Changyong Niu

Priority-based target coverage in directional sensor networks using a genetic algorithm

Sensor networks have been applied in a wide variety of situations. Recently directional sensor networks consisting of directional sensors have gained attention. As for the traditional target coverage problem, the limited sensing angle of directional sensors makes it even more challenging. Moreover, individual targets may also be associated with differentiated priorities. Considering the distance between the directional sensors and targets influences sensing quality, this paper proposes the priority-based target coverage problem and strives to choose a minimum subset of directional sensors that can monitor all targets, satisfying their prescribed priorities. Due to the NP-Complete complexity, the minimum subset of directional sensors is approximated by using a genetic algorithm. Simulation results reveal the effects of multiple factors on the size of the resulting subset.

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.

Randomized Approach for Target Coverage Scheduling in Directional Sensor Network

Recently directional sensors have been widely deployed as they are more practicable under constraints of manufacture, size and cost. One common functionality of networks formed by such directional sensors is to monitor a set of discrete targets continuously. Large scale deployment makes sensor recharge impossible. By abundant deployment, it is reasonable and necessary to select subsets of sensors to operate alternatively so as to prolong the network lifetime. Such problem has been proved to be NP-Complete. This paper approximates network lifetime problem by randomized algorithm. Through constructing elementary sessions, which denotes active subset of sensors covering all targets, and linear programming, the approximating solution is derived within extremely less duration comparing to previous works. Simulation results demonstrate the algorithms performance and sound explanation is also presented.

Cooperativeness prediction in P2P networks

Peer-to-peer systems are open communities, in which not only is there no overarching control, but neither is there any hierarchy of control among the system components. In such open communities where peers can join and leave freely and behave autonomously, selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers may be unreliable or dishonest. Reputation systems have been proposed to boost trust and enhance collaboration among peers. However, conventional computational reputation systems tend to generate trust based on ad hoc aggregation techniques thus produce reputation values with ambiguous meanings. In this paper we propose a probabilistic computational approach to model and generate reputation. By explicitly separating the reputation between providing services and giving recommendations, our solution represents the estimate of service quality for a specific transaction as a probability conditioned upon each retrieved recommendation, thus taking the innate behaviours of reporters into account. A Kalman filter is applied to learn further the service reputation from the estimate. The proposed approach works well even when there is sparse feedback from the reporting peers giving output with well-defined semantics and useful meanings.

Bus-based Anonymous Multicast in Peer-to-Peer Overlay

Peer-to-peer overlay is self-organized by individual hosts in the Internet. The overlay usually serves for collaboration-oriented applications and necessitates group communication. Consequently, anonymity is demanded in scenarios such as military, government applications for privacy protection. It prevents communicating party identity from being revealed by others. In this paper we propose Bus-based anonymous multicast upon peer-to-peer overlay, which delivers message from originator to consumers anonymously. The Bus rotates over an Euler tour formed by hosts and takes intentional messages to respective consumers. Theoretical analysis demonstrates that bus-based anonymous multicast gets higher anonymity level comparing to existing ones.

A Utility-Based Maximum Lifetime Algorithm for Directional Sensor Network

Recently directional sensors have been widely deployed as they are more practicable under constraints of manufacture, size or cost. One common functionality of networks formed by such directional sensors is to monitor a set of discrete targets continuously. Sensors are often unattended after deployment making recharging impossible. By abundant deployment, it is reasonable and necessary to select subsets of sensors to operate successively so as to prolong the network lifetime. Furthermore, distance between sensor and target usually dominates accuracy of gathered data. This paper first formulates the utility-based lifetime of directional sensor network monitoring target set, and proves such problem is NP-complete. Next, network lifetime is approximated by proposed randomized algorithm. Simulation results reveal different factors impacting on network lifetime.

A Trust-Enhanced Topology Adaptation Protocol for Unstructured P2P Overlays

In this paper, a novel probabilistic computational reputation based trust model is firstly introduced, which has the property of separating reputations between providing services and giving feedbacks. And then a topology adaptation protocol for unstructured P2P overlays is proposed based on this trust model. The basic principle of this adaptation protocol is that peers with higher service and feedback trust values have higher probabilities of being accepted as neighbors in the P2P overlay. We show via simulations that the proposed trust model has better accuracy of reputations generated. And the topology adaptation protocol can enhance trusts even further, resulting in fewer malicious transactions when highly dynamic peer behavior patterns exist in the P2P network.

Scheduling meetings in distance learning

Peer-to-peer technique becomes mature gradually, and multiple domain-involved applications emerge, such as IPTV, distance learning, chatting network. In the context of distance learning, small scale of teachers would serve large scale of geographically located students. Normally, knowledge points are associated with different difficulty levels. And students usually are interested in varied subsets of knowledge points. Also teachers are capable of serving knowledge point subsets. The objective to schedule meeting among students and teachers according to their respective interests and capabilities is to reduce total learning duration. After formulating meeting schedule as Integer Programming problem, this paper proposes three heuristic algorithms to approximate the optimal solution. To the best of our knowledge, such problem is firstly investigated in distance learning context. Performance evaluation demonstrates their behaviors and PKPA algorithm excels two others substantially.

On Feedback Similarity Measurement in Web of Trust

Trust modeling is currently a key part in building P2P and other internet-based systems. One way for trust generation is to view the trust among users as a web of trust in large scale internet settings, and the trust quantities are further computed from such a web. However, due to the large size and sparseness of the web of trust, how to effectively represent and propagate trust in such a web remains a challenging problem. In this paper, we first present a novel way for similarity measurement from the point of view of user feedback patterns, which can be further incorporated into a trust model. And then the efficiency and effectiveness of the proposed method is given in simulations.

Bandwidth-aware scheduling in media streaming under heterogeneous bandwidth

Data-driven media streaming has been deployed gradually over the internet. in such systems, node periodically exchanges media block availability and fetches desirable blocks from neighbors. The issue on optimizing fetching blocks is called block scheduling and receives focus, especially in heterogeneous overlay. in this paper Bandwidth-Aware Scheduling (BAS) formulates such problem by incorporating bandwidth and block deadline, as well as node importance. Simulation results verify that BAS performs much better than existing ones.