A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks

Abstract

Innovative Internet of Things (IoT) applications with strict performance and energy consumption requirements and where the agile collection of data is paramount are arising. Wireless sensor networks (WSNs) represent a promising solution as they can be easily deployed to sense, process, and forward data. The large number of Sensor Nodes (SNs) composing a WSN are expected to be autonomous, with a node’s lifetime dictated by the battery’s size. As the form factor of the SN is critical in various use cases, minimizing energy consumption while ensuring availability becomes a priority. Moreover, energy harvesting techniques are increasingly considered as a viable solution for building an entirely green SN and prolonging its lifetime. In the process of building a SN and in the absence of a clear and well-rounded methodology, the designer can easily make unfounded and suboptimal decisions about the right hardware components, their configuration, and reliable data communication techniques, such as automatic repeat request (ARQ) and forward error correction (FEC). In this paper, a methodology to design, configure, and deploy a reliable ultra-low power WSNs is proposed. A comprehensive energy model and a realistic path-loss (PL) model of the sensor node are also established. Through estimations and field measurements it is proven that, following the proposed methodology, the designer can thoroughly explore the design space and the make most favorable decisions when choosing commercial off-the-shelf (COTS) components, configuring the node, and deploying a reliable and energy-efficient WSN.

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

“…Each band was selected based on the its past access experiences. A framework to design, configure, and deploy a reliable ultralow power wireless sensor networks is proposed in [18], with the goal of achieving improved energy-efficiency, latency, and reliability. An in-depth energy model of the sensor node were presented, and validated through measurements.…”

“…However, the average number of packets in the IoT buffer, and hence the network stability are unaffected, since they depend on P F i , which is not affected by IoT mobility. To gain a complete understanding of the effect of IoT mobility on the network metrics, changes to the network model, assumptions and derivations must take place, 18 which entails a paper on its own to give it justice. Hence, it is deferred to future work.…”

Energy-Efficient Routing Protocol for Node Lifetime Enhancement in Wireless Sensor Networks

“…Two methods are used to detect node failure. In [8] the authors presented a realistic model of power consumption for WSN by involving the characteristics of a low power transceiver.…”

Design of the Squared Daisy: A Multi-Mode Energy Harvester, with Reduced Variability and a Non-Linear Frequency Response

“…Energy recovery systems and energy harvesting systems are the subjects of growing interest [1,2,3,4,5]. In addition, the number of connected objects is constantly increasing [6], and the energy requirements of these systems are still growing and many sensors are manufactured without it being physically possible or economically viable to replace their batteries once they are depleted [5]. The development of energy recovery and energy harvesting systems makes it possible to increase the lifespan of these objects, which is important notably in devices placed in remote locations or harsh environments.…”