Real-time surveillance systems with green wireless sensor networks (WSNs) are vital for maintaining high energy efficiency in many situations. This paper considers a scenario utilizing green WSNs to monitor the situation of Internet of Things (IoT), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ICT). More specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green WSNs. We first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. We then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. Hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. Simulation results indicate that the proposed Algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.

中文翻译：

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最小化绿色物联网中的聚合时间和能耗

具有绿色无线传感器网络 (WSN) 的实时监控系统对于在许多情况下保持高能效至关重要。本文考虑了利用绿色 WSN 监控物联网 (IoT) 状况的场景，物联网是信息和通信技术 (ICT) 中最重要的电力消耗来源之一。更具体地说，我们专注于优化集群结构，以最大限度地减少绿色 WSN 中聚合聚合的延迟和能耗。我们首先通过理论分析找到最小化延迟的网络簇半径的最优值。然后我们提出了一种新的集群网络架构，其中远离汇的集群很小，允许更早地处理集群间数据聚合，靠近sink的集群比较大，可以有更多的时间进行集群内的数据聚合。因此，可以在连续的时隙中调度传感器节点以减少状态转换的次数，从而达到最小化延迟和能耗的目标。仿真结果表明，所提出的算法在计划长度和生命周期方面均优于先前报告的解决方案，从而证明了其有效性。