An Overview of Using Mobile Sink Strategies to Provide Sustainable Energy in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) can effectively address the issue with the static sink’s sink-hole or hot spot brought on by multi-hop routing by data collecting using mobile sinks (MS). The optimal path’s design, however, is a famous NP-hard issue. Data transmission from source nodes to the base station that is both successful and efficient while reducing energy consumption and data loss is what determines the architecture’s overall performance. In WSNs, data collection is done via mobile sinks or static sinks (SS). he MS data collection methods are more effective than sink-based approaches because they can gather sensor node data efficiently. Nevertheless, the MS-based data collection methods have a number of shortcomings and restrictions such as energy usage, complexity, cost implications, and scalability issues. Designing a trajectory is therefore an NP-hard task. In this work, we suggest a survey that utilizes path optimization techniques such as swarm intelligence, ant colony optimization (ACO), machine learning and artificial intelligence. We also have an overview of different approaches for using SS and MS-based techniques to collect data from a sensor network, as well as different kinds of data collection using MS and some of the difficulties it encounters. Lastly, we offer a level-based categorization of the various trajectory techniques that were employed to gather the data. We divided schemes into three categories at the first level: static, dynamic, and Hybrid.