Towards AoI-aware Smart IoT Systems

Age of Information (AoI) has gained importance as a Key Performance Indicator (KPI) for characterizing the freshness of information in information-update systems and time-critical applications. Recent theoretical research on the topic has generated significant understanding of how various algorithms perform in terms of this metric on various system models and networking scenarios. In this paper, by the help of the theoretical results, we analyzed the AoI behavior on real-life networks, using our two test-beds, addressing IoT networks and regular computers. Excessive number of AoI measurements are provided for variations of transport protocols such as TCP, UDP and web-socket, on wired and wireless links. Practical issues such as synchronization and selection of hardware along with transport protocol, and their effects on AoI are discussed. The results provide insight toward application and transport layer mechanisms for optimizing AoI in real-life networks

Haberleşme ağlarında bilgi yaşı farkında çizelgeleme

In next-generation communication networks, various types of data traffic with different requirements will coexist. This type of coexistence requires new techniques based on new metrics to distinguish the various types of data traffic based on their value. The emerging metric Age of Information (AoI) quantifying the timeliness of the communication flow is a promising metric to prepare future networks for new technologies such as the Internet of Things and Autonomous Driving. In this thesis, we studied scheduling problems with the objective of minimizing AoI on different network models. With the help of the results obtained from the theoretical study of LCFS queues using the Stochastic Hybrid System method, we propose a prominent scheduling algorithm called Maximum Age Difference, which is useful for optimizing the network in terms of AoI. Additionally, we approached the age-minimizing scheduling problem with Reinforcement Learning-based methods, which is advantageous when the network statistics are unknownYeni nesil haberle¸sme aglarında farklı isterlere sahip olan çe¸sitli veri trafikleri birlikte ˘ bulunacak. Bu birliktelik yeni metriklere dayanan ve veri trafiklerini ayırt edebilen yeni tekniklere ihtiyaç duymaktadır. Haberle¸sme baglantılarının tazeli ˘ gini ölçen Bilgi ˘ Ya¸sı metrigi, yeni nesil haberle¸sme a ˘ glarını Nesnelerin ˘ ˙Interneti ve Otonom Sürü¸s gibi yeni teknolojilerine hazırlamak için ümit vadetmektedir. Bu tezde, bilgi ya¸sını dü¸süren çizelgeleme problemini farklı ag modellerinde inceledik. LCFS kuyruklarda ˘ Stochastic Hybrid System metoduyla yaptıgımız teorik çalı¸smaların sonuçları yardı- ˘ mıyla Maximum Age Difference isimli agları bilgi ya¸sı açısından optimize etmekte ˘ kullanılabilecek öne çıkan bir çizelgeleme algoritması önerdik. Ayrıca bilgi ya¸sı dü- ¸süren çizelgeleme problemini Reinforcement Learning tabanlı metodlarla birlikte inceledik. Bu yöntem özellikle agın istatistiksel bilgilerini bilinmedi ˘ gi zaman avantajlı ˘ olmaktadır.M.S. - Master of ScienceTUBITAK (Yurtici Lisansustu Burslar

Age Minimization of Multiple Flows using Reinforcement Learning

Age of Information (AoI) is a recently proposed performance metric measuring the freshness of data at the receiving side of a flow. This metric is particularly suited to status-update type information flows, like those occurring in machine-type communication (MTC), remote monitoring and similar applications. In this paper, we consider the problem of AoI-optimal scheduling of multiple flows served by a single server. The performance of scheduling algorithms proposed in previous literature has been shown under limited assumptions, due to the analytical intractability of the problem. The goal of this paper is to apply reinforcement learning methods to achieve scheduling decisions that are resilient to network conditions and packet arrival processes. Specifically, Policy Gradients and Deep Q-Learning methods are employed. These can adapt to the network without a priori knowledge of its parameters. We study the resulting performance relative to a benchmark, the MAF algorithm, which is known to be optimal under certain conditions

Minimizing Age of Information for Multiple Flows

Age of Information (AoI) is an emerging metric measuring the freshness of a flow at its destination, which is critical for time-sensitive applications. We consider multiple flows served by a single server. To minimize the average AoI over all flows, we formulate preemptive and non-preemptive Maximum Age Difference (MAD) algorithms, and experimentally observe their superior performance with respect to various other scheduling algorithms for multi-flow networks. We also observe that both MAD and MAF (Maximum Age First) respond favorably to flow diversity, with a reduction in AoI

Measuring age of information on real-life connections

Age of Information (AoI) is a relatively new metric to measure freshness of networked application such as real-time monitoring of status updates or control. The AoI metric is discussed in the literature mainly in a theoretical way. In this work, we want to point out the issues related to the measuring AoI-related values, such as synchronization and calculation of the values. We discussed the effect of synchronization error in the measurement and a solution for calculating an estimate of average AoI without any synchronization

Age of Information in Practice

While age of Information (AoI) has gained importance as a metric characterizing the freshness of information in information-update systems and time-critical applications, most previous studies on AoI have been theoretical. In this chapter, we compile a set of recent works reporting AoI measurements in real-life networks and experimental testbeds, and investigating practical issues such as synchronization, the role of various transport layer protocols, congestion control mechanisms, application of machine learning for adaptation to network conditions, and device related bottlenecks such as limited processing power

PROTOTİP IoT AĞI ÜZERİNDE EN İYİ BİLGİ YAŞI İÇİN YENİLİKÇİ ALGORİTMALARIN GERÇEKLENMESİ

Güncellemeye dayanan uygulamaların ortak bir başarım kriteri, verinin işlendiği ve kararların verildiği düğümdeki bilginin yeterince taze oluşudur. Bilgi Yaşı (Age of Information), alıcı tarafın elindeki en yeni veri güncellemesinin ne kadar eskimiş olduğunu, yani bu güncelleme kaynakta oluştuğundan itibaren geçmiş olan süreyi ifade eder. Bu konuda temel katkılar yapacak olan 117E215 numaralı “Optimal Bilgi Yaşı için Veri Örnekleme ve Çizelgeleme Kuramsal Sınırları, Algoritmalar ve Deneysel Uygulamalar” başlıklı TÜBİTAK 1001 projemiz, 3 kuramsal ve 1 deneysel iş paketinden oluşmaktadır.TÜBİTAK projelerinde sözleşme aşamasındaki gecikmeden ötürü (6 ay), teorik çalışmalara başlanmış olsa da teçhizat ihtiyacından ötürü deneysel çalışmalara başlanamamıştır. Ayrıca deneysel iş paketine ilave yapılarak deneysel çalışmanın kapsamı genişletilmiştir. BAP projesi kapsamında, sözleşmesi henüz gelmemiş TÜBİTAK 1001 projesinin deneysel iş paketin ilk bölümü olan IoT prototip ağını kurmak amaçlanmaktadır. Proje dahilinde üzerinde çalıştığımız teorik problemlerin deneysel olarak başarımlarını test edeceğimiz deneysel uygulama kısmı çeşitli bileşenlerden oluşacaktır. Bunlardan ilki laboratuvar ortamında çeşitli fiziksel verileri örnekleyecek algılayıcılardır. Tüm bu algılayıcılar, arabirimler, erişim noktaları ve uygulama yazılımları bir arada programlanarak heterojen bir uçtan-uca kontrol/otomasyon sistemi/sistemleri oluşturulacaktır