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Now showing 1 - 6 of 6
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    Dynamic control plane for sdn at scale
    (IEEE-Inst Electrical Electronics Engineers Inc, 2018) Görkemli, Burak; Tatlıcıoğlu, Sinan; Civanlar, Seyhan; Lokman, Erhan; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207
    As SDN migrates to wide area networks and 5G core networks, a scalable, highly reliable, low latency distributed control plane becomes a key factor that differentiates operator solutions for network control and management. In order to meet the high reliability and low latency requirements under time-varying volume of control traffic, the distributed control plane, consisting of multiple controllers and a combination of out-of-band and in-band control channels, needs to be managed dynamically. To this effect, we propose a novel programmable distributed control plane architecture with a dynamically managed in-band control network, where in-band mode switches communicate with their controllers over a virtual overlay to the data plane with dynamic topology. We dynamically manage the number of controllers, switches, and control flows assigned to each controller as well as traffic over control channels achieving both controller and control traffic load-balancing. We introduce "control flow table" (rules embedded in the flow table of a switch to manage in-band control flows) in order to implement the proposed distributed dynamic control plane. We propose methods for off-loading congested controllers and congested in-band control channels using control flow tables. A validation test-bed and experimental results over multiple topologies are presented to demonstrate the scalability and performance improvements achieved by the proposed dynamic control plane management procedures when the controller CPU and/or availability or throughput of in-band control channels becomes bottlenecks.
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    Energy and delay constrained maximum adaptive schedule for wireless networked control systems
    (IEEE-Inst Electrical Electronics Engineers Inc, 2015) N/A; Department of Electrical and Electronics Engineering; Şadi, Yalçın; Ergen, Sinem Çöleri; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; 246556; 7211
    Communication system design for wireless networked control systems (WNCSs) is very challenging since the strict timing and reliability requirements of control systems should be met by the wireless communication systems that introduce non-zero packet error probability and non-zero delay at all times. Particularly, the scheduling algorithms for WNCSs should be designed to provide maximum level of adaptivity accommodating packet losses and changes in network topology while exploiting periodic nature of the sensor node transmissions. Creating such a schedule has been previously studied for an Ultra Wide Band (UWB) based WNCS. in this paper, we extend the joint optimization problem of power control, rate adaptation and scheduling with the objective of providing maximum adaptivity for general WNCSs employing continuous rate transmission model in which Shannon's channel capacity formulation is used for the achievable transmission rate. Upon proving the NP-hardness of the problem, we provide a framework for the design of a heuristic algorithm for scheduling and propose an optimal polynomial time algorithm for the power control and rate adaptation problem following the derivation of the optimality conditions. We demonstrate via extensive simulations that the proposed algorithms outperform the existing algorithms with performance close to optimal solution and average runtime admissible for practical WNCSs.
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    Minimum length scheduling for power constrained harvest-then-transmit communication networks
    (IEEE, 2019) N/A; N/A; Department of Electrical and Electronics Engineering; Salık, Elif Dilek; Önalan, Aysun Gurur; Ergen, Sinem Çöleri; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 7211
    We consider a wireless powered, harvest-then-transmit communication network, which consists of a single antenna, energy and information access point (aP) and multiple, single antenna, batteryless users with energy harvesting capabilities. at the beginning of a time frame, the aP broadcasts energy in the downlink to the users. then, users transmit their data to the aP in the uplink, using their harvested energy. We formulate the optimization problem with the objective of minimizing the total schedule length, subject to the constraints on the minimum amount of data to be sent to the aP, and unlike previous studies, the maximum transmit power for the information transmission. This problem is nonlinear and non-convex. the solution is based on bi-level optimization, consisting of optimizing the transmit power allocation of the nodes for a given energy harvesting time and searching over harvesting time allocation. We also propose a heuristic algorithm in which we incorporate the optimal solution of a single user network. Simulation results demonstrate that under appropriate network conditions, our proposed algorithms provide close-to-optimal results with a reasonable run time compared to a previously proposed time minimization algorithm that does not integrate the uplink power constraint.
