Researcher:
Farayev, Bakhtiyar

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Master Student

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Bakhtiyar

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Farayev

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Farayev, Bakhtiyar

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2015 - 20203

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Researcher Of Publications: search.filters.isAuthorOfPublication.744ed7ae-d24b-413f-9d34-00f2aa85c238

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    Energy efficient robust scheduling of periodic sensor packets for discrete rate based wireless networked control systems
    (Elsevier, 2020) Ucar, Seyhan; Sadi, Yalcin; N/A; Department of Electrical and Electronics Engineering; Farayev, Bakhtiyar; Ergen, Sinem Çöleri; Master Student / PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 7211
    Wireless networked control systems (WNCSs) require the design of a robust scheduling algorithm that meets the stringent timing and reliability requirements of control systems, despite the limited battery resources of sensor nodes and adverse properties of wireless communication for delay and packet errors. In this article, we propose a robust delay and energy constrained scheduling algorithm based on the exploitation of the mostly pre-known periodic data generation nature of sensor nodes in control systems. We first formulate the joint optimization of scheduling, power control and rate adaptation for discrete rate transmission model, in which only a finite set of transmission rates are supported, as a Mixed-Integer Non-linear Programming problem and prove its NP-hardness. Next, we propose an optimal polynomial-time power control and rate adaptation algorithm for minimizing the transmission time of a node subset. We then design a novel polynomial-time heuristic scheduling algorithm based on first determining the concurrently transmitting node subsets and then distributing them uniformly over time by a modified Karmarkar-Karp algorithm. We demonstrate the superior performance of the proposed scheduling algorithm in terms of robustness, delay and runtime on the Low-Rate Wireless Personal Area Network (LR-WPAN) simulation platform, which we developed in network simulator-3 (ns3). (C) 2020 Elsevier B.V. All rights reserved.
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    Towards ultra-reliable M2M communication: scheduling policies in fading channels
    (IEEE, 2016) N/A; Department of Electrical and Electronics Engineering; Farayev, Bakhtiyar; 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
    Design of the scheduling algorithm that satisfies the stringent timing and reliability requirements of control systems, while considering the delay and packet errors in the wireless channel, and limited battery resources of the sensors is the main challenge of ultra-reliable machine-to-machine (M42M) control applications. Transmission delay and energy consumption of a sensor node are determined by the transmission power and rate of that sensor node and the concurrently transmitting nodes. Hence, the transmission schedule, and the power and rate parameters of the nodes should be jointly optimized. Joint optimization problem for M2M control applications has been previously formulated and solved by adopting a static channel. in this paper, we extend this framework for fading channels. First, we propose an offline scheduling algorithm that exploits the periodic nature of the transmissions under a priori known channel. then we propose an online scheduling algorithm based on the prediction of the channel information by using previous measurements, scheduling of the nodes using offline algorithm, and modification of the schedule and the transmission parameters of the nodes under the actual channel information as time evolves. Extensive simulations demonstrate the near optimal performance of online and offline scheduling algorithms for different network sizes and densities.
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    Optimal power control and rate adaptation for ultra-reliable M2M control applications
    (IEEE, 2015) Department of Electrical and Electronics Engineering; N/A; N/A; Ergen, Sinem Çöleri; Şadi, Yalçın; Farayev, Bakhtiyar; Faculty Member; PhD Student; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 7211; N/A; N/A
    The main challenge of ultra-reliable machine-to-machine (M2M) control applications is to meet the stringent timing and reliability requirements of control systems, despite the adverse properties of wireless communication for delay and packet errors, and limited battery resources of the sensor nodes. Since the transmission delay and energy consumption of a sensor node are determined by the transmission power and rate of that sensor node and the concurrently transmitting nodes, the transmission schedule should be optimized jointly with the transmission power and rate of the sensor nodes. Previously, it has been shown that the optimization of power control and rate adaptation for each node subset can be separately formulated, solved and then used in the scheduling algorithm in the optimal solution of the joint optimization of power control, rate adaptation and scheduling problem. However, the power control and rate adaptation problem has been only formulated and solved for continuous rate transmission model, in which Shannon's capacity formulation for an Additive White Gaussian Noise (AWGN) wireless channel is used in the calculation of the maximum achievable rate as a function of Signal-to-Interference-plus-Noise Ratio (SINR). In this paper, we formulate the power control and rate adaptation problem with the objective of minimizing the time required for the concurrent transmission of a set of sensor nodes while satisfying their transmission delay, reliability and energy consumption requirements based on the more realistic discrete rate transmission model, in which only a finite set of transmit rates are supported. We propose a polynomial time algorithm to solve this problem and prove the optimality of the proposed algorithm. We then combine it with the previously proposed scheduling algorithms and demonstrate its close to optimal performance via extensive simulations. © 2015 IEEE.