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Real-time Bidding (RTB) advertisers wish to \textit{know in advance} the expected cost and yield of ad campaigns to avoid trial-and-error expenses. However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal. We propose \textit{AdVance}, a time-aware framework that integrates local auction-level and global campaign-level modeling. User preference and fatigue are disentangled using a time-positioned sequence of clicked items and a concise vector of all displayed items. Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad. Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism. Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks. These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity. Considering the irregular time intervals between auctions, we make SSM's parameters dependent on the current auction embedding and the time interval. We further condition SSM's global predictions on the accumulation of local results. Extensive evaluations and ablation studies demonstrate its superiority over state-of-the-art methods. AdVance has been deployed on the Tencent Advertising platform, and A/B tests show a remarkable 4.5\% uplift in Average Revenue per User (ARPU)., Comment: 12 pages, 4 figures, accepted at ACM SIGKDD 2024

The rise of software applications in vehicles has transformed the automotive industry, leading to significant advancements in vehicle functionality and user experience. These advancements, however, are not for free and come at the cost of increased computational requirements. Vehicles nowadays perform many tasks, from critical tasks such as the ones related to accident prevention and monitoring, to other high priority and low priority tasks. Being able to perform all tasks on a timely manner in a vehicle requires enough computational resources, which sometimes may not be readily available to provide fast response times. Offloading the tasks is a possible approach to solving this problem, but this requires a common system of communication and critical safety measures. Furthermore, leveraging the distribution of the tasks in real time is not trivial, since many factors such as vehicle to vehicle distance and speed need to be taken into account, and system performance can vary a lot depending on the scheduling policy chosen. Addressing this challenge, utilising a simulator to get real life traffic data, this master's thesis builds upon the AVE framework to present a novel method that leverages asynchronous Multi-Agent Deep Reinforcement Learning (MADRL) for efficient task scheduling among nearby vehicles, in order to achieve higher software responsiveness in a multi-agent system without the need of a centralised system.

Because of the ambiguous and subjective property of the facial expression recognition (FER) task, the label noise is widely existing in the FER dataset. For this problem, in the training phase, current FER methods often directly predict whether the label of the input image is noised or not, aiming to reduce the contribution of the noised data in training. However, we argue that this kind of method suffers from the low reliability of such noise data decision operation. It makes that some mistakenly abounded clean data are not utilized sufficiently and some mistakenly kept noised data disturbing the model learning process. In this paper, we propose a more reliable noise-label suppression method called ReSup (Reliable label noise Suppression for FER). First, instead of directly predicting noised or not, ReSup makes the noise data decision by modeling the distribution of noise and clean labels simultaneously according to the disagreement between the prediction and the target. Specifically, to achieve optimal distribution modeling, ReSup models the similarity distribution of all samples. To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression. Specifically, ReSup utilize the property that two networks are less likely to make the same mistakes, making two networks swap decisions and tending to trust decisions with high agreement. Extensive experiments on three popular benchmarks show that the proposed method significantly outperforms state-of-the-art noisy label FER methods by 3.01% on FERPlus becnmarks. Code: https://github.com/purpleleaves007/FERDenoise

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs., Comment: Accepted by IEEE THMS

Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets - commonly referred to as big data - and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging of big data and emergency management (BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories of remote sensing, resilient communication networks, mobile communication networks, human mobility and urban sensing, social network analysis, and knowledge graphs. Finally, we outline several directions of future research. © 2022 IEEE.

Distributed access control is a crucial component for massive machine type communication (mMTC). In this communication scenario, centralized resource allocation is not scalable because resource configurations have to be sent frequently from the base station to a massive number of devices. We investigate distributed reinforcement learning for resource selection without relying on centralized control. Another important feature of mMTC is the sporadic and dynamic change of traffic. Existing studies on distributed access control assume that traffic load is static or they are able to gradually adapt to the dynamic traffic. We minimize the adaptation period by training TinyQMIX, which is a lightweight multi-agent deep reinforcement learning model, to learn a distributed wireless resource selection policy under various traffic patterns before deployment. Therefore, the trained agents are able to quickly adapt to dynamic traffic and provide low access delay. Numerical results are presented to support our claims., Comment: 6 pages, 4 figures, presented at VTC Fall 2022

In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.

