scispace - formally typeset
Search or ask a question
Author

Mao Ye

Other affiliations: Hewlett-Packard, Nanjing University
Bio: Mao Ye is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Social network. The author has an hindex of 19, co-authored 33 publications receiving 4624 citations. Previous affiliations of Mao Ye include Hewlett-Packard & Nanjing University.

Papers
More filters
Proceedings ArticleDOI
24 Jul 2011
TL;DR: This paper argues that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution, and develops a collaborative recommendation algorithm based on geographical influence based on naive Bayesian.
Abstract: In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative filtering and exploring social influence from friends, we put a special emphasis on geographical influence due to the spatial clustering phenomenon exhibited in user check-in activities of LBSNs. We argue that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution. Accordingly, we develop a collaborative recommendation algorithm based on geographical influence based on naive Bayesian. Furthermore, we propose a unified POI recommendation framework, which fuses user preference to a POI with social influence and geographical influence. Finally, we conduct a comprehensive performance evaluation over two large-scale datasets collected from Foursquare and Whrrl. Experimental results with these real datasets show that the unified collaborative recommendation approach significantly outperforms a wide spectrum of alternative recommendation approaches.

1,048 citations

Proceedings ArticleDOI
07 Apr 2005
TL;DR: This paper proposes a novel clustering schema EECS for wireless sensor networks, which better suits the periodical data gathering applications and elects cluster heads with more residual energy through local radio communication while achieving well cluster head distribution.
Abstract: Data gathering is a common but critical operation in many applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we propose a novel clustering schema EECS for wireless sensor networks, which better suits the periodical data gathering applications. Our approach elects cluster heads with more residual energy through local radio communication while achieving well cluster head distribution; further more it introduces a novel method to balance the load among the cluster heads. Simulation results show that EECS outperforms LEACH significantly with prolonging the network lifetime over 35%.

866 citations

Proceedings ArticleDOI
12 Dec 2005
TL;DR: An energy-efficient unequal clustering mechanism for periodical data gathering in wireless sensor networks that partitions the nodes into clusters of unequal size, and clusters closer to the base station can preserve some energy for the inter-cluster data forwarding.
Abstract: Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime

654 citations

Proceedings ArticleDOI
02 Nov 2010
TL;DR: A friend-based collaborative filtering approach for location recommendation based on collaborative ratings of places made by social friends is developed, and a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset is proposed.
Abstract: In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations/places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead.

493 citations

Journal ArticleDOI
TL;DR: An Unequal Cluster-based Routing (UCR) protocol is proposed that mitigates the hot spot problem in multihop sensor networks, and achieves an obvious improvement on the network lifetime.
Abstract: Clustering provides an effective method for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spot problem in multihop sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavier relay traffic and tend to die much faster, leaving areas of the network uncovered and causing network partitions. To mitigate the hot spot problem, we propose an Unequal Cluster-based Routing (UCR) protocol. It groups the nodes into clusters of unequal sizes. Cluster heads closer to the base station have smaller cluster sizes than those farther from the base station, thus they can preserve some energy for the inter-cluster data forwarding. A greedy geographic and energy-aware routing protocol is designed for the inter-cluster communication, which considers the tradeoff between the energy cost of relay paths and the residual energy of relay nodes. Simulation results show that UCR mitigates the hot spot problem and achieves an obvious improvement on the network lifetime.

475 citations


Cited by
More filters
Journal ArticleDOI

6,278 citations

01 Nov 2008

2,686 citations

Journal ArticleDOI
TL;DR: The concept of urban computing is introduced, discussing its general framework and key challenges from the perspective of computer sciences, and the typical technologies that are needed in urban computing are summarized into four folds.
Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

1,290 citations

Journal ArticleDOI
TL;DR: A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC, is proposed and evaluated, which achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.

1,131 citations

Journal ArticleDOI
TL;DR: This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.
Abstract: A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

1,097 citations