Constructing Chaotic Systems from a Class of Switching Systems

Zhang, Yuping; Liu, Xinzhi; Zhang, Huaiyue; Cui, Jia

doi:10.1142/s0218127418500323

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…Throughout the perturbed nonlinear chaotic systems study, unaccustomed aspects have been highlighted. Thereby, such systems are characterized by their strong sensitivity to initial conditions [29], [49].…”

Section: Lemma 2 [43]mentioning

confidence: 99%

Disturbed chaotic system synchronization via nonlinear robust interval state observers

Khemiri

Tlili

2022

Preprint

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In this paper, a nonlinear interval state observer design method is synthesized for chaos synchronization between master (drive) and slave (response) systems based on the Luenberger-type observer. The stability study, analyzed with Lyapunov’s approach, is formulated in terms of linear matrix inequalities (LMIs) and subsequently to calculate the observation gain matrix. The stability of the designed state observer is analyzed within the Lyapunov approach and the LMI paradigm. Moreover, the nonlinear interval observer, comprising the lower and upper bound state observers, is used for the state reconstruction of nonlinear perturbed chaotic sys- tems subject to unknown but bounded external disturbances. The effi- ciency and high performances of the aforementioned state observer-based synchronization technique are demonstrated by a large study of numeri- cal simulation for the state synchronization of perturbed unified chaotic systems. keywords : State synchronization Nonlinear perturbed chaotic sys- tems Nonlinear interval observer Lyapunov approach LMI paradigm

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Section: Lemma 2 [43]mentioning

confidence: 99%

Disturbed chaotic system synchronization via nonlinear robust interval state observers

Khemiri

Tlili

2022

Preprint

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Huan Zhou. at the network edge [6]. The cloud server is in charge of managing and monitoring the fog nodes and the entire network [7], [8]. In such an application scenario, the user and the in-vehicle navigation device generate a large amount of interaction, for example, the user may need the smart navigation device to personalize the content (bar, restaurant, and supermarket) that is of interest to the user [9].…”

Section: Introductionmentioning

confidence: 99%

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

et al. 2019

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With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived. Vehicle networking technology can not only facilitate people's travel but also effectively alleviate traffic congestion. The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV). Intelligent navigator is a very important part of human-computer interaction in IoV. It carries a large number of tasks of recommending content for users. In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. First, the user data are analyzed to establish the association rule mining algorithm. Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user. From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy. INDEX TERMS Content recommendation, association rules, Internet of Vehicles, fog computing.

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“…For the commercial and military applications, secure and reliable communications are crucial for maintaining data dependability and confidentiality [ 3 ]. In recent years, many security aspects in WSN have been reconsidered, such as routing, localization, data fusion and access control [ 4 , 5 , 6 , 7 ]. We take the secure routing as an example.…”

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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Constructing Chaotic Systems from a Class of Switching Systems

Cited by 12 publications

References 21 publications

Disturbed chaotic system synchronization via nonlinear robust interval state observers

Disturbed chaotic system synchronization via nonlinear robust interval state observers

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

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

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