Single sensor, such as 3D LiDAR camera, has relatively limited perception performance of providing comprehensive environmental information though the perception results from single sensor is accurate. Therefore, multiple sensors are perferred for surveillant tasks in either tactical or civilian scenarios. Cooperative perception is one of the solution to enable sensors to share sensory information with other sensors and infrastructure, extending coverage and enhancing the detection accuracy of surrounding objects for better safety and path planning. However, an efficient management of the large volume of sensory data across multiple sensors in the wirelss network is needed to maintain real-time sensing. In this work, we design a complete cooperative perception framework with varies networking, image processing and data fusion technologies integrated to enhance the situational awareness performance with multiple sensors. The framework uses information-centric networking and deep reinforc
The rapid growth of the demand for mobile sensing makes it difficult to process all sensing tasks on a single mobile device. Therefore, the concept of distributed computing was proposed, in which the computation tasks are distributed to all available devices in the same edge network to achieve faster data processing. However, in some critical scenarios, in which the network condition of the edge is poor, the bandwidth of the edge network is limited, and the connection is unstable, which can significantly affect the performance of distributed computing. To overcome such issues, we propose a resilient mobile distributed computing framework adopting an integrated solution combining Coded Computing (CC) and Named Data Networking (NDN). With NDN, the network traffic and information sharing within the edge network is optimized dynamically to adapt to the timevarying network condition. The CC technique can recover some of the missing computation results when an edge node is failed or disconne
KEYWORDS: Relays, Mobile devices, Energy efficiency, Unmanned aerial vehicles, Standards development, Optimization (mathematics), Mobile communications
Next-generation (5G & beyond) cellular networks promise much higher throughput and lower latency. However, mobile users experiencing poor channel quality not only suffer low data-rate connections with the base station but also reduce cell’s aggregate throughput and increase overall delay. In this paper, we consider a hybrid cellular and mobile ad hoc Device-to-Device (D2D) network that leverages the advantages of both wide-area cellular coverage and high-speed ad hoc D2D relaying to enhance network performance and scalability. Dedicated relay devices, such as Unmanned Aerial Vehicles (UAVs)/drones, can also be deployed to further improve network connectivity and thus throughput. The base station may send the packets destined for a mobile user with poor cellular channel quality to a proxy mobile device with better cellular channel quality. The proxy mobile device will relay the packets to the destination, thereby significanltly improving network throughput and delay. We formulate the data transmission problem and design an online reinforcement learning-based algorithm to achieve the best transmission performance.
Mobile edge computing (MEC) is an emerging and fast-growing distributed computing paradigm. It brings the computation and storage resources closer to mobile users while also processing data at the network edge to improve response time and save bandwidth. In tactical virtual training environments, latency is a key factor that affects training performance. Additionally, MEC provides both information service environment and cloud computing capabilities to enable real-time virtual training. Therefore, we designed a machine learning-based data caching and processing scheme for the virtual training networks. The design consists of three tiers, mobile devices, edge servers, and cloud servers, respectively. By pre-caching the critical content objects close to the mobile devices, our MEC network enables data transmission and processing at low latency. Utilizing machine learning techniques, our caching scheme can predict and select the content objects to be cached with optimal storage efficiency at network edge servers. Specifically, we decoupled the content caching problem into two subproblems, namely probability learning and content selection. For probability learning, the edge servers estimate the probability and frequency that each content object will be requested in the near future. The estimate is according to the content request pattern learned over time. For the content selection, the edge servers determine the content objects for caching to minimize the expected content delay with limited storage. To evaluate the performance of our proposed scheme, we developed a testbed with real mobile devices and servers. The experimental results validated the feasibility and significant performance gains of the proposed scheme.
Mobile edge computing is a new distributed computing paradigm which brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth in the dynamic mobile networking environment. Despite the improvements in network technology, data centers cannot always guarantee acceptable transfer rates and response times, which could be a critical requirement for many applications. The aim of mobile edge computing is to move the computation away from data centers towards the edge of the network, exploiting smart objects, mobile phones or network gateways to perform tasks and provide services on behalf of the cloud. In this paper, we design a task offloading scheme in the mobile edge network to handle the task distribution, offloading and management by applying deep reinforcement learning. Specifically, we formulate the task offloading problem as a multi-agent reinforcement learning problem. The decision-making process of each agent is modeled as a Markov decision process and deep Q-learning approach is applied to deal with the large scale of states and actions. To evaluate the performance of our proposed scheme, we develop a simulation environment for the mobile edge computing scenario. Our preliminary evaluation results with a simplified multi-armed bandit model indicate that our proposed solution can provide lower latency for the computational intensive tasks in mobile edge network, and outperforms than naïve task offloading method.
Conference Committee Involvement (1)
Sensors and Systems for Space Applications XVII
23 April 2024 | National Harbor, Maryland, United States
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