An Agent-Based Model for Secondary Use of Radio Spectrum

Wireless communications rely on access to radio spectrum. With a continuing proliferation of wireless applications and services, the spectrum resource becomes scarce. The measurement studies of spectrum usage, however, reveal that spectrum is being used sporadically in many geographical areas and times. In an attempt to promote efficiency of spectrum usage, the Federal Communications Commission has supported the use of market mechanism to allocate and assign radio spectrum. We focus on the secondary use of spectrum defined as a temporary access of existing licensed spectrum by a user who does not own a spectrum license. The secondary use of spectrum raises numerous technical, institutional, economic, and strategic issues that merit investigation. Central to the issues are the effects of transaction costs associated with the use of market mechanism and the uncertainties due to potential interference.The research objective is to identify the pre-conditions as to when and why the secondary use would emerge and in what form. We use transaction cost economics as the theoretical framework in this study. We propose a novel use of agent-based computational economics to model the development of the secondary use of spectrum. The agent-based model allows an integration of economic and technical considerations to the study of pre-conditions to the secondary use concept. The agent-based approach aims to observe the aggregate outcomes as a result of interactions among agents and understand the process that leads to the secondary use, which can then be used to create policy instruments in order to obtain the favorable outcomes of the spectrum management

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Formality and informality in the summative assessment of motor vehicle apprentices: a case study

This article explores the interaction of formal and informal attributes of competence‐based assessment. Specifically, it presents evidence from a small qualitative case study of summative assessment practices for competence‐based qualifications within apprenticeships in the motor industry in England. The data are analysed through applying an adaptation of a framework for exploring the interplay of formality and informality in learning. This analysis reveals informal mentoring as a significant element which influences not only the process of assessment, but also its outcomes. We offer different possible interpretations of the data and their analysis, and conclude that, whichever interpretation is adopted, there appears to be a need for greater capacity‐building for assessors at a local level. This could acknowledge a more holistic role for assessors; recognise the importance of assessors’ informal practices in the formal retention and achievement of apprentices; and enhance awareness of inequalities that may be reinforced by both informal and formal attributes of assessment practices

Blockchain-enabled resource management and sharing for 6G communications

The sixth-generation (6G) network must provide performance superior to previous generations to meet the requirements of emerging services and applications, such as multi-gigabit transmission rate, even higher reliability, and sub 1 ms latency and ubiquitous connection for the Internet of Everything (IoE). However, with the scarcity of spectrum resources, efficient resource management and sharing are crucial to achieving all these ambitious requirements. One possible technology to achieve all this is the blockchain. Because of its inherent properties, the blockchain has recently gained an important position, which is of great significance to 6G network and other networks. In particular, the integration of the blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently. Hence, in this paper, we discuss the potentials of the blockchain for resource management and sharing in 6G using multiple application scenarios, namely, Internet of things, device-to-device communications, network slicing, and inter-domain blockchain ecosystems

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Energy Management of Prosumer Communities

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity markets. Significant untapped potential remains in the exploitation and coordination of small and medium-sized distributed energy resources. However, such resources usually have a primary purpose, which imposes constraints on the exploitation of the resource; for example, the primary purpose of an electric vehicle battery is for driving, so the battery could be used as temporary storage for excess photovoltaic energy only if the vehicle is available for driving when the owner expects it to be. The aggregation of several distributed energy resources is a solution for coping with the unavailability of one resource. Solutions are needed for managing the electricity production and consumption characteristics of diverse distributed energy resources in order to obtain prosumers with more generic capabilities and services for electricity production, storage, and consumption. This collection of articles studies such prosumers and the emergence of prosumer communities. Demand response-capable smart loads, battery storages and photovoltaic generation resources are forecasted and optimized to ensure energy-efficient and, in some cases, profitable operation of the resources

Agent-Based Computational Economics: A Constructive Approach to Economic Theory

This chapter explores the potential advantages and disadvantages of Agent-based Computational Economics (ACE) for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, behaviors, and institutional arrangements. Extensive annotated pointers to ACE surveys, research, course materials, and software can be accessed here: http://www.econ.iastate.edu/tesfatsi/ace.htmagent-based computational economics; Learning; network formation; decentralized market economy

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing