e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Abstract

Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure.Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments. KEYWORDSadaptive sensing, data collection, energy-aware sensing, wireless sensor networks INTRODUCTIONAs a key enabler of the Internet of Things (IoT) and Smart Cities paradigms, wireless sensor networks (WSNs) are currently a technology capturing much attention, both from the academic community and industry. Sustained by the development of multifunctional low-cost wireless sensors, WSNs are applied in a large range of scenarios, many of then requiring operation without human intervention. 1,2 This versatility and difficult maintenance urge for mechanisms of self-management and power saving in order to allow a cost-effective adaptation of the sensing process to the application area, while optimising WSN lifetime. 3 Reducing energy consumption of sensor nodes for improving WSN lifetime has been a major topic of research in the last decade, being identified three major subsystems impacting on energy consumption-communication, sensing, and processing. The communication subsystem revealed to be the most demanding, even for devices able of energy harvesting. 4 This motivated solutions such as data gathering aggregation in order to reduce the number of transmission events.However, several works revealed that, depending on the operational scenario, acquiring and processing data may be more demanding than communicating. 5,6 This suggests that a versatile data gathering process should avoid acquiring redundant information, even when data aggregation strategies are in place.Int J Commun Syst. 2020;33:e4153.wileyonlinelibrary.com/journal/dac

Improvement of Power Consumption in Wireless Body Area Networks by PCM Sampling ​

“…In the past years several solutions and discussions proposed about mentioned challenges. Bradai et al [25] proposed data scheduling and aggregation under WBAN/WLAN healthcare network. They proposed scheduling algorithms to satisfy QoS needs and to overcome the starvation mode of the packets without the highest priority.…”