Scheduling data aggregation trees to extend network lifetime in sensor networks

Abstract

SummaryIncreasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree‐based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV‐based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics‐based technique of Local Path Reestablishment (LPR) and greedy‐based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments, and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

“…Scheduling DATs to improve NL is a known NP complete problem, 7 and several researchers have devised DAT construction techniques which suboptimally distribute the energy consumption of the nodes. [6][7][8][9][10][11][12][13][14][15]17,18 These techniques include various algorithmic strategies like approximation, [7][8][9][19][20][21] heuristics, 6,10,[15][16][17][18][22][23][24][25] soft computing, 26 linear programming, 27 genetic algorithms, and evolutionary algorithms. [28][29][30] Recent nature inspired meta-heuristic optimization algorithms such as black window optimization, 31 whale optimization, 32 and gray wolf optimization approaches 33 facilitate to solve NP complete problems.…”

“…In Liu et al, 40 the network performance is improved using RE-based techniques. In previous studies, 25,39,41,42 the effect of DA ratio α on the quality of constructed DAT is investigated where α is the amount of data that can be combined into a packet. There is a cost associated with DAT scheduling.…”