Human mobility based stable clustering for data aggregation in singlehop cell phone based wireless sensor network

Advances in 3G and 4G technology have offered many possibilities for developing novel applications using sensors embedded in hand held devices like cell phones. Mobility of cell phone based wireless sensor network has a critical issue of gathering sensed information in an energy efficient and delay sensitive manner. In this paper we provide a human mobility based stable clustering algorithm for data aggregation in single hop cell phone based sensor network incorporating mobility of cell phone users. We present a human mobility aware weighted clustering algorithm for data aggregation under Truncated Levy Walk (TLW) mobility model. Our approach is to select stable Cluster Heads (CH) to save the energy expenditure of network back bone formation. We have compared our algorithm with WCA [9] of mobile adhoc network and with MRECA [6] algorithm of mobile adhoc sensor network which we consider to be closely related with our work. WCA algorithm's mobility parameter is not effectively capturing mobility of human walk. Our Human mobility aware Weighted Cluster based Data Aggregation algorithm (Hm-WCDA) effectively captures human walk characteristics and thereby stabilizes the back bone network. We have evaluated performance of our algorithm primarily with stability related parameters such as number of dominant set (DS) updates, number of reaffiliations and number of cluster heads; which directly effects the energy consumption of the algorithm. The simulation results show that our algorithm is more energy-efficient and reduces the energy consumption by 18 percent as compared to MRECA and by 9 percent as compared to WCA for cluster radius of 400m