Opportunistic Transmission Mechanism Based on SI in Mobile Crowd Sensing Networks

Jia, Bing; Liu, Shuai; Zhou, Tao; Xu, Zhendong

doi:10.1109/qrs-c.2017.41

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Feature , virus propagation in the communication network depends on the network structure; therefore, we employ the SI [38][39][40] and SIR [41,42] models based on CNT to simulate virus propagation. In the SI model, the vertexes of the communication network are divided into two groups: susceptible set S and infectious set I.…”

Section: Virus Propagation Model Based On Cnt According Tomentioning

confidence: 99%

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

et al. 2019

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Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber-physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.

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Section: Virus Propagation Model Based On Cnt According Tomentioning

confidence: 99%

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

et al. 2019

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“…However, during a crisis, the SWN is constrained by interrupted transportation within the limited geographic topology. Reference [18] proposes an opportunistic data transmission mechanism based on a susceptible-infected model, and this model is used to analyze the social characteristics of mobile crowd sensing networks and obtain the table of evolutionary relationships between nodes. The evolutionary relationships are quantified and added to the opportunistic routing as additional information, to guide the nodes to reach the destination successfully.…”

Section: Related Workmentioning

confidence: 99%

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

Liu

Zhou

et al. 2019

Wireless Communications and Mobile Computing

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The opportunistic mobile sensor network has been extensively applied in various public safety applications such as the fire rescue and earthquake rescue, since it can provide a surveillance range with an inexpensive cost and avoid the dangers of humans staying in risk zones. However, due to some environmental events such as building structure damage, airflow push, and fire explosions, the sensor nodes sprinkled into the fire-rescue scenario may be kept moving. Thus, the contacts between nodes become momentary, and the data packets cannot be forwarded along stable communication paths. To this end, the opportunistic forwarding manner is adopted in the fire-rescue scenario to enable the data packets to be transferred to the rescue control center (RCC) through some discrete hops. The contributions of this paper are threefold. First, the nodes in the fire-rescue scenario are carefully investigated and classified into four types: small-range mobile nodes (SRNs), large-range mobile nodes (LRNs), firefighter nodes (FNs), and robot nodes (RNs). Second, we formulate the data forwarding problem, and the optimal proportions of SRNs, LRNs, and FNs in data holders are mathematically analyzed to obtain the maximum delivery ratio. Third, a data forwarding approach for fire-rescue scenario (DFAFR) is proposed. In DFAFR, the optimal proportions of SRNs, LRNs, and FNs in data holders are maintained as far as possible through selecting different types of data holder candidates, and then the new data holders are determined from these data holder candidates and the adjacent RNs on basis of their expected delivery delay. Finally, the performance of DFAFR is analyzed through simulations of the fire-rescue scenario, and the results indicate that DFAFR can enhance the delivery ratio and shorten the delivery delay while the forwarding overhead is restricted.

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Small Data in IoT: An MCS Perspective

Azmy

Sneineh

Hassanein

2018

EAI/Springer Innovations in Communication and Computing

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Opportunistic Transmission Mechanism Based on SI in Mobile Crowd Sensing Networks

Cited by 4 publications

References 11 publications

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

A Data Forwarding Approach for Fire-Rescue Scenario with Multi-Type Mobile Nodes

Small Data in IoT: An MCS Perspective

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