Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile ...Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.展开更多
With the rapid development of location-based services and online social networks,POI recommendation services considering geographic and social factors have received extensive attention.Meanwhile,the vigorous developme...With the rapid development of location-based services and online social networks,POI recommendation services considering geographic and social factors have received extensive attention.Meanwhile,the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services.However,there is a degree of distrust of the cloud by service providers.To protect digital assets,service providers encrypt data before outsourcing it.However,encryption reduces data availability,making it more challenging to provide POI recommendation services in outsourcing scenarios.Some privacy-preserving schemes for geo-social-based POI recommendation have been presented,but they have some limitations in supporting group query,considering both geographic and social factors,and query accuracy,making these schemes impractical.To solve this issue,we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users,which are named GSPR-S and GSPR-G.Specifically,we first utilize the quad tree to organize geographic data and the MinHash method to index social data.Then,we apply BGV fully homomorphic encryption to design some private algorithms,including a private max/min operation algorithm,a private rectangular set operation algorithm,and a private rectangular overlapping detection algorithm.After that,we use these algorithms as building blocks in our schemes for efficiency improvement.According to security analysis,our schemes are proven to be secure against the honest-but-curious cloud servers,and experimental results show that our schemes have good performance.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61370192, 61432015, 61428203, and 61572347)the US National Science Foundation (Nos. CNS-1319915 and CNS-1343355)
文摘Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.
基金supported by the National Key Research and Development Program of China(2021YFB3101300,2021YFB3101303)the Natural Science Foundation of China(U22B2030,62302374)+4 种基金Shaanxi Provincial Key Research and Development Program(2023-ZDLGY-35)China Postdoctoral Science Foundation(2022M722498)the Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-QN-0699)Qin Chuangyuan Cited High-level Innovative and Entrepreneurial Talents Project(QCYRCXM-2022-244)the Science and Technology on Communication Networks Laboratory(HHX23641X003)。
文摘With the rapid development of location-based services and online social networks,POI recommendation services considering geographic and social factors have received extensive attention.Meanwhile,the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services.However,there is a degree of distrust of the cloud by service providers.To protect digital assets,service providers encrypt data before outsourcing it.However,encryption reduces data availability,making it more challenging to provide POI recommendation services in outsourcing scenarios.Some privacy-preserving schemes for geo-social-based POI recommendation have been presented,but they have some limitations in supporting group query,considering both geographic and social factors,and query accuracy,making these schemes impractical.To solve this issue,we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users,which are named GSPR-S and GSPR-G.Specifically,we first utilize the quad tree to organize geographic data and the MinHash method to index social data.Then,we apply BGV fully homomorphic encryption to design some private algorithms,including a private max/min operation algorithm,a private rectangular set operation algorithm,and a private rectangular overlapping detection algorithm.After that,we use these algorithms as building blocks in our schemes for efficiency improvement.According to security analysis,our schemes are proven to be secure against the honest-but-curious cloud servers,and experimental results show that our schemes have good performance.
