Chaotification of a class of linear switching systems based on a Shilnikov criterion

Zhang, Yuping; Shi, Peng; Lim, Cheng‐Chew; Zhu, Hong; Hu, Jiangping; Zeng, Yong

doi:10.1016/j.jfranklin.2017.06.003

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…We take the secure routing as an example. As the foundation of the information perception and transmission in IoT, the selection of routing strategies is an important part for the security defense and efficient delivery [ 8 , 9 , 10 , 11 ]. The design process of a routing protocol needs to make tradeoff in complexity, energy consumption and other aspects.…”

Section: Introductionmentioning

confidence: 99%

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

Zhou

Song

2018

Sensors

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It is knotty for current routing protocols to meet the needs of reliable data diffusion during the Internet of Things (IoT) deployments. Due to the random placement, limited resources and unattended features of existing sensor nodes, the wireless transmissions are easily exposed to unauthorized users, which becomes a vulnerable area for various malicious attacks, such as wormhole and Sybil attacks. However, the scheme based on geographic location is a suitable candidate to defend against them. This paper is inspired to propose a smart collaborative routing protocol, Geographic energy aware routing and Inspecting Node (GIN), for guaranteeing the reliability of data exchanging. The proposed protocol integrates the directed diffusion routing, Greedy Perimeter Stateless Routing (GPSR), and the inspecting node mechanism. We first discuss current wireless routing protocols from three diverse perspectives (improving transmission rate, shortening transmission range and reducing transmission consumption). Then, the details of GIN, including the model establishment and implementation processes, are presented by means of the theoretical analysis. Through leveraging the game theory, the inspecting node is elected to monitor the network behaviors. Thirdly, we evaluate the network performances, in terms of transmission delay, packet loss ratio, and throughput, between GIN and three traditional schemes (i.e., Flooding, GPSR, and GEAR). The simulation results illustrate that the proposed protocol is able to outperform the others.

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Section: Introductionmentioning

confidence: 99%

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

Zhou

Song

2018

Sensors

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“…Chaos synchronization and control theory have a great interest due to their potential applications in several fields during the recent decades. [1][2][3][4][5] Different synchronization types have been introduced and many theoretical results have been achieved, such as complete synchronization, [6][7][8] antisynchronization, [9][10][11] lag synchronization, [12,13] projective synchronization, [13,14] function projective synchronization, [15] etc. Most of the pervious types of synchronization concentrate on synchronization between one drive and one response systems.…”

Section: Introductionmentioning

confidence: 99%

Double compound combination synchronization among eight n-dimensional chaotic systems

2018

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Depending on double compound synchronization and compound combination synchronization, a new kind of synchronization is introduced which is the double compound combination synchronization (DCCS) of eight n-dimensional chaotic systems. This kind may be considered as a generalization of many types of synchronization. In the communication, based on many of drive and response systems, the transmitted and received signals will be more secure. Using the Lyapunov stability theory and nonlinear feedback control, analytical formulas of control functions are obtained to insure our results. The corresponding analytical expression and numerical treatment are used to show the validity and feasibility of our proposed synchronization scheme. The eight memristor-based Chua oscillators are considered as an example. Other examples can be similarly investigated. The proposed synchronization technique is supported using the MATLAB simulation outcomes. We obtain the same results of numerical treatment of our synchronization using simulation observations of our example.

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“…Mobile Fog Computing (MFC) [1], with outstanding performance and specialties in certain aspects of network deployment [2][3][4], is gradually attracting attentions to solve the problems of practical usage benefitting impoverished regions. For countries such as China, with large gaps of development between urban and rural areas, there is an urgent need for a solution that could handle issues such as limited infrastructural facilities [5][6][7], tight distribution of current adjacent resources [8][9][10], and connecting remote districts to the world outside [11][12][13]. As rural vitalization process is on the way in many places, a comprehensive architecture utilizing modern technology could bring new development opportunities.…”

Section: Introductionmentioning

confidence: 99%

A Smart Collaborative Policy for Mobile Fog Computing in Rural Vitalization

Zhou

Shi

Song

2018

Wireless Communications and Mobile Computing

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Mobile Fog Computing (MFC), as a crucial supplement to cloud computing, has its own special traits in many aspects. As smart mobile devices grow and vary in shapes and formats over the years, the need for real-time interactions and an easy-to-use network is imminent. In this paper, we propose a smart collaborative policy for MFC scenarios by considering the target of rural vitalization. The challenges and drawbacks of extending cloud to fog are reviewed at the beginning. Then, the analysis of policy design is presented from the perspectives of feature comparisons, urgent requirements, and possible solutions. The details of policy establishment are introduced with necessary examples. Finally, performance evaluations are provided based on simulation platforms. Validation results related to round trip time and transmission time illustrate the significant improvements of our proposal in certain ways compared to the original candidate, which enables larger deployment in impoverished areas.

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Chaotification of a class of linear switching systems based on a Shilnikov criterion

Cited by 15 publications

References 18 publications

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios

Double compound combination synchronization among eight n-dimensional chaotic systems

A Smart Collaborative Policy for Mobile Fog Computing in Rural Vitalization

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