Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

Osamy, Walid; Aziz, Ahmed; Khedr, Ahmed M.

doi:10.7717/peerj-cs.463

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…This uncertainty manifests in the non-uniqueness and instability of complex system learning outcomes and the non-repeatability and unpredictability of system dynamic behavior discovery 20 , 21 . The primary challenges arising from nonlinear issues in complexity include the scientific description complexity of big data, structural complexity induced by the nonlinear correlation of primary components in complex systems, and evolutionary complexity stemming from the unpredictable regulation of these systems’ behaviors 22 – 24 . Multi-collinearity is observed in a set of independent variables when, although these variables are not precisely equal to zero when linearly combined with other independent variables, there exists a significant degree of inter-correlation among them.…”

Section: Methodsmentioning

confidence: 99%

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Song

2024

Sci Rep

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This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

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Section: Methodsmentioning

confidence: 99%

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Song

2024

Sci Rep

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“…However, the method was unable to balance the load on the network. To balance network load, Osamy et al 22 developed an adaptive and effective model on the basis of CS to enhance the efficiency of routing in WSN. The efficiency of the technique was analyzed using power‐efficient gathering in sensor information systems (PEGASIS).…”

Section: Literature Reviewmentioning

confidence: 99%

CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization

T P,

Srinadh,

et al. 2023

Int J Communication

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SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

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“…Internet to make our life easier [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. IoT has become a significant industrial and academic research field in recent years.…”

Section: Introductionmentioning

confidence: 99%

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

et al. 2023

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The Internet of Vehicles (IoV) has evolved from the classic Vehicular Ad-hoc NETworks (VANETs) as a result of the emergence of the Internet of Things (IoT). IoV is used for communication among vehicles in real-time with their drivers, other vehicles, pedestrians, fleet management systems, and roadside infrastructure. High vehicular speeds and frequent network topology changes make vehicle communication extremely difficult on the IoV network. More constraints are imposed on IoV communication performance in a huge network environment due to the difficult road conditions and the enormous quantity of vehicles. A promising approach to improve the IoV communication performance is through vehicle clustering. Minimizing the number of clusters and identifying a reliable Cluster head (CH) are some of the most challenging tasks. In this paper, we propose a Swarm optimization-based and mobility-aware clustering method termed SOMACA. SOMACA consists of two phases clustering phase and the routing phase. During the clustering phase, we combine mobility measures and cluster distance to generate the minimum number of clusters having stable CHs and employ the Sparrow Search algorithm (SSA) for CH selection. The routing phase consists of two steps (1) Route Formation and (2) Route Upkeep. The main target for route formation step is to build a secure routing path between IoV nodes and base station (BS) by establishing an optimal list of links that are ordered from high to low, and in each round, it selects the best one. Moreover, the Upkeep step aims to update and maintain the existing connection. The performance of SOMACA is assessed using simulation experiments with various metrics including average cluster lifetime, transmission range, and network grid size. The simulation results show that SOMACA reduces the average number of clusters by 42%, 48%, 47%, 9%, 22%,31%, 16%, and 43% less than CAVDO, GOA, GWOCNET, MFCA-IOV, MOGA-AWCP, HHOCNET, AMONE, and p-WOA algorithms respectively when the transmission ranges vary between 200 and 500. Moreover, SOMACA increases the average network lifetime by 11%, 12%, 9%, 6%, 7%, 9%, 3%, and 13% longer than the mentioned above compared algorithms, respectively.INDEX TERMS Clustering, the Internet of Things, the Internet of Vehicles, swarm intelligence, sparrow search algorithm, routing data, VANET.The associate editor coordinating the review of this manuscript and approving it for publication was Chan Hwang See.

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Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

Cited by 15 publications

References 29 publications

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

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