Abstract
This paper reports the design of a randomly deployed heterogeneous wireless sensor network (HWSN) with two types of nodes: a powerful node and an ordinary node. Powerful nodes, such as Cluster Heads (CHs), communicate directly to the data sink of the network, and ordinary nodes sense the desired information and transmit the processed data to powerful nodes. The heterogeneity of HWSNs improves the networks lifetime and coverage. This paper focuses on the design of a random network among HWSNs. In the design of a random HWSN, this paper uses algorithms based on the binary-valued versions of swarm intelligence, such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). The design is then considered to be an optimization problem of how many powerful and ordinary nodes will combine to minimize the network cost, while guaranteeing a desired coverage during a given period. Simulation results show the performance of each algorithm for solving the defined optimization problem.
Highlights
A heterogeneous wireless sensor network (HWSN) consists of more than two types of devices to improve the network’s lifetime, coverage, and scalability
We solve this optimization problem with more realistic and efficient approaches at the expense of an increase in complexity by applying other metaheuristic methods based on swarm intelligence, such as binary Artificial Bee Colony [16] and binary Ant Colony Optimization, which are the binary-valued versions corresponding to their original algorithms as well as the DPSO algorithm
The binACOR is first proposed in this paper. This algorithm is implemented by combining the binary concepts from the Binary Particle Swarm Optimization (binPSO) and the Binary Artificial Bee Colony (binABC) with the Ant Colony Optimization (ACO) algorithm for continuous domains, ACOR [17]
Summary
A heterogeneous wireless sensor network (HWSN) consists of more than two types of devices (or sensor nodes) to improve the network’s lifetime, coverage, and scalability. This paper focuses on the effective design of WSNs constructed by randomly deploying sensors, solving the optimization problem of how many powerful and ordinary nodes will combine to minimize the network cost while guaranteeing a desired coverage during a given period. Our previous paper [7] designed the HWSN with nodes and CHs that are able to sense information by defining the constrained integer optimization problem, converting it to the unconstrained problem and solving this problem using the discrete binary version of the PSO algorithm known as the Discrete PSO (DPSO) algorithm [15]. We solve this optimization problem with more realistic and efficient approaches at the expense of an increase in complexity by applying other metaheuristic methods based on swarm intelligence, such as binary Artificial Bee Colony (binABC) [16] and binary Ant Colony Optimization (binACOR), which are the binary-valued versions corresponding to their original algorithms as well as the DPSO algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Distributed Sensor Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
