Reliable Federated Learning for Mobile Networks

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, e.g., mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In the federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, e.g., the data poisoning attack, or unintentionally, e.g., low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes

A Survey on Privacy-preserving Blockchain Systems (PPBS) and A Novel PPBS-based Framework for Smart Agriculture

Blockchain and smart contracts have seen significant application over the last decade, revolutionising many industries, including cryptocurrency, finance and banking, and supply chain management. In many cases, however, the transparency provided potentially comes at the cost of privacy. Blockchain does have potential uses to increase privacy-preservation. This paper outlines the current state of privacy preservation utilising Blockchain and Smart Contracts, as applied to a number of fields and problem domains. It provides a background of blockchain, outlines the challenges in blockchain as they relate to privacy, and then classifies into areas in which this paradigm can be applied to increase or protect privacy. These areas are cryptocurrency, data management and storage, e-voting, the Internet of Things, and smart agriculture. This work then proposes PPSAF, a new privacy-preserving framework designed explicitly for the issues that are present in smart agriculture. Finally, this work outlines future directions of research in areas combining future technologies, privacy-preservation and blockchain

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Blockchain in Enterprise Solutions: Assessing the Suitability of Blockchain for Corporate Sustainability Reporting

The aim of this master’s thesis was to assess the suitability of blockchain technology as enterprise solution for corporate sustainability reporting through evaluating the technological properties and enterprise use-cases of blockchain technology and challenges in corporate sustainability reporting. Furthermore, a special focus on literature review was to demystify the properties of blockchain technology that provide its most well-known qualities. In this thesis, blockchain technology was assessed through a review of prior literature, and the current state of corporate sustainability reporting was mainly evaluated through data collected in expert interviews. Blockchain technology has been found to have potential as an enterprise solution for a large number of corporate functions, such as supply chain management and accounting. Global corporations have publicly announced to be piloting with the technology in recent years. However, a level of technological abstraction prohibits the visibility of how the technology is concretely improving existing processes. Prior research exists on both levels, on high-level enterprise use-cases and technological deep dives, but rarely together. Corporate sustainability reporting is becoming more harmonised and standardized due to EU regulations such as the corporate sustainability reporting directive (CSRD) and EU Taxonomy. Corporations subject to the regulations are facing challenges in sourcing and managing the data required for compliant reporting. For blockchain implementations in corporate sustainability reporting, prior research is very limited and research including a more detailed technological evaluation of blockchain does not seem to exist to the knowledge of this thesis. This thesis fills a gap in the literature by providing in-depth insights into the suitability of blockchain technology for enterprise solutions, with a specific focus on sustainability reporting. Unlike previous studies that primarily address high-level concepts, this research offers a comprehensive explanation of blockchain basics, catering to readers who may not be familiar with the technology. Furthermore, given the novelty of sustainability reporting solutions, it is crucial to explore alternative options beyond traditional systems. The main findings of this thesis validated the presumed challenges corporations face in accustoming to the new sustainability regulation and highlighted the need for efficient IT solutions to manage the vast amounts of data points and insights required for compliant reporting. While blockchain-based solutions certainly have the potential to streamline and manage the reporting process, no indications of advantages over more traditional systems built on shared databases were found. Rather, this thesis highlighted the very specific advantages and use-cases blockchain technology currently has over traditional data management solutions, which are not currently relevant