Mobile Sink-Based Path Optimization Strategy in Wireless Sensor Networks Using Artificial Bee Colony Algorithm

Abstract

In traditional static wireless sensor networks (WSNs), the unbalanced communication overhead in different regions will result in premature death of some monitoring nodes. The introduction of mobile sink in WSNs can not only balance the node traffic load, but also obtain even energy consumption of nodes, thus effectively avoiding the ''hot spot'' problem and prolonging the network lifetime. However, the mobility of the sink will lead to frequent changes in the aspect of network topology, which can aggravate the overhead of the node's reorganization in hierarchical WSNs. Therefore, it is essential to obtain the optimal trajectory design of the mobile sink so as to improve the ability of data gathering. In this paper, a mobile sink-based path optimization strategy in WSNs using artificial bee colony algorithm is proposed. First, the problem of overall energy consumption in the network can be transformed into the minimization of the total hops between all subnodes and the rendezvous points of the mobile sink. The objective function and the constraint criterion should be established. Second, an improved artificial bee colony algorithm is proposed to solve the problem. On the one hand, the cumulative factor is introduced to the position update of the employed bee stage to speed up the convergence of the algorithm. On the other hand, the Cauchy mutation operator is presented to increase the diversity of the feasible solution and enhance the global search ability of the algorithm. The simulation results show that the proposed algorithm is better than the traditional methods in the aspects of energy efficiency and the real-time performance of data collection.INDEX TERMS Wireless sensor networks, mobile sink, path optimization, artificial bee colony algorithm.

A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

“…In some researches the mobile Sink can be guided towards the energy-intensive area to balance energy consumption by means of controlling the maximum movable distance and the moving speed of the mobile Sink. Similar schemes can be found recently, such as the heuristic algorithm GMRE [98] , the Heuristic Obstacle Avoiding Algorithm (HOAA) [99] , the Tree-Cluster-Based Data Gathering Algorithm (TCBDGA) which establishes a weight tree rooted by a Rendezvous Point [100] , the Set Packing Algorithm and TSP mobility scheme (SPAT) [101] , MWSN-oriented location service (MLS) [102] , the Mobile Sink-based Path Optimization strategy using Artificial Bee Colony (MSPO-ABC) algorithm [103] , and so on.…”

“…Some intelligent algorithms, such as the ant colony algorithm [179] , the bee colony algorithm [180] , etc., are integrated to the clustering strategy to design the energy-efficient clustering routing. Among most of existing clustering strategies, the emphasis is put on the way that CH is selected so as to reduce the energy overhead.…”

Evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks

“…Based on the above analysis, the utility function of the game model is defined as the average expectation of nodes competing for new location of the sink. The utility function is calculated by equations (4) and (8) as follows…”

Pair‐based sink relocation and route adjustment in mobile sink WSN integrated IoT

“…The mobile sink in a sensor network can effectively reduce the hotspot problem and it also balances the energy consumption using MDF‐based fuzzy cluster head selection. A mobile sink‐based path optimisation strategy is used [9] in WSN. The path optimisation strategy in sensor network is achieved through the artificial bee colony algorithm.…”