Ensuring secure and energy-efficient data collection remains a critical challenge in wireless sensor networks (WSNs), where resource-constrained sensor nodes operate in vulnerable and dynamic environments. This article proposes S-EMSDC, a novel two-layer secure and energy-aware cluster-based data collection framework employing a mobile sink. The first layer integrates an Improved Discretized INFO (IDINFO) mobility optimization algorithm to minimize transmission distance and balance energy consumption through optimized sink trajectory planning, adaptive clustering, and dynamic cluster-head (CH) rotation. The second layer eliminates explicit key exchange and strengthens security by combining AES-256–based node authentication with HMAC-SHA256 integrity verification, ensuring early rejection of forged or manipulated data before aggregation. Extensive simulations on networks ranging from 100 to 500 nodes demonstrate the superiority of Secure and Energy-Efficient data collection framework for WSNs employing a Mobile Sink (S-EMSDC) over recent state-of-the-art schemes, including Energy-Efficient Mobile Sink-Based Intelligent Data Routing (EEMSR), Genetic algorithm based sink mobility for energy efficient data routing (GA-SMT), Balanced Inter-cluster and Inner-cluster Energy (BIIE), Group Teaching Algorithm by using the Bald Eagle (GTA-BE), and energy-aware path construction (EAPC). Results show that S-EMSDC extends network lifetime by up to 74%, reduces average energy consumption by over 60% compared to baseline methods and by 20–40% relative to recent protocols such as EA-PC and GA-SMT, depending on the number of rounds. The framework also achieves near-zero successful identity forgery across varying node densities and identifier lengths, confirming its robustness against identity-based attacks. Overall, S-EMSDC provides a secure, scalable, and energy-efficient solution suitable for mission-critical WSN applications.
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Alhammad et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69be37726e48c4981c677150 — DOI: https://doi.org/10.7717/peerj-cs.3731
Sarah M. Alhammad
Hassan Al-Mahdi
Mohamed Elshrkawey
PeerJ Computer Science
Suez Canal University
Princess Nourah bint Abdulrahman University
Arish University
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