Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) became essential in developing many applications, including smart cities and Internet of Things (IoT) applications. WSN has been used in many critical applications such as healthcare, military, and transportation. Such applications depend mainly on the performance of the deployed sensor nodes. Therefore, the deployment process has to be perfectly arranged. However, the deployment process for a WSN is challenging due to many of the constraints to be taken into consideration. For instance, mobile nodes are already utilized in many applications, and their localization needs to be considered during the deployment process. Besides, heterogeneous nodes are employed in many recent applications due to their efficiency and cost-effectiveness. Moreover, the development areas might have different properties due to their importance. Those parameters increase the deployment complexity and make it hard to reach the best deployment scheme. This work, therefore, seeks to discover the best deployment plan for a WSN, considering these limitations throughout the deployment process. First, the deployment problem is defined as an optimization problem and mathematically formulated using Integer Linear Programming (ILP) to understand the problem better. The main objective function is to maximize the coverage of a given field with a network lifetime constraint. Nodes’ mobility and heterogeneity are added to the deployment constraints. The importance of the monitored field subareas is also introduced in this paper, where some subareas could have more importance than others. The paper utilizes Swarm Intelligence as a heuristic algorithm for the large-scale deployment problem. Simulation experiments show that the proposed algorithm produces efficient deployment schemes with a high coverage rate and minimum energy consumption compared to some recent algorithms. The proposed algorithm shows more than a 30% improvement in coverage and network lifetime.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

“…About 35% of the reviewed studies worked on an update to swarm intelligence optimization algorithms [ 40 , 44 , 46 ] such as particle swarm optimization (PSO) [ 42 , 58 , 66 , 68 , 69 , 74 , 75 , 76 , 77 , 81 , 88 , 97 , 99 ], ant colony optimization (ACO) [ 33 ], and bee colony optimization (BCO) [ 48 , 65 ], due to their ability to solve complex problems and provide a satisfactory solution in a feasible time [ 90 ]. These algorithms are applied to enhance network performance by combining them with other approaches and then comparing the obtained results with other algorithms, such as the genetic, greedy, and multi-objective evolutionary algorithms.…”

“…Considering the mobility, environment properties and using heterogeneous nodes in the WSN increases its deployment problem complexity. Fatima H. et al (2021) [ 44 ] took these problems into account when designing their optimization deployment algorithm based on integer linear programming (ILP). The objective of this algorithm is to maximize coverage, while taking network lifetime, mobility, and heterogeneity as constraints.…”

“…This can be done either by proposing new algorithms or updating existing ones or by a combination of two different types of optimization algorithms, such as a hybrid algorithm between classical and meta-heuristic algorithms or meta-heuristic and artificial intelligence algorithms [11]. About 35% of the reviewed studies worked on an update to swarm intelligence optimization algorithms [40,44,46] such as particle swarm optimization (PSO) [42,58,66,68,69,[74][75][76][77]81,88,97,99], ant colony optimization (ACO) [33], and bee colony optimization (BCO) [48,65], due to their ability to solve complex problems and provide a satisfactory solution in a feasible time [90]. These algorithms are applied to enhance network performance by combining them with other approaches and then comparing the obtained results with other algorithms, such as the genetic, greedy, and multi-objective evolutionary algorithms.…”

Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities

“…Advancements in computing technology and networking have given rise to the Internet of Things (IoT), which aims to enable objects to gather and share data over the Internet using smart devices [1,2]. The IoT was envisioned as a large-scale network of Internetconnected objects that are uniquely addressable and reachable using standard networking protocols [3]. It has played a pivotal role in the development of smart cities, where interconnected devices and systems work together to enhance the efficiency, sustainability, and quality of urban life [4].…”

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism

“…Wireless Sensor Networks (WSNs) have a significant impact on increasing the capabilities of the Internet of Things (IoT) [ 1 , 2 ]. In WSNs, network devices are widely used for many practical applications, such as health [ 3 ], transportation [ 4 , 5 , 6 ], agriculture [ 7 , 8 ], and education [ 9 ].…”