Routing and flow rate assignment using multi-objective optimization in wireless sensor networks

Abstract

Wireless sensor networks (WSNs) have shown to be an efficient solution for physical parameters monitoring and activity detection. WSNs are usually designed using single optimization by optimizing one objective function such as energy consumption taking some constraints into consideration. In multi-objective optimization (MOPT) multiple and often conflicting objectives need to be optimized while satisfying some constraints. This paper proposes MOPT-based routing and flow rate assignment methodologies for WSNs. The first methodology employs Karush-Kuhn-Tucker (KKT) conditions to find the optimal solution, while the second one is based on genetic algorithms (GA). A new formulation is introduced jointly minimize the energy and the delay. Performance metrics such as the average end-to-end delay and energy consumption are evaluated and compared with those of previous work, namely the hybrid geographical routing (HGR) algorithm. The proposed work helps network designers to address design procedures efficiently and increase the flexibility in the energy delay tradeoff for communications in WSNs.