Querying Zero-streaming Cameras

Low-cost cameras grow rapidly, producing colossal videos that enable powerful analytics but also stress network and compute resources. An unexploited opportunity is that most of the videos remain "cold" without ever being queried. For resource efficiency, we advocate for these cameras to be zero-streaming: they capture videos directly to their cheap local storage and only communicate with the cloud when analytics is requested. To this end, we present a system that spans the cloud and cameras. Our key ideas are twofold. When capturing video frames, a camera learns accurate knowledge on a sparse sample of frames, rather than learning inaccurate knowledge on all frames; in executing one query, a camera processes frames in multiple passes with multiple operators trained and picked by the cloud during the query, rather than one pass processing with operator(s) decided ahead of the query. On diverse queries over 15 videos and with typical wireless network bandwidth and low-cost camera hardware, our system prototype runs at more than 100x video realtime. It outperforms competitive alternative designs by at least 4x and up to two orders of magnitude.Comment: Mengwei Xu and Tiantu Xu are co-primary authors. Xuanzhe Liu is the corresponding autho

Situation-Aware Integration and Transmission of Safety Information for Smart Railway Vehicles

Recent trend of railway train development can be characterized in several aspects : high speed, infortainment, intelligence in driving, and so on. In particular, trend of high speed in driving is prominent and competition for high speed amongst several techno-savvy countries is becoming severe. To achieve high speed, engines or motors are distributed over multiple vehicles of train to provide increased motive power, while a single engine or motor has been mostly used for conventional trains. Increased speed and more complicated powertrain system naturally incur much higher chance of massive accidents. From this perspective, importance of proactive safety control before accident takes place cannot be over-emphasized. To implement proactive safety control requires situation-aware integration and transmission of safety information obtained from IoT sensors. Types of critical IoT sensors depend on situational conditions. Thus, integration and transmission of safety information should be performed with IoT sensors providing the safety information proper for faced situation. This brief paper is to devise a methodology how to operate IoT sensor network enabling proactive safety control for railway vehicles and to propose a queue management based medium access control scheme.Comment: 15 page

Predictive Green Wireless Access: Exploiting Mobility and Application Information

The ever increasing mobile data traffic and dense deployment of wireless networks have made energy efficient radio access imperative. As networks are designed to satisfy peak user demands, radio access energy can be reduced in a number of ways at times of lower demand. This includes putting base stations (BSs) to intermittent short sleep modes during low load, as well as adaptively powering down select BSs completely where demand is low for prolonged time periods. In order to fully exploit such energy conserving mechanisms, networks should be aware of the user temporal and spatial traffic demands. To this end, this article investigates the potential of utilizing predictions of user location and application information as a means to energy saving. We discuss the development of a predictive green wireless access (PreGWA) framework and identify its key functional entities and their interaction. To demonstrate the potential energy savings we then provide a case study on stored video streaming and illustrate how exploiting predictions can minimize BS resource consumption within a single cell, and across a network of cells. Finally, to emphasize the practical potential of PreGWA, we present a distributed heuristic that reduces resource consumption significantly without requiring considerable information or signaling overhead

Energy-efficient Traffic Bypassing in LTE HetNets with Mobile Relays

One of the core technologies being standardized by 3GPP for LTE-A is the introduction of Relay Nodes (RNs). RNs are intended for ensuring coverage at cell edges as well as for the provision of enhanced capacity at hot spot areas. An extension to this concept is the Mobile Relay (MR). MRs can be mounted on vehicles and the original idea is to serve users inside high speed trains thus counter fighting the inherent severe fading and vehicle penetration loss. In this work we present a framework for exploiting Mobile Relay (MRs) even at low speeds in urban environments for bypassing traffic from nearby users, either within or outside the vehicles. In particular we show that apart from increased capacity and good quality coverage this approach achieves important energy savings for the mobile terminals.Comment: 6 pages, 6 figure