This paper studies the problem of information freshness-aware task offloading in an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). A third-party real-time application service provider provides computing services to the subscribed mobile users (MUs) with the limited communication and computation resources from the InP based on a long-term business agreement. Due to the dynamic characteristics, the interactions among the MUs are modelled by a non-cooperative stochastic game, in which the control policies are coupled and each MU aims to selfishly maximize its own expected long-term payoff. To address the Nash equilibrium solutions, we propose that each MU behaves in accordance with the local system states and conjectures, based on which the stochastic game is transformed into a single-agent Markov decision process. Moreover, we derive a novel online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks for each MU to approximate the Q-factor and the post-decision Q-factor. Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics. Numerical experiments examine the potentials of the proposed scheme in balancing the age of information and the energy consumption.

Facing the trend of merging wireless communications and multi-access edge computing (MEC), this article studies computation offloading in the beyond fifth-generation networks. To address the technical challenges originating from the uncertainties and the sharing of limited resource in an MEC system, we formulate the computation offloading problem as a multi-agent Markov decision process, for which a distributed learning framework is proposed. We present a case study on resource orchestration in computation offloading to showcase the potentials of an online distributed reinforcement learning algorithm developed under the proposed framework. Experimental results demonstrate that our learning algorithm outperforms the benchmark resource orchestration algorithms. Furthermore, we outline the research directions worth in-depth investigation to minimize the time cost, which is one of the main practical issues that prevent the implementation of the proposed distributed learning framework.

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works., [EN] In this paper, we propose a realistic model for simulating communications between unmanned aerial vehicles (UAVs), or drones, and ground vehicles, which can support mobile infrastructure to broadcast alerts in emergency situations. Three-dimensional positioning features should be considered in these simulations that involve UAVs and ground vehicles since communications links are not based on a flat surface. In fact, irregular terrains in the form of hills and mountains can greatly affect the communications by acting as obstacles that block radio signals partially or totally. Hence, in this paper, we propose a simulation model that conforms to this kind of communication and that was developed in the scope of the OMNeT++ simulator. The simulation results achieved showed a great degree of similarities with those results obtained in a real testbed for different scenarios. In addition, various path loss models and elevation models were considered to improve the level of realism of the simulation model.

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works., [EN] In this paper, we propose a realistic model for simulating communications between unmanned aerial vehicles (UAVs), or drones, and ground vehicles, which can support mobile infrastructure to broadcast alerts in emergency situations. Three-dimensional positioning features should be considered in these simulations that involve UAVs and ground vehicles since communications links are not based on a flat surface. In fact, irregular terrains in the form of hills and mountains can greatly affect the communications by acting as obstacles that block radio signals partially or totally. Hence, in this paper, we propose a simulation model that conforms to this kind of communication and that was developed in the scope of the OMNeT++ simulator. The simulation results achieved showed a great degree of similarities with those results obtained in a real testbed for different scenarios. In addition, various path loss models and elevation models were considered to improve the level of realism of the simulation model.

This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the terrestrial MEC system. The UAV processes the received computation tasks from the mobile users (MUs) by creating the corresponding virtual machines. Due to finite shared I/O resource of the UAV in the MEC system, each MU competes to schedule local as well as remote task computations across the decision epochs, aiming to maximize the expected long-term computation performance. The non-cooperative interactions among the MUs are modeled as a stochastic game, in which the decision makings of a MU depend on the global state statistics and the task scheduling policies of all MUs are coupled. To approximate the Nash equilibrium solutions, we propose a proactive scheme based on the long short-term memory and deep reinforcement learning (DRL) techniques. A digital twin of the MEC system is established to train the proactive DRL scheme offline. Using the proposed scheme, each MU makes task scheduling decisions only with its own information. Numerical experiments show a significant performance gain from the scheme in terms of average utility per MU across the decision epochs.

