Social relationship-based mobile node location prediction algorithm in participatory sensing systems

2014 ◽  
Author(s):  
J. Li ◽  
X. Xing ◽  
R. Yu ◽  
X. Wang ◽  
Y. Zhou
Keyword(s):  
Social Relationship ◽  
Mobile Node ◽  
Prediction Algorithm ◽  
Participatory Sensing ◽  
Location Prediction ◽  
Sensing Systems ◽  
Node Location

scholarly journals A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ruiyun Yu ◽  
Xingyou Xia ◽  
Shiyang Liao ◽  
Xingwei Wang
Keyword(s):  
Markov Model ◽  
Strong Relationship ◽  
Mobile Node ◽  
Prediction Algorithm ◽  
Participatory Sensing ◽  
Location Prediction ◽  
Mobile Nodes ◽  
Daily Routines ◽  
Sensing Systems ◽  
Node Location

Mobile node location predication is critical to efficient data acquisition and message forwarding in participatory sensing systems. This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes’ mobility. After considering the dynamism of users’ behavior resulting from their daily routines, the SMLPR algorithm preliminarily predicts node’s mobility based on the hidden Markov model in different daily periods of time and then amends the prediction results using location information of other nodes which have strong relationship with the node. Finally, the UCSD WTD dataset are exploited for simulations. Simulation results show that SMLPR acquires higher prediction accuracy than proposals based on the Markov model.

GPS Based Location Prediction and Authentication during Inter-MSC Handover

2014 ◽  
Vol 602-605 ◽  
pp. 2326-2329 ◽  
Author(s):  
K. Regin Bose ◽  
V. Sankaranarayanan
Keyword(s):  
Time Delay ◽  
Data Transfer ◽  
Mobile Node ◽  
Base Station ◽  
Location Prediction ◽  
Information Centre ◽  
Exact Location ◽  
Mobile Information ◽  
Node Location

In this paper a reliable secured handover with less time delay is suggested. To enhance the reliability of data transfer during handover, the exact location of the Mobile Node (MN) is to be known. Hence a GPS based Mobile Node location and direction prediction is suggested, which helps to handover to the appropriate Base Station. The security in transfer of data is ensured by a novel Mobile Information Centre (MIC) based dual authentication algorithm.

scholarly journals NPLP: An Improved Routing-Forwarding Strategy Utilizing Node Profile and Location Prediction for Opportunistic Networks

Information ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 306
Author(s):  
Bangyuan Chen ◽  
Lingna Chen
Keyword(s):  
Urban Areas ◽  
Data Dissemination ◽  
Opportunistic Networks ◽  
Mobile Node ◽  
Network Routing ◽  
Delivery Ratio ◽  
Location Prediction ◽  
Node Location ◽  
Forwarding Strategy ◽  
Routing State

Opportunistic networks are considered as the promising network structures to implement traditional and typical infrastructure-based communication by enabling smart mobile devices in the networks to contact with each other within a fixed communication area. Because of the intermittent and unstable connections between sources and destinations, message routing and forwarding in opportunistic networks have become challenging and troublesome problems recently. In this paper, to improve the data dissemination environment, we propose an improved routing-forwarding strategy utilizing node profile and location prediction for opportunistic networks, which mainly includes three continuous phases: the collecting and updating of routing state information, community detection and optimization and node location prediction. Each mobile node in the networks is able to establish a network routing matrix after the entire process of information collecting and updating. Due to the concentrated population in urban areas and relatively few people in remote areas, the distribution of location prediction roughly presents a type of symmetry in opportunistic networks. Afterwards, the community optimization and location prediction mechanisms could be regarded as an significant foundation for data dissemination in the networks. Ultimately, experimental results demonstrate that the proposed algorithm could slightly enhance the delivery ratio and substantially degrade the network overhead and end-to-end delay as compared with the other four routing strategies.

scholarly journals A Label Propagation Based User Locations Prediction Algorithm in Social Network

2020 ◽  
pp. 165-174
Author(s):  
Huan Ma ◽  
Wei Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Label Propagation ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Network Graph ◽  
Propagation Algorithm ◽  
Node Location ◽  
User Location

AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.

scholarly journals A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoguang Niu ◽  
Jiawei Wang ◽  
Qiongzan Ye ◽  
Yihao Zhang
Keyword(s):  
Dynamic Balance ◽  
Incentive Mechanism ◽  
Privacy Preserving ◽  
Circulation System ◽  
Participatory Sensing ◽  
Sensing Systems ◽  
Sensing Applications ◽  
Ideal Situation ◽  
Conflicting Objectives ◽  
Share Information

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Incentives-based preferences and mobility-aware task assignment in participatory sensing systems

2018 ◽  
Vol 117 ◽  
pp. 71-83 ◽  
Author(s):  
Rim Ben Messaoud ◽  
Yacine Ghamri-Doudane ◽  
Dmitri Botvich
Keyword(s):  
Task Assignment ◽  
Participatory Sensing ◽  
Sensing Systems
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