TVWS policies to enable efficient spectrum sharing

The transition from analogue to the Digital Terrestrial Television (DTV) in Europe is planned to be completed by the end of the year 2012. The DTV spectrum allocation is such that there are a number of TV channels which cannot be used for additional high power broadcast transmitters due to mutual interference and hence are left unused within a given geographical location, i.e. the TV channels are geographically interleaved. The use of geographically interleaved spectrum provides for the so-called TV white spaces (TVWS) an opportunity for deploying new wireless services. The main objective of this paper is to present the spectrum policies that are suitable for TVWS at European level, identified within the COGEU project. The COGEU project aims the efficient exploitation of the geographical interleaved spectrum (TVWS). COGEU is an ICT collaborative project supported by the European Commission within the 7th Framework Programme. Nine partners from seven EU countries representing academia, research institutes and industry are involved in the project. The COGEU project is a composite of technical, business, and regulatory/policy domains, with the objective of taking advantage of the TV digital switchover by developing cognitive radio systems that leverage the favorable propagation characteristics of the UHF broadcast spectrum through the introduction and promotion of real-time secondary spectrum trading and the creation of new spectrum commons regimes. COGEU will also define new methodologies for compliance testing and certification of TVWS equipment to ensure non-interference coexistence with the DVB-T European standard. The innovation brought by COGEU is the combination of cognitive access to TV white spaces with secondary spectrum trading mechanisms.telecommunications,spectrum management,secondary spectrum market,regulation,TV white spaces,cognitive radio

Privacy In The Smart Grid: An Information Flow Analysis

Project Final Report prepared for CIEE and California Energy Commissio

Federated Learning-Empowered AI-Generated Content in Wireless Networks

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations, e.g., the centralized training in the pre-training, fine-tuning and inference processes, especially their implementations in wireless networks with privacy preservation. Federated learning (FL), as a collaborative learning framework where the model training is distributed to cooperative data owners without the need for data sharing, can be leveraged to simultaneously improve learning efficiency and achieve privacy protection for AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content. Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results show that our scheme achieves advantages in effectively reducing the communication cost and training latency and privacy protection. Finally, we highlight several major research directions and open issues for the convergence of FL and AIGC.Comment: 8 pages, 3 figures and 2 tables. Submitted to IEEE Networ

Electronic security - risk mitigation in financial transactions : public policy issues

This paper builds on a previous series of papers (see Claessens, Glaessner, and Klingebiel, 2001, 2002) that identified electronic security as a key component to the delivery of electronic finance benefits. This paper and its technical annexes (available separately at http://www1.worldbank.org/finance/) identify and discuss seven key pillars necessary to fostering a secure electronic environment. Hence, it is intended for those formulating broad policies in the area of electronic security and those working with financial services providers (for example, executives and management). The detailed annexes of this paper are especially relevant for chief information and security officers responsible for establishing layered security. First, this paper provides definitions of electronic finance and electronic security and explains why these issues deserve attention. Next, it presents a picture of the burgeoning global electronic security industry. Then it develops a risk-management framework for understanding the risks and tradeoffs inherent in the electronic security infrastructure. It also provides examples of tradeoffs that may arise with respect to technological innovation, privacy, quality of service, and security in designing an electronic security policy framework. Finally, it outlines issues in seven interrelated areas that often need attention in building an adequate electronic security infrastructure. These are: 1) The legal framework and enforcement. 2) Electronic security of payment systems. 3) Supervision and prevention challenges. 4) The role of private insurance as an essential monitoring mechanism. 5) Certification, standards, and the role of the public and private sectors. 6) Improving the accuracy of information on electronic security incidents and creating better arrangements for sharing this information. 7) Improving overall education on these issues as a key to enhancing prevention.Knowledge Economy,Labor Policies,International Terrorism&Counterterrorism,Payment Systems&Infrastructure,Banks&Banking Reform,Education for the Knowledge Economy,Knowledge Economy,Banks&Banking Reform,International Terrorism&Counterterrorism,Governance Indicators

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible