## 4 Maintaining Maximum Coverage and Connectivity Scheme
### Design of the MCC-ACO Algorithm  
In the basic algorithm of the MCC-ACO, the basic ant colony optimization algorithm model is introduced in the design for sensor network deployment. However, as mentioned above, the initial background for proposing the ant colony optimization algorithm involves the solutions of non-deterministic polynomial (NP) problems that are represented by a TSP problem when the scale of the problem is limited. When the number of grids in the partitions is extremely large, there will be some scalability problems when the ant colony optimization algorithm is introduced into the node deployment problems. If ant colony optimization algorithms are used in this instance, this will result in poor scalability, low algorithm efficiency, and even situations that have no solutions. Therefore, there is no need to establish a model that is suitable for an ant colony optimization algorithm based on the sensor node deployment in grid partitions, but existing ant colony optimization algorithms should be revised to achieve some scalability. This paper established a suitable model for basic ant colony optimization algorithms and carried out appropriate revisions based on the application background, which is described as follows. (1) Introduction of the Greedy Mechanism  
As shown in Figure 3, as the ants move, more and more candidate grids can be selected for deploying sensor nodes in the next step (as shown by the blue coverage area in the figure). It will increase the chances of different choices of ants, so the search will be more random, the convergence speed of the algorithm will drop sharply, and the final result will be very close to the performance of the random algorithm. In the TSP problem, due to the small number of cities (tens of nodes), the non-random convergence of the ant colony algorithm can be guaranteed, but in the sensor network, when the number of grid nodes is greatly expanded (often tens of thousands of grids can be reached), ants will fall into the dilemma of the random search and random pheromone enhancement. Therefore, this paper introduces the greedy strategy in the ant search process. The core idea is to eliminate the grid points that do not contribute to the completion of the coverage in the candidate grid, so as to narrow the range of candidate grid points. For ease of explanation, three definitions are introduced below:  
**Definition 1**. : _Candidate grid point set \(\text{CandiSet}\): Define the candidate set of the next target grid point that can be selected when the ant moves to a grid point to be \(\text{CandiSet}\). After the ant is deployed in the initial position,  
Figure 3: Schematic diagram of the movement process of ants. the ant adds the grid point of the deployment location to the selected point set and then updates the set CandiSet according to the rules that can be connected to the sink (as shown in Figure 3. CandiSet can be expressed as:_  
\[CandiSet=\bigcup_{s_{i}\in A}\;\left\{\vec{X}_{q}\left|\left\|\vec{X}_{q}-\vec{ X}_{i}\right|\right.\leq r^{s}\right.\right\} \tag{7}\]  
_where \(\vec{X}_{q}\) is the coordinate of the candidate grid points in CandiSet; \(\vec{X}_{i}\) is the coordinate of the deployed nodes in monitored area \(A\)._  
**Definition 2**. : _Unfamiliar monitoring point: If a monitoring point is not covered by any deployed sensor nodes, define such monitoring points as \"unfamiliar monitoring points\"._  
**Definition 3**. : _Effective candidate grid point set \(EffiSet\): Define the effective grid point set \(EffiSet\) as the grid points in the set CandiSet that can cover the \"unfamiliar monitoring points\".