Showing papers by "Xiaoheng Deng published in 2016"

A reliable QoS-aware routing scheme for neighbor area network in smart grid

Abstract: A reliable bi-directional communication network is one of the key factors in smart grid (SG) to meet application requirements and improve energy efficiency. As a promising communication infrastructure, wireless mesh network (WMN) can provide high speed and cost-effect communication for SG. However, challenges remain to maintain high reliability and quality of service (QoS) when applying WMNs to SG. In this paper, we first propose a hybrid wireless mesh protocol (HWMP) based neighbor area network (NAN) QoS-aware routing scheme, named HWMP-NQ, to meet the QoS requirements by applying an integrated routing metric to route decision with effective link condition probing and queue optimization. To further improve the reliability of the proposed HWMP-NQ, we present a multi-gateway backup routing scheme along with a routing reliability correction factor to mitigate the impact of routing oscillations. Finally, we evaluate the performances of the proposed schemes on NS3 simulator. Extensive simulations demonstrate that HWMP-NQ can distinguish different applications and satisfy the QoS requirements respectively, and also improve the average packet delivery ratio and throughput with a reduced routing overhead, even with a high failure rate of mesh nodes.

QoS-Aware and Load-Balance Routing for IEEE 802.11s Based Neighborhood Area Network in Smart Grid

Abstract: Monitoring and transforming smart grid (SG) assets in a timely manner is highly desired for emerging smart grid applications. This critically requires the design of a neighborhood area network (NAN) which is capable of providing high-efficiency and reliable two-way last mile communication from meters to other SG domains. For this demand, IEEE 802.11s based wireless mesh network (WMN) is anticipated to be utilized in a NAN as it can provide high scalability, high-speed and cost-effective wireless transmission. In this paper, we propose a NAN QoS-aware and load-balance routing scheme (NQA-LB) based on the default hybrid wireless mesh protocol (HWMP) of IEEE 802.11s, which aims to address multiple QoS requirements from different NAN applications, and guarantee the highly reliability transfer of NAN traffic data towards gateway. With the NQA-LB, various QoS requirements can be satisfied through sufficient differentiated services as well as network congestion is mitigated by achieving load balance between multiple transmission paths. In order to improve the reliability of NQA-LB, we present an EDCA based adaptive priority adjustment scheme, called AP-EDCA, which dynamically adjusts packet's priority to increase the throughput under low load condition and to mitigate the collision under heavy load condition to improve the reliability of applications with high QoS requirements. Extensive simulation experiments demonstrate the superiority of the proposed scheme in terms of packet delivery ratio, end-to-end delay and throughput while satisfies various QoS requirements much better at the same time.

An imbalanced data classification method based on automatic clustering under-sampling

Abstract: Classification of imbalanced datasets has become one of the most challenging problems in big data mining. Because the number of positive samples is far less than the negative samples, low accuracy and poor generalization performance and some other defects always go with learning process of traditional algorithms. Ensemble construction algorithm is an important method to handle this problem. Especially, the ensemble construction algorithm based on random under-sampling or clustering can effectively improve the performance of classification. However, the former causes information loss easily and the latter increases complexity. In this paper, we propose ACUS, an improved ensemble algorithm based on automatic clustering and under-sampling. ACUS conducts clustering first according to the weight of samples, and then it constructs balanced-distributed dataset which consists of a certain percentage of the majority class and all of the minority class from each cluster. With Adaboost algorithm construction, these datasets are used to get an ensemble classifier. Experimental results demonstrate the advantages of our proposed algorithm in terms of accuracy, simplicity and high stability.

EPTR: expected path throughput based routing protocol for wireless mesh network

Abstract: For effective routing in wireless mesh networks, we proposed a routing metric, expected path throughput (EPT), and a routing protocol, expected path throughput routing protocol (EPTR), to maximize the network throughput . The routing metric EPT is based on the estimated available bandwidth of the routing path, considering the link quality, the inter- and intra-flow interference and the path length. To calculate the EPT of a routing path, we first calculate the expected bandwidth of the link and the clique, and then consider the decay caused by the path length. Based on EPT, a distributed routing protocol EPTR is proposed, aiming to balance the network load and maximize the network throughput. Extensive simulations are conducted to evaluate the performance of the proposed solution. The results show that the proposed EPTR can effectively balance the network load, achieve high network throughput, and out-perform the existing routing protocols with the routing metrics previously proposed for wireless mesh networks.

Credit distribution for influence maximization in online social networks with node features 1

Abstract: Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks How to evaluate the influence is essential to solve the influence maximization problem Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread However, since nodes’ degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users In this paper, besides the node features, the Credit Distribution (CD) model is extended to incorporate the time-critical aspect of influence in online social networks After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and

A Weighted Network Model Based on Node Fitness Dynamic Evolution

Abstract: Many complex networks in practice can be described by weighted networks. Currently, most existing weighted network models only consider the node strength in evolving conditions, but neglect the influence of node attraction on network evolution. In this paper, we propose an accurate and practical weighted evolving network model based on node fitness dynamic evolution, which takes both node strength and node attraction into consideration. Our theoretical analysis and numerical simulations have demonstrated the scale-free property of the network model, which has been widely observed in many real-world networks. Additionally, the phenomenon that very few nodes possess greater fitness is observed via numerical simulations of our network model, which can be referred to as the fitness property of network. Our network model's dual assessment of node strength and node attraction leads to fewer node clustering and stronger robustness of the whole network than other existing network growth models.

Credit distribution for influence maximization in online social networks

Abstract: Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since nodes' degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution (CD) model is extended to incorporate the time-critical aspect of influence in online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and diffusion process.