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

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Abstract

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.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Walid Osamy & Ahmed A. El-Sawy

  2. Computer Science Department, University of Sharjah, Sharjah, 27272, UAE

    Ahmed M. Khedr

  3. Mathematics Department, Zagazig University, Zagazig, Egypt

    Ahmed M. Khedr

  4. Qassim University, Buridah, Saudi Arabia

    Walid Osamy

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  1. Walid Osamy
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  2. Ahmed A. El-Sawy
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  3. Ahmed M. Khedr
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Correspondence to Walid Osamy.

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Osamy, W., El-Sawy, A.A. & Khedr, A.M. Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks. Peer-to-Peer Netw. Appl. 13, 796–815 (2020). https://doi.org/10.1007/s12083-019-00818-z

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