Compressive Data Gathering Based on Even Clustering for Wireless Sensor Networks

Qiao, Jianhua; Zhang, Xueying

doi:10.1109/access.2018.2832626

Cited by 33 publications

(17 citation statements)

References 40 publications

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“…J. Qiao and X. Zhang [25], the method of compressed data gathering is proposed in order to get rid of the problem of unbalanced position and the random selection. Even Clustering method is proposed based on the location, clustering is applied with the similar size of grids that ensures the positional balance.…”

Section: Literature Surveymentioning

confidence: 99%

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

Vimala¹,

Ranjan²

2020

IJECE

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IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.

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Section: Literature Surveymentioning

confidence: 99%

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

Vimala¹,

Ranjan²

2020

IJECE

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“…Consequently, in the green energy powered wireless access networks, the smart grid is an "unlimited energy storage device" with back up ongrid power supply. Moreover, energy trading between BSscan facilitates the radio resource exchange between the grid connected wireless access nodes [19], [20].…”

Section: Elements Of Resource Allocationmentioning

confidence: 99%

“…In [3], An energy efficiency and average throughput maximization for CRNs is studied in, which also analyzes the tradeoff between energy ef_ciency and average throughput under the imperfect sensing decisions and different PU trafcs. Chu and Zepernick [19] propose two optimal PA strategies for the hybrid interweave-underlay cognitive cooperative radio networks with the imperfect spectrum sensing to maximize channel capacity and minimize outage probability. In [20], under the spectrum sensing uncertainties, an interference-limited resource allocation algorithm including subcarrier allocation and PA in cognitive heterogeneous networks is investigated to reduce the interference to macrocell users and satisfy the requirement of femtocell users.…”

Section: Elements Of Resource Allocationmentioning

confidence: 99%

Energy Consummation in cognitive radio network using Different Methods

Gunjal¹,

Sharama²,

Todmal³

2019

IJEAT

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The cognitive radio and resource allocation techniques have been proposed for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum and allocate the available resources correctly. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with resource allocation can further improve the future wireless network performance and reduces the energy consummation. Efficient resource allocation in cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this Paper, we are going to discuss the different Localization Techniques, objectives and protocols used in the literature for resource allocation in CRN. This paper also highlights the use of power control, cooperation types, network configurations, and Energy consummation used in CRN. Finally, directions for future research are outlined of proposed algorithms for energy minimization.

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“…One of the common clustering techniques for the energy reduction is the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, 20 which selects the cluster head through the probabilistic technique. 21 As the nodes always communicate with the cluster head, there is a chance for increase of traffic load in the cluster head, and hence the cluster head topology to be rearranged from time to time. The dynamic clustering scheme has provided considerable scalability and energy efficiency and thus used in different research communities, and further adopted for various communication based applications.…”

Section: Introductionmentioning

confidence: 99%

A modified rider optimization algorithm for multihop routing in WSN

Augustine¹,

Ananth²

2020

Int J Numerical Modelling

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Wireless Sensor Networks (WSNs) have an important role in establishing the communication between the Internet of things (IoT) devices. Routing is an important research field in the WSN as it helps in finding the suitable paths for the communication. This paper proposes a multipath routing algorithm based on the optimization approach. The proposed routing algorithm has two important phases, namely, Cluster head selection and multipath routing. Initially, the cluster head selection is carried out by the kernel based Fuzzy C Means (kernel FCM) algorithm. Then, the multipath routing is established by the newly developed Rider Salp Swarm Optimization algorithm (RSSA), which is the integration of the Rider Optimization Algorithm (ROA) and Salp Swarm Algorithm (SSA). The fitness function of the proposed RSSA is designed by considering the several factors, such as energy, QoS and trust. The simulation of the proposed work is carried out using different WSN setups, and the results are compared with several comparative techniques. From the results, it can be summarized that the proposed RSSA based multipath routing has better performance with values of 0.2526, 0.0764, 0.5, and 26 for delay, energy, throughput and number of alive nodes, respectively.

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Compressive Data Gathering Based on Even Clustering for Wireless Sensor Networks

Cited by 33 publications

References 40 publications

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

Energy Consummation in cognitive radio network using Different Methods

A modified rider optimization algorithm for multihop routing in WSN

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