Geyong Min

A performance model for wormhole-switched interconnection networks under self-similar traffic

Many recent studies have convincingly demonstrated that network traffic exhibits a noticeable self-similar nature, which has a considerable impact on queuing performance. However, the networks used in current multicomputers have been primarily designed and analyzed under the assumption of the traditional Poisson arrival process, which is inherently unable to capture traffic self-similarity. Consequently, it is crucial to reexamine the performance properties of multicomputer networks in the context of more realistic traffic models before practical implementations show their potential faults. In an effort toward this end, we propose the first analytical model for wormhole-switched k-ary n-cubes in the presence of self-similar traffic. Simulation experiments demonstrate that the proposed model exhibits a good degree of accuracy for various system sizes and under different operating conditions. The analytical model is then used to investigate the implications of traffic self-similarity on network performance. We reveal that the network suffers considerable performance degradation when subjected to self-similar traffic, stressing the great need for improving network performance to ensure efficient support for this type of traffic.

An Intelligent Information Forwarder for Healthcare Big Data Systems With Distributed Wearable Sensors

An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed.

Reliable and Energy-Efficient Multipath Communications in Underwater Sensor Networks

Weak reliability and low energy efficiency are the inherent problems in Underwater Sensor Networks (USNs) characterized by the acoustic channels. Although multiple-path communications coupled by Forward Error Correction (FEC) can achieve high performance for USNs, the low probability of successful recovery of received packets in the destination node significantly affects the overall Packet Error Rate (PER) and the number of multiple paths required, which in turn becomes a critical factor for reliability and energy consumption. In this paper, a novel Multiple-path FEC approach (M-FEC) based on Hamming Coding is proposed for improving reliability and energy efficiency in USNs. A Markovian model is developed to formulate the probability of M-FEC and calculate the overall PER for the proposed decision and feedback scheme, which can reduce the number of the multiple paths and achieve the desirable overall PER in M-FEC. Compared to the existing multipath communication scheme, extensive simulation experiments show that the proposed approach achieves significantly lower packet delay while consuming only 20-30 percent of energy in multiple-path USNs with various Bit Error Rates (BER).

Content delivery networks: a bridge between emerging applications and future IP networks

With the rapid development of network applications, the Internet has evolved from a content-based communication infrastructure to a social-based community network. The emerging applications require the Internet to preserve not only the existing advantages of simplicity and scalability, but also demand varying amounts of capability, availability, reliability, flexibility, and differentiated quality of service. Therefore, it is of paramount importance to bridge the gap between these applications and the IP networks which were originally designed and developed for supporting one-size-fits-all functionality. An efficient solution is to build a virtual network on top of a generic IP transport layer in order to provide additional functionality and flexibility. The content delivery networks technique is one of the successful virtual networks rapidly developed over the last decade with the specific advantage of optimizing the Internet. Nowadays, the CDN has become one of the most important parts of the Internet architecture for content distribution. In this article we highlight the innovative technologies in CDNs and present their evolution triggered by ever newer emerging applications. By presenting in-depth discussion about the architecture, challenges, and applications of CDNs, we demonstrate their importance for the future Internet.

A distributed reputation and trust management scheme for mobile peer-to-peer networks

In peer-to-peer (P2P) networks, trust ratings aggregation and peer ranking are unreliable, time-consuming and space-demanding operations. The swift expansion of emerging P2P techniques towards the domain of mobile computing poses significant challenges for trust and security management. Several trust management schemes have been proposed recently to counter the security threat on P2P systems. However, due to the difficulties caused by system mobility and dynamic network topology, there is an increasing requirement of decentralized and distributed trust management schemes. In this paper, we initially investigate and analyze four typical decentralized and distributed trust management schemes. Based on the findings of this analysis, a robust distributed reputation and trust management scheme, referred to as M-trust, is proposed for mobile P2P networks. The new scheme utilizes confidence in reputation, based on interactions among peers, to reduce the computation complexity. Furthermore, distributed algorithms are presented for accurate and reliable trust ratings aggregation and space management. The performance of M-trust is evaluated in comparison to the existing schemes using extensive simulation experiments. The results demonstrate that M-trust possesses the excellent overall performance in terms of accuracy, reliability, convergence speed, and detection rate under various constraints of mobility, trust threshold and network out-degree.

