TSS: A Primary User Mobility Model in Cognitive Internet

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

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Effect of User Mobility upon Trust Building among Autonomous Content Routers in an Information-Centric Network

Wireless Communications and Mobile Computing ◽

10.1155/2018/8612817 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Jerzy Konorski ◽

Jakub Grochowski

Keyword(s):

Autonomous Agents ◽

High Mobility ◽

Mobility Model ◽

Trust Building ◽

User Mobility ◽

Negative Effect ◽

Current Content ◽

Content Availability ◽

The One ◽

Willingness To Cooperate

The capability of proactive in-network caching and sharing of content is one of the most important features of an information-centric network (ICN). We describe an ICN model featuring autonomous agents controlling the content routers. Such agents are unlikely to share cached content with other agents without an incentive to do so. To stimulate cooperation between agents, we adopt a reputation and trust building scheme that is able to explicitly account for both objective current content availability and subjective willingness to cooperate. The scheme is further complemented with a so-called one-time goodwill mechanism introduced to avoid penalizing agents failures to provide temporarily unavailable content. In a simulated ICN environment under a modified Random Waypoint user mobility model, we investigate the resiliency of the reputation and trust building scheme to subversion, that is, strategic (selfish or malicious) agents acquiring higher trust values than honest ones, for varying user mobility scenarios. The scheme proves resilient in low-mobility scenarios, while increased user mobility is shown to have a negative effect. The one-time goodwill mechanism partly remedies this for high-mobility scenarios. We validate the results by comparison with an existing reputation and trust building scheme and with an alternative user mobility model.

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A Purpose-Driven User Mobility Model for Opportunistic Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.171-172.804 ◽

2010 ◽

Vol 171-172 ◽

pp. 804-809

Author(s):

Jian Bo Xu ◽

Guang Yang

Keyword(s):

Human Movement ◽

Mobility Model ◽

Opportunistic Networks ◽

User Mobility ◽

Node Mobility ◽

Opportunistic Network ◽

Data Forwarding ◽

Challenging Tasks ◽

Efficient Data ◽

Two Sides

An opportunistic Network is a network consisting exclusively of users’ mobile devices, with mobility being one of its essential features. Under the circumstances that a path may never exist between the two sides of communication, an opportunistic network exploits node mobility to realize delayed data delivery by capturing the opportunities of node meeting to relay messages. Designing efficient data forwarding strategies is one of the most challenging tasks in opportunistic network research, while currently the validation of any protocol for data forwarding almost absolutely relies on simulations of which node mobility models are one of the fundamental components. In this paper, we suggest a purpose-driven user mobility model for opportunistic networks which, to our best knowledge, is the first work considering the factor of purposes behind users’ movement. On the basis of location functionalization, our model can gain a better approximation of human movement patterns.

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