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  • 1.
    Ahmed, Ejaz
    et al.
    The Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya.
    Yaqoob, Ibrar
    The Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya.
    Hashem, Ibrahim Abaker Targio
    The Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya.
    Khan, Imran
    Schneider Electric Industries, Grenoble.
    Ahmed, Abdelmuttlib Ibrahim Abdalla
    The Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya.
    Imran, Muhammad
    College of Computer and Information Sciences, King Saud University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    The role of big data analytics in Internet of Things2017In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 129, no 2, p. 459-471Article in journal (Refereed)
    Abstract [en]

    The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions.

  • 2. Carr-Motyckova, Lenka
    et al.
    Carr, David
    Luleå tekniska universitet.
    A cluster-tree protocol for reliable multicasting2005In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 49, no 6, p. 707-726Article in journal (Refereed)
    Abstract [en]

    Distributed simulations and conferences require a reliable multicast protocol that guarantees safe data delivery in a reasonably short time. Such high-quality service demands substantial network resources. As these applications grow in use, scalability becomes an important issue. One way to achieve scalability is through clustering. The overall load is distributed among clusters so that large multicast groups avoid overloading the network. We propose a protocol for reliable multicasting, based on a cluster structure. We prove that in the cluster, the leader is an ancestor of all cluster members with respect to the multicast routing tree. This relationship yields an efficient acknowledgement structure. We also describe an acknowledgement algorithm based on a pulsing mechanism and prove that it has constant latency for acknowledging data. Finally, we show that the protocol is scalable by proving that it generates a constant load for all nodes.

  • 3.
    Dinh, Thanh
    et al.
    School of Electronic Engineering, Soongsil University.
    Kim, Younghan
    School of Electronic Engineering, Soongsil University.
    Gu, Tao
    School of Computer Science, RMIT University, Melbourne.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    L-MAC: A Wake-up Time Self-learning MAC Protocol for Wireless Sensor Networks2016In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 105, p. 33-46Article in journal (Refereed)
    Abstract [en]

    This paper analyzes the trade-off issue between energy efficiency and packet delivery latency among existing duty-cycling MAC protocols in wireless sensor networks for low data-rate periodic-reporting applications. We then propose a novel and practical wake-up time self-Learning MAC (L-MAC) protocol in which the key idea is to reuse beacon messages of receiver-initiated MAC protocols to enable nodes to coordinate their wakeup time with their parent nodes without incurring extra communication overhead. Based on the self-learning mechanism we propose, L-MAC builds an on-demand staggered scheduler to allow any node to forward packets continuously to the sink node. We present an analytical model, and conduct extensive simulations and experiments on Telosb sensors to show that L-MAC achieves significant higher energy efficiency compared to state-of-the-art asynchronous MAC protocols and a similar result of latency compared to synchronous MAC protocols. In particular, under QoS requirements with an upper bound value for one-hop packet delivery latency within 1 s and a lower bound value for packet delivery ratio within 95%, results show that the duty cycle of L-MAC is improved by more than 3.8 times and the end-to-end packet delivery latency of L-MAC is reduced by more than 7 times compared to those of AS-MAC and other state-of-the-art MAC protocols, respectively, in case of the packet generation interval of 1 minute. L-MAC hence achieves high performance in both energy efficiency and packet delivery latency.

  • 4.
    Kamal, Ahmed E.
    et al.
    Department of Electrical & Computer Engineering, Iowa State University, USA.
    Imran, Muhammad
    College of Computer and Information Sciences, King Saud University, Saudi Arabia.
    Chen, Hsiao-Hwa
    Department of Engineering Science, National Cheng Kung University, Taiwan.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Survivability strategies for emerging wireless networks2017In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 128, p. 1-4Article in journal (Refereed)
  • 5.
    Liu, Kai
    et al.
    School of Electronics and Information Engineering, Beihang University, Beijing.
    Chang, Xiaoyang
    School of Electronics and Information Engineering, Beihang University, Beijing.
    Liu, Feng
    School of Electronics and Information Engineering, Beihang University, Beijing.
    Wang, Xin
    Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Cooperative MAC Protocol with Rapid Relay Selection for Wireless Ad hoc Networks2015In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 91, p. 262-282Article in journal (Refereed)
    Abstract [en]

