Energy-Efficiency Clustering and Data Collection for Wireless Sensor Networks in Industry 4.0

Alrashidi, Malek; Nasri, Nejah; Khediri, Salim El; Kachouri, Abdennaceur

doi:10.1007/s12652-020-02146-0

Cited by 27 publications

(29 citation statements)

References 23 publications

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“…This section discusses the simulations of the anticipated UAV inner and outer, UAV outer methods, and existing Alrashidi et al 18 method by deploying four UAVs in the monitoring filed. In UAV inner and outer scenario, two UAVs are moving inside the sensor node deployed area, and two UAVs are moving outside the sensor node deployed area whereas in UAV outer methods, all four UAVs are considered for outside of the monitoring area as shown in Figures 2 and 3.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The enactment effectiveness of the proposed and existing protocols is investigated using MATLAB (R2014a) using Intel(R) core (TM) i7, processor (2.4 GHz), window 10 with RAM 16GB. The results are viz a viz (or as compared) with the existing Alrashidi et al 18 method by using stability period, network lifetime, remaining network energy, number of CHs, and throughput are considered for analyzing the network performance. The definitions of the performance metrics are given as follows: Stability period: The stability period of a network can be defined as the first node in the network depletes their complete energy.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…This method did not accommodate to space and time elements of the energy hole. Alrashidi et al discuss an energy‐efficient clustering and information gathering for WSNs which optimized routing by determining the shortest path to reach all the CHs 18 . The routing path is used for data collection from the networks by the drone, and the complete process is repeated for every iteration.…”

Section: Literature Reviewmentioning

confidence: 99%

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A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

Singh

Malik

Kumar

et al. 2021

Int J Communication

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SummaryUnmanned aerial vehicle (UAV)‐enabled wireless sensor networks (WSNs) have evolved as the significant paradigms to communicate the information among the devices in today's scenario instantly. These technologies support for developing smart crop spraying, smart infrastructure, aerial photography and videography, emergency services, etc. in providing smart solutions. Therefore, there is a dire need to develop an energy‐efficient solution for data gathering through UAVs. In this paper, an optimum weighted probability function is proposed by considering residual node energy, node spacing, and average energy of networks. This function helps in optimizing the clustering process for three‐tier heterogeneity by minimizing the energy of the proposed network. The proposed methods achieve 29.45% and 52.48% increment in the network lifespan as compared to the existing algorithm. The reason for such a gigantic enhancement in the network lifespan is the selection of the highest capable node as a cluster head using the proposed function.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

Singh

Malik

Kumar

et al. 2021

Int J Communication

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“…1. Similar to the UAV network, many wireless sensor networks (WSN) also use clustering technology and the main goal is to enhance the battery life by utilizing the CH switching techniques [11]. However, the high mobility of the UAV in the spatial domain, makes it more challenging for the CH to provide a stable network coverage where the CH switching is a lot more frequent than the WSN.…”

Section: Introductionmentioning

confidence: 99%

Safeguarding Cluster Heads in UAV Swarm Using Edge Intelligence: Linear Discriminant Analysis-Based Cross-Layer Authentication

Wang

Fang

Wang

2021

IEEE Open J. Commun. Soc.

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While the unmanned aerial vehicles (UAVs) swarm travels under a dynamic environment, the cluster head (CH) switching is unavoidable due to the mitigation of mobility, quality of service, and energy consumption. If an attacker becomes the new CH, the entire swarm will be controlled and the sensitive data will be leaked. Unlike the other mobile networks with constant network connectivity, the authentication in the UAV swarm suffers from intermittent connection with the ground station under a hostile environment or spectrum constraint condition. Hence, this paper proposes a novel CH safeguarding mechanism enabled by edge intelligence utilizing a situational-aware authentication scheme. This low-latency mechanism provides extra security at the CH selection and switching without cloud server support. By adopting the unique cross-layer attributes, the system security is significantly improved based on the extracted multi-dimensional information. The Linear Discriminant Analysis (LDA) algorithm fuses the authentication decision accurately by projecting the high dimensional estimations into a low dimensional space for maximum separability by only keeping the necessary attributes. A situation-aware cross-layer attribute selection algorithm is developed to select a minimum number of attributes so that the time required for attribute estimation and computation overhead of authentication can be reduced. The simulation results demonstrate that our scheme performs better under a dynamic environment compared with the physical layer authentication scheme and some existing state-of-the-art authentication techniques.

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“…Clustering supports both types of routing effectively, but multi-hop is mostly preferred in large area networks Singh et al (2017b). In clustering, optimal cluster formation and cluster head (CH) selection are the major challenges that we have to resolve using the fuzzy controller to select CH and form a cluster Alrashidi et al (2020). In static BS and multi-hop routing, the hotspot is a severe issue that severely affects network performance.…”

mentioning

confidence: 99%

Multi-Criteria Decision-based Path Planning for Data Collection in Fuzzy-Cluster based Large Sensor Networks

Singh¹,

Kumar²

2021

Preprint

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Wireless sensor networks (WSN) are widely used to gather information using wireless communication, but due to the confined power of sensor nodes, it is a predominant task to make WSN energy-efficient. Generally, a sensor node finds routes towards the base station (BS) to transmit the data. The sensor node transmits information at once or via neighbor nodes in a multi-hop manner. The nodes close to the BS transmit more data than other nodes. The nodes close to the BS tend to deplete their power quicker than different nodes in the network. This issue is known as the hotspot problem, leading to network separation and reducing the lifetime of the sensor network. The mobile sink as a better strategy to dispose of the hotspot problem in the course of information transmission, but the most critical challenges are finding sojourn points and path planning. This paper makes use of a cluster head (CH) as a sojourn point and multi-criteria decision strategy for path planning by the mobile sink at some stage in information collection. This paper contributes a fuzzy-based clustering algorithm that uses fuzzy logic to decide on cluster heads (CHs). The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach used to figure out the mobile sink's route. Based on this route, the mobile sink will move to collect data from every CH in the network. This scheme receives an order of CHs to visit and collect the data with the mobile sink. The simulation outcomes exhibit that this strategy performs better than the other protocols, which uses multi-hop information transmission for extending the lifetime of wireless sensor networks and one parameter for route planning.

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Energy-Efficiency Clustering and Data Collection for Wireless Sensor Networks in Industry 4.0

Cited by 27 publications

References 23 publications

A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

Safeguarding Cluster Heads in UAV Swarm Using Edge Intelligence: Linear Discriminant Analysis-Based Cross-Layer Authentication

Multi-Criteria Decision-based Path Planning for Data Collection in Fuzzy-Cluster based Large Sensor Networks

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