Theses supervised by Prof. Dr. Nail Akar
10 theses · İhsan Doğramacı Bilkent University
Bilgi yaşi fonksiyonlarinin minimizasyonu
Age of Information (AoI) is a metric that quantifies freshness of information in a status update system, making it crucial for applications where timely updates are essential, such as real-time monitoring systems, IoT networks, and mission-critical communication systems. This thesis explores age-agnostic cyclic scheduling in multi-source, single-server Generate-At-Will (GAW) status update systems, where the goal is to minimize the expected value of a non-linear polynomial function of AoI in a discrete-time setting. In the literature, approaches that aim to minimize the weighted sum of average AoI often rely solely on the first two moments of packet service times. However, the use of non-linear functions of age as the information freshness metric, requires an analytical model to obtain the distribution of AoI which also uses the distributions of packet service times as input to the model. In this work, given a cyclic transmission pattern, we use the theory of discrete-time absorbing Markov chains to obtain the distribution of the AoI of each user, which then allows us to find the expected value of any non-linear function of individual ages, referred to as the Value of Information (VoI) in this work. Subsequently, using the proposed analytical method, we propose a metaheuristic based space-search algorithm, specifically leveraging the Simulated Annealing (SA) technique, to obtain a cyclic schedule with the goal of minimizing a polynomial cost function of age. Numerical results are presented to validate the proposed approach.
Güneş enerjisi harmanlayan kablosuz algılama düğümleri için uyarlamalı enerji yönetimi
Wireless Sensor Networks (WSN) will have a key role in the upcoming era of the Internet of Things (IoT) as they will be forming the basis of communication infrastructure. Energy harvesting has been a widely used instrument for prolonging the battery life and enhancing the quality of service (QoS) of sensor nodes (SN). In this study, we investigate adaptive transmission policies for a solar-powered wireless sensor node which is tasked with sending status updates to a gateway as frequently as possible with energy-neutral operation constraints. On the basis of empirical data, we model the daily variations of the solar energy harvesting process with a Discrete Time Markov Chain (DTMC). When the number of states of the DTMC is increased, the harvesting process is modeled more accurately. Using the DTMC model, we formulate the energy management problem of the WSN node as a Markov Decision Process (MDP); and based on this model, we use the policy iteration algorithm to obtain optimal energy management policies so as to minimize the average Age of Information (AoI) of the corresponding status update system. We validate the effectiveness of the proposed approach using datasets belonging to two different locations with 20 years of solar radiance data.
Kablosuz ağlarda enerji-gecikme ödünleşiminin kuyruk sistemi analizi
Energy-efficiency of wireless communication systems has been an important research topic in recent years. For such a system, a transmission profile is described by the transmission power and the modulation and coding scheme (MCS) to be used for packet transmission. For a given channel condition, higher order MCSs offer higher throughput at the expense of requiring more transmission power. Average power consumption of the system can be reduced by using lower order MCSs at the expense of increased queuing delays. Using this observation, the goal of this study is the development of transmission profile selection policies so as to minimize the average power consumption while meeting a statistical delay constraint for a wireless link. For the purpose of assessing the proposed policies, the system is modeled as an M/M/1 queue where transmission speeds of packets are dynamically selected based on the queuing delay already experienced by them. This setting is shown to give rise to a multi-regime Markov fluid queue model which is used to obtain the waiting time distributions of packets as well as the average power consumption. In the numerical examples, proposed profile selection policies are evaluated for different system parameters using realistic transmission profiles obtained from LTE simulations. A proposed energy-aware profile selection policy is shown to consistently outperform all other proposed policies in terms of energy-efficiency whereas a reasonable performance is also obtained with a simpler-to-implement policy.
Nesnelerin interneti için düşük güç kullanan kayıplı ağlarda yönlendirme protokolüne yönelik yük dengeleme iyileştimeleri
The internet today is shifting from the Internet of people to the Internet of Things (IoT). Particularly, in IoTs, wireless sensors connect edge devices to the Internet via a gateway that provides connectivity between wireless sensor networks (WSNs) and the Internet. IoT includes a variety of heterogeneous network applications ranging from smart grid automated metering infrastructures (AMIs), industrial and environmental monitoring networks to building automation. In WSNs, congestion causes a plenty of impairments such as increased packet losses, lower throughput, and energy wastage thus decreasing the lifetime and performance of wireless sensor applications. IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) is envisioned to be used in the majority of IoT applications. Recently, the Internet Engineering Task Force (IETF) Routing over Low power and Lossy Networks (ROLL) working group has proposed a Routing Protocol for Low power and Lossy networks called RPL. RPL is often studied in a multipoint-to-point sink node (MP2P) scenarios. We investigate the load balancing and congestion problem of RPL. RPL suffers from congestion and unbalanced load distribution due to the use of a single path for multipoint-to-point traffic. In particular, we propose queue utilization-based multipath RPL (QU-MRPL). In QU-MRPL, multiple parents are selected based on their queue size information. We demonstrate that QU-MRPL achieves load balance in the network and thus increases the packet delivery ratio.
Rezervasyon tabanlı çerçeve-dilimli ALOHA'nın çok-sınıflı IoT ağlar için kullanılması
Reservation Frame Slotted ALOHA for Multi-Class IoT Networks The Internet of Things (IoT) is a promising technology capable of revolutionizing our work and daily lives. ALOHA based medium access schemes are widely used in IoT applications due to their low complexity despite lower throughput figures. In this study, we aim to improve the performance of Frame Slotted ALOHA (FSA) for a single hop IoT network without increasing the overall complexity. Duty cycling is a key concept for managing energy consumption of wireless networks with battery powered nodes having maximum duty cycle constraints. The goal of this study is to improve the performance of Frame Slotted ALOHA by exploiting duty cycle patterns in these networks and using reservations in advance. We discuss the system model for a single class IoT network and study via simulations the performance of Reservation Frame Slotted ALOHA (RFSA) as compared to FSA, as well as the performace implications of different system parameters related to traffic patterns. With the insight gained from this preliminary study, we next model a multi-class IoT network with nodes belonging to different classes with different duty cycle constraints. Adopting RFSA for such a network requires different schemes for allocating channel resources for each class. We propose several static and dynamic channel allocation schemes based on our traffic model and study their performance as compared to FSA. Static partitioning has better performance for low traffic loads but dynamic partitioning offers better throughput at higher traffic loads. Selection of an appropriate channel allocation scheme can vary according to the load as well as several system parameters of the network.
Aşağı yönlü güç alanlı NOMA için adil kullanıcı seçimi
Non-Orthogonal Multiple Access (NOMA) has been proposed as a new radio access technique in which multiple users (the case of user pairs or triples are covered in this thesis) are allowed to use the wireless channel simultaneously in a way to improve the overall system capacity. A bucket-based Temporal Fair Scheduling algorithm (TFS) has been proposed in the literature for Orthogonal Multiple Access (OMA) systems. In this thesis, we extend this existing work to downlink power domain NOMA by which user pairs or triples are to be scheduled with the goal of maximizing system capacity under temporal fairness constraints. The effectiveness of the proposed fair user scheduling algorithm for NOMA is validated with simulations in which the effects of transmit power and coverage radius of the base station, as well as the number of users are thoroughly studied.
Tek kaynaklı ve çok kaynaklı durum güncelleme sistemlerinde bilgi yaşı ve zirve bilgi yaşının kesin dağılımı
In this thesis, we first study bufferless and single-buffer single-source queueing models of a status update system with various accompanying buffer management schemes. Next, we study the bufferless multi-source queueing model of a status-update system with probabilistic preemption. For both single-source and multi-source queueing models, we obtain the exact distributions of the associated Age of Information (AoI) and Peak Age of Information (PAoI) processes. For this purpose, we propose a Markov Fluid Queue (MFQ) model for both scenarios out of which the exact AoI distributions are derived. The numerical solution obtained from the proposed model provides the distributional expressions in matrix-exponential form out of which one can easily obtain their high order moments. We validate the accuracy of our proposed algorithm by comparing our results with simulations and also existing results in the literature in certain sub-cases. Numerical results are presented to provide engineering insight on how state update systems need to be configured and operated.
Tcp, etkin kuyruk yönetimi ve kablosuz bağ uyarlaması arasındaki etkileşimin performans modellemesi ve çözümlemesi
We propose a novel workload-dependent queuing model of a wireless router link which employs active queue management and is offered with a number of persistent TCP flows. As opposed to existing work that focus only on the average queue occupancy as the performance metric of interest, the proposed analytical method obtains the more informative steady-state queue occupancy distribution of the wireless link. With the intention of maximizing TCP throughput, this analytical method is used to study traffic agnostic link adaptation schemes with and without hybrid ARQ. Moreover, a novel cross-layer queue-aware link adaptation scheme is proposed to improve the TCP throughput relative to the case where adaptive modulation and coding decisions are made based solely on the physical layer parameters. A fixed-point analytical model is proposed to obtain the aggregate TCP throughput attained at wireless links employing active queue management and queue-aware link adaptation. Allowing packet retransmissions and generalizing the scope from a single link to a network of such links, we propose an energy efficient queue-aware link adaptation scheme with hybrid ARQ which jointly adapts the transmission power and rate of the wireless links based on the queue occupancy levels and the channel conditions. Furthermore, we provide a fixed-point analytical method for such networks.
