To promote development of Mobile Crowdsensing Systems (MCSs), numerous auction schemes have been proposed to motivate mobile users' participation. But, task diversity of MCSs has not been fully ...explored by most existing works. To further exploit task diversity and improve performance of MCSs, in this paper, we investigate the joint problem of sensing task assignment and schedule with considering multi-dimensional task diversity, including partial fulfillment, bilaterally-multi-schedule, attribute diversity, and price diversity. First, task owner-centric auction model is formulated and two distributed auction schemes (CPAS and TPAS) are proposed such that each task owner can locally process auction procedure. Then, mobile user-centric auction model is established and two distributed auction schemes (VPAS and DPAS) are developed to facilitate local auction implementation. These four auction schemes differ in their approaches to determine winners and compute payments. We further rigorously prove that all the four auction schemes (CPAS, TPAS, VPAS, and DPAS) are computationally-efficient, individually-rational, and incentive-compatible and that both CPAS and TPAS are budget-feasible. Finally, we comprehensively evaluate the effectiveness of CPAS, TPAS, VPAS, and DPAS via comparing with the state-of-the-art in real-data experiments.
Broadcast Scheduling for Cognitive Radio Networks Shouling Ji; Mingyuan Yan; Yueming Duan ...
2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS),
2013-April
Conference Proceeding
In this poster, we propose a novel Mixed Broadcast Scheduling (MBS) algorithm, which finishes a broadcast task intelligently by employing mixed unicast and broadcast communication modes. We also ...examine the latency and redundancy performance of MBS theoretically and by simulations.
Self-configuration management of mobile ad-hoc networks (MANETs) depends upon the hierarchical architecture and topology change of the network. By analyzing role shift and behavior changes of nodes ...in the clustering-based MANETs, a self-configuration management model is proposed in this paper. In view of unpredictability of the node behavior, a cluster unit provides the auto-configuration mechanisms with the node entry-exit and the clusterhead replacement control loops to adjust to changes of the network. The node entry-exit control loop resolves configuration as a result of nodes' movements. Configuration processes such as parameter reconfiguration, service publication will be executed once again in the clusterhead replacement control loop when the original clusterhead loses effectiveness. Based on this, two scenarios are depicted to give an analysis in model commonality aspects. Results of simulation prove a better performance of our model. In this paper, we describe the interoperate actions between cluster members and the cluster head to enable each cluster system to self-maintain in its life cycle.
With the rapid development of the Internet of Things (IoT) and the rapid popularization of 5 G networks, the data that needs to be processed in Mobile Crowdsourcing (MCS) system is increasing every ...day. Traditional cloud computing can no longer meet the needs of crowdsourcing for real-time data and processing efficiency, thus, edge computing was born. Edge computing can be calculated at the edge of network so that greatly improve the efficiency and real-time performance of data processing. In addition, most of the existing privacy protection technologies are based on the trusted third parties. Therefore, in view of the semi-trustworthiness of edge servers and the transparency of blockchain, this paper proposes a triple real-time trajectory privacy protection mechanism (T-LGEB) based on edge computing and blockchain. Through combining the localized differential privacy and multiple probability extension mechanism, the T-LGEB mechanism is proposed to send the requests and data to the edge server in this paper. Then, through the spatio-temporal dynamic pseudonym mechanism proposed in the paper, the entire trajectory of task participants is divided into multiple unrelated trajectory segments with different pseudonymous identities in order to protect the trajectory privacy of task participants while ensuring high data availability and real-time data. Through a large number of experiments and comparative analysis on multiple real data sets, the proposed T-LGEB has extremely high privacy protection capabilities and data availability, and the resource consumption caused is relatively low.
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust ...relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.
