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  • 1.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Xiag, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing2015In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 12, p. 2997-3010Article in journal (Refereed)
    Abstract [en]

    The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

  • 2.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Energy efficient monitoring of water distribution networks via compressive sensing2015In: 2015 IEEE International Conference on Communications (ICC), IEEE conference proceedings, 2015, Vol. 2015, p. 6681-6686Conference paper (Refereed)
    Abstract [en]

    The recent development of low cost wireless sensors enables water monitoring through dense wireless sensor networks (WSN). Sensor nodes are battery powered devices, and hence their limited energy resources have to be optimally managed. The latest advancements in compressive sensing (CS) provide ample promise to increase WSNs lifetime by limiting the amount of measurements that have to be collected. Additional energy savings can be achieved through CS-based scheduling schemes that activate only a limited number of sensors to sense and transmit their measurements, whereas the rest are turned off. The ultimate objective is to maximize network lifetime without sacrificing network connectivity and monitoring performance. This problem can be approximated by an energy balancing approach that consists of multiple simpler subproblems, each of which corresponds to a specific time period. Then, the sensors that should be activated within a given period can be optimally derived through dynamic programming. The complexity of the proposed CS-based scheduling scheme is characterized and numerical evaluation reveals that it achieves comparable monitoring performance by activating only a fraction of the sensors.

  • 3.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    On Maximizing Sensor Network Lifetime by Energy Balancing2018In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 5, no 3Article in journal (Refereed)
    Abstract [en]

    Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

  • 4.
    Gkatzikis, Lazaros
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Koutsopoulos, Iordanis
    AUEB, Athens, Greece.;Ctr Res & Technol Hellas CERTH, Thermi, Greece..
    Mobiles on Cloud Nine: Efficient Task Migration Policies for Cloud Computing Systems2014In: 2014 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD NETWORKING (CLOUDNET), IEEE , 2014, p. 204-210Conference paper (Refereed)
    Abstract [en]

    Due to limited processing and energy resources, mobile devices outsource their computationally intensive tasks to the cloud. However, clouds are shared facilities and hence task execution time may vary significantly. In this paper, we investigate the potential of task migrations to reduce contention for the shared resources of a mobile cloud computing architecture in which local clouds are attached to wireless access infrastructure (e.g. wireless base stations or access points). We devise online migration strategies that at each time make migration decisions according to the instantaneous load and the anticipated execution time. We explicitly take into account the interaction of co-located tasks in a server and the cost of migrations. We propose three classes of migration policies, ranging from fully uncoordinated ones, in which each user or server autonomously makes its migration decisions, up to cloud-wide ones, where migration decisions are made by the cloud provider. The key underlying idea is that a migration should occur only if it is beneficial for the processing time of the task, including the migration delay.

  • 5.
    Gkatzikis, Lazaros
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Koutsopoulos, Iordanis
    Mobiles on Cloud Nine: Efficient Task MigrationPolicies for Cloud Computing Systems2014In: Efficient Task MigrationPolicies for Cloud Computing Systems, IEEE conference proceedings, 2014, p. 204-210Conference paper (Refereed)
    Abstract [en]

    Due to limited processing and energy resources, mobile devices outsource their computationally intensive tasks to the cloud. However, clouds are shared facilities and hence task execution time may vary significantly. In this paper, we investigate the potential of task migrations to reduce contention for the shared resources of a mobile cloud computing architecture in which local clouds are attached to wireless access infrastructure (e.g. wireless base stations or access points). We devise online migration strategies that at each time make migration decisions according to the instantaneous load and the anticipated execution time. We explicitly take into account the interaction of co-located tasks in a server and the cost of migrations. We propose three classes of migration policies, ranging from fully uncoordinated ones, in which each user or server autonomously makes its migration decisions, up to cloud-wide ones, where migration decisions are made by the cloud provider. The key underlying idea is that a migration should occur only if it is beneficial for the processing time of the task, including the migration delay.

  • 6.
    Gkatzikis, Lazaros
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sourlas, Vasilis
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Koutsopoulos, Iordanis
    Dán, György
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Clustered Content Replication for Hierarchical Content Delivery Networks2015In: 2015 IEEE International Conference On Communications (ICC), 2015, Vol. 2015, p. 5872-5877Conference paper (Refereed)
    Abstract [en]

    Caching at the network edge is considered a promising solution for addressing the ever-increasing traffic demand of mobile devices. The problem of proactive content replication in hierarchical cache networks, which consist of both network edge and core network caches, is considered in this paper. This problem arises because network service providers wish to efficiently distribute content so that user-perceived performance is maximized. Nevertheless, current high-complexity replication algorithms are impractical due to the vast number of involved content items. Clustering algorithms inspired from machine learning can be leveraged to simplify content replication and reduce its complexity. Specifically, similar items could be clustered together, e.g., according to their popularity in space and time. Replication on a cluster-level is a problem of substantially smaller dimensionality, but it may result in suboptimal decisions compared to item-level replication. The factors that cause performance loss are identified and a clustering scheme that addresses the specific challenges of content replication is devised. Extensive numerical evaluations, based on realistic traffic data, demonstrate that for reasonable cluster sizes the impact on actual performance is negligible.

     

  • 7.
    Shokri-Ghadikolaei, Hossein
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Beam-searching and Transmission Scheduling in Millimeter Wave Communications2015In: 2015 IEEE International Conference on Communications (ICC), IEEE conference proceedings, 2015, Vol. 2015, p. 1292-1297Conference paper (Refereed)
    Abstract [en]

    Millimeter wave (mmWave) wireless networks relyon narrow beams to support multi-gigabit data rates. Nevertheless, the alignment of transmitter and receiver beams is a time consuming operation, which introduces an alignment-throughput tradeoff. A wider beamwidth reduces the alignment overhead,but leads also to reduced directivity gains. Moreover, existing mmWave standards schedule a single transmission in eachtime slot, although directional communications facilitate multiple concurrent transmissions. In this paper, a joint consideration ofthe problems of beamwidth selection and scheduling is proposed to maximize effective network throughput. The resulting optimization problem requires exact knowledge of network topology,which may not be available in practice. Therefore, two standard compliant approximation algorithms are developed, which relyon underestimation and overestimation of interference. The first one aims to maximize the reuse of available spectrum, whereas the second one is a more conservative approach that schedules together only links that cause no interference. Extensive performance analysis provides useful insights on the directionality level and the number of concurrent transmissions that should bepursued. Interestingly, extremely narrow beams are in general not optimal.

  • 8. Vassilaras, S.
    et al.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control. University of Thessaly, Greece.
    Liakopoulos, N.
    Stiakogiannakis, I. N.
    Qi, M.
    Shi, L.
    Liu, L.
    Debbah, M.
    Paschos, G. S.
    The Algorithmic Aspects of Network Slicing2017In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 55, no 8, p. 112-119, article id 8004165Article in journal (Refereed)
    Abstract [en]

    Network slicing is a technique for flexible resource provisioning in future wireless networks. With the powerful SDN and NFV technologies available, network slices can be quickly deployed and centrally managed, leading to simplified management, better resource utilization, and cost efficiency by commoditization of resources. Departing from the one-Type-fits-All design philosophy, future wireless networks will employ the network slicing methodology in order to accommodate applications with widely diverse requirements over the same physical network. On the other hand, deciding how to efficiently allocate, manage, and control the slice resources in real time is very challenging. This article focuses on the algorithmic challenges that emerge in efficient network slicing, necessitating novel techniques from the communities of operation research, networking, and computer science.

1 - 8 of 8
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  • fi-FI
  • nn-NO
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