Greg Cirincione

Bio: Greg Cirincione is an academic researcher from United States Army Research Laboratory. The author has contributed to research in topics: Mobile computing & Wireless sensor network. The author has an hindex of 7, co-authored 17 publications receiving 462 citations.

Internet-based mobile ad hoc networking

Abstract: Internet-based mobile ad hoc networking is an emerging technology that supports self-organizing, mobile networking infrastructures. The technology enables an autonomous system of mobile nodes, which can operate in isolation or be connected to the greater Internet. Mobile ad hoc networks (Manets) are designed to operate in widely varying environments, from forward-deployed military Manets with hundreds of nodes per mobile domain to applications of low-power sensor networks and other embedded systems. Before Manet technology can be easily deployed, however, improvements must be made in such areas as high-capacity wireless technologies, address and location management, interoperability and security. The authors give an overview of Manet technology and current IETF efforts toward producing routing and interface definition standards that support it within the IP suite.

Quality-of-information aware networking for tactical military networks

Abstract: In tactical military networks, decisions must often be made quickly based on information at hand. It is a challenge to provide decision makers with a notion of the quality of the information they have, or to provide a method by which decision makers can specify a required quality of information. It is a further challenge to honor requests for a required quality of information when selecting information sources, transporting information through a highly-dynamic network, and perhaps performing processing on that information. In this paper we motivate the need for a general, but formal, definition of quality-of-information so that this metric may be specified and potentially optimized by algorithms that operate a tactical network. Furthermore, we define a new notion, the operational information content capacity, to capture the amount and quality of information that a network can deliver.

Intelligence, Surveillance, and Reconnaissance fusion for coalition operations

Abstract: Coalition operations rely on the fusion, sharing and dissemination of information for a network of disparate intelligence, surveillance and reconnaissance (ISR) assets such as sensors, sensing platforms, human intelligence, data fusion and networking elements. One prominent aspect of this research is the design of policy-aware fusion, that is, fusion that takes policy related to security, resource control, command-and-control, etc. into account. Processes are described for development of fusion algorithms and policy protocols that will enable rapid assembly/dynamic control of ISR assets and associated policy agreements that govern the sharing and dissemination of information to support multiple concurrent coalition missions.

Security of current mobile IP solutions

Abstract: Due to the operation of an IP's addressing and routing algorithms, mobile nodes (such as notebooks, portable workstations and palmtop computers) cannot currently participate while roaming without being reconfigured in tactical wired and wireless networks, strategic networks or the Internet. A node's IP address encodes the network access point to which the node is connected. This prevents IP packets from reaching the node if it moves to a new location and tries to connect to its home network from within a different network. Changing the IP address of a node when it moves is not possible while keeping existing transport level connections open. This change requires the termination of all current network activity and the user making a number of configuration changes followed by rebooting of the node. There are a number of proposed solutions for supporting mobile nodes (leading approaches are: mobility support in IPv4, route optimized mobile IP for IPv4, and mobility support in IPv6), that are compatible with the TCP/IP protocol suite. This paper addresses the security needs for IP mobile hosts in the tactical battlefield, the security capabilities and deficiencies of these solutions, and proposed solutions for identified security deficiencies.

Approaches to address the data skew problem in federated learning

Abstract: A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments.

System and method for monitoring and controlling remote devices

Abstract: The present invention is generally directed to a system for monitoring a variety of environmental and/or other conditions within a defined remotely located region. In one aspect, a system is configured to monitor utility meters (613) in a defined area. The system is implemented by using a plurality of wireless transmitters (614), each integrated into a sensor (612) adapted to monitor a particular data input. The system also includes a plurality of transceivers (221) that are dispersed throughout the region at defined locations. The system uses a local gateway (210) to translate and transfer information from the transmitters to a dedicated computer (260) on a network (230). The dedicated computer collects, compiles and stores the data for retrieval upon client demand across the network. The computer further includes means for evaluating the received information and identifying an appropriate control signal to be applied at a designated actuator.

Mobile ad hoc networking: imperatives and challenges

Abstract: Mobile ad hoc networks (MANETs) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary, ‘‘ad-hoc’’ network topologies, allowing people and devices to seamlessly internetwork in areas with no pre-existing communication infrastructure, e.g., disaster recovery environments. Ad hoc networking concept is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad hoc paradigm. Recently, the introduction of new technologies such as the Bluetooth, IEEE 802.11 and Hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent evolutions have been generating a renewed and growing interest in the research and development of MANET. This paper attempts to provide a comprehensive overview of this dynamic field. It first explains the important role that mobile ad hoc networks play in the evolution of future wireless technologies. Then, it reviews the latest research activities in these areas, including a summary of MANETs characteristics, capabilities, applications, and design constraints. The paper concludes by presenting a set of challenges and problems requiring further research in the future. � 2003 Elsevier B.V. All rights reserved.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL

Handbook of wireless networks and mobile computing

Abstract: If you want to get Handbook of Internet Computing pdf eBook copy write by good Handbook of Wireless Networks and Mobile Computing Google Books. Mobile Computing General. Handbook of Algorithms for Wireless Networking and Mobile Computing by Azzedine Boukerche (Editor). Call Number: TK 5103.2. CITS4419 Mobile and Wireless Computing software projects related to wireless networks, (2) write technical reports and documentation for complex computer.