α-Fraction First Strategy for Hierarchical Model in Wireless Sensor Networks

Energy hole refers to the critical issue near the sinks for data collecting, this problem effects the lifetime of wireless sensor network to a great extent. Frequently data forwarding from distributed sensors to the sink will speed up the energy consumption of the sensors near the sink. This circumstance shortens the lifetime of the sensor network. In this paper, an α-fraction first strategy was proposed to build a hierarchical model of wireless sensor networks that concerning the energy consumption. The model mixes the so-called relay nodes into the network for transmitting and collecting data from the other sensor nodes. We studied the Farthest First traversal and Harel methods, then combined the proposed α-fraction first strategy with the two methods respectively. Three algorithms of FF+Fr(α), HD+Fr(α), and HL+Fr(α) were designed for determining the relay nodes in sensor networks. The algorithms can be used to construct a two-tier sensor network with fewer relay nodes than the results of the Farthest First traversal and Harel methods. The proposed strategy also could be used with any other algorithms that regarding for choosing one of many options. The simulation results show that our proposed algorithms perform well. Thus, the network lifetime can be prolonged.

[1]  Hadi Larijani,et al.  A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[2]  Wei Wang,et al.  A simple greedy approximation algorithm for the minimum connected kk-Center problem , 2016, J. Comb. Optim..

[3]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[4]  Teofilo F. GONZALEZ,et al.  Clustering to Minimize the Maximum Intercluster Distance , 1985, Theor. Comput. Sci..

[5]  Yu-Chee Tseng,et al.  A Survey of Solutions for the Coverage Problems in Wireless Sensor Networks , 2005 .

[6]  Yin-Feng Xu,et al.  An incremental version of the k-center problem on boundary of a convex polygon , 2015, J. Comb. Optim..

[7]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[8]  Narendra Sharma,et al.  Comparison the various clustering algorithms of weka tools , 2012 .

[9]  Maziar Ahmad Sharbafi,et al.  Using Earliest Deadline First Algorithms for Coalition Formation in Dynamic Time-critical Environment , 2011 .

[10]  T. Velmurugan,et al.  Lung Cancer Data Analysis by k-means and Farthest First Clustering Algorithms , 2015 .

[11]  Rong Ge,et al.  Joint Cluster Analysis of Attribute Data and Relationship Data: the Connected k-Center Problem , 2006, SDM.

[12]  Chandan Tripathi,et al.  A centralized approach for resolving physical interference between robots using nearest first swarm method , 2014, 2014 6th International Conference on Computer Science and Information Technology (CSIT).

[13]  Andreas Emil Feldmann Fixed-Parameter Approximations for k-Center Problems in Low Highway Dimension Graphs , 2018, Algorithmica.

[14]  Daniel J. Rosenkrantz,et al.  An analysis of several heuristics for the traveling salesman problem , 2013, Fundamental Problems in Computing.

[15]  David Peleg,et al.  The Fault Tolerant Capacitated k-Center Problem , 2012, SIROCCO.

[16]  Trong-The Nguyen,et al.  A Compact Articial Bee Colony Optimization for Topology Control Scheme in Wireless Sensor Networks , 2015, J. Inf. Hiding Multim. Signal Process..

[17]  Chu-Sing Yang,et al.  An adaptive joining mechanism for improving the connection ratio of ZigBee wireless sensor networks , 2010, Int. J. Commun. Syst..

[18]  Trong-The Nguyen,et al.  An Energy-based Cluster Head Selection Algorithm to Support Long-lifetime in Wireless Sensor Networks , 2016, J. Netw. Intell..

[19]  David B. Shmoys,et al.  A Best Possible Heuristic for the k-Center Problem , 1985, Math. Oper. Res..

[20]  Arindam Banerjee,et al.  Active Semi-Supervision for Pairwise Constrained Clustering , 2004, SDM.

[21]  Deepshree A. Vadeyar,et al.  Farthest First Clustering in Links Reorganization , 2014 .

[22]  Mihaela Cardei,et al.  Improved sensor network lifetime with multiple mobile sinks , 2009, Pervasive Mob. Comput..

[23]  André Baresel,et al.  Fitness Function Design To Improve Evolutionary Structural Testing , 2002, GECCO.