Zhenhua Tan

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.

An Improvement to the Chord-Based P2P Routing Algorithm

Chord has been widely used as a routing protocol in structured peer-to-peer overlay networks. A fundamental problem of peer-to-peer applications is to efficiently locate the node that stores a particular data item. In fact, performance of structured peer-to-peer overlay networks depends on the routing protocols. The original Chord routing protocol based on DHT uses Finger Table to route. However, in the original model, there is redundancy information in the Finger Table. This paper analyzes the routing algorithm of Chord protocol and presents an improvement strategy of original Chord routing algorithm. Results from theoretical analysis and experiments show that the routing performance of structured Chord-based overlay networks is improved.

An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

User-based collaborative filtering is an important technique used in collaborative filtering recommender systems to recommend items based on the opinions of like-minded nearby users, where similarity computation is the critical component. Traditional similarity measures, such as Pearson’s correlation coefficient and cosine Similarity, mainly focus on the directions of co-related rating vectors and have inherent limitations for recommendations. In addition, CF-based recommendation systems always suffer from the cold-start problem, where users do not have enough co-related ratings for prediction. To address these problems, we propose a novel similarity measure inspired by a physical resonance phenomenon, named resonance similarity (RES). We fully consider different personalized situations in RES by mathematically modeling the consistency of users’ rating behaviors, the distances between the users’ opinions, and the Jaccard factor with both the co-related and non-related ratings. RES is a cumulative sum of the arithmetic product of these three parts and is optimized using learning parameters from data sets. Results evaluated on six real data sets show that RES is robust against the observed problems and has superior predictive accuracy compared with the state-of-the-art similarity measures on full users’, grouped users’, and cold-start users’ evaluations.

A Novel Iterative and Dynamic Trust Computing Model for Large Scaled P2P Networks

Trust management has been emerging as an essential complementary part to security mechanisms of P2P systems, and trustworthiness is one of the most important concepts driving decision making and establishing reliable relationships. Collusion attack is a main challenge to distributed P2P trust model. Large scaled P2P systems have typical features, such as large scaled data with rapid speed, and this paper presented an iterative and dynamic trust computation model named IDTrust (Iterative and Dynamic Trust model) according to these properties. First of all, a three-layered distributed trust communication architecture was presented in IDTrust so as to separate evidence collector and trust decision from P2P service. Then an iterative and dynamic trust computation method was presented to improve efficiency, where only latest evidences were enrolled during one iterative computation. On the basis of these, direct trust model, indirect trust model, and global trust model were presented with both explicit and implicit evidences. We consider multifactors in IDTrust model according to different malicious behaviors, such as similarity, successful transaction rate, and time decay factors. Simulations and analysis proved the rightness and efficiency of IDTrust against attacks with quick respond and sensitiveness during trust decision.

Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems

Abstract Data sparsity is a well-recognized issue for Top-N item recommendation, which depends on user preference gathered from their historical behaviors (i.e., implicit feedback). However, only few works have considered multiple types of auxiliary implicit feedback (e.g, click, wanted) when building recommendation models. This paper aims to resolve the data sparsity problem by (a) generating target data (e.g., purchase) from a linear regression of auxiliary feedback, and from the nearest neighbors with a set of purchased items in multiple dimensions; (b) proposing a novel ranking model to accommodate both the original and generated data. We provide an intuitive comprehension regarding the relationship between one kind of auxiliary feedback and target feedback. A series of experiments are conducted on two real-world datasets and demonstrate the superiority of our approach to other counterparts.

Distributed secret sharing scheme based on personalized spherical coordinates space

Threshold secret sharing schemes are ideal to protect confidential information. In this paper, we propose a novel distributed threshold secret sharing scheme based on spherical coordinates. As four non-coplanar points can determine a unique sphere, we design transformation algorithms to generate secret as sphere center and mapping algorithms to convert participants to be sphere surface points. An algorithm to generate shadow secrets is proposed based on spherical coordinates. Verifiability and proactivity secret sharing are considered during the procedures of generating shadows and recovering secret and four or more participants could recover secret in our scheme. Performance analysis proves that the proposed scheme has relatively advantage in computation complexity, storage space and communication amounts during distribution and reconstruction processes, and it can tolerate collusion attacks and detect dishonest participants.

A Hybrid Algorithm to Solve Traveling Salesman Problem

The TSP problem is a typical one in the field of combinatorial optimization. After study other researchers’ related works, this paper presents a hybrid algorithm based on simulated annealing, ant colony and genetic in reference to previous research, in order to improve computing performance. Algorithms of this paper are used for solving traveling salesman problem, and the simulation contrast test results show that the algorithm has better convergence speed and optimal results; it also shows that the algorithm is feasible and effective.

A P2P Network Topology Optimized Algorithm Based on Minimum Maximum K-Means Principle

The most popular routing algorithm in p2p network is that every node keeps a route table which records a certain number of other nodes. Such algorithm selects neighbor nodes at random, and this reduces routing efficiency. In this paper a new algorithm of optimizing p2p overlay network topology is proposed, which is based on minimum maximum k-means principle. According to the communication history, the division presents a steady network topology with k clusters and k cluster center nodes. This optimized p2p network topology presents a high performance of routing efficiency. The minimum maximum principle makes the division more appropriate, so that there will be less probability of being unsteady for the p2p network. The conclusions show that this algorithm is an efficient p2p network topology optimized algorithm.

Research on Traffic Information Service Grid Based on GT4

With the rapid prevalence of grid technologies, more attentions have to be focused on grid applications. The paper presents a framework for traffic information service grid (TISG) in order to eliminate information islands and to implement resource sharing and collaboration in the intelligent transportation systems (ITS). Based on Red Had Linux, Globus Toolkit 4.0 (GT4), and other related tools, the paper proposes the implementation model of TISG and investigates the key techniques to build TISG. Therefore, an application platform of traffic grid is built, which conforms to the standard of OGSA in the aspects of common use, convenience, efficiency, and security. The research on traffic laws is conducted in TISG. Applying grid to ITS will promote the development and popularization of grid applications.

A Reputation Model for Aggregating Ratings based on Beta Distribution Function

With the speed growth of financial technology (Fintech), modern electronic marketing has typically deployed the use of the World Wide Web. This has come with great challenges especially in decision making and in engaging the pre-tail for launching new products and services in an open environment susceptible to high risks and threats. A prodigious need to build a sellers reputation and trust between the seller and the buyer so as to diminish such risks and threats in online trading birthed the idea of reputation systems. The emergence of reputation systems has attracted a lot of researchers to propose rating aggregation methods such as simple mean and normal distribution based method. However, the existing methods cannot accurately produce reputation score in some cases. Hence, this paper proposes a new model aiming to producing even more accurate and effective reputation score. Our model uses the standard beta-distribution considering the received rating distribution, so as to generate the weights of each ratings and then derive the level weights of ratings. The final reputation score is the level weighted aggregation of the rating levels. The proposed model is innovative in the aspect that the ratings are not directly aggregated to the reputation score, but are treated as the samples in evaluating each respective rating levels. Through case studies, the model is demonstrated to achieve desired accuracy and effectiveness, and even performs better than the existing models.