An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Abstract

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

“…In this section, we simulate CoWSN with NS2 to evaluate its performance. The simulation results are compared with the scheme of Rahmani et al 5 , CCM-RL 32 , and CCA 33 . We assume that the network includes 250–2000 heterogeneous sensor nodes, which are randomly distributed in the network.…”

“…In 5 , an area coverage approach based on fuzzy logic (FL) and shuffled frog-leaping algorithm (SFLA) is proposed. This approach balances energy consumption, increases network lifetime and improves coverage quality.…”

“…WSNs consist of a high number of sensor nodes distributed in the network environment to monitor the Region of Interest (RoI) 3 , 4 . Initially, these networks were specifically designed for military applications 5 , 6 . However, the growing advances in these networks have led to new technologies such as the Internet of Things (IoT) 7 , 8 .…”

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

“…When designing a learning model in the healthcare field, datasets are used for training, validating, and testing. Healthcare datasets may include demographic information, images, laboratory results, genomic data, and data obtained from sensors [54,55]. Various platforms are used to produce or collect these data, for example network servers, e-health records, genome data, personal computers, smartphones, mobile applications, and wearable devices [56,57].…”

A Distributed Optimization Accelerated Algorithm with Uncoordinated Time-Varying Step-Sizes in an Undirected Network

“…In recent years, with the rapid development of artificial intelligence, big data, etc., there has been much attention to distributed optimization problems in multi-agent systems. As one of the most important fields, distributed optimization methods have gained significant growing interest due to the widespread applications in science and engineering areas such as the transmission of information in wireless sensor networks [1][2][3], the collaboration of vehicles in formation control [4,5], speeding up the optimization process in distributed machine learning [6,7], distributed resource allocation in smart-grid networks [8][9][10], distributed control in nonlinear dynamical systems [11,12], etc. Specifically, a distributed optimization framework can avoid the establishment of long-distance communication between agents while providing better load balancing for the network.…”