Improved clustering algorithms for target tracking in wireless sensor networks

Darabkh, Khalid A.; Albtoush, Wijdan Y.; Jafar, Iyad F.

doi:10.1007/s11227-016-1898-1

Cited by 72 publications

(29 citation statements)

References 52 publications

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“…In contrast to Reference [11], Razaque et al [12] selected CHs based on the residual energy of the sensor node and the distance from the node to the base station. Darabkh et al [13] proposed two clustering algorithms for target tracking in WSNs. In order to satisfy the requirement of target tracking, the CH in Reference [13] is selected based on the node's residual energy, the distance between the detecting sensor nodes and the sink node, and the distance between the target and detecting sensor node.…”

Section: Related Workmentioning

confidence: 99%

“…Darabkh et al [13] proposed two clustering algorithms for target tracking in WSNs. In order to satisfy the requirement of target tracking, the CH in Reference [13] is selected based on the node's residual energy, the distance between the detecting sensor nodes and the sink node, and the distance between the target and detecting sensor node. In contrast to the above work, Lin et al [14] proposed an energy-efficient clustering strategy for WSNs that selects a node as CH in a precalculated central area of each cluster.…”

Section: Related Workmentioning

confidence: 99%

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An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks

Ren

Yao

2019

Sensors

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Concerning the large amount of energy consumption during the cluster head selection stage and the unequal harvested energy among nodes in energy-harvesting wireless sensor networks (EH-WSNs), an energy-efficient cluster head selection scheme called EECHS is proposed in this paper. The scheme divides all nodes from one cluster into three types: cluster head (CH), cluster member (CM), and scheduling node (SN). The SN is designed to monitor and store real-time information about the residual energy of all nodes, including CMs and the CH, in the same cluster. In the CH selection stage, the SN specifies a corresponding CM as the new CH according to the monitored results, thereby reducing the energy consumption caused by CH selection. In this way, the task of CH selection is migrated from CHs to SNs and, thus, the CHs can preserve more energy for data forwarding. Moreover, the EECHS adjusts the transmission radius of some nodes dynamically to prevent these nodes from discarding the harvested energy if their batteries are fully charged. A series of experiments were conducted to verify the effectiveness of the proposed EECHS, and the results demonstrate that EECHS can provide an efficient CH selection scheme for EH-WSNs and is able to use the harvested energy more efficiently than corresponding competitors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks

Ren

Yao

2019

Sensors

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“…These path selection routing protocols are utilized by the sensor nodes when they are not able to make forwarding directly to the base station (BS). 15 The data forwarding path from sensor nodes to the sink is dependent on the sink's current position. In hierarchal routing protocol, the network is separated into clusters.…”

Section: Of 14mentioning

confidence: 99%

“…In accumulation, the location-based routing protocol picks the sensor node based on their locality. 15 The data forwarding path from sensor nodes to the sink is dependent on the sink's current position.…”

Section: Of 14mentioning

confidence: 99%

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Kumar

Chaurasiya

2018

Concurrency and Computation

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To give the complete description of an environment or to take a robust decision, a number of observations are collected and combined from multiple sensor nodes. The process of combining and analyzing the observations is called multisensor data fusion. The fusion is used to produce more consistent, accurate, and useful information rather than provided by any individual sensor node. Data fusion finds wide application in many areas of such as object recognition, wireless sensor network, image processing, environment mapping, and localization. Nowadays, the Internet of Things (IoT) utilizes wireless sensor network (WSN) as a necessary platform for data sensing and communication. For efficiency, data fusion is performed on the sensed sample collected by the sensor nodes. However, fusion of the network parameters is also essential to select an appropriate sensor node for the forwarding of data. Therefore, in this paper, we propose a multisensor data fusion (MDF) strategy that performs fusion of the collected network parameters like bandwidth and centrality for the selection of an appropriate path. Extensive simulations were made, and related results showed that our proposed scheme has a better performance compared with other schemes in our simulated scenarios. KEYWORDS data fusion, Internet-of-Things, performance analysis, wireless sensor network INTRODUCTIONA wireless sensor network (WSN) is a type of network that contains large number of nodes having sensing capability such that they can easily detect any changes in the surrounding real-world environment. The nodes in WSN are used to carry sensed information from one to another desired location for further processing. 1,2 Technological advances are being developed daily, and a WSN plays a rapid contribution for providing smart communication with the use of smart devices. These smart devices perform communication at the different locations by providing a level of transparency among users and maintained an interconnected smart network. A number of sensor nodes are constituted by the smart network, which is used to send sensed information and termed as the Internet of Things (IoT). 3 In this paper, we called this network as an IoT oriented WSN. In this network, the users communicate with each other by exchanging of sensed data, monitoring of events, and surrounding and reacting autonomously. 4 The difference between the WSN and IoT oriented WSN is only the few sensors are deployed in WSN rather than along with smart devices for applications.However, the IoT oriented WSN consists of devices having sensors with full network connectivity. The network of IoT oriented WSN is concerned with the loss of packets and performance of the links for achieving high throughput in less delay.The services of IoT oriented WSN are provided by using a standard interface such that users can create a query, retrieve information, and change their states accordingly. An Internet link is used to provide the standard interface between users and IoT devices. The hardware components of a sensor no...

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“…Nowadays, digital communications become an essential part of our daily life [1] [2] [3] [4] [5]. A lot of applications are based on the Internet and in some cases it is required that communication have to be secret.…”

Section: Introductionmentioning

confidence: 99%

Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing

Al-Dhamari¹,

Darabkh²

2017

JSEA

Self Cite

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The main purpose in developing the steganographic algorithms lies in achieving most of the steganographic objectives which comprise the embedding capacity, imperceptibility, security, robustness and complexity. In this paper, we propose a high quality steganographic algorithm using new block structure which makes a good use of both modulus function and pixel-value differencing, namely, MF-PVD. We have made many experiments with various test images from several galleries, such as USC-SIPI and UWATERLOO-LINK. The performance of our proposed algorithm is verified using three different performance metrics which include peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and embedding capacity (EC). Experimental results and comparisons with six pertinent state-of-art algorithms are given to prove the validation and efficiency of the proposed algorithm.

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Improved clustering algorithms for target tracking in wireless sensor networks

Cited by 72 publications

References 52 publications

An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks

An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

Block-Based Steganographic Algorithm Using Modulus Function and Pixel-Value Differencing

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