# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 2 Related Work  
One of the main concerns in the design of IWSNs is power efficiency targeting the prediction and extension of the network lifetime. While several energy efficient routing algorithms were proposed, to the best of our knowledge, no studies have been found to have a systematic approach (i.e., considering the specific requirements of an IIoT application) and a hardware evaluation platform. Moreover, the PEAR solution is assessed on a heterogeneous dual-standard WSN platform comprising the ISA100.11a and WirelessHART industrial communication protocols [11], thus further emphasizing the novelty of the study. PEAR is designed to add energy-efficient policies suitable for industrial monitoring and control applications and respecting the ISA100.11a standard. The research carried out on routing algorithms employ simulations based on network models. Surveys such as [6; 12; 13] have classified the network models with respect to the traffic pattern, network structure, communication model, topology, and network management. Given this classification, the ISA100.11a network can be defined as a centralized WSN where routing and resource allocation decisions are the responsibility of a single Gateway entity. It uses a hierarchical network structure in which the sensor nodes can assume different roles namely, router and input/output. A router node is capable of forwarding the traffic packets of its neighbors while input/output nodes can route only their own data packets. A dynamic self-configuring and self-healing mesh network [14] is employed having up to five layers and respecting the convergence traffic pattern where nodes on lower layers forward the data packets of their upper-layer neighbors. Thus, the data load is cumulative and higher for nodes closer to the sink node. Network models presented in [8; 15; 16] aim to monitor an area and assume that only a set of the sensing nodes need to be active at a certain time. The network is considered depleted only if the remaining sensors do not satisfy the expected monitoring performance. On the other hand, in order to reflect the demands of real IWSNs deployments we regard that all nodes should sense data during the network lifetime and that energy cannot be balanced by rotating the active set of nodes. In what follows, PEAR will be contextualized using a top-down approach. In [1] the need for energy-efficient solutions is underlined as a part of the challenges and directions for IIoT systems. To address such demands, upper-layer designs are recognized to have a major impact. However, current research is not applicable to real IIoT environments. Among the upper-layer energy-saving mechanisms presented in [5] the routing algorithms play an important role in the efficient use of the bandwidth and battery resources [17]. Two routing paradigms are the most related to PEAR namely, cluster architectures and energy as a routing metric. A considerable amount of research has been carried on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol and its variants developed in more than 15 years [18; 19; 20]. It is a hierarchical protocol in which a part of the sensor nodes assumes the cluster head (CH) role to coordinate the neighboring nodes forming a cluster. PEAR overcomes the drawbacks of the LEACH protocol by having a deterministic resource allocation and topology control and not employing a random load rotation among CHs. Moreover, PEAR adapts to the nodes residual energy level and is not limited to single hop communication. Thus, larger areas can be covered, and the transmission power is lower due to shorter transmission distances [21; 22]. These disadvantages were addressed by LEACH-based protocols such as [23; 24; 25]. However, the centralized multi-hop variants imply high overheads and complexity.