Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali - Ihsan , Ali (2024)
Nowadays, Sensor nodes are widely employed to monitor environmental conditions but face significant challenges, including power supply and rapid, reliable data transmission. The reliance on batteries, especially rechargeable ones, poses a challenge as recharging or replacing them is often not feasible in hazardous environments such as war zones or disaster-stricken areas. Consequently, prolonging battery life is crucial in designing Wireless Sensor Networks (WSNs). Equally important is the synchronization of time between sensor nodes; without it, significant connectivity issues could arise within the network, leading to delayed transmissions and the loss of critical data. Thus, developing an effective routing strategy is key to extending battery life, conserving power, reducing transmission delays, and enhancing throughput. These factors necessitate the creation of an energy-efficient routing algorithm to minimize energy consumption and extend sensor lifetimes. In smart communities, WSNs support various applications, including healthcare and environmental monitoring. The use of multiple mobile sinks across the network for prompt data collection presents a challenge in cost-effective data management, especially in time-sensitive WSNs applications. The presence of several mobile sinks creates a trade-off between maximizing network lifetime and minimizing delay in large-scale, time-sensitive WSNs. The primary challenge is to find a balanced solution that optimizes network lifespan and minimizes communication delays. Different methods for data collection in WSNs have been extensively investigated in existing research, yet the results (total energy consumption, throughput, and end-to-end delay) remain inconclusive. To address relay node selection and data scheduling issues, Energy-Efficient Scheduling (EES) and Energy-Efficient Un-Scheduling (EEUS) methods have been introduced using the Improved Discrete Bat Algorithm (IDBA) along with the Adaptive Warshal Floyd algorithm (AWF). The hybrid method, IDBA-AFW, aims to enhance the original IDBA by incorporating the Adaptive Floyd-Warshall algorithm for graph transformation and optimization. The main objectives are to identify optimally reliable nodes and perform effective data scheduling in WSNs, thereby maximizing throughput, minimizing energy consumption, and reducing end-to-end delay. The AFW algorithm reduces unnecessary computations by focusing only on useful edge entries in the graph, thereby expediting the optimization process. The IDBA-AFW evaluates the fitness of relay nodes based on multiple criteria, such as energy efficiency, throughput, and end-to-end delay. Both Bat Algorithm parameters and AFW parameters are adaptively tuned to balance exploration and exploitation throughout the optimization process. Evaluations and comparisons with existing solutions show that EES and EEUS using IDBA-AWF significantly improve data collection in terms of energy consumption, throughput, and end-to-end delay.