Design exploration of energy-performance trade-offs for wireless sensor networks

Abstract

Wireless sensor networks (WNSs) are gradually evolving from a promising technology to a well-established reality in a large set of different domains. In order to fulfill the requirements of the specific scenario, a WSN must provide the right tradeoff between performance and lifetime, which is heavily determined by the network design. However, although the complexity of WSNs is increasing, the design space exploration is often carried out manually without the support of a general analytical methodology. In this paper, we advocate a model-based approach as an efficient and scalable way to explore the energy-performance tradeoffs during the design. In particular, we show that it is possible to define systemlevel models to describe wide classes of WSNs, providing a quick and accurate network evaluation. As a proof of concept, we propose a general model that describes the main characteristics of a class of WSNs for human health monitoring, and we apply it to a real case study. The results show that the energy-performance estimation error of the model never exceeds 1.74% compared to real data, while the evaluation time is reduced by up to 6 orders of magnitude with respect to an accurate network simulation.

A Wireless Body Sensor Network for Activity Monitoring with Low Transmission Overhead

“…In fact, wearable nodes typically feature very basic microcontroller architectures with integer arithmetic modules and modest memory resources, thus imposing significant limits on the complexity of the algorithms they can execute. Sensor nodes usually include a radio for data transmission, which typically represents the most energy-hungry component of the system [6].…”

“…In order to minimize the subject's discomfort while wearing a WBSN for a prolonged time, a minimum number of nodes has to be deployed, and their size has to be miniaturized. The latter requirement poses significant limitations on the size of the batteries used by the wearable nodes, thus forcing them to limit their computational effort and the volume of data they transmit [6]. A common strategy to increase the lifetime of a WBSN [2] is the preliminary extraction of a set of features from the sensed data: this operation, performed directly on the wearable node, massively reduces the amount of transmitted data.…”

Analytical modelling of the A-ANCH clustering algorithm for WSNs

“…For a large number of topologies, using physical studies and also running a network simulator is unfeasible due to the time cost. Algorithm modelling on the other hand offers an effective and economic tool which can help to evaluate the benefits of an algorithm [13].…”

BodySim

“…[3,9]) have typically concentrated on the BSN components (both software and hardware). There has been some limited modeling of the relationship between the BSN and environment [10,2].…”