# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 2 Related Work
The cost-effectiveness of a WSN is best shown by include both expensive and cheap sensor nodes. The second challenge is how to deploy sensor nodes once the system is in operation. When it comes to WSNs, heterogeneous sensor nodes are the most important. The ranges of sensor nodes may vary to meet the needs of WSNs. While this adjustment helps lower energy use, it also introduces heterogeneity within WSNs. Authors of [13] proposed a weight-aware sensor deployment method based on each sensing grid's weight. The sensor grid is evaluated using Seesaw mapping to reflect factors that include the risk of abnormalities, their consequences, and the time it takes to tolerate such incidents. In order to get an optimum balance between reliability, the existence of field obstacles, and the number of sensors, several weight-aware sensor deployment algorithms are developed to address each of these criteria. However, sensor nodes have important characteristics that these algorithms do not account address. Some sensor nodes use more energy, the number of times may switch from on to off or vice versa, and the number of movements. The lifespan of sensor nodes is a critical challenge in WSN since its operation is highly dependent on such nodes. Indeed, neglecting the decrease in node lifetime caused by its movement across the various zones and its status transitioning from on to off may decrease network performance. In the context of time-varying differential surveillance requirements, the authors of [6] presents a modelling framework for heterogeneous sensor deployment. It first formulates the problem as an optimization problem using a mixed-integer mathematical programming to maximize field coverage. This is accomplished by using two meta-heuristics. The first heuristic solution uses a Genetic Method (GA), while the second heuristic solution utilizes a Simulated Annealing (SA) algorithm. This framework was created to support large-scale surveillance operations, including integrating multiple sets of heterogeneous sensors into a single deployment strategy. The sensors' main characteristics such as energy, allowed number of state-switching, allowed number of moves, and reliability are considered. Meanwhile, the analysis of the proposed framework in [6] shows that the movement distance and movement consumed energy are not considered. This might have a negative influence on network performance, as described above. A two Flower Pollination Algorithms (FPA) are proposed in [20] to solve the heterogeneous node deployment optimization problem in WSNs with obstacles in the monitoring area. Due to the shortcomings of FPA, such as the low convergence speed and the precision, different enhancements are proposed [20]. The first algorithm is called an Improved Flower Pollination Algorithm (IFPA), while the second algorithm is the Non-dominant Sorting Multi-Objective Flower Pollination Algorithm (NSMOFPA). Via simulation results, the NSMOFPA proved to have a good optimization effect and can provide a better solution for WSN deployment [20]. The optimal sensor deployment problem to minimize sensing uncertainty with a network lifetime constraint is presented in [21]. The authors considered both homogeneous and heterogeneous mobile WSNs. They also consider connectivity to make its model more practical and minimize the sensing uncertainty in a homogeneous network. Two centralized sensor relocation algorithms were designed: centralized constrained movement Lloyd algorithm and Backwards-stepwise centralized constrained movement Lloyd algorithm. A primary goal is to make it easier to balance the trade-off between uncertainty sensing and network lifespan [21]. This paper focuses on how to deploy heterogeneous sensor networks with varying surveillance requirements over time. The problem is mathematically formulated as a 0/1 integer linear programming (ILP) to maximize the area coverage. Then, a meta-heuristic method called Ant-Colony optimization is proposed for large-scale deployment problems.