Kalman Filter Algorithm for Security of Network Clock Synchronization in Wireless Sensor Networks

Xuan, Xinggang; He, Jingsha; Zhai, Peng; Basabi, Alireza Ebrahimi; Liu, Gongzheng

doi:10.1155/2022/2766796

Cited by 3 publications

(5 citation statements)

References 18 publications

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“…Time synchronization is a fundamental support technology for WSNs. 5–9 Due to the battery-powered characteristics of WSNs, energy efficiency is a virtual factor to be considered when designing corresponding time synchronization protocols. 10…”

Section: Introductionmentioning

confidence: 99%

“…Time synchronization is a fundamental support technology for WSNs. [5][6][7][8][9] Due to the batterypowered characteristics of WSNs, energy efficiency is a virtual factor to be considered when designing corresponding time synchronization protocols. 10 In WSNs, the energy consumed by sensor nodes for communication (sending, receiving, and so on) is much larger than that consumed for computing, 11 and the information exchange mode can significantly influence the energy consumed for time synchronization.…”

Section: Introductionmentioning

confidence: 99%

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New timestamp mark–based energy efficient time synchronization method for wireless sensor networks

Huang

Zhang

Kong

et al. 2022

International Journal of Distributed Sensor Networks

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Aimed at the demands of wireless sensor networks for high energy-efficient time synchronization, the reduction of synchronization energy consumption is studied from the aspects of both accurate timestamps marking and synchronous information transmission mechanism. First, the network is divided into several parent–child groups periodically. The group-wise pair selection algorithm is used to select the network’s pairwise synchronization nodes, and chain-type network topology is thus generated. Second, the sequential multi-hop synchronization algorithm is introduced to realize the synchronization information exchange among pairwise synchronization nodes. The overhearing synchronization (OS) nodes obtain the synchronization information packet based on a one-way overhearing mechanism. Moreover, the accurate acquisition of the synchronization ack packet’s timestamp is carried out through the use of receiving-time-plus-fixed-delay mode. Third, the joint maximum likelihood method and the minimum variance unbiased estimation method are used to estimate the clock offsets of pairwise synchronization nodes and overhearing nodes to the parent nodes, respectively, based on which the child nodes adjust their local virtual clocks. Periodically, the pairwise synchronization nodes initiate the network’s time synchronization, estimate, and broadcast the relative offset to the gateway node, assisting the upper layer child nodes in synchronizing to the gateway node. Simulation results show that the proposed method not only achieves the millisecond level synchronization accuracy but also reduces the synchronization energy consumption and thus improves the network lifetime.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New timestamp mark–based energy efficient time synchronization method for wireless sensor networks

Huang

Zhang

Kong

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…WSN is composed of a large number of low-power, microsmart sensor nodes that are randomly deployed to perform sensing tasks in the monitoring area. In recent years, WSNs have played an important role in the production of life, environmental monitoring, national security, and other fields [1][2][3][4][5]. Because sensor nodes are usually powered passively with limited energy and weak computing power, it is important to build an energy-efficient WSN.…”

Section: Introductionmentioning

confidence: 99%

“…The CM is used to collect data and transmit the results to the CH, and then the CH merges the data within the cluster and forwards it to the remote base station (BS) through multi-hops transmission afterwards. The process is mainly divided into two stages: (1) the network topology construction stage, which carries out network clustering and cluster head node screening; and (2) the steady-state stage, which carries out data communication, data fusion, and data transmission.…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

Jiang

Ding

et al. 2022

Mobile Information Systems

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The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.

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“…Interference may also be generated when many wireless networks are located in close proximity to one another, as is possible in places with a high population density. This phenomenon is referred to as "interference" from other networks [27,28,29,30]. Interference may slow down the speeds at which data is sent, result in mistakes during data transmission, and possibly result in the loss of the signal entirely.…”

mentioning

confidence: 99%

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

Shrivastav¹,

Karmore

2023

IJEER

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Performance of dense wireless sensor networks is often degraded due to communication interference and time synchronization issues. Existing machine learning & deep learning models that propose bioinspired & pre-emptive packet-analysis solutions for these tasks either have high complexity, or high deployment costs. Moreover, these models cannot be scaled for heterogeneous node & traffic types, which limits their applicability when applied to real-time scenarios. To overcome these issues, this text proposes design of an interference-aware routing model with time synchronization capabilities for dense wireless sensor network deployments. The network initially collects temporal clock states & packet delivery performance of different nodes on heterogeneous traffic scenarios. These traffic patterns are converted into frequency, entropy, Gabor, and Wavelet components. The converted components are used to train an ensemble set of Naïve Bayes (NB), k Nearest Neighbour (kNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. These classifiers assist in identification of optimal clock deviations and set of routing paths. These routing paths are further fine-tuned via use of a Bacterial Foraging Optimization (BFO) Model, which assists in identification of interference-aware paths. The BFO Model uses a temporal fitness function that fuses throughput, communication delay, energy levels, and packet delivery performance for different set of contextual communications. Due to which, the model is able to showcase lower end-to-end delay, higher throughput, lower energy consumption, and higher packet delivery performance when compared with existing routing methods under high density nodes & heterogeneous network scenarios. The model showcases 99% PDR, 18.3% lower delay, 19.5% higher energy efficiency and 10.4% lower delay levels when compared with existing methods.

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Kalman Filter Algorithm for Security of Network Clock Synchronization in Wireless Sensor Networks

Cited by 3 publications

References 18 publications

New timestamp mark–based energy efficient time synchronization method for wireless sensor networks

New timestamp mark–based energy efficient time synchronization method for wireless sensor networks

A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks

IARMTS: Design of an Interference-Aware Routing Model with Time Synchronization Capabilities for Dense Wireless Sensor Network Deployments

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