Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

Aziz, Ahmed; Osamy, Walid; Khedr, Ahmed M.; El‐Sawy, A. A.; Singh, Karan

doi:10.1007/s11276-020-02265-8

Cited by 30 publications

(16 citation statements)

References 33 publications

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“…In the middle and late last century, a CMC-1 type associated decision-making monitoring system with 128 monitoring points was introduced to monitor environmental parameters [11]. With the advancement of science and technology and the advent of advanced instruments, many countries have developed many monitoring and control systems [12]. Kougianos et al [13] are the first to propose the use of grey correlation sensor networks in the Internet of ings to realize worker tracking and positioning and environmental parameter monitoring.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

et al. 2021

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This paper analyzes the current real-time monitoring system based on grey-related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey-related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three-dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real-time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.

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Section: Introductionmentioning

confidence: 99%

Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

et al. 2021

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“…Aziz et al [38] proposed a grey wolf-based compressive algorithm (EMCA-CS) for IoT data aggregation. The EMCA-CS algorithm divides the entire network into hexagonal clusters of equal size.…”

Section: Related Workmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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“…In addition, within CS schemes, the focus is currently on its use in conjunction with clustering, where interesting works have been published [ 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. More recently we must mention the works in [ 81 , 82 , 83 ].…”

Section: Energy Conservationmentioning

confidence: 99%

Current Trends on Green Wireless Sensor Networks

López-Ardao

Rodríguez-Rubio

Suárez-González

et al. 2021

Sensors

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The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

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Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

Cited by 30 publications

References 33 publications

Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Current Trends on Green Wireless Sensor Networks

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