    We propose a cooperative MAC protocol with rapid relay selection (RRS-CMAC) to improve the cooperation efficiency and multiple access performance in wireless ad hoc networks. In this protocol, if the data rate between a sender and its recipient is low, an optimal relay is selected by a rate differentiation phase (RDP), priority differentiation phase (PDP), and contention resolution phase (CRP) for relays with the same priority. In the RDP, each contending relay determines its data rate level based on the data rate from the sender to itself and that from itself to the recipient, and then broadcasts busy tones to its neighbor nodes or senses the channel according to the values of its binary digits, which are determined by its data rate level. Relays with the highest data rate levels win and continue to the next phase. In PDP, these winning relays send busy tones or sense the channel according to their own priority values, with the highest priority relays winning in this phase. Then CRP is performed using k-round contention resolution (k-CR) to select a unique optimal relay. Relays sending busy tones earliest and for the longest duration proceed to the next round, while others, sensing a busy tone, subsequently withdraw from contention. A packet piggyback mechanism is adopted to allow data packet transmission without reservation if the winning relay has a packet to send, and the direct transmission rate to its recipient is high. This reduces reservation overhead and improves channel utilization. Both theoretical analysis and simulation results show that the throughput of the proposed protocol is better than those of the CoopMACA and 2rcMAC protocols.

  • 6.
    Socievole, Annalisa
    et al.
    DIMES, Ponte P. Bucci, University of Calabria.
    Zivani, Artur
    National Laboratory for Scientific Computing (LNCC).
    de Rango, Floriano
    DIMES, Ponte P. Bucci, University of Calabria.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yoneki, Eiko
    University of Cambridge Computer Laboratory, JJ Thomson Avenue Cambridge.
    Cyber-physical systems for Mobile Opportunistic Networking in Proximity (MNP)2016In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 111, p. 1-5Article in journal (Refereed)
  • 7.
    Wang, Bin
    et al.
    School of Software, Shanghai Jiao Tong University.
    Qi, Zhengwei
    School of Software, Shanghai Jiao Tong University.
    Ma, Ruhui
    School of Software, Shanghai Jiao Tong University.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Guan, Haibing
    School of Software, Shanghai Jiao Tong University.
    A survey on data center networking for cloud computing2015In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 91, p. 528-547Article in journal (Refereed)
    Abstract [en]

    Data Center Networks (DCNs) are an essential infrastructure that impact the success of cloud computing. A scalable and efficient data center is crucial in both the construction and operation of stable cloud services. In recent years, the growing importance of data center networking has drawn much attention to related issues including connective simplification and service stability. However, existing DCNs lack the necessary agility for multi-tenant demands in the cloud, creating poor responsiveness and limited scalability. In this paper, we present an overview of data center networks for cloud computing and evaluate construction prototypes based on these issues. We provide, specifically, detailed descriptions of several important aspects: the physical architecture, virtualized infrastructure, and DCN routing. Each section of this work discusses and evaluates resolution approaches, and presents the use cases for cloud computing service. In our attempt to build insight relevant to future research, we also present some open research issues. Based on experience gained in both research and industrial trials, the future of data center networking must include careful consideration of the interactions between the important aspects mentioned above

  • 8.
    Wang, Sen
    et al.
    School of Software Engineering, Chongqing University, Chongqing, China.
    Bi, Jun
    Network Architecture & IPv6 Research Division, Institute for Network Sciences and Cyberspace of Tsinghua University, China.
    Wu, Jianping
    Network Research Center, Tsinghua University, Beijing, China.
    Vasilakos, Athanasios V.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Fan, Qilin
    School of Software Engineering, Chongqing University, Chongqing, China.
    VNE-TD: a Virtual Network Embedding Algorithm Based on Temporal-Difference Learning2019In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 161, p. 251-263Article in journal (Refereed)
    Abstract [en]

    Recently, network virtualization is considered as a promising solution for the future Internet which can help to overcome the resistance of the current Internet to fundamental changes. The problem of embedding Virtual Networks (VN) in a Substrate Network (SN) is the main resource allocation challenge in network virtualization. The major challenge of the Virtual Network Embedding (VNE) problem lies in the contradiction between making online embedding decisions and pursuing a long-term objective. Most previous works resort to balancing the SN workload with various methods to deal with this contradiction. Rather than passive balancing, we try to overcome it by learning actively and making online decisions based on previous experiences. In this article, we model the VNE problem as Markov Decision Process (MDP) and develop a neural network to approximate the value function of VNE states. Further, a VNE algorithm based on Temporal-Difference Learning (one kind of Reinforcement Learning methods), named VNE-TD, is proposed. In VNE-TD, multiple embedding candidates of node-mapping are generated probabilistically, and TD Learning is involved to evaluate the long-run potential of each candidate. Extensive simulation results show that VNE-TD outperforms previous algorithms significantly in terms of both block ratio and revenue.

