Radio Sleep Mode Optimization in Wireless Sensor Networks

Abstract

Energy-efficiency is a central challenge in sensor networks, and the radio is a major contributor to overall energy node consumption. Current energy-efficient MAC protocols for sensor networks use a fixed low power radio mode for putting the radio to sleep. Fixed low power modes involve an inherent tradeoff: deep sleep modes have low current draw and high energy cost and latency for switching the radio to active mode, while light sleep modes have quick and inexpensive switching to active mode with a higher current draw. This paper proposes adaptive radio low power sleep modes based on current traffic conditions in the network. It first introduces a comprehensive node energy model, which includes energy components for radio switching, transmission, reception, listening, and sleeping, as well as the often disregarded micro-controller energy component for determining the optimal sleep mode and MAC protocol to use for given traffic scenarios. The model is then used for evaluating the energy-related performance of our recently proposed RFIDImpulse protocol enhanced with adaptive low power modes, and comparing it against BMAC and IEEE 802.15.4, for both MicaZ and TelosB platforms under varying data rates. The comparative analysis confirms that RFIDImpulse with adaptive low power modes provides up to 20 times lower energy consumption than IEEE 802.15.4 in low traffic scenario. The evaluation also yields the optimal settings of low power modes on the basis of data rates for each node platform, and it provides guidelines and a simple algorithm for the selection of appropriate MAC protocol, low power mode, and node platform for a given set of traffic requirements of a sensor network application.

Energy-efficient peer-to-peer networking for constrained-capacity mobile environments

“…Most of the existing M2M processing architectures and wireless protocols achieve ultra-low power connectivity by extensive use of sleep modes (Jurdak et al 2010, Rault et al 2014, Xing et al 2007,Gomez & Paradells 2010, which is basically impossible with current P2P protocols due to their frequent signalling traffic. By addressing this problem, ADHT contributes to an important research topic, since it is predicted that M2M device connections worldwide will grow rapidly during the coming years (Analysys Mason 2013, CISCO Infographic 2013.…”

“…Also limited battery life affects the above mentioned features through strict energy saving requirements (Vergara & Tehrani 2013, Hoque et al 2015. The use of sleep modes, for example, may significantly increase network latencies (Jurdak et al 2010, Gomez et al 2012, Ayoub et al 2011. However, major differences in the hardware capacity between peer nodes pose even a more substantial challenge for the P2P network functionality than the limited hardware capacity itself would inflict (Rao et al 2003, Kim & Park 2006.…”

“…These schemes include smart duty cycling (activity scheduling) (Lu et al 2011, Jurdak et al 2010, and passive wake-up radios (Rault et al 2014, Ba et al 2013. In smart duty cycling, nodes periodically wake up to listen to messages from neighbour nodes and return to a sleep mode if no activity is detected.…”

“…These optimization methods include, e.g. adaptive transmission power management on radio interfaces and duty cycling, allowing radio interfaces and processors to have long idle periods between active periods (Bontu & Illidge 2009, Rault et al 2014, Xing et al 2007, Jurdak et al 2010. In order to obtain the maximal benefit of these technologies, higher-layer solutions must, among other measures, exploit these features as efficiently as possible.…”

Minimum energy broadcast in duty cycled wireless sensor networks

“…Thus, in a day of operation of the network, the total energy consumption of a node due to wakeup is equal to 15μA*3V *86400s = 3.9J that can be utilized to transmit or receive almost 21 Mbits of data. Recently several papers have pointed out that neglecting the so-called switching energy to switch from one mode to another can lead to algorithms with sub-optimal energy consumption or reduce network lifetime [5,9,23,24]. Ruzzelli et al [38] report measurements on three different chipsets for sensor nodes that show that at low traffic load, the switching energy can dominate the energy required for transmission.…”