Design Workload Aware Data Collection Technique for IoT-enabled WSNs in Sustainable Smart Cities

Abstract

Load balancing in IoT-based Wireless Sensor Networks (WSNs) is essential for improving energy efficiency, reliability, and network lifetime, promoting the development of smart and sustainable cities through informed decision-making and resource optimization. This paper introduces a Workload Aware Clustering Technique (WLACT) to enhance energy efficiency and extend the network lifespan of IoT-based WSNs. WLACT focuses on overcoming challenges such as uneven workload distribution and complex scheme designs in existing clustering methods, highlighting the importance of load balancing, optimized data aggregation, and effective energy resource management in IoT-based heterogeneous WSNs. WLACT adapts Chicken Swarm Optimization (CSO) for efficient workload-aware clustering of WSNs, while also introducing the concept of average imbalanced workload parameter for clustered WSNs and utilizing it as an evaluation metric. By considering node heterogeneity and formulating an objective function to minimize workload imbalances among nodes during clustering, WLACT aims to achieve efficient energy resource utilization, improved reliability, and long-term operational support within smart city environments. A new cluster joining procedure for non-CHs based on multiple factors is also designed. Results reveal the superior performance of WLACT in terms of energy efficiency, workload balance, reliability, and network lifetime, making it a promising technique for sustainable smart city development.