# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 2 Related Work
\\  & & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\Considering the inadequacy of the abovementioned algorithms, a routing algorithm, which is an energy-efficient fuzzy logic Dijkstra-based routing scheme for WSNs, is proposed in this study. In the proposed scheme, the nodes are organized in the form of clusters using a K-means clustering scheme. Each cluster is then assigned a CH node by calculating a weighted sum function for each cluster node and then comparing with the standard weight of the cluster. Once the network is set-up, the data transmission phase starts on the detection of an event. The node near the location of the event senses the information and transmits the data to the final node through an energy-efficient and delay resistant route by considering the neighbor nodes' residual energy and the cost of intra-cluster communication. This approach avoids unnecessary energy consumption of the relay nodes by load balancing; therefore, it is more helpful for the optimization of energy consumption and extends the lifetime of the network system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Clustering Routing** & \multicolumn{2}{c}{**Key Features**} & **Advantages** & **Disadvantages** \\ Protocol & 1. & Network area is divided into three horizontal layers each & & \\ Hierarchical unequal & & split into grids. & Improves network lifetime as & Linguistic variables provide \\ clustering fuzzy & 2. & CH selection mechanism & compared to LEACH. & inefficient results when node \\ algorithm (HUCA) & & based on fuzzy logic. & & mobility is increased. \\ \hline  & 1. & Calculates an evaluation & & 1. & Residual energy of \\ Energy saving routing & & index-based link cost to search & Minimizes energy consumption & neighboring node is not \\ algorithm based on & & path-between two nodes. & by accounting the energy & considered while \\ Dijkstra (ESRAD) & 2. & Dijkstra-algorithm is used to identify path with minimum energy consumption. & dissipation in information & 2. & Inapplicable for real-time \\  & & energy consumption. & transmission phase. & applications due to \\ \hline  & 1. & Pool manager (PM) node is & & 1. & Ignores the residual energies \\ Shortest path evaluation & & responsible for selecting & & of the nodes in the network. \\ using fuzzy logic (SPFL) & 2. & Fuzzy logic function is & Provides delay–efficient path & 2. & Large overhead owing to \\  & & proposed for data routing & from source to destination node. & control messages sent to PM \\  & & in WSN. & & node for finding least \\ \hline  & 1. & Fuzzy membership function is & & 1. & Node mobility is \\ Fuzzy maximum lifetime & & used to calculate link weight. & Maximizes network lifetime by & not considered. \\ (FML) & & Minimum weighted path is & taking the residual energy of & 2. & Inapplicable to the \\  & & selected via & source node into account. & time-constrained applications. \\ \hline  & 1. & Unequal clustering & & \\ Distributed unequal & mechanism is proposed. & & balances energy consumption & Neighbor node’s residual energy is \\ clustering using fuzzy logic (DUCF) & 2. & Determines cluster-size and & among clusters by forming & not taken into account which may \\  & & CH via fuzzy logic. & appropriate sized clusters. & lead to load imbalance while data \\ \hline  & & 1. & Resolves reallocation time & \\ Low-energy adaptive & & & slot problem among & \\ clustering hierarchy- & & & candidate and current & Uneven distribution of CHs may \\ dynamic threshold & & used to select CHs. & 2. & balances energy \\ (LEACH-DT) & & & consumption of nodes in & network. \\ \hline  & 1. & Clustering approach of & & \\  & & threshold sensitive energy & & \\ Tree-cluster based & & efficient sensor network & & \\ shortest path (TCBSP) & 2. & & & \\ \end{tabular}
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Table 1: _Cont._