Toward Green Media Delivery: Location-Aware Opportunities and Approaches

Mobile media has undoubtedly become the predominant source of traffic in wireless networks. The result is not only congestion and poor Quality-of-Experience, but also an unprecedented energy drain at both the network and user devices. In order to sustain this continued growth, novel disruptive paradigms of media delivery are urgently needed. We envision that two key contemporary advancements can be leveraged to develop greener media delivery platforms: 1) the proliferation of navigation hardware and software in mobile devices has created an era of location-awareness, where both the current and future user locations can be predicted; and 2) the rise of context-aware network architectures and self-organizing functionalities is enabling context signaling and in-network adaptation. With these developments in mind, this article investigates the opportunities of exploiting location-awareness to enable green end-to-end media delivery. In particular, we discuss and propose approaches for location-based adaptive video quality planning, in-network caching, content prefetching, and long-term radio resource management. To provide insights on the energy savings, we then present a cross-layer framework that jointly optimizes resource allocation and multi-user video quality using location predictions. Finally, we highlight some of the future research directions for location-aware media delivery in the conclusion

Design Considerations for a 5G Network Architecture

The data rates of up to 10 GB/s will characterize 5G networks telecommunications standards that are envisioned to replace the current 4G/IMT standards. The number of network-connected devices is expected to be 7 trillion by the end of this year and the traffic is expected to rise by an order of magnitude in the next 8 years. It is expected that elements of 5G will be rolled out by early 2020s to meet business and consumer demands as well as requirements of the Internet of Things. China's Ministry of Industry and Information Technology announced in September 2016 that the government-led 5G Phase-1 tests of key wireless technologies for future 5G networks were completed with satisfactory results. This paper presents an overview of the challenges facing 5G before it can be implemented to meet expected requirements of capacity, data rates, reduced latency, connectivity to massive number of devices, reduced cost and energy, and appropriate quality of experience.Comment: 16 pages, 10 figure

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper

Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy efficient video streaming with the popular HTTP-based Adaptive Streaming (AS) protocols (e.g. DASH). To this end, we develop an energy-efficient Predictive Green Streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives 1) minimize the required transmission airtime without causing streaming interruptions, 2) minimize total downlink Base Station (BS) power consumption for cases where BSs can be switched off in deep sleep, and 3) enable a trade-off between AS quality and energy consumption. Our framework is first formulated as a Mixed Integer Linear Program (MILP) where decisions on multi-user rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.Comment: 14 pages, 14 figures, accepted for publication in IEEE Transactions on Vehicular Technolog

A Survey on High-Speed Railway Communications: A Radio Resource Management Perspective

High-speed railway (HSR) communications will become a key feature supported by intelligent transportation communication systems. The increasing demand for HSR communications leads to significant attention on the study of radio resource management (RRM), which enables efficient resource utilization and improved system performance. RRM design is a challenging problem due to heterogenous quality of service (QoS) requirements and dynamic characteristics of HSR wireless communications. The objective of this paper is to provide an overview on the key issues that arise in the RRM design for HSR wireless communications. A detailed description of HSR communication systems is first presented, followed by an introduction on HSR channel models and characteristics, which are vital to the cross-layer RRM design. Then we provide a literature survey on state-of-the-art RRM schemes for HSR wireless communications, with an in-depth discussion on various RRM aspects including admission control, mobility management, power control and resource allocation. Finally, this paper outlines the current challenges and open issues in the area of RRM design for HSR wireless communications.Comment: 40 pages, 10 figures. Submitted to Computer Communication

Artificial Intelligence-Defined 5G Radio Access Networks

Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G systems. This article discusses the intelligent agent in 5G base station which combines sensing, learning, understanding and optimizing to facilitate these enablers. We present a flexible, rapidly deployable, and cross-layer artificial intelligence (AI)-based framework to enable the imminent and future demands on 5G and beyond infrastructure. We present example AI-enabled 5G use cases that accommodate important 5G-specific capabilities and discuss the value of AI for enabling beyond 5G network evolution