Performance and efficiency optimization of multi-layer IoT edge architecture

Abstract. Internet of Things (IoT) has become a backbone technology that connects together various devices with diverse capabilities. It is a technology, which enables ubiquitously available digital services for end-users. IoT applications for mission-critical scenarios need strict performance indicators such as of latency, scalability, security and privacy. To fulfil these requirements, IoT also requires support from relevant enabling technologies, such as cloud, edge, virtualization and fifth generation mobile communication (5G) technologies. For Latency-critical applications and services, long routes between the traditional cloud server and end-devices (sensors /actuators) is not a feasible approach for computing at these data centres, although these traditional clouds provide very high computational and storage for current IoT system. MEC model can be used to overcome this challenge, which brings the CC computational capacity within or next on the access network base stations. However, the capacity to perform the most critical processes at the local network layer is often necessary to cope with the access network issues. Therefore, this thesis compares the two existing IoT models such as traditional cloud-IoT model, a MEC-based edge-cloud-IoT model, with proposed local edge-cloud-IoT model with respect to their performance and efficiency, using iFogSim simulator. The results consolidate our research team’s previous findings that utilizing the three-tier edge-IoT architecture, capable of optimally utilizing the computational capacity of each of the three tiers, is an effective measure to reduce energy consumption, improve end-to-end latency and minimize operational costs in latency-critical It applications

Decision model for U.S.-Mexico border security measures

The Department of Homeland Security (DHS) has invested billions of dollars to prevent illegal drugs, immigration, weapons, and currency from transiting across the U.S.–Mexico border. DHS has not created a sufficient standardized method to measure whether an investment in a security measure is cost-effective when combining assets. To take it one step further, DHS has not created a model that combines cost-effectiveness of a security asset while simultaneously determining how it will contribute to achieving operational control of the border. This study provides an in-depth look into the current risk-based model DHS uses, the administrative and physical infrastructure of U.S.-Mexico border security, and a critical view of DHS’ annual budget. A decision model is presented that will give policymakers a process to choose a combination of border security investments that will achieve operational control of the border while remaining within budgeting constraints. A lot of work needs to be done for DHS to determine the correct security assets to be placed at the U.S.-Mexico Border to maintain operational control and cost-effectiveness. This study does not determine which security assets need to be put into place, but it provides a decision process that will be an asset for policymakers to save federal time and money assigned to border security investments.http://archive.org/details/decisionmodelfor1094556150Lieutenant, United States NavyApproved for public release; distribution is unlimited

Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks

The vision of the upcoming 6G technologies, characterized by ultra-dense network, low latency, and fast data rate is to support Pervasive AI (PAI) using zero-touch solutions enabling self-X (e.g., self-configuration, self-monitoring, and self-healing) services. However, the research on 6G is still in its infancy, and only the first steps have been taken to conceptualize its design, investigate its implementation, and plan for use cases. Toward this end, academia and industry communities have gradually shifted from theoretical studies of AI distribution to real-world deployment and standardization. Still, designing an end-to-end framework that systematizes the AI distribution by allowing easier access to the service using a third-party application assisted by a zero-touch service provisioning has not been well explored. In this context, we introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart system. This platform aims to standardize the pervasive AI at all levels of the architecture and unify the interfaces in order to facilitate the service deployment across application and infrastructure domains, relieve the users worries about cost, security, and resource allocation, and at the same time, respect the 6G stringent performance requirements. As a proof of concept, we present a Federated Learning-as-a-service use case where we evaluate the ability of our proposed system to self-optimize and self-adapt to the dynamics of 6G networks in addition to minimizing the users' perceived costs.Comment: IEEE Communications Magazin

The Ironies of Automation Law: Tying Policy Knots with Fair Automation Practices Principles

Rapid developments in sensors, computing, and robotics, including power, kinetics, control, telecommunication, and artificial intelligence have presented opportunities to further integrate sophisticated automation across society. With these opportunities come questions about the ability of current laws and policies to protect important social values new technologies may threaten. As sophisticated automation moves beyond the cages of factories and cockpits, the need for a legal approach suitable to guide an increasingly automated future becomes more pressing. This Article analyzes examples of legal approaches to automation thus far by legislative, administrative, judicial, state, and international bodies. The case studies reveal an interesting irony: while automation regulation is intended to protect and promote human values, by focusing on the capabilities of the automation, this approach results in less protection of human values. The irony is similar to those pointed out by Lisanne Bainbridge in 1983, when she described how designing automation to improve the life of the operator using an automation-centered approach actually made the operator\u27s life worse and more difficult. The ironies that result from automation-centered legal approaches are a product of the neglect of the sociotechnical nature of automation: the relationships between man and machine are situated and interdependent, humans will always be in the loop, and reactive policies ignore the need for general guidance for ethical and accountable automation design and implementation. Like system engineers three decades ago, policymakers must adjust the focus of Meg Leta (Ambrose) Jones, J.D., Ph.D. is an Assistant Professor of Communication, legal treatment of automation to recognize the interdependence of man and machine to avoid the ironies of automation law and meet the goals of ethical integration. The Article proposes that the existing models utilized for safe and actual implementation for automated system design be supplemented with principles to guide ethical and sociotechnical legal approaches to automation

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network