Related papers: AWARE: Evaluating PriorityFresh Caching for Offlin…
Online Reinforcement Learning (RL) is typically framed as the process of minimizing cumulative regret (CR) through interactions with an unknown environment. However, real-world RL applications usually involve a sequence of tasks, and the…
This letter investigates a cache-enabled multiuser mobile edge computing (MEC) system with dynamic task arrivals, taking into account the impact of proactive cache placement on the system's overall energy consumption. We consider that an…
The deterministic (timing) behavior of real-time systems (RTS) can be used by adversaries - say, to launch side channel attacks or even destabilize the system by denying access to critical resources. We propose a protocol (named REORDER) to…
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this paper, we introduce a novel prediction framework called PRIME, which stands for Prediction with Model-based Planning. Unlike recent…
Many real-world applications require machine-learning models to be able to deal with non-stationary data distributions and thus learn autonomously over an extended period of time, often in an online setting. One of the main challenges in…
Mobile crowdsensing (MCS) networks enable large-scale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge…
This paper presents a novel Advanced Activity-Aware (AAA) scheme to optimize and improve Multi-Channel Operations based on the IEEE 1609.4 standard in wireless access vehicular environments (WAVE). The proposed scheme relies on the…
Classifying network traffic according to their application-layer protocols is an important task in modern networks for traffic management and network security. Existing payload-based or statistical methods of application identification…
Competitive analysis of online algorithms has commonly been applied to understand the behaviour of real-time systems during overload conditions. While competitive analysis provides insight into the behaviour of certain algorithms, it is…
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM…
Memory tiering systems seek cost-effective memory scaling by adding multiple tiers of memory. For maximum performance, frequently accessed (hot) data must be placed close to the host in faster tiers and infrequently accessed (cold) data can…
This paper studies content caching in cloud-aided wireless networks where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay)…
Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for…
The success of deep neural networks (DNN) in machine perception applications such as image classification and speech recognition comes at the cost of high computation and storage complexity. Inference of uncompressed large scale DNN models…
Distributed caching systems such as content distribution networks often advertise their content via lightweight approximate indicators (e.g., Bloom filters) to efficiently inform clients where each datum is likely cached. While…
We present the notion of stateful priorities for imposing precise restrictions on system actions, in order to meet safety constraints. By using stateful priorities we are able to exclusively restrict erroneous system behavior as specified…
This article explores one of the key enablers of beyond $4$G wireless networks leveraging small cell network deployments, namely proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage,…
Reservoir computing is a novel machine learning algorithm that uses a nonlinear dynamical system to efficiently learn complex temporal patterns from data. The objective of this thesis is to investigate the principles of reservoir computing…
Emerging applications, such as smart factories and fleets of drones, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated…
In this paper, we investigate a cache updating system with a server containing $N$ files, $K$ relays and $M$ users. The server keeps the freshest versions of the files which are updated with fixed rates. Each relay can download the fresh…