Enerji harmanlayan kablosuz algılama düğümlerinde yaşam süresi kısıtlamalı enerji yönetimi
Advancements in the "Internet of Things (IoT)" concept enables large numbers of low-power wireless sensors and electronic devices to be connected to the Internet and outside world over a wide area wireless network without a need for human interaction. Using rechargeable batteries with energy harvesting to power these wireless sensors has been shown to preserve the self-sustainability and selfsufficiency of a sensor node and prolong its lifetime, hence the whole network it belongs to. However, it brings the question of how to intelligently manage the energy in the battery so that the node maintains its functionalities by keeping the battery level over zero for an extended duration of time, known as the life-horizon. We propose a risk-theoretic Markov fluid queue model to compute the battery outage probability of a wireless sensor node for a given finite life-horizon. The proposed method enables the performance evaluation of a wide spectrum of energy management policies including those with adaptive sensing rate (or duty cycling). In this model, the node gathers data from the environment according to a Poisson process whose rate is to depend on the instantaneous battery level and/or the state of the energy harvesting process (EHP) which is characterized by a Continuous Time Markov Chain (CTMC). Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing rate policies are obtained that maximize the information sensing rate of the sensor node while meeting lifetime constraints given in terms of battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using two-state CTMC-based EHPs.
Çok kaynaklı bilgi güncelleme sistemlerinde bilgi tazeliği için çizelgeleme ve kuyruk yönetimi
Timely delivery of information to its intended destination is essential in many existing and emerging time-sensitive applications. While conventional performance metrics like delay, throughput, or loss have been extensively studied in the literature, research concerning the management of age-sensitive traffic is relatively immature. Recently, a number of information freshness metrics have been introduced for quantifying the timeliness of information in networked systems carrying age-sensitive traffic, primarily the Age of Information (AoI) and peak AoI (PAoI) metrics as well as their alternatives including Age of Synchronization (AoS), version age, binary freshness, etc. The focus of this thesis is the development and performance modeling of age-agnostic scheduling and queue management policies in various multi-source status update systems carrying age-sensitive traffic, using the recently introduced information freshness metrics. In this thesis, first, the exact distributions of the AoI and PAoI for the probabilistic Generate-At-Will (GAW) and Random Arrival with Single Buffer (RA-SB) servers are studied with general number of heterogeneous information sources with phase-type (PH-type) service time distributions for which an absorbing Continuous-Time Markov Chains (CTMC) based analytical modeling method, namely AMC (Absorbing Markov Chains) method, is proposed. Secondly, a homogeneous multi-source status update system with Poisson information packet arrivals and exponentially distributed service times is studied for which the server is equipped with a queue holding the freshest packet from each source referred to as Single Buffer Per-Source Queueing (SBPSQ). For this case, two SBPSQ-based scheduling policies are studied, namely First Source First Serve (FSFS) and the Earliest Served First Serve (ESFS) policies, using the AMC method, and it is shown that ESFS presents a promising scheduler for this special setting. Third, a general status update system with two heterogeneous information sources is studied, i.e., sources have different priorities and generally distributed service times, for Deterministic GAW (D-GAW) and Deterministic RA-SB (D-RA-SB) servers. The aim in both servers is to minimize the system AoI/AoS that is time-averaged and weighted across the two sources. For the D-GAW server, the optimal update policy is obtained in closed form. A packet replacement policy, referred to as Pattern-based Replacement (PR) policy, is then proposed for the D-RA-SB server based on the optimal policy structure of the D-GAW server. Finally, scheduling in a cache update system is investigated where a remote server delivers time-varying contents of multiple items with heterogeneous popularities and service times to a local cache so as to maximize the weighted sum binary freshness of the system, and the server is equipped with a queue that holds the most up-to-date content for each item. A Water-filling based Scheduling (WFS) policy and its extension, namely Extended WFS (E-WFS) policy, are proposed based on convex optimization applied to a relaxation of the original system, with low computational complexity and near optimal weighted sum binary freshness performance.