Electronic trading (e-trading) provides a virtual marketplace (e-Marketplace) where buyers and sellers can engage in business activities through electronic media rather than direct physical contact. Although negotiation is a fundamental component of e-trading, the critical risks of missing out on top utility offers that expire before client's negotiation deadline has not been addressed. In order to address these problems, we propose a mobile-agent based secure one-to-many bilateral e-trade negotiation framework that efficiently manages the risk of losing top utility offers and maximizes client's utility taking into account various temporal constraints. Theoretical and empirical analysis of the proposed approach is performed. We evaluated the performance of the proposed strategy in terms of client's utility and negotiation time and compared it with two baseline negotiation strategies. The experimental analysis shows that the proposed strategy maximizes client's utility, shortens negotiation time, and ensures adequate market search. Proofs of validity of the proposed utility function are presented. The security protocol is formally verified and the verification shows that the protocol is free of security flaws and hence, negotiation data are secured.

Summary With the advantages of extremely high access speed, low energy consumption, nonvolatility, and byte addressability, nonvolatile memory (NVM) device has already been setting off a revolution in storage field. Conventional storage architecture needs to be optimized or even redesigned from scratch to fully explore the performance potential of NVM device. However, most previous NVM‐related works only explore its low access latency and low energy consumption. Few works have been done to explore the appropriate way to use NVM device for improving virtual machine's storage performance. In this paper, we comprehensively evaluate and analyze conventional virtual machine's storage architecture. We find that, even with cutting‐edge optimization technologies, virtual machine can only achieve 30% of NVM device's original performance. Based on this observation, we propose a memory bus–based storage architecture, which we named MBSA. Memory bus–based storage architecture can greatly shorten the length of virtual machine's storage input/output stack and improve NVM device's use flexibility. In addition, an efficient wear‐leveling algorithm is proposed to prolong NVM device's lifespan. To evaluate the new architecture, we implement it as well as the wear‐leveling algorithm on real hardware and software platform. Experimental results show that MBSA can provide a big performance improvement, about 2.55X, and the wear‐leveling algorithm can efficiently balance write operations on NVM device with a negligible performance overhead (no more than 3%).

Summary This paper addresses the problem of efficient federation formation by the cloud providers (CPs) with an aim to fulfill the dynamic resource demands of users for supporting data‐intensive workloads. Existing works only focus on forming federations based on the highest profit gained by each of the CPs in a federation. Therefore, these approaches often suffer from the risk of selecting unreliable CPs in the federation resulting in additional penalty cost and loss of CPs's reputation due to service level agreement violation between the users and the federation. In contrast, we argue that a trust model is necessary to find the most promising cloud collaborators. Accordingly, we propose a novel cloud federation formation mechanism by utilizing a trust‐based cooperative game theory, which enables the CPs to dynamically form a federation based on profit maximization and penalty cost minimization as a result of selecting the trustworthy CPs. Simulation results show that the cloud federation formed by the proposed mechanism is stable, satisfies the fairness property, and yields higher profit for the participating CPs in the long run without incurring penalty cost as compared with the state‐of‐the‐art approaches. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.

Presents correcttions to the paper, “A performance evaluation of machine learning-based streaming spam tweets detection,” (Chen ], C.; et al) , IEEE Trans. Comput. Social Syst., vol. 2, no. 3, pp. 65–76, Sep. 2015.

Abstract With the widespread deployment of cloud services, data center networks are developing toward large‐scale, multi‐path networks. Conventional switching‐oriented data center network meets difficulties in terms of scalability and flexibility to support increasing bandwidth requirements for cloud services. To solve this problem, a simple and scalable architecture, MatrixDCN, is proposed in this paper. MatrixDCN is an approximate non‐blocking network, in which switches and servers are arranged in rows and columns that compose a matrix structure. A MatrixDCN network can accommodate up to hundreds of thousands of servers without bandwidth bottlenecks. Furthermore, the physical topology of a MatrixDCN network can be designed consistently with its logic topology, which helps to reduce the complexity of the management and maintenance of a data center. An efficient routing algorithm, named fault‐avoidance routing (FAR), is well designed for MatrixDCN to fully leverage the regularity in the topology. FAR builds two routing tables for a router. A BRT is built based on local topology, and a novel negative routing table (NRT) is increasingly built based on learned partial network failures, which really avoids the problem of network convergence and further shortens the calculating time of routing tables. FAR also greatly reduces the size of routing tables by introducing NRTs at routers. Theoretical analysis and simulations show that MatrixDCN has advantages on the scalability of topology, network throughput, and the performance of FAR. Copyright © 2015 John Wiley & Sons, Ltd.

Summary In a mobilead hocnetwork, the data packet may fail to be delivered for various reasons mostly for route failure, congestion, and battery energy drain. Hence, providing reliable and timely data delivery in this network in an energy‐efficient way is challenging. Although there exist several solutions to solve these problems, they can handle either route failure or congestion or energy‐efficient routing. Hence, to cope up with all the problems simultaneously, we propose a route failure and congestion‐aware energy‐efficient cross‐layer design that spans the transport and network layer. In the transport layer, we introduce the concept of local packet buffering during link failure and congestion. As a result, the packet dropping rate of the network and energy consumption decreases. In the network layer, a routing protocol is proposed for selecting the energy‐efficient path for data transmission. It uses the buffering mechanism in case of route maintenance. In addition, we employ a multilevel congestion detection and control mechanism at the source and intermediate nodes that can judiciously take the most appropriate decision for congestion control in the network proactively. The simulation results showed that the proposed cross‐layer design provided better performance as compared with the state‐of‐the‐art protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Nowadays, smart‐card‐based user authentication becomes one of the most important security issues. But many schemes of that kind are under different attacks. Recently, Kumari et al. pointed that Chen et al.‘s scheme and Li et al.‘s scheme with the smart card were not secure. They proposed two improved schemes. Unfortunately, we find that the two schemes are not secure. The first scheme of Kumari et al. is under the de‐synchronization attack and lacks strong forward security. The second has the weaknesses including no user anonymity and password leaking. Also, it cannot withstand the user‐impersonation attack. We present a new scheme also based on the smart card overcoming common disadvantages and give a formal proof. We also use the tool ProVerif to verify the security of our scheme. Compared with some recent schemes, our scheme performs well, and it is fit for network applications. Copyright © 2015 John Wiley & Sons, Ltd.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption.

Cognitive radio (CR) networks are becoming an increasingly important part of the wireless networking landscape due to the ever-increasing scarcity of spectrum resources throughout the world. Nowadays CR media is becoming popular wireless communication media for disaster recovery communication network. Although the operational aspects of CR are being explored vigorously, its security aspects have gained less attention to the research community. The existing research on CR network mainly focuses on the spectrum sensing and allocation, energy efficiency, high throughput, end-to-end delay and other aspect of the network technology. But, very few focuses on the security aspect and almost none focus on the secure anonymous communication in CR networks (CRNs). In this research article we would focus on secure anonymous communication in CR ad hoc networks (CRANs). We would propose a secure anonymous routing for CRANs based on pairing based cryptography which would provide source node, destination node and the location anonymity. Furthermore, the proposed research would protect different attacks those are feasible on CRANs.