1,721,020 research outputs found
A survey on 360-degree video:Coding, quality of experience and streaming
No full textThe commercialization of Virtual Reality (VR) headsets has made immersive and 360-degree video streaming the subject of intense interest in the industry and research communities. While the basic principles of video streaming are the same, immersive video presents a set of specific challenges that need to be addressed. In this survey, we present the latest developments in the relevant literature on four of the most important ones: (i) omnidirectional video coding and compression, (ii) subjective and objective Quality of Experience (QoE) and the factors that can affect it, (iii) saliency measurement and viewport prediction, and (iv) the adaptive streaming of immersive 360-degree videos. The final objective of the survey is to provide an overview of the research on all the elements of an immersive video streaming system, giving the reader an understanding of their interplay and performance.</p
Age of Information Resilience With a Strategic Out-of-Band Relay
No full textThe age of information (AoI) is a metric representing the freshness of the information available at the receiver in a system which involves the exchange of status updates over an error-prone time-slotted channel between a sensing source and a receiver. We consider such a system, with the addition of a relay node that is able to assist the transmission to improve the resilience against failures, and compute the expected AoI over a discrete time-slotted channel when both the sensor and relay are intermittently and independently active. Furthermore, we present a game theoretic formulation of the optimization of the activity rate for both nodes when transmissions are expensive, managing the tradeoff between cost and AoI. The Nash equilibrium (NE) of the resulting game is found to be both efficient from the perspective of the resulting performance and computationally lightweight for a distributed robust control implementation
A Web of Things approach for learning on the Edge–Cloud Continuum
Full text linkInternet of Things (IoT) devices provide constant, contextual data that can be leveraged to automatically reconfigure and optimize smart environments. Artificial Intelligence (AI) and deep learning techniques are tools of increasing importance for this, as Deep Reinforcement Learning (DRL) can provide a general solution to this problem. However, the heterogeneity of scenarios in which DRL models may be deployed is vast, making the design of universal plug-and-play models extremely difficult. Moreover, the real deployment of DRL models on the Edge, and in the IoT in particular, is limited by two factors: firstly, the computational complexity of the training procedure, and secondly, the need for a relatively long exploration phase, during which the agent proceeds by trial and error. A natural solution to both these issues is to use simulated environments by creating a Digital Twin (DT) of the environment, which can replicate physical entities in the digital domain, providing a standardized interface to the application layer. DTs allow for simulation and testing of models and services in a simulated environment, which may be hosted on more powerful Cloud servers without the need to exchange all the data generated by the real devices. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies DRL techniques on real time data. We discuss the theoretical properties of DRL training using DTs, showcasing our system in an existing real deployment, comparing its performance with a legacy system. Our findings show that the implementation of a DT, specifically for DRL models, allows for faster convergence and finer tuning, as well as reducing the computational and communication demands on the Edge network. The use of multiple DTs with different complexities and data requirements can also help accelerate the training, progressing by steps
On the Limits of Digital Twins for Safe Deep Reinforcement Learning in Robotic Networks
Full text linkSuccessfully training models which address the complex nature of real environments in Smart Cities and robotic networks is challenging, due to the vast amount of data needed. When employing Reinforcement Learning (RL) models, it is also impossible to let them explore all the actions in the real world, due to potential damages and inappropriate actions. The inherent trial-and-error nature of RL, especially in real-world applications like traffic management, makes an integration with Digital Twins (DTs) attractive: DTs provide a secure environment for iterative training and testing, ensuring the refinement of the models before testing. However, the use of DTs has some significant limitations, as the difference between the model and reality may cause significant risks. This study focuses on the adaptive and self-learning characteristics of this approach, considering a standard navigation task in a highway environment, and analyzes its advantages and potential pitfalls
Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources
Full text linkAs the use of Internet of Things (IoT) devices for monitoring purposes becomes ubiquitous, the efficiency of sensor communication is a major issue for the modern Internet. Channel coding is less efficient for extremely short packets, and traditional techniques that rely on source compression require extensive signaling or pre-existing knowledge of the source dynamics. In this work, we propose an encoding and decoding scheme that learns source dynamics online using a Hidden Markov Model (HMM), puncturing a short packet code to outperform existing compression-based approaches. Our approach shows significant performance improvements for sources that are highly correlated in time, with no additional complexity on the sender side
Joint Scheduling and Coding for Reliable, Latency-Bounded Transmission over Parallel Wireless Links
Full text linkLow-Latency Massive Access with Multicast Wake Up Radio
Full text linkThe use of Wake-Up Radio (WUR) in Internet of Things (IoT) networks can significantly improve their energy efficiency: battery-powered sensors can remain in a low-power (sleep) mode while listening for wake-up messages using their WUR and reactivate only when polled, saving energy. However, polling-based Time Division Multiple Access (TDMA) may significantly increase data transmission delay if packets are generated sporadically, as nodes with no information still need to be polled. In this paper, we examine the effect of multicast polling for WUR-enable wireless nodes. The idea is to assign nodes to multicast groups so that all nodes in the same group can be solicited by a multicast polling message. This may cause collisions, which can be solved by requesting retransmissions from the involved nodes. We analyze the performance of different multicast polling and retransmission strategies, showing that the optimal approach can significantly reduce the delay over TDMA and ALOHA in low-traffic scenarios while keeping good energy efficiency
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