# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Target Coverage  
A number of studies have focused on addressing the target coverage problem. The objective of the authors in [16] for example, was to have the maximum number of targets monitored by sensor nodes before they run out of energy. To extend the network lifetime, they proposed an approach to calculate the maximum number of disjoint sensor covers with a heuristic greedy algorithm using three parameters: (1) the set covers; (2) the number of targets; (3) the number of sensors. The proposed approach finds the set covers that can be used to maximize the network lifetime of a WSN in polynomial time. They showed that it gives a local maximum solution due to its heuristic search. Maintaining full connectivity of the WSN was the primary focus of [17], in which the authors proposed an algorithm for ensuring that both conditions (i.e., coverage and connectivity) are met for conserving energy and extending the lifetime of the WSN. It relies on a distributed response procedure to locate the routes between the sensors and the sinks. The procedure allows for several routes leading from a sink to individual sensors to be discovered. Depending on the set of routes, a Minimum Distance Path (MDP) greedy algorithm is derived from resolving the Maximum Set Cover (MSC) issue with the goal of locating the highest possible number of set covers. The MDP-MSC algorithm works with the concept of choosing the sensors possessing the shortest distance to the set cover under construction, in order to include them into the contemporary set cover after each process. In this algorithm, the discovered routes, which are determined by the maximum number of routes per cover, influence the choice of sensors that are added to the set cover. The authors stated that the optimum value of the maximum number of routes per cover, as well as the level of density of the sensors within the target area, all impact the performance and control of transmission overhead in the WSN. Based on simulation and experimental analysis using MDP-MSC, a number of set covers received a noticeable growth by 13% and 21% compared to the Greedy-MSC [18] and Heuristic Approach-Multidimensional Scaling (HA-MDS) algorithms [19]. MDP-MSC did not just outperform Greedy-MSC and HA-MDS; it also had the lowest time complexity between them. MDP-MSC achieved a balanced consumption of energy and less computational time. On the other hand, the authors in [20] proposed a distributed lifetime coverage optimization (DiLCO) protocol to preserve coverage as well as boost the WSN's lifetime. The protocol begins by applying a divide and conquer algorithm to split the target location into smaller regions known as sub-locations, after which it is concurrently carried out in every single sub-location. Assumptions are made that the partitioning of the sub-locations is carried out in a regular manner and that the sensors are set uniformly in each sub-location. The periodic DiLCO protocol possesses four stages in each period: information exchange, leader election, decision, and sensing. Every period possesses a single set cover handling the sensing operation. This type of cyclic scheduling boosts the reliability of the WSN against sensor failures. Sensors without sufficient power to successfully carry out a period or those that fail before the decision phase is completed are not included in the scheduling procedure. If a sensor fails after being chosen, the quality is only reduced for the duration of the period as another functioning sensor is selected in the next period. Two types of packets were used when carrying out the DiLCO protocol: info and Active/Sleep. Info packets are transmitted from a sensor to all others within the same sub-location with the purpose of providing information. Active/Sleep packets are transmitted by the leader to the other sensors within the same sub-location to notify them to set their status to either Active or Sleep in the sensing stage.