Smart City Gnosys

Smart city article details

Title Swarm Intelligence For Resource Management In Internet Of Things
ID_Doc 54133
Authors Hassanien A.E.; Darwish A.
Year 2020
Published Swarm Intelligence for Resource Management in Internet of Things
DOI http://dx.doi.org/10.1016/B978-0-12-818287-1.00016-4
Abstract Internet of Things (IoT) is a new platform of various physical objects or “things” equipped with sensors, electronics, smart devices, software, and network connections. IoT represents a new revolution of the Internet network which is driven by the recent advances of technologies such as sensor networks (wearable and implantable), mobile devices, networking, and cloud computing technologies. IoT permits these the smart devices to collect, store and analyze the collected data with limited storage and processing capacities. Swarm Intelligence for Resource Management in the Internet of Things presents a new approach in Artificial Intelligence that can be used for resources management in IoT, which is considered a critical issue for this network. The authors demonstrate these resource management applications using swarm intelligence techniques. Currently, IoT can be used in many important applications which include healthcare, smart cities, smart homes, smart hospitals, environment monitoring, and video surveillance. IoT devices cannot perform complex on-site data processing due to their limited battery and processing. However, the major processing unit of an application can be transmitted to other nodes, which are more powerful in terms of storage and processing. By applying swarm intelligence algorithms for IoT devices, we can provide major advantages for energy saving in IoT devices. Swarm Intelligence for Resource Management in the Internet of Things shows the reader how to overcome the problems and challenges of creating and implementing swarm intelligence algorithms for each application. © 2020 Elsevier Inc.
Author Keywords


Similar Articles


Id Similarity Authors Title Published
7786 View0.872Xing P.; Zhang H.; Derbali M.; Sefat S.M.; Alharbi A.H.; Khafaga D.S.; Sani N.S.An Efficient Algorithm For Energy Harvesting In Iiot Based On Machine Learning And Swarm IntelligenceHeliyon, 9, 7 (2023)
4256 View0.865Sharma J.; Sangwan A.; Singh R.P.A Review On Evolving Domains Of Internet Of Things: Architecture, Applications, And Technical ChallengesInternational Journal of Communication Systems, 36, 18 (2023)
36301 View0.864Huang J.; Hua K.Managing The Internet Of Things: Architectures, Theories And ApplicationsManaging the Internet of Things: Architectures, theories and applications (2016)
32936 View0.864Hassan R.; Qamar F.; Hasan M.K.; Aman A.H.M.; Ahmed A.S.Internet Of Things And Its Applications: A Comprehensive SurveySymmetry, 12, 10 (2020)
54131 View0.864Zedadra O.; Guerrieri A.; Jouandeau N.; Spezzano G.; Seridi H.; Fortino G.Swarm Intelligence And Iot-Based Smart Cities: A ReviewInternet of Things (2019)
1045 View0.861Saqib M.; Moon A.H.A Concise Review On Internet Of Things: Architecture, Enabling Technologies, Challenges, And ApplicationsInternational Journal of Sensors, Wireless Communications and Control, 12, 9 (2022)
46091 View0.858Farooq J.; Zhu Q.Resource Management For On-Demand Mission-Critical Internet Of Things ApplicationsResource Management for On-Demand Mission-Critical Internet of Things Applications (2021)
44439 View0.855Abolhassani Khajeh S.; Saberikamarposhti M.; Rahmani A.M.Real-Time Scheduling In Iot Applications: A Systematic ReviewSensors, 23, 1 (2023)
37791 View0.852Tolba A.; Al-Makhadmeh Z.Modular Interactive Computation Scheme For The Internet Of Things Assisted Robotic ServicesSwarm and Evolutionary Computation, 70 (2022)