# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
###### Abstract
With the previously stated constraint, nodes lifetime depends on the its energy source. Consequently, it is highly required for any deployment algorithm to employ nodes' available energy when selecting which node to move. These node failures can cause areas in the FoI to become devoid of sensor coverage. These areas are called coverage holes. Depending on these coverage holes' severity, they can degrade performance or disrupt the network entirely [7; 8]. Figure 1: WSN structure. Figure 2: Modernized Wireless Sensor Network (WSN). Considering the network coverage goal, the highest priority areas or zones should be covered by at least one node. In WSN with mobile nodes, moving a node to high priority zones, more distance to that zone leads to more consumed energy during the movement. Hence, the closer a node it is to the target zone, the better performance in terms of energy efficiency. Therefore, considering the movement distance in the design of the deployment algorithm is highly required. As in the WSN with mobile nodes, the node deployment referees to finding the optimal possible location of such mobile nodes, covering n-target zones with the minimum number of nodes, are proved to be an NP-complete problem [9]. To address the deployment problem, nature-inspired algorithms were developed. Swarm Intelligence (SI) technique is one of these algorithms being explored to solve WSN problems via efficient optimization. They are very efficient in solving complex problems through simple behavior. One of the most used SI techniques is Ant Colony Optimization (ACO), as it is highly efficient and performs well in a complex environment. The ACO imitates the swarms' behavior of social ants. Mainly, ants use pheromone as a chemical messenger, and the pheromone concentration can also be interpreted as the solution quality indicator of different interesting issues. Hence, it is possible to describe each mobile node's motion with ACO [9; 10; 11; 12]. The motivation behind this work is the advances of wireless network technology, including Unmanned ariel vehicles and robotics. These advances in technologies lead to a new generation of wireless networks that combine various types of sensing technologies, robotics, and UAVs. This combination is beneficial in different applications; also, new challenges are generated. One of these challenges is the deployment problem, especially when heterogeneous devices are deployed on a large scale. This paper is a step towards an efficient solution to the deployment problem. This paper studies the sensor relocation problem in WSN to maximize network coverage, considering three key aspects: nodes' residual energy, movement distance, and movement consumed energy. Therefore, the main contributions of this paper focus on:  
1. Describing the deployment problem in the form of 0/1 integer programming,
2. Considering heterogeneous sensor deployment, including mobile nodes such as robots or UAVs,
3. Taking into consideration the zone priority to decide a new location of each node,
4. Considering the nodes' residual energy when selecting which node to move,
5. Considering the movement distance and energy to minimize network overall energy consumption. Finally, developing the Swarm Intelligence algorithm as a near-optimal solution for large-scale deployment problems in reasonable running time enables the use of such algorithms in applications that require real-time sensor relocation. The paper is laid out as follows: some examples of the related work are given in Section 2. The sensor deployment problem is described in Section 3. The optimal solution approach for this problem is elaborated in Section 4, whereas Section 5 states that the solution being suggested to address deployment problem. The performance results are discussed later in Section 6, and the paper is concluded in Section 7.