Emotion is well-recognized as a distinguished symbol of human beings, and it plays a crucial role in our daily lives. Existing vision-based or sensor-based solutions are either obstructive to use or rely on specialized hardware, hindering their applicability. This paper introduces EmoSense, a first-of-its-kind wireless emotion sensing system driven by computational intelligence. The basic methodology is to explore the physical expression of emotions from wireless channel response via data mining. The design and implementation of EmoSense {face} two major challenges: extracting physical expression from wireless channel data and recovering emotion from the corresponding physical expression. For the former, we present a Fresnel zone based theoretical model depicting the fingerprint of the physical expression on channel response. For the latter, we design an efficient computational intelligence driven mechanism to recognize emotion from the corresponding fingerprints. We prototyped EmoSense on the commodity WiFi infrastructure and compared it with main-stream sensor-based and vision-based approaches in the real-world scenario. The numerical study over $3360$ cases confirms that EmoSense achieves a comparable performance to the vision-based and sensor-based rivals under different scenarios. EmoSense only leverages the low-cost and prevalent WiFi infrastructures and thus constitutes a tempting solution for emotion sensing., Comment: 11 pages, accepted by IEEE Transactions On Emerging Topics In Computational Intelligence

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an ``all-mmWave'' cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the ``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time-varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of-art baselines, in terms of average queue length and average packet dropping rate.

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme., Comment: Accepted to IEEE JSAC, May 2019

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained., Comment: 7 pages, 7 figures

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep $Q$-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

The advent of SDN has brought a plethora of new architectures and controller designs for many use-cases and scenarios. Existing SDN deployments focus on campus, datacenter and WAN networks. However, little research efforts have been devoted to the scenario of effectively controlling a full deployment of end-nodes (e.g. smartphones) that are transient and scattered across the Internet. In this paper, we present a rigorous analysis of the challenges associated with an SDN architecture for end-nodes, show that such challenges are not found in existing SDN scenarios, and provide practical design guidelines to address them. Then, and following these guidelines we present a reference architecture based on a decentralized, distributed and symmetric controller with a connectionless pull-oriented southbound and an intent-driven northbound. Finally, we measure a proof-of-concept deployment to assess the validity of the analysis as well as the architecture.

Current TCP/IP based network is suffering from the tremendous usage of IP. Recently, content centric network (CCN) is proposed as an alternative of the future network architecture. In CCN, data itself, which is authenticated and secured, is a name and can be directly requested at the network level instead of using IP and DNS. Moreover, routers in CCN have caching abilities. Then end users can obtain the data from routers instead of remote server if the content has been stored in the routers, thus the overall network performance could be improved by reducing the transmission hops. Orthogonally, video plays a more and more important role nowadays and dominates the network traffic. Response time of each video request greatly affects the quality of user experience (QoE), users may even abandon the requested video service if they have to wait for long time before the video playback. Hence how to provide fast-start video delivery in CCN is critical. In this paper, we target to provide users fast-start video delivery in CCN. Specifically, we design a new caching policy for popularity-aware video caching in topology-aware CCN. And we propose to encode the video using scalable video coding (SVC) for fast-start video delivery and cache each video layer separately following the designed caching policies. Given an assigned weight by users, the tradeoff between the waiting time and received video quality is studied. Simulations are conducted to verify the performances and the results show that the proposed scheme outperforms state-of-the-art schemes significantly in typical scenarios.

Current TCP/IP based network is suffering from the tremendous usage of IP. Recently, content centric network (CCN) is proposed as an alternative of the future network architecture. In CCN, data itself, which is authenticated and secured, is a name and can be directly requested at the network level instead of using IP and DNS. Moreover, routers in CCN have caching abilities. Then end users can obtain the data from routers instead of remote server if the content has been stored in the routers, thus the overall network performance could be improved by reducing the transmission hops. Orthogonally, video plays a more and more important role nowadays and dominates the network traffic. Response time of each video request greatly affects the quality of user experience (QoE), users may even abandon the requested video service if they have to wait for long time before the video playback. Hence how to provide fast-start video delivery in CCN is critical. In this paper, we target to provide users fast-start video delivery in CCN. Specifically, we design a new caching policy for popularity-aware video caching in topology-aware CCN. And we propose to encode the video using scalable video coding (SVC) for fast-start video delivery and cache each video layer separately following the designed caching policies. Given an assigned weight by users, the tradeoff between the waiting time and received video quality is studied. Simulations are conducted to verify the performances and the results show that the proposed scheme outperforms state-of-the-art schemes significantly in typical scenarios.

