# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 2 Related Work  
Researchers have proposed numerous ways to prolong the network lifetime. Mobile sinks are one of the most effective solutions for reducing WSN energy usage and improving network lifetime. Due to its relevance in real-world applications, academics and researchers are interested in mobile sink path planning. WSNs are the foundation of IoT applications; mobile sink path planning is considered one of the key factors underpinning their efficacy. Recent research divides the sink mobility problem into optimum mobile sink paths with and without clustering. Since the research most related to our proposal is the optimal mobile sink path planning with clustering, this section starts with the description of the algorithms that are mostly related to the proposed solution presented in [18; 19; 20; 21; 22; 23; 24]. Then, it discusses their differences from our proposal as well. Finally, as the research presented in [18; 19; 20; 21; 22; 23; 24] does not address the cluster-based routing problem for inter-cluster communication, which is one of this paper's main objectives, this section discusses the proposed solution compared to the related research presented in [27; 28]. An energy-efficient route planning strategy for wireless sensor networks (WSNs) with several mobile sinks is proposed by the authors in [18]. This method is based on a stable election algorithm (SEA) and clustering algorithms for homogeneous and heterogeneous sensor networks. This work aimed to increase network life and connection. The stable election algorithm reduces message flow between sensor nodes and prevents redundant cluster head rotation. Cluster initiation, head rotation, and data collecting make up the SEA. After sensor node deployment, cluster initiation starts. The sensor node with the most neighbors in a homogeneous network is the initial cluster head. The initial cluster head depends on surrounding nodes and node energy in a heterogeneous network. When residual energy drops below a threshold, cluster heads rotate. The node with the greatest rotation index will be the cluster leader. The sink node receives data from nearby cluster heads. Mobile sink path planning examined sojourn locations using the minimum weighted vertex cover problem (MWVCP). The optimal sink path should visit all sojourn spots; however, an MOEA is used to decrease cost, distance, and time. The authors of [19] present an evolutionary game-based mobile sink trajectory design algorithm (EGTDA). The model of the evolutionary game considered each cluster's average residual and inter-cluster energy consumption. The mobile sink travels to a cluster with the highest residual energy and the shortest distance to surrounding clusters. This model proposes a clustering algorithm that compares each node to its neighbors to find the cluster head. When two nodes have the same amount of remaining energy, a smaller node ID is selected. This is done to ensure that each cluster has only one cluster head. KH-TSP optimizes mobile sink paths in large-scale wireless sensor networks [20]. Iterative filtering is used to aggregate the collected data into cluster heads. K-mean clustering is utilized to group randomly placed sensor nodes. It utilizes residual energy and node proximity to construct second-level clusters. TSP used these clusters to build the optimal path sequence. The sequences number is merged to produce a population for the -kill optimization method, where the average delay in each sequence is evaluated. The population was designed to reduce delay. The KH-TSP facilitates large-scale WSN data collection, conserves energy, and extends the network's lifetime. The clustering-based movable sink route approach was suggested by the authors of [21]. To minimize the sink distance between sensor nodes, they used fuzzy logic. They divided the field into 16 equal zones and made an attempt to calculate the sensor residual energy that should be taken into account. Fuzzy logic and mobile sink nodes increased network longevity, particularly in large-scale networks.