900 research outputs found
Efficient Data Collection in Multimedia Vehicular Sensing Platforms
Vehicles provide an ideal platform for urban sensing applications, as they
can be equipped with all kinds of sensing devices that can continuously monitor
the environment around the travelling vehicle. In this work we are particularly
concerned with the use of vehicles as building blocks of a multimedia mobile
sensor system able to capture camera snapshots of the streets to support
traffic monitoring and urban surveillance tasks. However, cameras are high
data-rate sensors while wireless infrastructures used for vehicular
communications may face performance constraints. Thus, data redundancy
mitigation is of paramount importance in such systems. To address this issue in
this paper we exploit sub-modular optimisation techniques to design efficient
and robust data collection schemes for multimedia vehicular sensor networks. We
also explore an alternative approach for data collection that operates on
longer time scales and relies only on localised decisions rather than
centralised computations. We use network simulations with realistic vehicular
mobility patterns to verify the performance gains of our proposed schemes
compared to a baseline solution that ignores data redundancy. Simulation
results show that our data collection techniques can ensure a more accurate
coverage of the road network while significantly reducing the amount of
transferred data
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Coded Distributed Tracking
We consider the problem of tracking the state of a process that evolves over
time in a distributed setting, with multiple observers each observing parts of
the state, which is a fundamental information processing problem with a wide
range of applications. We propose a cloud-assisted scheme where the tracking is
performed over the cloud. In particular, to provide timely and accurate
updates, and alleviate the straggler problem of cloud computing, we propose a
coded distributed computing approach where coded observations are distributed
over multiple workers. The proposed scheme is based on a coded version of the
Kalman filter that operates on data encoded with an erasure correcting code,
such that the state can be estimated from partial updates computed by a subset
of the workers. We apply the proposed scheme to the problem of tracking
multiple vehicles. We show that replication achieves significantly higher
accuracy than the corresponding uncoded scheme. The use of maximum distance
separable (MDS) codes further improves accuracy for larger update intervals. In
both cases, the proposed scheme approaches the accuracy of an ideal centralized
scheme when the update interval is large enough. Finally, we observe a
trade-off between age-of-information and estimation accuracy for MDS codes.Comment: Accepted for publication at IEEE GLOBECOM 201
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Since the traffic conditions change over time, machine learning models that
predict traffic flows must be updated continuously and efficiently in smart
public transportation. Federated learning (FL) is a distributed machine
learning scheme that allows buses to receive model updates without waiting for
model training on the cloud. However, FL is vulnerable to poisoning or DDoS
attacks since buses travel in public. Some work introduces blockchain to
improve reliability, but the additional latency from the consensus process
reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme
that reduces the latency of aggregation to improve efficiency, but the learning
performance is unstable due to unreasonably weighted local models. To address
the above challenges, this paper offers a blockchain-based asynchronous
federated learning scheme with a dynamic scaling factor (DBAFL). Specifically,
the novel committee-based consensus algorithm for blockchain improves
reliability at the lowest possible cost of time. Meanwhile, the devised dynamic
scaling factor allows AFL to assign reasonable weights to stale local models.
Extensive experiments conducted on heterogeneous devices validate outperformed
learning performance, efficiency, and reliability of DBAFL
Towards Dynamic Vehicular Clouds
Motivated by the success of the conventional cloud computing, Vehicular Clouds were introduced as a group of vehicles whose corporate computing, sensing, communication, and physical resources can be coordinated and dynamically allocated to authorized users. One of the attributes that set Vehicular Clouds apart from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new computing resources become available while others depart, creating a volatile environment where the task of reasoning about fundamental performance metrics becomes very challenging. The goal of this thesis is to design an architecture and model for a dynamic Vehicular Cloud built on top of moving vehicles on highways. We present our envisioned architecture for dynamic Vehicular Cloud, consisting of vehicles moving on the highways and multiple communication stations installed along the highway, and investigate the feasibility of such systems. The dynamic Vehicular Cloud is based on two-way communications between vehicles and the stations. We provide a communication protocol for vehicle-to-infrastructure communications enabling a dynamic Vehicular Cloud. We explain the structure of the proposed protocol in detail and then provide analytical predictions and simulation results to investigate the accuracy of our design and predictions. Just as in conventional clouds, job completion time ranks high among the fundamental quantitative performance figures of merit. In general, predicting job completion time requires full knowledge of the probability distributions of the intervening random variables. More often than not, however, the data center manager does not know these distribution functions. Instead, using accumulated empirical data, she may be able to estimate the first moments of these random variables. Yet, getting a handle on the expected job completion time is a very important problem that must be addressed. With this in mind, another contribution of this thesis is to offer easy-to-compute approximations of job completion time in a dynamic Vehicular Cloud involving vehicles on a highway. We assume estimates of the first moment of the time it takes the job to execute without any overhead attributable to the working of the Vehicular Cloud. A comprehensive set of simulations have shown that our approximations are very accurate. As mentioned, a major difference between the conventional cloud and the Vehicular Cloud is the availability of the computational nodes. The vehicles, which are the Vehicular Cloud\u27s computational resources, arrive and depart at random times, and as a result, this characteristic may cause failure in executing jobs and interruptions in the ongoing services. To handle these interruptions, once a vehicle is ready to leave the Vehicular Cloud, if the vehicle is running a job, the job and all intermediate data stored by the departing vehicle must be migrated to an available vehicle in the Vehicular Cloud
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