on Industrial engineering and other applications of applied intelligent systems. Volume Part III Pages 260-270
Murshed, MG. & Allen, AR. (2012). ‘Energy Efficient Dynamic Routing Protocol for Wireless Sensor Networks’. in N Meghanathan, N Chaki & D Nagamalai (eds), Advances in Computer Science and Information Technology: Computer Science and Engineering.. vol. 85, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol. 85, Springer-Verlag, pp. 41-52.
Iwendi C. O.; Murshed M.G,; Toolit G.; Allen A. R,; (under review) “Ensuring Security in Energy Efficient Routing Technique in Wireless Sensor Network” IEEE Africon 2013 Conference Mauritius