  • 9.
    Yannuzzi, M.
    et al.
    Networking and Information Technology Lab (NetIT Lab), Technical University of Catalonia (UPC).
    Siddiquia,, M. S.
    Networking and Information Technology Lab (NetIT Lab), Technical University of Catalonia (UPC).
    Sällström, Annika
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Pickering, B
    IT Innovation.
    Serral-Gracia, R
    Networking and Information Technology Lab (NetIT Lab), Technical University of Catalonia (UPC).
    ́ınez, A Mart
    Networking and Information Technology Lab (NetIT Lab), Technical University of Catalonia (UPC).
    Chen, W
    dIT.
    Taylor, S
    IT Innovation.
    Benbadis, F
    THALES Group.
    Leguay, E
    Thales.
    Borrelli, E
    Institut National de Recherche en Informatique et Automatique (INRIA).
    Ormaetxea, I
    Software Quality System.
    Campowsky, K
    Fraunhofer Institute FOKUS.
    Giammatteo, G
    Engineering, Ingegneria Informatica.
    Aristomenopoulos, S
    Institute of Communications and Computer Systems (ICCS), National Technical University of Athens (NTUA).
    Papavassiliou, S
    Kuczynski, T
    Poznan Supercomputing and Networking Center (PSNC).
    Zielinski, S
    Poznan Supercomputing and Networking Center (PSNC).
    Seigneur, J M
    University of Geneva.
    Lafuentel, C Ballester
    University of Geneva.
    Johansson, Jeaneth
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design. Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Masip-Bruin, X
    Advanced Network Architectures Lab (CRAAX), Technical University of Catalonia (UPC).
    Caria, M
    Technische Universität Braunschweig (TUBS).
    Junior, J R Ribeiro
    University of Sao Paolo.
    Salageanu, E
    ActiveEon.
    Latanicki, J
    THALES Group.
    TEFIS: A Single Access Point for Conducting Multifaceted Experiments on Heterogeneous Test Facilities2014In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 63, p. 147-172Article in journal (Refereed)
    Abstract [en]

    A few years ago, an experimental facility composed of networking gear and simulation tools was sufficient for testing the main features of a prototype before the final product could be launched to the Internet market. This paradigm has certainly changed, but the lack of platforms enabling the realistic assessment of the different facets of a product, including cross-cutting trials across different testbeds, poses strong limitations for researchers and developers. In light of this, we present an open platform that offers a versatile combination of heterogeneous experimental facilities called “TEstbed for Future Internet Services” (TEFIS). TEFIS provides a single access point for conducting cutting-edge experiments on testbeds that supply different capabilities, including testbeds dedicated to network performance, software performance, grid computing, and living labs. We shall show that TEFIS covers the entire life-cycle of a multifaceted experiment, with the advantage that a single testrun can seamlessly execute across different experimental facilities. In order to demonstrate the potential and versatility of the TEFIS platform, we describe the deployment of four distinct experiments and provide a set of results highlighting the benefits of using TEFIS. The experiments described in this article cover: i) the experimentation with an open API called OPENER (which is an open and programmable environment for managing experimentation with SDN applications); ii) an application for skiers and tourists at the Megève ski resort in France; iii) an application that can dynamically adapt the Quality of Experience (QoE) of multimedia services for mobile users; and iv) an augmented reality workspace for remote education and learning purposes based on videoconferencing.

  • 10.
    Zhan, Yufeng
    et al.
    School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, PR China.
    Xia, Yuanqing
    School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, PR China.
    Vasilakos, Athanasios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Future directions of networked control systems: A combination of cloud control and fog control approach2019In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 161, p. 235-248Article in journal (Refereed)
    Abstract [en]

    Currently, we have witnessed that networked control technology has played a key role in Internet of Things (IoT). However, the volume, variety and velocity properties of big data from IoT make the traditional networked control systems (NCSs) can not meet the current requirements. Due to this, cloud control systems have emerged as a new control paradigm which bring lots of benefits and have played a key role in current IoT society. Despite cloud control systems have tremendous advantages, there are still lots of tough challenges such as latency, network congestion and etc., which hinder the development of cloud control systems. For these challenges, we extend the cloud control systems to the cloud fog control systems which bring the fog computing into the NCSs design. First, some recent studies of fog computing have been surveyed. Second, a new architecture of NCSs based on cloud computing and fog computing has been proposed. Then, an incentive mechanism has been designed for the cloud fog control systems. In the end, the cases of control tasks offloading and a simple platform of cloud fog control systems have been studied.

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