# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 1 Introduction
Therefore, unequal clustering techniques are usually adopted to ensure load balancing in the system. While designing a cluster-based routing algorithm, selection of CH should be performed efficiently because a CH executes energy intensive functions in a cluster such as data receiving, aggregation,  
Figure 1: Cluster-based wireless sensor network (WSN) with different data communication scenarios: (**a**) single-hop communication and (**b**) multi-hop communication from sensor nodes to the end user through base station (BS). and transmission to the BS. These functions consume more amounts of energy compared to the data sensing tasks performed by the surrounding nodes. Furthermore, the distance between the selected node and the BS should be relatively small to increase the energy conservation in the network. Therefore, to elect the appropriate CH, factors such as node residual energy and the distance of the node to BS should be considered to ensure selection of an efficient sensor node among the other cluster nodes. This study proposes an efficient CH selection method based on a weighted sum method to ensure the selection of an energy rich node in the whole network for each cluster. Moreover, the maximum amount of energy is consumed in the steady state phase wherein data is transmitted from the sensor nodes to the main access point, i.e., the BS in the network [6]. This leads to the need for an energy efficient routing algorithm that can solve the data routing problem of energy-constrained nodes. In WSNs, a CH consumes much more energy than the member nodes, owing to its particular responsibilities. However, during the intra-cluster multi-hop communication, the sensor nodes located closer to the CH are required to receive and forward data more frequently compared to the farther nodes, which may lead to more energy depletion. This extra dissipation of energy minimizes the lifetime of the corresponding relay nodes and induces an energy imbalance in the whole network system [9]. In WSNs, while monitoring the environment, if the data forwarding nodes die owing to insufficient residual energy during each routing round, then the further associated nodes have to rediscover the routing path and reestablish the communication link, which will increase the energy consumption in the system. This behavior destructs the stability of the communication and leads to inefficient network performance. Therefore, the residual energy of the relay nodes within a cluster has a great influence on the network energy efficiency, and it plays a significant role in the data transmission process. Furthermore, selecting an efficient path from the source to the destination node also helps in minimizing the network energy consumption. In WSNs, the weights or costs of the links (edges) between neighbor nodes are calculated based on certain factors, and are assigned to the edges. This link or edge weight decides the selection of the optimal path comprising consecutive edges for which the overall cost is minimized. Several algorithms are used in this regard such as Floyd-Warshall, Dijkstra's, and Bellman-Ford techniques [10; 11]. These algorithms are diverse in nature owing to the consideration of different processing times and the amount of information required from the nodes, thereby making each of them applicable to a specific routing scenarios. Furthermore, Machine learning (ML) is a significant tool to utilize distributive characteristics of WSNs and improve the network lifetime. Neural networks (NNs), fuzzy logic (FL), evolutionary algorithms (EAs), reinforcement learning (RL), and swarm intelligence (SI) are different ML techniques used in WSNs to overcome the challenges offered by the resource constrained nature of sensor nodes. Fuzzy logic, owing to its potential, has been fully explored in various fields such as signal processing, facial and speech recognition, medical, robotics, marketing, and networking [12; 13].