The success of a disaster relief and response process is largely dependent on timely and accurate information regarding the status of the disaster, the surrounding environment, and the affected people. This information is primarily provided by first responders on-site and can be enhanced by the firsthand reports posted in real-time on social media. Many tools and methods have been developed to automate disaster relief by extracting, analyzing, and visualizing actionable information from social media. However, these methods are not well integrated in the relief and response processes and the relation between the two requires exposition for further advancement. In this survey, we review the new frontier of intelligent disaster relief and response using social media, show stages of disasters which are reflected on social media, establish a connection between proposed methods based on social media and relief efforts by first responders, and outline pressing challenges and future research directions., Comment: 14 pages, 3 figures, 104 references, accepted in SIGKDD Explorations

Broadcasting of real-time video, especially accidents relevant, plays an important role in Vehicle Ad-hoc Networks (VANETs). Multi-view video enables the perception of the targeted scene from multiple angles. By subscribing the real-time multi-view video broadcast, drivers can obtain better knowledge of the highway traffic and road conditions. However, compared with the traditional single view video transmission, the multiview video transmission is more challenging, because of the VANETs' channel variation and a much larger network data rate requirement. The inherent problem thus becomes how to utilize the limited network bandwidth to stream the real-time multi-view video. As far as the authors understand, this problem has not yet been solved in any formal way. This paper focuses on the realtime multi-view broadcast over VANETs and formulates it into an optimization problem with the objective to maximize the average video quality received by users. By exploring the VANET channel characteristic, the correlations inside the multi-view and video popularity, an algorithm named as RMV is proposed to solve the optimization problem. Extensive simulations are conducted and simulation results show that the proposed scheme can outperform the comparison schemes in typical VANET scenarios to a great extent. © 2016 IEEE.

Broadcasting of real-time video, especially accidents relevant, plays an important role in Vehicle Ad-hoc Networks (VANETs). Multi-view video enables the perception of the targeted scene from multiple angles. By subscribing the real-time multi-view video broadcast, drivers can obtain better knowledge of the highway traffic and road conditions. However, compared with the traditional single view video transmission, the multiview video transmission is more challenging, because of the VANETs' channel variation and a much larger network data rate requirement. The inherent problem thus becomes how to utilize the limited network bandwidth to stream the real-time multi-view video. As far as the authors understand, this problem has not yet been solved in any formal way. This paper focuses on the realtime multi-view broadcast over VANETs and formulates it into an optimization problem with the objective to maximize the average video quality received by users. By exploring the VANET channel characteristic, the correlations inside the multi-view and video popularity, an algorithm named as RMV is proposed to solve the optimization problem. Extensive simulations are conducted and simulation results show that the proposed scheme can outperform the comparison schemes in typical VANET scenarios to a great extent. © 2016 IEEE.

Broadcasting of real-time video, especially accidents relevant, plays an important role in Vehicle Ad-hoc Networks (VANETs). Multi-view video enables the perception of the targeted scene from multiple angles. By subscribing the real-time multi-view video broadcast, drivers can obtain better knowledge of the highway traffic and road conditions. However, compared with the traditional single view video transmission, the multiview video transmission is more challenging, because of the VANETs' channel variation and a much larger network data rate requirement. The inherent problem thus becomes how to utilize the limited network bandwidth to stream the real-time multi-view video. As far as the authors understand, this problem has not yet been solved in any formal way. This paper focuses on the realtime multi-view broadcast over VANETs and formulates it into an optimization problem with the objective to maximize the average video quality received by users. By exploring the VANET channel characteristic, the correlations inside the multi-view and video popularity, an algorithm named as RMV is proposed to solve the optimization problem. Extensive simulations are conducted and simulation results show that the proposed scheme can outperform the comparison schemes in typical VANET scenarios to a great extent. © 2016 IEEE.

The base station (BS) in a multi-channel cognitive radio (CR) network has to broadcast to secondary (or unlicensed) receivers/users on more than one broadcast channels via channel hopping (CH), because a single broadcast channel can be reclaimed by the primary (or licensed) user, leading to broadcast failures. Meanwhile, a secondary receiver needs to synchronize its clock with the BS's clock to avoid broadcast failures caused by the possible clock drift between the CH sequences of the secondary receiver and the BS. In this paper, we propose a CH-based broadcast protocol called SASS, which enables a BS to successfully broadcast to secondary receivers over multiple broadcast channels via channel hopping. Specifically, the CH sequences are constructed on basis of a mathematical construct---the Self-Adaptive Skolem sequence. Moreover, each secondary receiver under SASS is able to adaptively synchronize its clock with that of the BS without any information exchanges, regardless of any amount of clock drift., Comment: A full version with technical proofs. Accepted by IEEE VTC 2016 Spring

