# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 1 Introduction  
Wireless sensor networks (WSNs) have received great attention owing to their wide usage in potential applications in numerous fields [1]. These networks comprise a large number of sensing devices known as sensor nodes. These sensor nodes monitor the target area of deployment and collectively transmit the monitored information to the base station (BS). Additionally, the sensors have limited power, sensing, communication, processing, and memory resources. The small size and economical cost of these sensors contribute to their limited resources. The size of a sensor node is application specific. It may vary from a microscopic particle, e.g., a tooth sensor, to a brick, e.g., a weather station. Their cost also differs with the degree of the required sensor capabilities, e.g., sensors equipped with simple hardware design have a low cost and those with complex hardware design have a higher cost. Therefore, changes in the footprint and price of the sensor have a significant impact on its resource limitations [2]. Sensor nodes are primarily battery operated devices; therefore, the battery is the most critical and precious resource of a sensor. It has considerable effect on the overall lifetime of a WSN. Energy depletion of sensor node batteries results in coverage holes in the network, i.e., such nodes become non-operational and cease to participate in information sensing or data collection. Moreover, this may lead to connectivity loss in different parts of the network because the communication routes may bedamaged owing to node failure events [3]. Therefore, the energy consumption of the sensor node must be correctly managed to increase the network lifetime and functionality for a rational amount of time. The lifetime of a network is defined in the literature as the time duration elapsed until the first node in the network dies, i.e., depletes its energy [4]. Network topology control provides an efficient approach for addressing such challenges in the WSN [5]. Topology control is defined as a set of techniques or methods that can transform an underlying network structure in an efficient manner, thereby maximizing system performance or reducing transmission cost [6]. Several techniques of topology control have been proposed for WSNs; however, clustering has proven to be the most efficient and widely used scheme for managing network structure. Moreover, cluster-based routing mechanisms have been presented in the Refs. [5; 6; 7]; these mechanisms comprise two phases: the set-up phase and the steady state phase. In the set-up phase, all the sensor nodes are organized in the form of clusters. In each cluster, a cluster head (CH) selection algorithm is executed to elect a node as the leader of the corresponding cluster and perform related tasks. Therefore, each cluster consists of member nodes and a CH. After the CH selection, the steady-state phase starts and the CH aggregates the data received from surrounding member nodes and transmits it to the BS directly (single-hop communication) or through other CH nodes (multi-hop communication) as illustrated in Figure 1.  
On the contrary, multi-hop communication within a cluster can reduce the number of communication links and avoid the communication congestion compared to direct communication. This is owing to the fact that the CH has to communicate with more member nodes simultaneously. Additionally, multi-hop communication can enable member nodes to assist the CH in sharing the task of data aggregation and minimize the energy consumption of CHs. This leads to the enhanced lifetime of the underlying network. Cluster heads gather the collected information from all the member nodes. After the data aggregation process, the CHs forward the data to the BS in multi-hop manner to perform the data upload [8]. In cluster-based network systems, the nodes are randomly deployed with different distances and energy consumptions. The formed clusters with equal number of nodes always lead to uneven energy consumption in the network.