Department of Information and Communication Engineering, Chungbuk National University, Chungbuk 28644, Korea.
Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48104, USA.
Sensors (Basel). 2021 May 4;21(9):3198. doi: 10.3390/s21093198.
Wireless sensor nodes are heavily resource-constrained due to their edge form factor, which has motivated increasing battery life through low-power techniques. This paper proposes a power management method that leads to less energy consumption in an idle state than conventional power management systems used in wireless sensor nodes. We analyze and benchmark the power consumption between Sleep, Idle, and Run modes. To reduce sensor node power consumption, we develop fine-grained power modes (FGPM) with five states which modulate energy consumption according to the sensor node's communication status. We evaluate the proposed method on a test bench Mica2. As a result, the power consumed is 74.2% lower than that of conventional approaches. The proposed method targets the reduction of power consumption in IoT sensor modules with long sleep mode or short packet data in which most networks operate.
无线传感器节点由于其边缘外形因素而受到严重的资源限制，这促使人们通过低功耗技术来提高电池寿命。本文提出了一种电源管理方法，与无线传感器节点中使用的传统电源管理系统相比，该方法可在空闲状态下消耗更少的能量。我们分析和基准测试了 Sleep、Idle 和 Run 模式之间的功耗。为了降低传感器节点的功耗，我们开发了具有五个状态的细粒度电源模式 (FGPM)，根据传感器节点的通信状态来调节能耗。我们在测试台 Mica2 上评估了所提出的方法。结果表明，所消耗的功率比传统方法低 74.2%。所提出的方法针对具有长睡眠模式或短数据包的数据的 IoT 传感器模块的功耗降低，大多数网络都在此类模块中运行。
