Energy-Efficient Dynamic Clustering for IoT Applications: A Neural Network Approach

Manman, Li; Xin, Qin; Goswami, Pratik; Mukherjee, Amrit; Yang, Lixia

doi:10.1109/comnet47917.2020.9306092

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…With the advancement in artificial intelligence coupled with IoT systems is providing lots of benefits in real-time applications connectivity. For the energy efficiency of such kind of applications [60] propose an improved dynamic clustering algorithm to address the energy efficiency issues. The proposed algorithm can be implemented in IoT applications which consist of different wireless sensor networks.…”

Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

et al. 2022

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The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

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Section: Discussionmentioning

confidence: 99%

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

et al. 2022

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“…Where x and y are characterized by equations ( 13) and ( 14), two normal distribution variables with the standard deviations of and are defined as follows: = (0, 2 ), ( 13) = (0, 2 ), ( 14) While the standard deviations are described in Eqs. (13,14), are defined as follows:…”

Section: Lévy Flightmentioning

confidence: 99%

“…Bear in mind these protocols are forming the underlyingly data transmission mechanism within the IoT for FinTech that introduce crucial security challenge [11,12]. Inherently, these types of protocols require simple security solutions that could save energy and avoid complex procedures [13,14]. Therefore, the wise selection of connection link within FinTech is really challenging as any successful attack would cause the loss of huge amount of money, considering the fact that the projection of having $3.9 trillion to $11.1 trillion per year in 2025 adding value to the global economy over IoT for FinTech technologies [15].…”

Section: Introductionmentioning

confidence: 99%

Trustworthy and Efficient Routing Algorithm for IoT-FinTech Applications Using Nonlinear Lévy Brownian Generalized Normal Distribution Optimization

Sadiq

Dehkordi

Mirjalili

et al. 2023

IEEE Internet Things J.

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The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses' income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to

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“…References [33][34] proposed a modified version of the original whale optimization approach and the Harris hawk optimizer to solve complex optimization problems. Reference [35] used neural networks for dynamic clustering in IoT. Reference [36] presented multiagent system clustering for efficient resource assignment in massive IoT.…”

Section: Related Workmentioning

confidence: 99%

“…Reference [36] presented multiagent system clustering for efficient resource assignment in massive IoT. Reference [35] and [36] employed backpropagation neural networks and convolutional neural networks, respectively, for IoT performance optimization. Reference [37] used distributed artificial intelligence for active resource allocation in IoT, the results of which suggest that high-performance resource management is achieved by merging cognitive radio with wireless sensor networks (WSNs).…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Kaushik

Al-Raweshidy

2022

IEEE Access

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Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%,

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Energy-Efficient Dynamic Clustering for IoT Applications: A Neural Network Approach

Cited by 11 publications

References 19 publications

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

An Energy-Efficient Data Aggregation Mechanism for IoT Secured by Blockchain

Trustworthy and Efficient Routing Algorithm for IoT-FinTech Applications Using Nonlinear Lévy Brownian Generalized Normal Distribution Optimization

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

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