Adaptive Path Selection for Efficient Data Collection in Wireless Sensor Networks

With the rise of interest in the Internet of Things (IoT), Wireless Sensors Network (WSN) has drawn a lot of attention as they are seen as the foundation of IoT-based systems and can be deployed in a wide range of delay-intolerant applications. However, energy remains one of the major concerns for Wireless Sensors Network (WSN). Data collection was proposed as one of the methods that can help preserve energy. And, the use of Mobile Sink (MS) has been widely employed in WSN as a preferred approach for data collection. This approach can significantly prolong the network lifetime by reducing radio communication between nodes, thus, reserving the overall energy. Employing MS can also enhance the Quality of Service (QoS) by increasing throughput and reducing End-to-End (E2E) delay if the MS trajectory is chosen wisely. However, this can also pose many unique challenges that must be solved. In this paper, we propose an adaptive algorithm that chooses the optimal MS trajectory regardless of the network topology. The MS will choose the shortest path to collect critical data, ensuring minimal delay and maximum throughput. We present different scenarios and analyze the performance of the suggested scheme in a simulated environment. The findings show that our algorithm outperforms other existing approaches.