Energy Aware Obstacle Avoidance Data Routing Scheme for IoT Enabled Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are an integral element of any Internet of Things (IoT) based consumer application. In such applications, Mobile Sink (MS) gathers sensed data by traversing through selected Rendezvous Points (RPs). Consumer applications generate a significant amount of multidimensional data and face various physical obstacles. These obstacles prevent communication between sensor nodes and hinder the MS movement in WSNs. This causes increased energy consumption and higher data collection delays. Most of the existing obstacle-avoiding data-gathering schemes suffer from the following major limitations: (i) high collision risks due to lack of a safety margin between MS paths and obstacles, (ii) imbalanced energy consumption and premature node failures due to suboptimal RP selection, and (iii) failure to design smooth MS paths which leads to sharp turns and inefficient MS movement. To address these challenges, this paper proposes an energy-aware obstacle avoidance data routing scheme for IoT-enabled WSNs using MS. It uses a Manta-ray Foraging Optimization (MRFO) algorithm to identify optimal RPs. Furthermore, the EBS-A* algorithm is used to identify a smooth obstacle-avoiding optimal route for MS. The proposed MRFO-based RP selection mechanism minimizes transmission distance and hop count. It balances the energy load among sensor nodes and prevents premature node failure. Therefore, network lifetime is improved. The EBS-A* algorithm ensures smooth MS movement by avoiding sharp turns. The EBS-A* algorithm also maintains a safety margin from obstacles, which reduces the risk of collision between MS and obstacles. The simulation and testbed results show that the proposed approach outperforms existing state-of-the-art approaches in terms of residual energy, network lifetime, stability period, data collection delay, and MS safety assessment.