Finding the sensors location and the number of sensors in sensor networks with a genetic algorithm

Sensor networks have recently emerged as a premier research topic. Sensor networks pose a number of new conceptual and optimization problems. Some, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. While designing the sensor networks according to performed computation, the limited number of sensors to cover an area will be considered, so the proper placing of this limited number of sensors will cause costs to reduce regarding to coverage and development of the network in the next stage. In this paper we will present a genetic algorithm to solve the designing issue of the sensor network. The most important property of the presented algorithm is to find the sensors location and to discover the number of these sensors for the proper coverage and also to minimize the costs. This algorithm includes the ability to find the number of essential sensor for each real map.

[1]  Xiaoli Li,et al.  A map-growing localization algorithm for ad-hoc wireless sensor networks , 2004, Proceedings. Tenth International Conference on Parallel and Distributed Systems, 2004. ICPADS 2004..

[2]  S. Pattem,et al.  Distributed online localization in sensor networks using a moving target , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[3]  Dragos Niculescu,et al.  Positioning in ad hoc sensor networks , 2004, IEEE Network.

[4]  Ren C. Luo,et al.  Nodes localization through data fusion in sensor network , 2005, 19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA papers).

[5]  Erik G. Larsson,et al.  Cramer-Rao bound analysis of distributed positioning in sensor networks , 2004, IEEE Signal Processing Letters.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Ying Zhang,et al.  Localization from connectivity in sensor networks , 2004, IEEE Transactions on Parallel and Distributed Systems.

[8]  Hongchi Shi,et al.  SHARP: a new approach to relative localization in wireless sensor networks , 2005, 25th IEEE International Conference on Distributed Computing Systems Workshops.

[9]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[10]  Hongchi Shi,et al.  A new algorithm for relative localization in wireless sensor networks , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[11]  Sankar K. Pal,et al.  Genetic Algorithms for Pattern Recognition , 2017 .

[12]  Mahmood Fathy,et al.  An Energy-Efficient Algorithm for Positioning and Map Extracting by Connectivity in Wireless Sensor Network , 2006, Wireless and Optical Communications.

[13]  Kristina Lerman,et al.  Distributed online localization in sensor networks using a moving target , 2004, IPSN.

[14]  Jae-Hyuk Oh,et al.  High precision positioning with a sensor network , 2004, Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004..