A two-prong approach to energy-efficient WSNs: Wake-up receivers plus dedicated, model-based sensing

Abstract

Energy neutral operation of WSNs can be achieved by exploiting the idleness of the workload to bring the average power consumption of each node below the harvesting power available. This paper proposes a combination of state-of-the-art low-power design techniques to minimize the local and global impact of the two main activities of each node: sampling and communication. Dynamic power management is adopted to exploit low-power modes during idle periods, while asynchronous wake-up and prediction-based data collection are used to opportunistically activate hardware components and network nodes only when they are strictly required. Furthermore, the concept of “model-based sensing” is introduced to push prediction-based data collection techniques as close as possible to the sensing elements. The results achieved on representative real-world WSN case studies show that the combined benefits of the design techniques adopted is more than linear, providing an overall power reduction of more than three orders of magnitude

Low Power Wide Area Networks: An Overview

“…Duty cycling can also be extended beyond the transceiver to other hardware components, as explored in the context of many low-power embedded networks [25], [26]. Modular hardware design may provide ability to choose different operational modes and turn on or off individual hardware components (such as auxiliary components and storage and microcontrollers) [27].…”

On the Stability of a Hardware Compensation Mechanism for Embedded Energy Harvesting Emulators

“…The emulator has been tested in four different configurations of the connected load, namely: Cases 1, 2, and 3, corresponding to the three configurations analyzed in Section 4 and in a fourth configuration (denoted as Case 4) where the emulator load was a real VirtualSense WSN node [20]. Off-the-shelf discrete components that were used for the experiments take the following values:…”

2.3.3 SMERS - Energieautarkes drahtloses Sensornetzwerk zur Detektion von Katastrophen und zur Lagebilderfassung in U-Bahnsystemen

“…Dabei wurden meist Autotunnel [5,6,7] oder Minentunnel [8,9] betrachtet. Weniger im Fokus liegen Zug- [10,11] und U-Bahn-Tunnel [12,13]. Bennet et al [12] installierten ein drahtloses Sensornetzwerk in der Londoner und der Prager U-Bahn.…”