Abstract
Wireless Sensor Networks (WSN) refer to networks where the sensors are deployed in an environment to sense and select data. WSN sensor nodes have limited power and cannot be recharged easily. Consequently, the faster sensor nodes to deplete their energy budget are those close to the sink as they have to relay all data emanating from any sensor in the network. Thus, a hole of energy around the sink is created as the sink coverage nodes have drained their initial energy thus leading to sink unreachability. The WSN lifetime maximization problem has always been a hot research topic. Collecting data in WSN using a mobile sink is an efficient approach for achieving WSN longevity and preventing the energy hole problem. However, finding the optimal trajectory along with its appropriate flow routing is a challenging problem since many constraints should be considered. This paper discusses and compares several existing WSN-lifetime-maximization using sink mobility solutions. These solutions are mainly classified into two types: Linear Programming and Artificial Intelligence-based solutions. The state-of-the-art solutions are compared in terms of network topology, sojourn points and duration, buffer size, and overhearing. Finally, a discussion of the WSN lifetime maximization constraints is provided to define a promising sink mobility model.