# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 1 Introduction  
Industrial wireless sensor networks (IWSN) are deployed in harsh, hardly accessible environments to serve as control and monitoring infrastructure solutions. The sensor nodes have limited energy resources and their availability as well as maintenance cost is greatly influenced by the battery replacement procedure. Moreover, the adoption of heterogeneous nodes in terms of communication protocol, energy consumption, and initial battery level further emphasizes the concern over the network lifetime [1]. The approach towards energy-saving in IWSNs should consider the application specifics so that more efficient use of the residual energy allows network requirements to be achieved. For instance, the main characteristics of industrial IoT applications are bounded delay, deterministic communication, robustness, and security. Hence, industrial communication standards such as ISA100.11a [2] and WirelessHART [3] are poised to assure application characteristics while implementing energy efficiency techniques. Several factors are influencing the energy consumption within IWSNs, such as network topology and protocols, data acquisition, resource allocation, and radio module architecture [4]. To address them, the literature proposes the following energy-efficient mechanisms: radio optimization, data reduction, sleep/wakeup schemes, battery harvesting, and routing schemes [5, 6]. In this paper, we propose the predictive energy-aware routing (PEAR) solution to enhance the network lifetime in terms of prediction, sensor node energy consumption profiles, and flexible energy balancing. The particularities of this solution closely reflect the requirements and constraints of the Industrial WSN applications. For instance, a cost-efficient battery replacement procedure should be predictable and scheduled for the entire network or cluster. To suit this, the time until the first sensor node in the network exhausts its energy is considered the network lifetime. Moreover, given the limited hardware resources in conjunction with the need for real-time hierarchical network management, a low complexity routing algorithm is required [7]. Considering industrial IoT application, the expected maximum network uptime is typically related to sensor specifications (i.e., battery capacity, consumption rate) and data update period. The performance of the proposed routing technique is not evaluated against a theoretical maximum lifetime [8; 9] but rather against the expected network or cluster lifetime thus, not targeting a consumption lower than anticipated. IIoT deployments comprise assorted sensors arguing the need for energy consumption profiles to accommodate different sensor lifetime expectancy within a WSN. The senor nodes are grouped into homogeneous consumption clusters based on the adoption of energy profiles. This approach allows for lifetime prediction and battery replacement scheduling on a per cluster basis. Furthermore, the overall network lifetime can be enhanced by balancing the energy use rate between consumption clusters. This perspective implies an increase in lifetime expectancy for the entire network at the expense of decreasing the lifetime of one or more consumption clusters. The main contributions of this work are as follows:  
* Firstly, this research gives the technical, feasibility and application details of the WSNs lifetime problematics contextualized for IIoT systems deployed in harsh industrial environments. The applicability of the related theoretical solutions is assessed indicating the need to consider the application specifics. * Secondly, a generic conceptual model is proposed for adding energy aware solutions in IWSNs. The model presents the building blocks of energy-aware IWSNs and the method for interconnecting them in a coherent manner. * Thirdly, the major contribution of this paper is the customization of the energy aware model targeting industrial control and monitoring, namely the PEAR solution.