Mobile Sink Data Gathering and Path Determination in Wireless Sensor Networks: A Review

Abstract

The core challenge in wireless sensor networks is to design an efficient data gathering algorithm that improves the energy efficiency and lifetime of the node without delay. Data gathering is a process of collecting the sensed readings at predefined points to perform the analysis to process it. In a static-based approach, the collected data is flooded through the network and the node near to base station is highly affected resulting in non-uniform energy consumption, becoming vulnerable to hotspots or energy hole problems. As the static sink is inadequate and inefficient in energy utilization causing network performance degradation and shortening of network lifetime, the sink mobility concept is put forward to mitigate the energy hole issues among the nodes. Finding and selecting the optimal mobility trajectory of the mobile sink to gather the data efficiently become a challenging issue in sensor networks. Bio-inspired and machine learning based mobile sink path determination recently proves to be an effective method of data collection. This paper reviews different mobile sink data aggregation and path determination approaches to select the appropriate method for various applications.