A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Review of SDN-based load-balancing methods, issues, challenges, and roadmap

The development of the Internet and smart end systems, such as smartphones and portable laptops, along with the emergence of cloud computing, social networks, and the Internet of Things, has brought about new network requirements. To meet these requirements, a new architecture called software-defined network (SDN) has been introduced. However, traffic distribution in SDN has raised challenges, especially in terms of uneven load distribution impacting network performance. To address this issue, several SDN load balancing (LB) techniques have been developed to improve efficiency. This article provides an overview of SDN and its effect on load balancing, highlighting key elements and discussing various load-balancing schemes based on existing solutions and research challenges. Additionally, the article outlines performance metrics used to evaluate these algorithms and suggests possible future research directions

Rough Consensus and Running Code: Integrating Engineering Principles into Internet Policy Debates

Symposium: Rough Consensus and Running Code: Integrating Engineering Principles into Internet Policy Debates, held at the University of Pennsylvania\u27s Center for Technology Innovation and Competition on May 6-7, 2010

Asynchronously Replicated Shared Workspaces for a Multi-Media Annotation Service over Internet

This paper describes a world wide collaboration system through multimedia Post-its (user generated annotations). DIANE is a service to create multimedia annotations to every application output on the computer, as well as to existing multimedia annotations. Users collaborate by registering multimedia documents and user generated annotation in shared workspaces. However, DIANE only allows effective participation in a shared workspace over a high performance network (ATM, fast Ethernet) since it deals with large multimedia object. When only slow or unreliable connections are available between a DIANE terminal and server, useful work becomes impossible. To overcome these restrictions we need to replicate DIANE servers so that users do not suffer degradation in the quality of service. We use the asynchronous replication service ODIN to replicate the shared workspaces to every interested site in a transparent way to users. ODIN provides a cost-effective object replication by building a dynamic virtual network over Internet. The topology of this virtual network optimizes the use of network resources while it satisfies the changing requirements of the users

Testing a Cloud Provider Network for Hybrid P2P and Cloud Streaming Architectures

The number of online real-time streaming services deployed over network topologies like P2P or centralized ones has remarkably increased in the recent years. This has revealed the lack of networks that are well prepared to respond to this kind of traffic. A hybrid distribution network can be an efficient solution for real-time streaming services. This paper contains the experimental results of streaming distribution in a hybrid architecture that consist of mixed connections among P2P and Cloud nodes that can interoperate together. We have chosen to represent the P2P nodes as Planet Lab machines over the world and the cloud nodes using a Cloud provider's network. First we present an experimental validation of the Cloud infrastructure's ability to distribute streaming sessions with respect to some key streaming QoS parameters: jitter, throughput and packet losses. Next we show the results obtained from different test scenarios, when a hybrid distribution network is used. The scenarios measure the improvement of the multimedia QoS parameters, when nodes in the streaming distribution network (located in different continents) are gradually moved into the Cloud provider infrastructure. The overall conclusion is that the QoS of a streaming service can be efficiently improved, unlike in traditional P2P systems and CDN, by deploying a hybrid streaming architecture. This enhancement can be obtained by strategic placing of certain distribution network nodes into the Cloud provider infrastructure, taking advantage of the reduced packet loss and low latency that exists among its datacenters

The Changing Patterns of Internet Usage

Symposium: Essays from Time Warner Cable\u27s Research Program on Digital Communications

Revolutionizing Healthcare Image Analysis in Pandemic-Based Fog-Cloud Computing Architectures

The emergence of pandemics has significantly emphasized the need for effective solutions in healthcare data analysis. One particular challenge in this domain is the manual examination of medical images, such as X-rays and CT scans. This process is time-consuming and involves the logistical complexities of transferring these images to centralized cloud computing servers. Additionally, the speed and accuracy of image analysis are vital for efficient healthcare image management. This research paper introduces an innovative healthcare architecture that tackles the challenges of analysis efficiency and accuracy by harnessing the capabilities of Artificial Intelligence (AI). Specifically, the proposed architecture utilizes fog computing and presents a modified Convolutional Neural Network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can enhance the efficiency and accuracy of medical image analysis, leading to improved patient care and outcomes

Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation

A social network analysis of actors involved in wild pig (\u3ci\u3eSus scrofa\u3c/i\u3e) management in Missouri

Wild pigs (Sus scrofa) cause significant damage to agriculture and native ecosystems and can transmit diseases to animals and people. Management responses designed to reduce population numbers are needed to mitigate these threats. Identifying networks of key actors, including the ways in which they interact, is valuable for purposes of better understanding opportunities or constraints that generate or impede effective management responses. The goal of our study was to understand the network of organizations, and the personnel working within them, that were active in wild pig management, research, or policy initiatives in Missouri during 2018–2020 by 1) identifying individuals and organizations involved in the network, 2) investigating the attributes of relevant personnel, 3) determining the structural patterns of the network, and 4) examining how the network structure could be optimized to improve communication and collaboration efforts. Results from a social network analysis identified 150 personnel affiliated with 26 organizations actively working on wild pig issues in Missouri. The network was largely homogenous based on respondents\u27 attributes, had low density, and was relatively fragmented, small, decentralized with few ties per node, and separated with few brokers. We emphasize the importance of understanding the strengths and weaknesses of a network\u27s structure in facilitating effective collective action to manage wild pigs

Getting on the E List: E-Mail Use in a Community of Service Provider

This case examines how a community of organizations providing service to people experiencing homelessness made use of an electronic mail list. Current economic conditions have encouraged organizations in various sectors—including nonprofits—that might normally compete for scarce resources to collaborate with one another to increase their chances of survival. One set of tools likely to be of value in such relationships includes various online discussion technologies. An examination of this community’s email list use over a three-year period suggests a somewhat complex picture regarding technology use. More specifically, some issues both constrain and enable use. Additionally, seemingly basic and minimal uses of the list provided not only the greatest functionality for the users, but also led to several unanticipated consequences for those involved