This is the author’s version of a work that was accepted for publication in Ad Hoc Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ad Hoc Networks, vol. 24, (2015) DOI 10.1016/j.adhoc.2014.07.030, Long-distance real-time video flooding over Vehicular Ad-hoc Networks (VANETs) is challenging due to the dynamic nature of vehicular networks. Real-time video transmission has high requirements in terms of bandwidth and delay, while VANETs are characterized by very limited radio resources and high mobility. Our objective is to compare ten different flooding schemes specially designed for this task and select the best in terms of packet arrival ratio and Peak Signal-to-Noise Ratio (PSNR). Additionally, we propose Automatic Copies Distance Based (ACDB), an improved flooding scheme, to cope with variable vehicle density situations. Furthermore, to ensure a good behavior under all types of circumstances, we also study the impact of Global Positioning System (GPS) drift on these schemes. Simulation results show that long-distance real-time video flooding transmission is feasible under certain vehicle density conditions and the quality achieved is enough to allow using such solutions in future applications.

This is the author’s version of a work that was accepted for publication in Ad Hoc Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ad Hoc Networks, vol. 24, (2015) DOI 10.1016/j.adhoc.2014.07.030, Long-distance real-time video flooding over Vehicular Ad-hoc Networks (VANETs) is challenging due to the dynamic nature of vehicular networks. Real-time video transmission has high requirements in terms of bandwidth and delay, while VANETs are characterized by very limited radio resources and high mobility. Our objective is to compare ten different flooding schemes specially designed for this task and select the best in terms of packet arrival ratio and Peak Signal-to-Noise Ratio (PSNR). Additionally, we propose Automatic Copies Distance Based (ACDB), an improved flooding scheme, to cope with variable vehicle density situations. Furthermore, to ensure a good behavior under all types of circumstances, we also study the impact of Global Positioning System (GPS) drift on these schemes. Simulation results show that long-distance real-time video flooding transmission is feasible under certain vehicle density conditions and the quality achieved is enough to allow using such solutions in future applications.

Current TCP/IP based network is suffering from the usage of IP especially in the era of Internet of things (IoT). Recently Content Centric Network (CCN) is proposed as an alternative of the future network architecture. In CCN, data itself, which is authenticated and secured, is a name and can be directly requested at the network level instead of using IP and Domain Name System (DNS). Another difference between CCN and traditional networks is that the routers in CCN have the caching abilities. Then the end users can obtain the data from routers instead of from the remote server if the content has been stored in the router. Hence the overall network performance can be improved by reducing the required transmission hops and the advantage of the CCN caching has been shown in literature. In this paper, we design a new caching policy for the popularity-aware video caching in CCN to handle the `redundancy' problem in the existing schemes, where the same content may be stored multiple times along the road from server to users, thus leading to a significant performance degradation. Simulations are conducted and we could observe that the proposed scheme performs better comparing with the existing caching policies., Location: Taipei, TAIWAN, Date: AUG 19-20, 2015

Current TCP/IP based network is suffering from the usage of IP especially in the era of Internet of things (IoT). Recently Content Centric Network (CCN) is proposed as an alternative of the future network architecture. In CCN, data itself, which is authenticated and secured, is a name and can be directly requested at the network level instead of using IP and Domain Name System (DNS). Another difference between CCN and traditional networks is that the routers in CCN have the caching abilities. Then the end users can obtain the data from routers instead of from the remote server if the content has been stored in the router. Hence the overall network performance can be improved by reducing the required transmission hops and the advantage of the CCN caching has been shown in literature. In this paper, we design a new caching policy for the popularity-aware video caching in CCN to handle the `redundancy' problem in the existing schemes, where the same content may be stored multiple times along the road from server to users, thus leading to a significant performance degradation. Simulations are conducted and we could observe that the proposed scheme performs better comparing with the existing caching policies., Location: Taipei, TAIWAN, Date: AUG 19-20, 2015

Lately, all the top truck manufacturers are investing considerable resources in the research and development of platooning systems which would allow vehicles to save fuel and improve safety by travelling in a close-following manner. The platoon-ing system requires frequent and reliable vehicle-to-vehicle communications. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However , TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if re-transmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability considerably compared to pure 802.11p., SafeCOP - Safe Cooperating Cyber-Physical Systems using Wireless Communication

