Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT

Dhumane, Amol V.; Prasad, Jayashree R.

doi:10.1007/s11276-017-1566-2

Cited by 99 publications

(64 citation statements)

References 50 publications

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“…In previous studies, 29–32 the authors use meta‐heuristic algorithms to improve the performance of the network. Dhumane and Prasad 29 propose a fractional gravitational search algorithm (FGSA) to find the optimal CHs in the IoT‐based networks. FGSA employs an efficient fitness function considering different factors such as distance, delay, link lifetime, and energy.…”

Section: Related Workmentioning

confidence: 99%

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Sadeghi

Avokh

2020

Int J Communication

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This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster-based scheme named "Cuckoo-search Clustering with Two-hop Routing Tree (CC-TRT)" to develop a two-hop load-balanced data aggregation routing tree in the network. CC-TRT uses a modified energyaware cuckoo-search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo-search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC-TRT scheme to present two methods for constructing multi-hop data aggregation routing trees, named "Cuckoo-search Clustering with Multi-Hop Routing Tree (CC-MRT)" and "Cuckoo-search Clustering with Weighted Multi-hop Routing Tree (CC-WMRT)." Both CC-MRT and CC-WMRT rely on a two-level structure; they not only use an energy-aware cuckoo-search algorithm to fairly select the best CHs but also adopt a load-balanced high-level routing tree to route the aggregated data of CHs to the sink node. However, CC-WMRT slightly has a better performance thanks to its low-level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC-TRT, CC-MRT, and CC-WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.

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Section: Related Workmentioning

confidence: 99%

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Sadeghi

Avokh

2020

Int J Communication

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“…Dhumane and Prasad [11] had proposed a new Multi-objective Fractional GSA (MOFGSA) for identifying optimal cluster heads for the routing protocol that is energy efficient. For extending the node lifetime, a Fractional GSA (FGSA) was proposed to find out an optimal cluster head node in an iterative manner for the IoT network mode.…”

Section: Related Workmentioning

confidence: 99%

“…This is initialized by using maximum value ωMax and is also linearly decreased to a minimum value ωMin, and this is subject to the actual maximum number of steps in the algorithm. 11…”

Section: B Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

2020

IJRTE

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Abstract: The Internet of Things (IoT) has a likely future for all technologies that are associated to the life of humans. Any communication between the social environments along with the user contexts will be made through the smart interfaces. The IoT will have to link to different diverse devices found in the Wireless Sensor Network (WSN) . Thus, the routing optimization which is energy efficient has become the primary factor in the performance of the network in the IoT. The widely popular routing used in WSN, Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, is found to be energy inefficient. The work will deal with the choice of finding the optimal path in routing through meta heuristic techniques to improve the lifespan of the network and the energy efficiency of the network. There are different techniques of metaheuristics such as Teacher Learning Based Optimization (TLBO) andParticle Swarm Optimization (PSO) were used effectively for finding optimal solutions for problems. In this work, the PSO-based algorithms were used for locating an optimal sink position to their nodes that make the network efficient in terms of energy. The TLBO metaheuristic is population-based and is based on the concept of teaching and the learning procedure observed in a classroom, which is adapted to the routing problem. The results of the experiment prove that the proposed technique was achieved better levels of performance compared to the other methods..

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“…The energy loss follows the free space and multipath fading model. For estimating the energy dissipation in every IoT node, the hardware of receiver and transmitter is employed [5]. At the transmitter, the power amplifier and radio electronics is used for measuring the energy dissipation, whereas in the receiver, radio electronics is used for measuring the energy dissipation.…”

Section: A Energy Model Of Iotmentioning

confidence: 99%

“…The geographically related characteristics of WSN are considered in the Location-based Protocols, like Geographic Aware Routing, Minimum Energy Communication network and Energy Aware Routing. The prerequisites of security and QoS are met by the protocols like, energy awarded security protocol, Network flow protocol, Intrude torrent routing protocols, QoS Routing Protocol, QoS aware, and Energy-aware protocol [5].…”

Section: Introductionmentioning

confidence: 99%

Katsinovas Framework Prototype Apps Hardware as based Innovation Readiness Level

Waliulu*,

Saptono,

Suryani

et al. 2020

IJITEE

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Internet of Things (IoT) is a developing technology used for enhancing the communication and providing flexibility to humans. The data packets are transmitted through better routing path using the IoT nodes in the network. This research presents an effective optimization algorithm for selecting the effective routing path for the transmission of the speech signal to the destination effectively. The optimization algorithm namely, chronological atom search optimization (chronological ASO) algorithm is developed for selecting the routing path. The chronological ASO algorithm is developed by integrating the chronological concept in ASO algorithm. After the initialization of the network, the effective routing path is selected using the fitness parameters, such as energy, delay and distance. Then, the speech signal is transmitted from the source node to the destination node by selecting the routing path having maximum fitness function. The performance analysis of the proposed chronological ASO algorithm is done based on the metrics, such as energy, throughput, and PDR. When compared to the existing methods, the proposed method provided a maximal energy of 0.2375, maximal PDR of 46.1538, and maximal throughput of 0.0792 for 50 nodes and obtained a maximal energy of 0.1381, maximal PDR of 32.0988, and maximal throughput of 0.0395, respectively.

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Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT

Cited by 99 publications

References 50 publications

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

Katsinovas Framework Prototype Apps Hardware as based Innovation Readiness Level

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