# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 2 Related Work
In addition to this, several reinforcement learning techniques have been introduced for improving network lifetime in WSNs [26]. In Ref. [27], a two-stage sleep scheduling approach is introduced based on reinforcement learning for providing area coverage in WSNs. This approach enables sensor nodes to sparsely cover the target area without the need for node location information. This is done by Q-learning algorithm to select a limited number of nodes which can appropriately cover the deployment area. As a result, the network lifetime is improved and the desired area coverage is achieved. In Ref. [28], a learning algorithm is proposed to determine the optimal cell selection which includes a power allocation mechanism. Each user in a two-tier femtocell network is associated with a QoS-aware function which ensures the selection of appropriate cell to connect and maximizes QoS-based performance. A study on energy-saving routing algorithm for WSNs (LEACH-DT) is proposed in Ref. [29]. This scheme introduces a dynamic energy threshold value to reduce the energy consumption during the CH selection and rotation phase. Additionally, it resolves the issue of reallocation of time slots for each round by exchanging the time slot information between the candidate CH and the current CH. In Ref. [30], a study was documented on optimizing the relay nodes by tree-cluster based shortest path (TCBSP) in WSNs. The routing scheme ensures proper connectivity among each cluster member node by organizing the nodes in the form of a tree cluster structure. This method improves the lifespan of the individual nodes in the network because it first aggregates the data in a cluster form and then transmits it to another cluster in the form of a tree. The shortcoming of this scheme is that it does not consider the residual energy of individual nodes while selecting the CH because each node has equal probability of becoming the CH, which may result in the selection of a node with less energy as the CH, and further causes network failure and critical data loss. Table 1 shows a summary of performance characteristics. It summarizes the key features, advantages, and disadvantages of some of the clustering-based routing algorithms discussed above. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clustering Routing Protocol** & & **Key Features** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Low-energy adaptive clustering hierarchy (LEACH)} & 1. & Introduced the concept of clustering and cluster head (CH) for wireless sensor network (WSN). & 1. & Localized coordination and control for cluster set-up and operation. & 1. & Inapplicable to time-constrained applications. \\  & 1. & Equal-sized cluster formation. & 1. & Maximization & 2. & Inapplicable to large scale due to single-hop communication. \\  & 1. & Equal-sized cluster formation. & 1. & Minimizes intra-cluster communication & \\  & 2. & CH selection takes place on the basis of primary and secondary & 2. & 1. & Minimizes intra-cluster communication & \\  & 1. & Equal-sized cluster formation. & 1. & & \\  & 2. & CH selection takes place on the basis of primary and secondary & 1. & & \\  & 1. & Network parameters. & 1. & & \\  & 1. & Cluster formation takes place on the basis of various events. & 1. & & \\  & 2. & Distance-based link cost is calculated to compute & 1. & & \\  & 2. & Distance-based link cost is calculated to compute & 1. & & \\  & 1. & Multi-objective weighted function is calculated as link cost between nodes. & 1. & & \\  & 2. & Investigates the impact of varying weighting factor on wireless mesh network & 1. & & \\  & 2. & Multi-objective weighted function is calculated as link cost between nodes. & 1. & Well-suited for time-constrained applications. & \\  & 2. & Investigates the impact of varying weighting factor on wireless mesh network & 2. & Minimizes number of hops and provides good balance & Inappropriate for energy-efficient scenarios.