Lately, all the top truck manufacturers are investing considerable resources in the research and development of platooning systems which would allow vehicles to save fuel and improve safety by travelling in a close-following manner. The platooning system requires frequent and reliable vehicle-to-vehicle communications. As platooning takes place in a vehicular ad hoc network, the use of IEEE 802.11p is close to mandatory. However, the 802.11p medium access method suffers from packet collisions and random delays. Most ongoing research suggests using TDMA on top of 802.11p as a potential remedy. However, TDMA requires synchronization and is not very flexible if the beacon frequency needs to be updated, the number of platoon members changes, or if re-transmissions for increased reliability are required. We therefore suggest a token-passing medium access method where the next token holder is selected based on beacon data age. This has the advantage of allowing beacons to be re-broadcasted in each beacon interval whenever time and bandwidth are available. We show that our token-based method is able to reduce the data age and considerably increase reliability considerably compared to pure 802.11p., This work was partially supported by the Ministerio de Economa y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R. Balador is funded by the ERASMUS+ programme and by the National Institute of Informatics International Internship Program. Bohm and Uhlemann are funded by the Knowledge Foundation through the ACDC project., ACDC

In cognitive radio (CR) networks, "TTR", a.k.a. time-to-rendezvous, is one of the most important metrics for evaluating the performance of a channel hopping (CH) rendezvous protocol, and it characterizes the rendezvous delay when two CRs perform channel hopping. There exists a trade-off of optimizing the average or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH protocol leads to the best "average" TTR without ensuring a finite "maximum" TTR (two CRs may never rendezvous in the worst case), or a high rendezvous diversity (multiple rendezvous channels). On the other hand, many sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR) and a high rendezvous diversity, while they inevitably yield a larger average TTR. In this paper, we strike a balance in the average-maximum TTR trade-off for CR rendezvous by leveraging the advantages of both random and sequence-based CH protocols. Inspired by the neighbor discovery problem, we establish a design framework of creating a wake-up schedule whereby every CR follows the sequence-based (or random) CH protocol in the awake (or asleep) mode. Analytical and simulation results show that the hybrid CH protocols under this framework are able to achieve a greatly improved average TTR as well as a low upper-bound of TTR, without sacrificing the rendezvous diversity., Comment: Accepted by IEEE International Conference on Communications (ICC 2015, http://icc2015.ieee-icc.org/)

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., [EN] Quickly identifying the severity of highway acci-dents, as well as the resources required to assist the people involved in those accidents, is a basic requirement for future intelligent transportation systems. In this context, vehicular communication technologies currently being standardized are able to provide novel solutions to address this problem. In this work we study the feasibility of combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to deliver a video stream from the place of the accident to the traffic authorities. Our approach relies on vehicles as data relays, thus having the additional advantage of providing drivers with a clear view about the accident, thereby helping to reduce stress and improving traffic flow. An experimental analysis comparing different traffic flooding mechanisms for wireless networks show that the proposed system is viable for highways with moderate/high amounts of traffic, although highlighting the need for more efficient mechanisms specifically addressing broadcast propagation in highway envi-ronments.

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., [EN] Quickly identifying the severity of highway acci-dents, as well as the resources required to assist the people involved in those accidents, is a basic requirement for future intelligent transportation systems. In this context, vehicular communication technologies currently being standardized are able to provide novel solutions to address this problem. In this work we study the feasibility of combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to deliver a video stream from the place of the accident to the traffic authorities. Our approach relies on vehicles as data relays, thus having the additional advantage of providing drivers with a clear view about the accident, thereby helping to reduce stress and improving traffic flow. An experimental analysis comparing different traffic flooding mechanisms for wireless networks show that the proposed system is viable for highways with moderate/high amounts of traffic, although highlighting the need for more efficient mechanisms specifically addressing broadcast propagation in highway envi-ronments.

Visual saliency is a probabilistic estimate of how likely a given spatial area in an image or video is to attract human visual attention relative to other areas. Bottom-up saliency models aggregate low-level image features like luminance and color contrast, flicker, 2D motion, etc. to construct a plausible saliency map. In this paper, we introduce 3D motion (object movements towards or away from the observer) into bottom-up video saliency modeling. Given availability of per-pixel depth maps, we first propose a novel algorithm to estimate 3D motion vectors (3DMVs) for arbitrarily shaped sub-blocks in texture-plus-depth videos. We then derive two feature channels from 3DMVs to be incorporated into a widely accepted bottom-up saliency model. Experiments on subjective quality of Region-of-Interest (ROI) based video coding show that our enriched saliency model with 3DMV channels is more accurate in estimating human visual attention. © 2013 IEEE.

