# MCCM: An Approach for Connectivity and Coverage Maximization
## 2 Related Works  
Maintaining connectivity between devices and maximizing coverage is big challenge to reduce the cost of deployment, and to ensure the reliability of data. They are key issues in WSNs as they measure how effectively a network field is monitored by its sensor devices. Coverage is to guarantee that each area is monitored by at least one node, and connectivity is to guarantee that any device can communicate with each other [12]. Protocols related to area coverage are classified based on the sensor types (mobile, static) and the coverage requirement (1-coverage or \(k\)-coverage) [13]. Different algorithms [14; 15; 16; 17; 18; 19] were proposed in the literature and have good impact on the coverage optimization. Protocols can be classified into either distributed or centralized. The type of sensors in the network can be homogeneous (The sensors have the same sensing range and technical properties) or heterogeneous. Studies such as \(CTC_{k}\)[16] and \(DCTC_{k}\)[16] take into account energy efficiency for coverage and connectivity optimization. Their algorithms work for centralized and distributed coverage in heterogeneous networks and tackle the k-coverage problem. In \(DCHGA\)[20] (Dynamic Clustering of Heterogeneous WSNs using Genetic Algorithm), authors present a genetic algorithm. The proposed work can operate on both homogeneous and heterogeneous environments. This algorithm requires a Central Station (CS) with advanced resources to control the devices distributed in the network. Another work (\(HMRC\))[21] uses the harmony search algorithm to improve connectivity and coverage within an area of interest. The algorithm in [21] only works on networks where the devices are homogeneous and controlled by a Sink Node (SN) or Central Station (CS). The authors in [22], presented an asynchronous algorithm (\(AAEECC\)) that is energy efficient and provides integrated coverage and connectivity. It is fully distributed and scalable. They also presented a fault-tolerance connectivity maintenance algorithm (\(FTCM\)) to increase the resistance against failure that might occur during the nodes scheduling. In [23], authors presented an algorithm based on Voronoi partition. The algorithm eliminates the isolated nodes and reduces the redundant coverage. Nodes move to their final best position to maintain network connectivity. A deployment technique (\(ABDC\)) is suggested in [24] to enhance coverage, connectivity, and reliability. The idea is to use the angle between sensor nodes and their neighbors. The work showed higher coverage percentagesand good connectivity compared to the Glowworm Swarm Optimization (GSO) approach [25]. This algorithm works on homogeneous environments only. Another algorithm (OPDCC) [26] addresses the \(k-\)coverage and \(m-\)connected problem in WSN. It estimates the minimum number of nodes (and their locations) required for the connectivity and coverage. The proposed algorithm requires a central station (CS) to do all the calculations. Another novel algorithm for Coverage Maximization of Heterogeneous Wireless Network (\(CMHN\)) is proposed in [27]. The algorithm is centralized. Devices are heterogeneous and the sensing range of each device has a circular shape. The circumference of the circle is represented as an interval of an angle (0, 2 TI). For any two connected devices \(D_{i}\) and \(D_{j}\) with sensing ranges \(r_{i}\) and \(r_{j}\) respectively, the distance \(d_{ij}\) between them is less than the sum of their ranges. A device moves (after random deployment) to a position that ensures a maximum coverage and connects to a closest device to maintain connectivity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices and keep it to a small margin to maximize the coverage while maintaining connectivity. \(CMHN\) ensures that every device is connected to at least \(k=2\) devices. In this algorithm, the notion of \(m-\)ratio is introduced.