An adaptive QoS framework for integrated cellular and WLAN networks

The design of a network architecture that can efficiently integrate WLAN and cellular networks is a challenging task, particularly when the objective is to make the interoperation between the two networks as seamless and as efficient as possible. To provide end-to-end quality of service (QoS) support is one of the key stages towards such a goal. Due to various constraints, such as the unbalanced capacity of the two systems, handoff from user mobility and unreliable transmission media, end-to-end QoS is difficult to guarantee. In this paper, we propose a generic reservation-based QoS model for the integrated cellular and WLAN networks. It uses an adaptation mechanism to address the above issues and to support end-to-end QoS. The validity of the proposed scheme is demonstrated via simulation experiments. The performance results reveal that this new scheme can considerably improve the system resource utilization and reduce the call blocking probability and handoff dropping probability of the integrated networks while maintaining acceptable QoS to the end users.

Green Communication in Energy Renewable Wireless Mesh Networks: Routing, Rate Control, and Power Allocation

The increasing demand for wireless services has led to a severe energy consumption problem with the rising of greenhouse gas emission. While the renewable energy can somehow alleviate this problem, the routing, flow rate, and power still have to be well investigated with the objective of minimizing energy consumption in multi-hop energy renewable wireless mesh networks (ER-WMNs). This paper formulates the problem of network-wide energy consumption minimization under the network throughput constraint as a mixed-integer nonlinear programming problem by jointly optimizing routing, rate control, and power allocation. Moreover, the min-max fairness model is applied to address the fairness issue because the uneven routing problem may incur the sharp reduction of network performance in multi-hop ER-WMNs. Due to the high computational complexity of the formulated mathematical programming problem, an energy-aware multi-path routing algorithm (EARA) is also proposed to deal with the joint control of routing, flow rate, and power allocation in practical multi-hop WMNs. To search the optimal routing, it applies a weighted Dijkstras shortest path algorithm, where the weight is defined as a function of the power consumption and residual energy of a node. Extensive simulation results are presented to show the performance of the proposed schemes and the effects of energy replenishment rate and network throughput on the network lifetime.

Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage

Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner’s data honestly. To date, a number of RDIC protocols have been proposed in the literature, but most of the constructions suffer from the issue of a complex key management, that is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the system complexity and the cost for establishing and managing the public key authentication framework in PKI-based RDIC schemes. We formalize ID-based RDIC and its security model, including security against a malicious cloud server and zero knowledge privacy against a third party verifier. The proposed ID-based RDIC protocol leaks no information of the stored data to the verifier during the RDIC process. The new construction is proven secure against the malicious server in the generic group model and achieves zero knowledge privacy against a verifier. Extensive security analysis and implementation results demonstrate that the proposed protocol is provably secure and practical in the real-world applications.

A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction

Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction.

Energy-Efficient Distributed Relay and Power Control in Cognitive Radio Cooperative Communications

In cognitive radio cooperative communication (CR-CC) systems, the achievable data rate can be improved by increasing the transmission power. However, the increase in power consumption may cause the interference with primary users and reduce the network lifetime. Most previous work on CR-CC did not take into account the tradeoff between the achievable data rate and network lifetime. To fill this gap, this paper proposes an energy-efficient joint relay selection and power allocation scheme in which the state of a relay is characterized by the channel condition of all related links and its residual energy. The CR-CC system is formulated as a multi-armed restless bandit problem where the optimal policy is decided in a distributed way. The solution to the restless bandit formulation is obtained through a first-order relaxation method and a primal-dual priority-index heuristic, which can reduce dramatically the on-line computation and implementation complexity. According to the obtained index, each relay can determine whether to provide relaying or not and also can control the corresponding transmission power. Extensive simulation experiments are conducted to investigate the effectiveness of the proposed scheme. The results demonstrate that the power consumption is reduced significantly and the network lifetime is increased more than 40%.