Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform

Abstract

In industrial wireless sensors networks (IWSNs), the sensor lifetime predictability is critical for ensuring continuous system availability, cost efficiency and suitability for safety applications. When deployed in a real-world dynamic and centralised network, the sensor lifetime is highly dependent on the network topology, deployment configuration and application requirements. (In the absence of an energy-aware mechanism, there is no guarantee for the sensor lifetime). This research defines a conceptual model for enhancing the energy predictability and efficiency of IWSNs. A particularization of this model is the predictive energy-aware routing (PEAR) solution that assures network lifetime predictability through energy-aware routing, energy balancing and profiling. The PEAR solution considers the requirements and constraints of the industrial ISA100.11a communication standard and the VR950 IIoT Gateway hardware platform. The results demonstrate the PEAR ability to ensure predictable energy consumption for one or multiple network clusters. The PEAR solution is capable of intracluster energy balancing, reducing the overconsumption 10.4 times after 210 routing changes as well as intercluster energy balancing, increasing the cluster lifetime 2.3 times on average and up to 3.2 times, while reducing the average consumption by 23.6%. The PEAR solution validates the feasibility and effectiveness of the energy-aware conceptual indicating its suitability within IWSNs having real world applications and requirements.

Hybrid Optical and Thermal Energy Conversion System to Power Internet of Things Nodes

“…The maximum values of current and voltage generated by the connector can be calculated from Equations ( 9) and (10) [36,39]:…”

“…Enhancing the operating time of IoT/WSN nodes can be achieved through several methods. One of these is the implementation of energy-efficient routing and communication channels, as communication is where the node typically consumes the most energy [10][11][12]. Another solution is equipping the node with an efficient energy buffer [13,14].…”

Multi-Network Latency Prediction for IoT and WSNs

“…First, the study elucidates how high data transmission within a limited bandwidth induces network congestion, potentially owing to multiple factors [5]. Second, it outlines challenges pertaining to WSNs' energy conservation-focused design and underscores the role of robust network prediction software in addressing trade-offs such as miniature device usage, decreased hardware and software processes, and low-rate communication [6,7]. Third, as the IoT network expands, the pivotal role of WSNs in connection and communication is highlighted, emphasizing the potential for enhanced integration through scalable network gateways and improved routing options with network failure prediction [8][9][10].…”