scholarly journals ARS: Adaptive Robust Synchronization for Underground Coal Wireless Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4981
Author(s):  
Kuiyuan Zhang ◽  
Mingzhi Pang ◽  
Yuqing Yin ◽  
Shouwan Gao ◽  
Pengpeng Chen
Keyword(s):  
Internet Of Things ◽  
Clock Synchronization ◽  
Sampling Period ◽  
Upper And Lower Bounds ◽  
Communication Overhead ◽  
Wireless Internet ◽  
Robust Synchronization ◽  
Time Stamps ◽  
Communication Links ◽  
Incomplete Measurement

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

scholarly journals Rechained: Sybil-Resistant Distributed Identities for the Internet of Things and Mobile Ad Hoc Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3257
Author(s):  
Arne Bochem ◽  
Benjamin Leiding
Keyword(s):  
Internet Of Things ◽  
Ad Hoc ◽  
Upper And Lower Bounds ◽  
Colored Petri Nets ◽  
Security Level ◽  
Internet Connectivity ◽  
Mobile Ad Hoc ◽  
Hoc Networks ◽  
Detection Schemes ◽  
The Internet Of Things

Today, increasing Internet of Things devices are deployed, and the field of applications for decentralized, self-organizing networks keeps growing. The growth also makes these systems more attractive to attackers. Sybil attacks are a common issue, especially in decentralized networks and networks that are deployed in scenarios with irregular or unreliable Internet connectivity. The lack of a central authority that can be contacted at any time allows attackers to introduce arbitrary amounts of nodes into the network and manipulate its behavior according to the attacker’s goals, by posing as a majority participant. Depending on the structure of the network, employing Sybil node detection schemes may be difficult, and low powered Internet of Things devices are usually unable to perform impactful amounts of work for proof-of-work based schemes. In this paper, we present Rechained, a scheme that monetarily disincentivizes the creation of Sybil identities for networks that can operate with intermittent or no Internet connectivity. We introduce a new revocation mechanism for identities, tie them into the concepts of self-sovereign identities, and decentralized identifiers. Case-studies are used to discuss upper- and lower-bounds for the costs of Sybil identities and, therefore, the provided security level. Furthermore, we formalize the protocol using Colored Petri Nets to analyze its correctness and suitability. Proof-of-concept implementations are used to evaluate the performance of our scheme on low powered hardware as it might be found in Internet of Things applications.

scholarly journals Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey

2021 ◽  
Author(s):  
Mohamed Said Frikha ◽  
Sonia Mettali Gammar ◽  
Abdelkader Lahmadi ◽  
Laurent Andrey
Keyword(s):  
Reinforcement Learning ◽  
Internet Of Things ◽  
Wireless Internet

Coding Algorithms and Network Plan for Context-Aware Data Collection Based on Internet of Things in Large Marine Ships

2014 ◽  
Vol 701-702 ◽  
pp. 957-960
Author(s):  
Feng Xie
Keyword(s):  
Internet Of Things ◽  
Formal Analysis ◽  
Cluster Head ◽  
Communication Overhead ◽  
Context Aware ◽  
Sensing Data ◽  
Communication Efficiency ◽  
Data Volume ◽  
Using Data ◽  
Data Context

The equipment maintenance in large marine ships may rely on Internet of Things to provide monitoring of equipment status instantly. The data volume of sensing data is huge as the number of equipments is large. It is critical to decrease the communication overhead of uploading sensing data for efficiently and timely monitoring. In this paper, we propose several coding algorithms by using data context that is modeled by our normal forms on the base of our observations. The communication efficiency is improved, which is justified by formal analysis and rigorous proof. We also propose several network plan policies for further improvement of the communication efficiency by using data context and cluster head deployment.

scholarly journals Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7449
Author(s):  
Fangqiuzi He ◽  
Junfeng Xu ◽  
Jinglin Zhong ◽  
Guang Chen ◽  
Shixin Peng
Keyword(s):  
Internet Of Things ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Cluster Head ◽  
Descent Method ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Upper And Lower Bounds ◽  
Battery Capacity ◽  
Intelligent Management

In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.

A strategy for opportunistic cognitive channel allocation in wireless Internet of Things

2014 ◽  
Author(s):  
Roni F. Shigueta ◽  
Mauro Fonseca ◽  
Aline Carneiro Viana ◽  
Artur Ziviani ◽  
Anelise Munaretto
Keyword(s):  
Internet Of Things ◽  
Channel Allocation ◽  
Wireless Internet

Spectrum coordination for intelligent wireless Internet of Things networks

2017 ◽  
Author(s):  
Zorica Nikolic ◽  
Milorad Tosic ◽  
Nenad Milosevic ◽  
Valentina Nejkovic ◽  
Filip Jelenkovic
Keyword(s):  
Internet Of Things ◽  
Wireless Internet
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