# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 2 Related Work  
The routing in IoT sensor-based networks is one of the most remarkable research areas in communication networks. There are lots of research articles related to this field. There are various parameters in the network that can be used to optimize the routing, for example, optimization with load balancing (traffic load distribution), as discussed in [16]. A proactive tree-based routing protocol, the routing protocol for low-power and lossy networks (RPL), is defined by RoLL [17]. RPL is a standard protocol that operates on an IPv6-based IoT network. It brought an opportunity to develop WSNs on a very large scale. Routing and message control are the RPL's most important mechanisms for establishing and maintaining an effective and stable network. Despite its standing as the standard routing protocol for IoT networks, RPL has had various flaws since its inception, and other approaches have emerged to address them [18]. Among these, routing loops are critical. Most of the recent studies that have aimed at energy efficiency and load balancing in WSNs and WSN-based IoT networks preferred the cluster-based approach. In [14], the authors proposed the integration of the bat algorithm and low-energy adaptive clustering hierarchy (LEACH) for the efficient cluster head selection to reduce energy consumption and balance load among the network nodes. The nodes are also bound to follow a schedule for the transmission of their data packets. This mechanism primarily focuses on the cluster head selection by considering the nodes' energy levels. Each cluster head is bound to have a particular number of connected cluster members. However, unlike this mechanism, the distribution of nodes in a network can be random, which makes it difficult to specify the number of nodes for each cluster. [PERSON] and [PERSON] [19] introduced a fully distributed energy-aware multi-level (FDEAM) routing and clustering mechanism for WSN-based IoT networks. The two-level and multi-level inter-cluster transmission methods are represented in this work. In the second level, the communication and transmission strength are determined by considering the distance between the nodes and the base station (BS). The clusters are statically distributed over the entire network. However, the option for re-clustering the network is also defined. Self-arranged nodes elect the limits for clustering, and cluster heads are selected by executing the FDEAM method. However, this method is inappropriate for non-uniform node distribution and has the shortcoming of being reliant on a dominant source. The authors of [20] presented an energy-efficient architecture of a self-sustaining WSN based on an energy-collecting BS and a mobile charger considering the cost of deployment. They conducted extensive simulations and demonstrated the efficacy of their proposed strategy by showing that it maximizes the expected network lifetime while minimizing deployment costs. The main idea is focused on the usage of mobile chargers and the energy-harvesting BS. However, the work did not primarily deal with energy-efficient routing. The position responsive routing protocol (PRRP) was proposed in [21]. The main objective of this proposed work was to minimize energy consumption by incorporating the global positioning system (GPS) into the nodes. The network is divided into equally sized grids with a static or dynamically distributed number of nodes. The nodes can adjust their transmission power by using GPSs while communicating with each other. To balance energy consumption within each cluster, [PERSON] et al. [22] suggested uneven cluster generation and distributed cluster head rotation based on residual energy and relative location. The authors also designed a routing path updating system to prevent node energy depletion. The selection of the cluster head is based on the level of residual energy of the nodes. The routing paths are dynamic and also associated with the nodes' energy levels.