Sleep-wakeup Scheduling Algorithm for Lifespan Maximization of Directional Sensor Networks: An Advanced Discrete Cuckoo Search Optimization Algorithm

Directional sensor networks are ad hoc networks which are utilized in different applications to monitor and coverage all of the specific targets in the observing fields permanently. These kinds of networks include several configurable directional sensors in which they can be adjusted in one the possible directions along with one of its adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization is still a challenge. This paper formulates the expansion of directional sensor network lifespan to a discrete optimization problem. Several proposals were presented in literature to solve the stated problem, but majority of them are threatened to get stuck in local optimum and led low efficiency. To solve this combinatorial problem, an advanced discrete cuckoo search algorithm is designed and is called several times until the remaining battery associated to alive sensors do not let observe all targets. In each time, algorithm returns an efficient cover along with its activation time. A cover is a sub set of available sensors capable of monitoring all targets in the observing field. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Designing miscellaneous discrete walking around procedures makes to reach a good balance between exploration and exploitation in search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in variety circumstances proves the superiority of the proposed algorithm is about 19.33%, 14.83%, 13.50%, and 5.33% in term of average lifespan improvement against H-MNLAR, ACOSC, GA, and HDPSO algorithms respectively.

Solving target coverage problem in directional sensor networks with ability to adjust sensing range using continuous learning automata

Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime.

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.