Visual saliency is a probabilistic estimate of how likely a given spatial area in an image or video is to attract human visual attention relative to other areas. Bottom-up saliency models aggregate low-level image features like luminance and color contrast, flicker, 2D motion, etc. to construct a plausible saliency map. In this paper, we introduce 3D motion (object movements towards or away from the observer) into bottom-up video saliency modeling. Given availability of per-pixel depth maps, we first propose a novel algorithm to estimate 3D motion vectors (3DMVs) for arbitrarily shaped sub-blocks in texture-plus-depth videos. We then derive two feature channels from 3DMVs to be incorporated into a widely accepted bottom-up saliency model. Experiments on subjective quality of Region-of-Interest (ROI) based video coding show that our enriched saliency model with 3DMV channels is more accurate in estimating human visual attention. © 2013 IEEE.

Includes bibliographical references, Wavelength division multiplexing(WDM) techonology has emerged as a promising technology for backbone networks. The oprical layer based on WDM techonology provides optical routing services to the upper layers such as the packet switching layer and the time-division multiplexing(TDM) layer over the generalized malti-protocol label switching(GMPLS) paradigm. The set of all-optical communication channels(lightpaths)in the opticak layer defines the logical topology for the upper layer applications. Since the traffic demand of upper layer applications fluctuates from time to time, it is required to reconfigure the underlyaing logical topology in the potical layer accordingly. However, the reconfiguration for the logical topology is reluctantly disruptive to the network since some traffic has to be buffered or rerouted during the reconfiguration process. It therefore needs to have an efficient transition method from the crrent logical topology to the new one so as to minimize the disruption to the network. In this paper, we focus on the reconfiguration transition approaches for logical topologies in large-scale wavelength-routed optical networks. We propose several heuristics that move the current logical topology efficiently to the given target logical topology. Our algorithms limit the disrupution to the network as little as possible during the reconfiguration process. For this purpose, a lightpath is taken as the minimum unit for the reconfiguration. Our algorithms construct the new logical topology starting from a lightpath with the largest benefit contributed to the reconfiguration. The proposed algorithms are evaluated in comparison with existing algorithms in an NSFNET-like network model with 16 nodes and 25 links. The results show that the proposed algorithms yield mach better performance(less disruption to the network) than previous algorithms mostly with comparable computation time., http://www.tulips.tsukuba.ac.jp/limedio/dlam/B24/B2428029/1.pdf

Applying a Base Station (BS) sleep approach during low traffic periods has recently been advocated as a strategy for reducing energy consumption in cellular networks. The complete switching off of certain BS however, can lead to coverage holes and severe performance degradation in terms of off-cell user throughput, greater transmit power dissipation in both the up and downlinks, and more complex interference management. This paper presents a novel cellular network energy saving model in which certain BS rather being turned off are switched to Relay Station (RS) mode during low traffic periods. The switched RS and other shared RS deployed at the cross border of each cell are responsible for upholding the same quality of service (QoS) provision as when all BS are active. A centralised adaptive switching threshold algorithm is also introduced to undertake the switching decision, instead of using a fixed threshold. Simulation results confirm the new BS-RS Switching model using an adaptive threshold can reduce network energy consumption by more than half, as well as improving off-cell users’ throughput.

Applying a Base Station (BS) sleep approach during low traffic periods has recently been advocated as a strategy for reducing energy consumption in cellular networks. The complete switching off of certain BS however, can lead to coverage holes and severe performance degradation in terms of off-cell user throughput, greater transmit power dissipation in both the up and downlinks, and more complex interference management. This paper presents a novel cellular network energy saving model in which certain BS rather being turned off are switched to Relay Station (RS) mode during low traffic periods. The switched RS and other shared RS deployed at the cross border of each cell are responsible for upholding the same quality of service (QoS) provision as when all BS are active. A centralised adaptive switching threshold algorithm is also introduced to undertake the switching decision, instead of using a fixed threshold. Simulation results confirm the new BS-RS Switching model using an adaptive threshold can reduce network energy consumption by more than half, as well as improving off-cell users’ throughput.