An energy-efficient evolutionary clustering technique for disaster management in IoT networks
2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 9, article id 2647Article in journal (Refereed) Published
Abstract [en]
Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Place, publisher, year, edition, pages
MDPI AG , 2020. Vol. 20, no 9, article id 2647
Keywords [en]
Disaster management, Energy-efficient clustering and routing, Evolutionary algorithms, Forest fire, IoT networks, Non-uniform distribution of events, Tree encoding, Disaster prevention, Disasters, Energy efficiency, Energy utilization, Particle swarm optimization (PSO), Sensor nodes, Trees (mathematics), Evolutionary clustering, Harmony search algorithms, Hybrid Particle Swarm Optimization, Internet of Things (IOT), Safety critical applications, State-of-the-art approach, Wireless connectivities, Wireless sensor network (WSNs), Internet of things
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-48057DOI: 10.3390/s20092647ISI: 000537106200211PubMedID: 32384693Scopus ID: 2-s2.0-85084720707OAI: oai:DiVA.org:mdh-48057DiVA, id: diva2:1432863
2020-05-282020-05-282022-02-10Bibliographically approved