in the case of corporate sustainability reporting. As both enterprise blockchains and corporate sustainability reporting systems continue to evolve and mature, this research emphasizes the need for a fresh perspective and deeper examination of the topic. By shedding light on the challenges faced by corporations in adapting to new sustainability regulations and evaluating the potential of blockchain technology as an enterprise solution for sustainability reporting, this thesis offers valuable insights and calls for further exploration in this rapidly evolving field.Tämän pro gradu -tutkielma tavoitteena oli arvioida lohkoketjuteknologian soveltuvuutta teknologiaratkaisuksi yritysten kestävyysraportointiin arvioimalla lohkoketjuteknologian teknologisia ominaisuuksia sekä yrityskäyttötapauksia, että yritysten kestävyysraportoinnin haasteita. Lisäksi kirjallisuuskatsaukseen keskityttiin erityisesti lohkoketjuteknologian ominaisuuksien esittelemiseen, jotka ovat sen tunnetuimpien ominaisuuksien takana. Tässä tutkielmassa lohkoketjuteknologiaa arvioitiin kirjallisuuskatsauksen avulla, ja yritysten kestävän kehityksen raportoinnin nykytilaa arvioitiin pääasiassa asiantuntijahaastatteluissa kerätyn tiedon avulla. Lohkoketjuteknologialla on todettu olevan potentiaalia yritysratkaisuna monissa yritystoiminnoissa, kuten toimitusketjujen hallinnassa ja kirjanpidossa. Suuret globaalit yritykset ovat viime vuosina julkisesti ilmoittaneet pilotoivansa teknologiaa. Teknologinen abstraktiotaso estää kuitenkin usein suoraan näkemästä, miten lohkoketjuteknologia todellisuudessa parantaa nykyisiä prosesseja. Aiempaa tutkimusta on tehty molemmilla tasoilla, sekä korkean tason yrityskäyttötapauksista, että teknologian syvemmistä tasoista, mutta harvoin yhdessä. Yritysten kestävän kehityksen raportointi on yhdenmukaistumassa ja standardisoitumassa EU:n säädösten, kuten yritysten kestävän kehityksen raportointia koskevan direktiivin (CSRD) ja EU taksonomian ansiosta. Säädösten piiriin kuuluvilla yrityksillä on haasteita vaatimustenmukaiseen raportointiin tarvittavien tietojen hankinnassa ja hallinnassa. Lohkoketjutoteutuksia yritysten kestävän kehityksen raportointia varten on tutkittu hyvin vähän, eikä tämän tutkielman tietämyksen mukaan näytä olevan olemassa tutkimusta, joka myös sisältäisi lohkoketjujen yksityiskohtaisemman teknologisen arvioinnin. Tämä tutkielma täyttää kirjallisuudessa olevan aukon tarjoamalla syvällistä tietoa lohkoketjuteknologian soveltuvuudesta yritysratkaisuihin keskittyen erityisesti kestävän kehityksen raportointiin. Toisin kuin aiemmissa tutkimuksissa, joissa käsitellään pääasiassa korkean tason käsitteitä, tämä tutkimus tarjoaa kattavan selityksen lohkoketjun perusteista, mikä palvelee lukijoita, jotka eivät ehkä tunne teknologiaa. Lisäksi kestävyysraportointiratkaisujen uutuuden vuoksi on tärkeää tutkia vaihtoehtoja perinteisten järjestelmien lisäksi. Tämän tutkielman tärkeimmät tulokset vahvistivat haasteet, joita yritykset kohtaavat kestävyysraportointiprosesseissaan, ja korostivat tehokkaiden tietoteknisten ratkaisujen tarvetta, jotta voidaan tehokkaammin hallita raportointiin vaadittua määrää informaatiota. Vaikka lohkoketjupohjaisilla ratkaisuilla on varmasti potentiaalia virtaviivaistaa raportointiprosessia, ei havaittu mitään viitteitä siitä, että niillä olisi etuja verrattuna perinteisempiin jaettuihin tietokantoihin perustuviin järjestelmiin. Pikemminkin tässä tutkimuksessa nousi esiin viitteitä siitä, että lohkoketjuteknologialla ei ole tällä hetkellä perinteisiin tiedonhallintaratkaisuihin verrattuna merkittäviä etuja yritysjärjestelminä, etenkään kestävyysraportoinnin piirissä

Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems

This PhD dissertation concludes a three-year long research journey on the integration of Fog Computing and Blockchain technologies. The main aim of such integration is to address the challenges of each of these technologies, by integrating it with the other. Blockchain technology (BC) is a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism. It was initially proposed for decentralized cryptocurrency applications with practically proven high robustness. Fog Computing (FC) is a geographically distributed computing architecture, in which various heterogeneous devices at the edge of network are ubiquitously connected to collaboratively provide elastic computation services. FC provides enhanced services closer to end-users in terms of time, energy, and network load. The integration of FC with BC can result in more efficient services, in terms of latency and privacy, mostly required by Internet of Things systems

On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship