Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. distributed edge computing ...
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ISBN:
(纸本)9781510674431;9781510674424
Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. distributed edge computing has emerged as an essential paradigm to facilitate cooperative AR. We designed and implemented a distributed system to enable fast, reliable, and scalable cooperative AR. In this paper, we present a novel approach and architecture that integrates advanced sensing, communications, and processing techniques to create such a cooperative AR system, and demonstrate its capability with HoloLens and edge servers connected over a wireless network. Our research addresses the challenges of implementing a distributed cooperative AR system capable of capturing data from a multitude of sensors on HoloLens, performing fusion and accurate object recognition, and seamlessly projecting the reconstructed 3D model into the wearer's field of view. The paper delves into the intricate architecture of the proposed cooperative AR system, detailing its distributed sensing and edgecomputing components, and the Apache Storm-integrated platform. The implementation encompasses data collection, aggregation, analysis, object recognition, and rendering of 3D models on the HoloLens, all in real-time. The proposed system enhances the AR experience while showcasing the vast potential of distributed edge computing. Our findings illustrate the feasibility and advantages of merging distributed cooperative sensing and edgecomputing to offer dynamic, immersive AR experiences, paving the way for new applications.
By combining edgecomputing and parallel computing, distributed edge computing has emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate computation at the edge, i.e., the inference t...
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By combining edgecomputing and parallel computing, distributed edge computing has emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate computation at the edge, i.e., the inference tasks for DNN-driven applications, the parallelism of both computation and communication needs to be considered for distributed edge computing, and thus, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is proposed. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. First, the MLCCS problem under a general model is formulated and proved to be NP-hard. Second, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of $1+\delta$1+delta, where $\delta$delta is related to devices' communication rates. An interesting property under such a computing model is identified and proved, i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the workload cannot be split arbitrarily, an approximation algorithm with a ratio of at most $2\cdot (1+\delta)$2(1+delta) is proposed. Additionally, for handling the dynamically changing network scenarios, several algorithms are also proposed accordingly. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% for inference tasks at the edge.
This article proposes an information security vehicle-to-grid (V2G) scheduling solution, which combines federated deep learning with distributed edge computing for V2G operation. In this framework, each charging point...
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This article proposes an information security vehicle-to-grid (V2G) scheduling solution, which combines federated deep learning with distributed edge computing for V2G operation. In this framework, each charging point is equipped with an intelligent computing module to conduct distributededge scheduling for the connected electric vehicle (EV), so that not only the computation of inference process is efficient, but also the privacy-preserving of EV users is guaranteed. Besides, the desensitized V2G data of charging points are used to train the deep neural network model in each charging station. Therefore, the accurate future data acquisition problem and the uncertainty handling challenges under traditional optimization methods is avoided. At the same time, the spatial-based and time-based clustering methods are applied to improve the accuracy of prediction. Finally, through federated learning, each charging station uploads the local model to the cloud server, and a stochastic client selection pattern is designed to improve the scalability of model aggregation in the cloud server. In this way, the digital assets of each charging station are protected, and computing and communication costs are reduced. Simulation results on real datasets show that the proposed framework has superior performance in terms of training accuracy, communication burden, and computing performance, while maintaining the privacy of EV users and the digital assets of charging stations.
The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task...
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The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task is partitioned into multiple small subtasks and executed in parallel on multiple edge nodes (ENs) to reduce computation delay. In early works of this area, the computation results of the subtasks are often transmitted back in a non-cooperative manner, which may lead to suboptimal downlink communication delay. Replicated edgecomputing can alleviate this issue by replicating the computing task over multiple ENs to enable cooperative transmission in the downlink. However, this will inevitably entail multi-fold increase of computation costs. To bridge the gap between the conventional distributed edge computing and the replicated edgecomputing, a novel partial replication based distributed edge computing scheme is proposed in this work. In particular, by judiciously determining the portion of task to be replicated at the ENs, the proposed scheme can harvest cooperative transmission gains while avoiding excessive computational replication costs. Accordingly, a partial replication based delay minimization problem is formulated. By leveraging the generic alternating optimization framework, this problem can be divided into two subproblems of power allocation and task partitioning. Through analysis, a semi-closed form solution is derived for the former non-convex subproblem, while the latter subproblem turns out to be linear. Simulation results are presented to corroborate the effectiveness of the proposed scheme.
The rapid growth of intelligent connected vehicles fosters the development of Internet-of-Vehicle (IoV) applications. Due to the complex traffic environments, integrated sensing, communication and computation (ISCC) t...
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The rapid growth of intelligent connected vehicles fosters the development of Internet-of-Vehicle (IoV) applications. Due to the complex traffic environments, integrated sensing, communication and computation (ISCC) technology would be seen as an efficient paradigm to support a plethora of IoV applications, which causes such a surge of computation-intensive tasks that the task computation with a single server node cannot keep energy-efficient under the strict delay constraints. Inspired by the idea of distributedcomputing, a scheme of distributed edge computing-based task offloading for ISCC is designed and various assisted edge nodes (AENs) are introduced in a stochastic geometry approach to alleviate the dilemma of energy consumption. After analyzing the proposed joint optimization problem for energy minimization, a double-iteration joint optimization algorithm (DIJOA) is developed to derive the solution. The results of the performance evaluation not only verify the plausibility of the proposed model, but also indicate that the introduction of AENs can significantly reduce system energy consumption and the proposed algorithm outperforms other schemes by over 2.5% in energy consumption, which corroborates the superiority of the proposed scheme through numerical simulations.
Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task pr...
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Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edgecomputing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edgecomputing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.
With the proliferation of delay-sensitive and computation-intensive mobile applications, recent years have witnessed a transition towards more advanced distributed edge computing. However, the diverse computation resu...
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The recently advocated reconfigurable holographic surface (RHS) is anticipated to bring substantial improvement in communication throughput, especially for the multi-node scenarios. From this viewpoint, RHS is a natur...
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To cope with limited capabilities of mobile devices, task offloading in distributed edge computing (DEC) environments is perceived as a promising solution. However, the mobility of devices makes the task offloading a ...
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ISBN:
(纸本)9781728191010
To cope with limited capabilities of mobile devices, task offloading in distributed edge computing (DEC) environments is perceived as a promising solution. However, the mobility of devices makes the task offloading a more challenging issue. In this paper, we investigate mobility-awareness for optimal task offloading in DEC environments. To this end, we formulate an optimization problem to minimize the response time of offloaded tasks. Simulation results demonstrate that the mobilitya ware task offloading scheme can reduce the response time by 14% similar to 21% compared with the conventional task offloading schemes without any mobility-awareness.
To overcome the limitation of standalone edgecomputing in terms of computing power and resource, a concept of distributed edge computing has been introduced, where application tasks are distributed to multiple edge c...
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ISBN:
(纸本)9781728129273
To overcome the limitation of standalone edgecomputing in terms of computing power and resource, a concept of distributed edge computing has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge computing, we formulate an optimization problem of task allocation minimizing the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation algorithm (DATA). Evaluation results demonstrate that DATA can reduce the completion time up to by 18% compared to conventional dependency-unaware task allocation schemes.
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