Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks

• Published in: Peer-to-peer networking and applications 2020-05, Vol.13 (3), p.796-815
• Main Authors: Osamy, Walid, El-Sawy, Ahmed A., Khedr, Ahmed M.
• Format: Article
• Language: English
• Subjects: Communications Engineering; Computer Communication Networks; Computer simulation; Data collection; Engineering; Genetic algorithms; Information Systems and Communication Service; Network latency; Networks; Schedules; Scheduling; Signal,Image and Speech Processing; Time Division Multiple Access; Wireless networks; Wireless sensor networks
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-019-00818-z

Data collection is a major operation in Wireless Sensor Networks (WSNs) and minimizing the delay in transmitting the collected data is critical for a lot of applications where specific actions depend on the required deadline, such as event-based mission-critical applications. Scheduling algorithms such as Time Division Multiple Access (TDMA) are extensively used for data delivery with the aim of minimizing the time duration for transporting data to the sink. To minimize the average latency and the average normalized latency in TDMA, we propose a new efficient scheduling algorithm (ETDMA-GA) based on Genetic Algorithm(GA). ETDMA-GA minimizes the latency of communication where two dimensional encoding representations are designed to allocate slots and minimizes the total network latency using a proposed fitness function. The simulation results show that the performance of the proposed algorithm outperforms the existing state-of-the-art approaches such as Rand-LO, Depth-LO, DepthRe-LO, IDegRe-LO, and IDeg-LO in terms of average latency, average normalized latency, and average schedule length.