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    Reclaimer scheduling in dry bulk terminals
    (IEEE-inst Electrical Electronics Engineers inc, 2020) Ünsal, Özgür; PhD Student; Graduate School of Sciences and Engineering; N/A
    This paper studies a complex parallel scheduling problem with non-crossing constraint, sequence dependent setup times, eligibility restrictions, and precedence relationships motivated by reclaimer scheduling in dry bulk terminals. in a stockyard of any dry bulk terminal, stockpiles are handled by reclaimers. therefore, improving the operational efficiency of reclaimers is critical for the overall performance of these terminals which are struggling with increasing workload. We study the variants of this problem with and without stacking operations. for each variant, we present a lower and an upper bound. a strong lower bound is obtained by relaxing the non-crossing constraint and solving the resulting problem to the optimality. While this relaxation still addresses a challenging scheduling problem, proposed arc-time-indexed formulation copes with the instances of practical size. We develop a novel constraint programming formulation to provide an upper bound for the problem. Computational experiments show this robust approach is able to generate near-optimal schedules for different stockyard configurations within a minute.
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    Video frame prediction via deep learning
    (IEEE, 2020) N/A; Department of Electrical and Electronics Engineering; Yılmaz, Mustafa Akın; Tekalp, Ahmet Murat; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26207
    This paper provides new results over our previous work presented in ICIP 2019 on the performance of learned frame prediction architectures and associated training methods. More specifically, we show that using an end-to-end residual connection in the fully convolutional neural network (FCNN) provides improved performance. in order to provide comparative results, we trained a residual FCNN, A convolutional RNN (CRNN), and a convolutional long-short term memory (CLSTM) network for next frame prediction using the mean square loss. We performed both stateless and stateful training for recurrent networks. Experimental results show that the residual FCNN architecture performs the best in terms of peak signal to noise ratio (PSNR) at the expense of higher training and test (inference) computational complexity. the CRNN can be stably and efficiently trained using the stateful truncated backpropagation through time procedure, and requires an order of magnitude less inference runtime to achieve an acceptable performance in near real-time.
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    Visible light communication based vehicle localization for collision avoidance and platooning
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) N/A; Department of Electrical and Electronics Engineering; Soner, Burak; Ergen, Sinem Çöleri; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 7211
    Collision avoidance and platooning applications require vehicle localization at cm-level accuracy and at least 50 Hz rate for full autonomy. The RADAR/LIDAR and camera based methods currently used for vehicle localization do not satisfy these requirements, necessitating complementary technologies. Visible light positioning (VLP) is a highly suitable complementary technology due to its high accuracy and high rate, exploiting the line-of-sight propagation feature of the visible light communication (VLC) signals from LED head/tail lights. However, existing vehicular VLP algorithms impose restrictive requirements, e.g., use of high-bandwidth circuits, road-side lights and certain VLC modulation strategies, and work for limited relative vehicle orientations, thus, are not feasible for general use. This paper proposes a VLC-based vehicle localization method that eliminates these restrictive requirements by a novel VLC receiver design and associated vehicular VLP algorithm. The VLC receiver, named QRX, is low-cost/size, and enables high-rate VLC and high-accuracy angle-of-arrival (AoA) measurement, simultaneously, via the usage of a quadrant photodiode. The VLP algorithm estimates the positions of two head/tail light VLC transmitters (TX) on a neighbouring vehicle by using AoA measurements from two QRXs for localization. The algorithm is theoretically analyzed by deriving its Cramer-Rao lower bound on positioning accuracy, and simulated localization performance is evaluated under realistic platooning and collision avoidance scenarios. Results demonstrate that the proposed method performs at cm-level accuracy and up to 250 Hz rate within a 10 m range under realistic harsh road and channel conditions, demonstrating its eligibility for collision avoidance and safe platooning.