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<p>Wireless sensor networks (WSNs) function under strict energy limitations, whereas numerous applications additionally demand tolerable packet latency. Queue-driven wake-up approaches may lower energy usage; nevertheless, they can worsen delay behaviour whenever packet processing is postponed. Within this study, we examine a two-threshold working vacation (TTWV) queued wake-up method for a finite-buffer sensor node. The system is represented through a continuous-time Markov chain (CTMC), while the steady-state distribution is obtained to assess important performance metrics, namely mean energy consumption (E) and mean system delay (W). To reconcile these two objectives, a weighted-sum optimisation problem is formulated and resolved through Tabu Search (TS) alongside Particle Swarm Optimisation (PSO). Numerical findings across diverse traffic and service scenarios indicate that the optimised TTWV scheme enhances the trade-off between energy consumption and system delay relative to baseline configurations, emphasising the benefit of energy–delay optimisation instead of energy-only optimisation.</p>

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Energy-delay trade-offs in threshold-based wake-up scheduling for wireless sensor networks

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Bachira Boutoumi,
  • Nawel Gharbi,
  • Mourad Labidi

摘要

Wireless sensor networks (WSNs) function under strict energy limitations, whereas numerous applications additionally demand tolerable packet latency. Queue-driven wake-up approaches may lower energy usage; nevertheless, they can worsen delay behaviour whenever packet processing is postponed. Within this study, we examine a two-threshold working vacation (TTWV) queued wake-up method for a finite-buffer sensor node. The system is represented through a continuous-time Markov chain (CTMC), while the steady-state distribution is obtained to assess important performance metrics, namely mean energy consumption (E) and mean system delay (W). To reconcile these two objectives, a weighted-sum optimisation problem is formulated and resolved through Tabu Search (TS) alongside Particle Swarm Optimisation (PSO). Numerical findings across diverse traffic and service scenarios indicate that the optimised TTWV scheme enhances the trade-off between energy consumption and system delay relative to baseline configurations, emphasising the benefit of energy–delay optimisation instead of energy-only optimisation.