Bounds on Dynamic Modulation Scaling for Wireless Sensor Networks

Improving Energy Efficiency in QoS-Constrained Wireless Sensor Networks

“…Chu and Sethu [42] propose a game-theoretic approach to control transmission power of every SN taking into consideration the uneven energy consumption distribution. However, transmission International Journal of Distributed Sensor Networks 19 Wake-up receivers Triggering the deactivated receiver whenever an incoming packet is detected Communication delay [31][32][33] UWB communication Transmission of very short impulses emitted in periodic sequences Connectivity, communication reliability [34] Memory leakage control Reducing the memory leakage current - [35,36] Low-power -OSs Energy-aware task scheduling and resource management Run-time operation [35,37] Energy-aware CRSNs A self-adaptive mechanism for allocating radio resources Latency, complexity [38,39] Scaling Adapting the operating voltage and frequency based on instantaneous computational load Processing latency [40,41] Transmission power control Adapting the transmission power according to the residual energy and network load Connectivity, interference, communication reliability [42] power decrease may have negative impacts on communication latency, interference, and connectivity. Specifically, reducing the transmission power leads to increasing the number of hops to convey data.…”

“…This method is referred to as dynamic voltage scaling (DVS) [40]. Additionally, dynamic modulation scaling (DMS) is a well-known method for reducing broadcasting energy with respect to the number of packets that need to be transmitted at that particular time interval [41]. However, DMS methods may increase the overall system latency due to the inherent complexity.…”

Energy-aware wireless networked control using radio-mode management

“…In this paper, we focus on the radio chip as it is admitted that the energy it consumes represents 50% to 80% of the overall consumption of a smart sensor node, see e.g. [11], [13], [20]. An often-quoted rule of thumb is that executing 3 million instructions is equivalent to transmitting 1000 bits at a distance of 100 meters in terms of expended energy [15].…”