ENAMS: Energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence.

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dc.contributor.author Al-Obaidi, Mohanad
dc.date 2010 en
dc.date.accessioned 2011-09-01T16:16:02Z
dc.date.available 2011-09-01T16:16:02Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/2086/5187
dc.description.abstract Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space. en
dc.language.iso en en
dc.publisher De Montfort University en
dc.subject genetic algorithms en
dc.subject clustering en
dc.subject evolutionary computation en
dc.subject sensor networks en
dc.subject swarm intelligence en
dc.subject energy optimization en
dc.subject particle swarm optimization en
dc.title ENAMS: Energy optimization algorithm for mobile wireless sensor networks using evolutionary computation and swarm intelligence. en
dc.type Thesis or dissertation en
dc.publisher.department Faculty of Technology en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en

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