Sensor Node Deployment Based on Electromagnetism-Like Algorithm in Mobile Wireless Sensor Networks

Abstract

The dynamic deployment of sensors in wireless networks significantly affects the performance of the network. However, the efficient application of dynamic deployments which determines the positions of the sensors within the network increases the coverage area of the network. As a result of this, dynamic deployment increases the efficiency of the wireless sensor networks (WSNs). In this paper, dynamic deployment was applied to WSNs which consist of mobile sensors by aiming at increasing the coverage area of the network with electromagnetism-like (EM) algorithm which is a population-based optimization algorithm. A new approach has been improved in calculating the coverage rate of the sensors by using binary detection model so as to carry out the dynamic deployments of sensors and it has been thought to reach realistic results efficiently. Simulation results have shown that the EM algorithm can be preferred in the dynamic deployment of mobile sensors within the wireless networks.

Research on Pseudo-Node Detection Algorithm in Wireless Sensor Networks

“…At this point P 3 ' can be calculated according to formula 7 without occurring both event W 1 and W 2 : (8) According to the third criterion, the replicated node can be checked out as long as any set of nodes (x k1 , x k2 ) satisfies the condition. And then based on the Bernoulli equation, the probability P3 can be calculated: (9) In view of the above calculation, the copy attack detection probability P based on joint ranging can be obtained:…”

“…This method caters for different ranging precision in intrusion detection of replicated nodes in WSN. By adjusting the key contributing parameters to detection rate in combination with the flexible and reliable protocol, the detection success rate can be optimized to meet the requirements of WSN intrusion detection [8][9][10][11].…”

Kablosuz algılayıcı ağlarda hedef kapsama problemi için algılayıcı dağıtımı ile ağın yaşam süresinin optimizasyonu

“…[7,8] İkili Tarama Modeli (Binary Detection Model) ve Olasılıksal Tarama Modeli (Probabilistic Detection Model)'ni kullanarak geliştirdikleri ABC algoritmasını düğümlerin dinamik dağıtımlarına uygulayarak ağın kapsama alanını optimize etmeye çalışmışlardır. Recep ve Ali [9,10] ilgili alanın kapsanma oranını artırmak için EM algoritmasını kullanarak geliştirdikleri yeni bir dinamik dağıtım metodu ile düğümlerin konumlarını optimize ederek kısmen de olsa ağın yaşam süresini uzatmışlardır. Ayrıca Feyza ve Suat [11] çok amaçlı evrimsel algoritmalar ile mobil düğümlerin dinamik dağıtımlarını yaparak KAA'ların kapsama alanını optimize etmişler ve düğümlerin yer değiştirme mesafelerinin optimizasyonunu sağlayarak algılayıcıların enerji tüketimlerini minimize etmişlerdir.…”

Resource Allocation Algorithm Based on Profit Maximization for Crowd Sensing

“…In summary, resource allocation problem is equivalent to the virtual unit placement; how to allocate the virtual unit to physical nodes becomes the key factor influencing crowd sensing performance [2,[7][8][9].…”