A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks

Latha, A.; Prasanna, S.; Hemalatha, S.; Sivakumar, B.

doi:10.1016/j.cogsys.2018.11.006

Cited by 33 publications

(16 citation statements)

References 15 publications

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“…Firstly, a proper routing protocol can reduce communications of sensors in IWSNs. In [13], the authors use a threefold method to improve the lifetime of the IWSNs by adjusting the duty cycle process of sensor nodes, then switch between the active mode and the sleep mode according to the trust value obtained by the nodes. On the other hand, to better improve the service quality of IWSNs, the paper [14] proposes an AODV routing protocol for surplus energy, which realizes the reduction of energy consumption of IWSNs through cross-layer design.…”

Section: Related Workmentioning

confidence: 99%

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Liu

Xiao

et al. 2021

Journal of Sensors

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The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.

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Section: Related Workmentioning

confidence: 99%

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Liu

Xiao

et al. 2021

Journal of Sensors

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“…In matching pursuit algorithm, the EDADA-RPL recovers the data d from the input y = r × d. The residual error (r error ) is initialized as y. The sparse matrix in each parent node PN is generated and it is given in Equation (13) PN = d i−1 + r T × r error (13) where r T indicates the random number in each parent node (PN). The data recovery process will be continued until both original and recovered data are the same.…”

Section: Data Reconstruction Using Matching Pursuit Algorithmmentioning

confidence: 99%

“…In energy-aware routing, the existing protocols are energy-aware solar routing, node reliance techniques, multi-hop hierarchical clustering, duty cycling, and data-driven approaches to extend the network lifetime [12].Data aggregation is a process of collecting the data and aggregates it from the sensor's node. It is one of the essential processes to remove redundant data and save energy [13]. The primary objective of the data aggregation is to collect and aggregate the data.…”

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confidence: 99%

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Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Sankar¹,

Balasubramaniyam²,

Luhach

et al. 2019

Sensors

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Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

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“…Depends on energy, if remaining energy is minimizing, the marking probability will be decreased. And if remaining energy is greater, then marking probability is increasing[99] [100]. We tested CADS with 100, 150, 300 nodes.…”

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confidence: 99%

Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

Khan¹,

Rahman²,

Almaiah³

et al. 2020

IEEE Access

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Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as "The Internet of Things". WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called "A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs". CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

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A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks

Cited by 33 publications

References 15 publications

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

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