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  • 1.
    Bate, Iain
    et al.
    University of York.
    Poulding, Simon
    University of York.
    Call for Papers: Practical Aspects of Search-Based Software Engineering2009In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 39, no 9, p. 867-868Article in journal (Other academic)
  • 2.
    Bate, Iain
    et al.
    University of York.
    Poulding, Simon
    University of York.
    Editorial for the special issue on search-based software engineering2011In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 41, no 5, p. 467-468Article in journal (Other academic)
  • 3.
    Faragardi, H. R.
    et al.
    University of Innsbruck, Innsbruck, Austria.
    Vahabi, Maryam
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fotouhi, Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fahringer, T.
    University of Innsbruck, Innsbruck, Austria.
    An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems2018In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 48, no 10, p. 1893-1919Article in journal (Refereed)
    Abstract [en]

    Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time-critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software-defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run-time. It is of paramount importance to select a proper number of these devices (i.e., software-defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time-critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA-MSCP, an algorithm hybridizing a parallel version of the max-min ant system with simulated annealing for multiple-sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well-known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.

  • 4.
    Faragardi, Hamid Reza
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dehnavi, Saed
    University of Tehran, Iran.
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kargahi, Mehdi
    An Energy-Aware Time-Predictable Cloud Data CenterIn: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024XArticle in journal (Refereed)
  • 5.
    Faragardi, Hamid Reza
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Univ Innsbruck, Innsbruck, Austria.
    Dehnavi, Saeid
    Univ Tehran, Sch Elect & Comp Engn, Coll Engn, Tehran, Iran..
    Nolte, Thomas
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kargahi, Mehdi
    Univ Tehran, Sch Elect & Comp Engn, Coll Engn, Tehran, Iran.;Inst Res Fundamental Sci IPM, Sch Comp Sci, Tehran, Iran..
    Fahringer, Thomas
    Univ Innsbruck, Inst Comp Sci, Distributed & Parallel Syst Grp, Innsbruck, Austria.
    An energy-aware resource provisioning scheme for real-time applications in a cloud data center2018In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 48, no 10, p. 1734-1757Article in journal (Refereed)
    Abstract [en]

    Based on a pay-as-you-go model, cloud computing provides the possibility of hosting pervasive applications from both academic and business domains. However, data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and large carbon footprints to the environment. Energy-aware resource provisioning is an effective solution to diminish the energy consumption of cloud data centers. Recently, a growing trend has emerged, where cloud technology is used to run periodic real-time applications such as multimedia, telecommunication, video gaming, and industrial applications. In order for a real-time application to be able to use cloud services, cloud providers have to be able to provide timing guarantees. In this paper, we introduce an energy-aware resource provisioning mechanism for cloud data centers, which are capable of serving real-time periodic tasks following the Software as a Service model. The proposed method is compared against an energy-aware version of the RT-OpenStack. RT-OpenStack is a recently proposed approach to provide a time-predictable version of OpenStack. The experimental results manifest that our proposed resource provisioning method outperforms energy-aware version of the RT-OpenStack by 16.01%, 25.45%, and 25.45% in terms of energy consumption, number of used servers, and average utilization of used servers, respectively. Moreover, from the scalability perspective, the preference of the proposed method for large-scale data centers is more considerable.

  • 6.
    Nhan Vo, Van
    et al.
    International School, Duy Tan University, Vietnam.
    Nguyen, Gia Tri
    International School, Duy Tan University, Vietnam.
    So-In, Chakchai
    Khon Kaen University, Khon Kaen, Thailand.
    Tran, Hung Vinh
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Nguyen Tat Thanh University, Vietnam.
    Surasak, Sanguanpong
    Kasetsart University, Bangkok, Thailand.
    Secrecy Performance in the Internet of Things: Optimal Energy Harvesting Time Under Constraints of Sensors and Eavesdroppers2019In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 1, p. 1-16Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the physical layer security (PLS) performance for the Internet of Things (IoT), which is modeled as an IoT sensor network (ISN). The considered system consists of multiple power transfer stations (PTSs), multiple IoT sensor nodes (SNs), one legitimate fusion center (LFC) and multiple eavesdropping fusion centers (EFCs), which attempt to extract the transmitted information at SNs without an active attack. The SNs and the EFCs are equipped with a single antenna, while the LFC is equipped with multiple antennas. Specifically, the SNs harvest energy from the PTSs and then use the harvested energy to transmit the information to the LFC. In this research, the energy harvesting (EH) process is considered in the following two strategies: 1) the SN harvests energy from all PTSs, and 2) the SN harvests energy from the best PTS. To guarantee security for the considered system before the SN sends the packet, the SN’s power is controlled by a suitable power policy that is based on the channel state information (CSI), harvested energy, and security constraints. An algorithm for the nearly optimal EH time is implemented. Accordingly, the analytical expressions for the existence probability of secrecy capacity and secrecy outage probability (SOP) are derived by using the statistical characteristics of the signal-to-noise ratio (SNR). In addition, we analyze the secrecy performance for various system parameters, such as the location of system elements, the number of PTSs, and the number of EFCs. Finally, the results of Monte Carlo simulations are provided to confirm the correctness of our analysis and derivation.

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