## 3 Literature Review
The authors of article [20] suggest a jamming-resilient multipath routing protocol to ensure that purposeful interruption and jamming and isolated and localized failures do not interfere with the overall performance of networks. The JarmRout relies on a mix of three critical schemes to accomplish this objective. These are connection quality, traffic load, and geographic distance schemes. For fast and secure data transfer, the authors of article [17] suggest a stochastic packet forwarding (SPF) technique. TheSPF's fundamental tenet is utilizing a number of real-time network indicators to create a random selection of forwarding drones. The SPF calculates the forwarding availability of each candidate drone by objectively allocating the weight to several real-time network parameters based on the entropy weight theory. To facilitate effective, trustworthy communication and data transfer in network, the authors of paper [31] suggest a link-quality and traffic-load-aware optimal link state routing protocol (also known as LTA-OLSR). The suggested link quality system uses the statistical information of received signal strength indication (RSSI) of packets to discriminate between the connection qualities of a node and its neighbor nodes. To effectively and safely transmit data packets to ground targets in WSNs, authors propose a hybrid packet forwarding method, HYBD fwd, in an article [42]. End-to-end routing and delay-tolerant forwarding make up the HYBD fwd. In order to get data packets from the drone to their final destination on the ground, end-to-end routing uses a route discovery technique. The Distributed Energy and Coverage Aware Routing algorithm (DECAR) [12] has been developed to extend the lifespan of WSN. DECAR solves the problem of data transmission hot spots. Tables 1-3 provide a comprehensive summary of the above material and reveal the common shortcomings of the algorithms. These difficulties are solved to some extent by CPCP but the computational cost on sensor nodes is prohibitive. As a result, a new algorithm, EEWBP, has been developed to address these concerns. To save energy, EEWBP employs some simple techniques in an algorithm. This reduces the computational strain on SN, resulting in a more extended period for complete area coverage, flexibility, and enhanced scalability. There are substantial tactical differences between the cluster creation processes used in CPCP and CUCA. Our weighted-sum approach chooses the most efficient CMs and CHs. The remaining energy, node density, and degree of the SN are all energy and trust value criteria considered when determining weight values. During cluster creation, the CPCP only feels each sensor node's energy. However, the EEWBP considers all factors that have the highest impact on the network's lifespan. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Published Year** & **Type of Mode** & **Approach** & **Sensing Model** & **Drawbacks** \\ \hline \multirow{3}{*}{LEACH [1]} & \multirow{3}{*}{2000} & \multirow{3}{*}{Homogenous} & \multirow{3}{*}{Centralized} & \multirow{3}{*}{Disc} & CH nodes are distributed in a non-uniform manner. CH is chosen at random and in each cluster, the nodes are not spread uniformly. \\ \hline \multirow{3}{*}{I-LEACH [5]} & \multirow{3}{*}{2015} & \multirow{3}{*}{Homogenous} & \multirow{3}{*}{Centralized} & \multirow{3}{*}{Disc} & Unlike nodes that receive distinct data, CH combines collected data to cut data transmission costs. \\ \cline{1-1} \cline{5-5} \cline{7-7}