Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.Comment: This paper appears in IEEE Internet of Things Journal (IoT-J

Eliciting Truthful Data from Crowdsourced Wireless Monitoring Modules in Cloud Managed Networks

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as indepen- dent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu

Formal Modeling and Verification of a Blockchain-Based Crowdsourcing Consensus Protocol

Crowdsourcing is an effective technique that allows humans to solve complex problems that are hard to accomplish by automated tools. Some significant challenges in crowdsourcing systems include avoiding security attacks, effective trust management, and ensuring the system’s correctness. Blockchain is a promising technology that can be efficiently exploited to address security and trust issues. The consensus protocol is a core component of a blockchain network through which all the blockchain peers achieve an agreement about the state of the distributed ledger. Therefore, its security, trustworthiness, and correctness have vital importance. This work proposes a Secure and Trustworthy Blockchain-based Crowdsourcing (STBC) consensus protocol to address these challenges. Model checking is an effective and automatic technique based on formal methods that is utilized to ensure the correctness of STBC consensus protocol. The proposed consensus protocol’s formal specification is described using Communicating Sequential Programs (CSP#). Safety, fault tolerance, leader trust, and validators’ trust are important properties for a consensus protocol, which are formally specified through Linear Temporal Logic (LTL) to prevent several security attacks, such as blockchain fork, selfish mining, and invalid block insertion. Process Analysis Toolkit (PAT) is utilized for the formal verification of the proposed consensus protocol

Open Infrastructure for Edge Computing

Edge computing, bringing the computation closer to end-users and data producers, has now firmly gained the status of enabling technology for the new kinds of emerging applications, such as Virtual/Augmented Reality and IoT. The motivation backing this rapidly developing computing paradigm is mainly two-fold. On the one hand, the goal is to minimize the latency that end-users experience, not only improving the quality of service but empowering new kinds of applications, which would not even be possible given higher delays. On the other, edge computing aims to save core networking bandwidth from being overwhelmed by myriads of IoT devices, sending their data to the cloud. After analyzing and aggregating IoT streams at edge servers, much less networking capacity will be required to persist remaining information in distant cloud datacenters. Having a solid motivation and experiencing continuous interest from both academia and industry, edge computing is still in its nascency. To leave adolescence and take its place on a par with the cloud computing paradigm, finally forming a versatile edge-cloud environment, the newcomer needs to overcome a number of challenges. First of all, the computing infrastructure to deploy edge applications and services is very limited at the moment. Indeed, there are initiatives supported by the telecommunication industry, like Multi-access Edge Computing. Also, cloud providers plan to establish their facilities near the edge of the network. However, we believe that even more efforts will be required to make edge servers generally available. Second, to emerge and function efficiently, the ecosystem of edge computing needs practices, standards, and governance mechanisms of its own kind. The specificity originates from the highly dispersed nature of the edge, implying high heterogeneity of resources and diverse administrative control over the computing facilities. Finally, the third challenge is the dynamicity of the edge computing environment due to, e.g., varying demand, migrating clients, etc. In this thesis, we outline underlying principles of what we call Open Infrastructure for Edge (OpenIE), identify its key features, and provide solutions for them. Intended to tackle the challenges we mentioned above, OpenIE defines a set of common practices and loosely coupled technologies creating a unified environment out of highly heterogeneous and administratively partitioned edge computing resources. Particularly, we design a protocol capable of discovering edge providers on a global scale. Further, we propose a framework of Ingelligent Containers (ICONs), capable of autonomous decision making and forming a service overlay on a large-scale edge-cloud setting. As edge providers need to be economically incentivized, we devise a truthful double auction mechanism where edge providers can meet application owners or administrators in need of deploying an edge service. Due to truthfulness, in our auction, it is the best strategy for all participants to bid one's privately known valuation (or cost), thus making complex market behavior strategies obsolete. We analyze the potential of distributed ledgers to serve for OpenIE decentralized agreement and transaction handling and show how our auction can be implemented with the help of distributed ledgers. With the key building blocks of OpenIE, mentioned above, we hope to make an entrance for anyone interested in service provisioning at the edge as easy as possible. We hope that with the emergence of independent edge providers, edge computing will finally become pervasive.Reunalaskenta, joka tuo laskentakapasiteettia lähemmäksi loppukäyttäjiä ja datan tuottajia, on noussut uudentyyppisten sovelluksien, kuten virtuaalisen ja lisätyn todellisuuden (VR/AR) sekä esineiden internetin (IoT) keskeiseksi mahdollistajaksi. Reunalaskennan kehitystä tukevat pääosin kaksi sen tuomaa etua. Ensiksi, reunalaskenta minimoi loppukäyttäjien kokemaa latenssia mahdollistaen uudentyyppisiä sovelluksia. Toiseksi, reunalaskenta säästää ydinverkon tiedonsiirtokapasiteettia, esimerkiksi IoT-laitteiden pilveen lähettämien tietojen osalta. Kun reunapalvelimet analysoivat ja aggregoivat IoT-virrat, verkkokapasiteettia tarvitaan paljon vähemmän. Reunalaskentaan on panostettu paljon, sekä teollisuuden, että tutkimuksen osalta. Reunalaskennan kehittymispolulla monipuoliseksi reunapilviympäristöksi on edessä useita haasteita. Ensinnäkin laskentakapasiteetti tietoverkkojen reunalla on tällä hetkellä hyvin rajallinen. Vaikka teleoperaattorit ja pilvipalvelujen tarjoajat suunnittelevat lisäävänsä laskentakapasiteettia reunalaskennan tarpeisiin, uskomme kuitenkin, että enemmän ponnisteluja tarvitaan, jotta reunalaskennan edut olisivat yleisesti saatavilla. Toiseksi, toimiakseen tehokkaasti, reunalaskennan ekosysteemi tarvitsee omat käytäntönsä, standardinsa ja hallintamekanisminsa. Reunalaskenan erityistarpeet johtuvat resurssien heterogeenisyydestä, niiden suuresta maantieteellisesta hajautuksesta ja hallinnollisesta jaosta. Kolmas haaste on reunalaskentaympäristön dynaamisuus, joka johtuu esimerkiksi vaihtelevasta kysynnästä ja asiakkaiden liikkuvuudesta. Tässä väitöstutkimuksessa esittelemme Avoimen Infrastruktuurin Reunalaskennalle (OpenIE), joka vastaa edellä mainittuihin haasteisiin, ja tunnistamme ongelman pääominaisuudet ja tarjoamme niihin ratkaisuja. OpenIE määrittelee joukon yleisiä käytäntöjä ja löyhästi yhdistettyjä tekniikoita, jotka luovat yhtenäisen ympäristön erittäin heterogeenisistä ja hallinnollisesti jaetuista reunalaskentaresursseista. Suunnittelemme protokollan, joka kykenee etsimään reunaoperaattoreita maailmanlaajuisesti. Lisäksi ehdotamme Älykontti (ICON) -kehystä, joka kykenee itsenäiseen päätöksentekoon ja muodostaa palvelupäällysteen laajamittaisessa reunapilviympäristössä. Koska reunaoperaattoreita on kannustettava taloudellisesti, suunnittelemme totuudenmukaisen huutokauppamekanismin, jossa reunapalveluntarjoajat voivat kohdata sovellusten omistajia tai järjestelmien omistajia, jotka tarvitsevat reunalaskentakapasiteettia. Totuudenmukaisessa huutokaupassa paras strategia kaikille osallistujille on tehdä tarjous yksityisesti tunnetun arvostuksen perusteella, mikä tekee monimutkaisen markkinastrategian kehittämisen tarpeettomaksi. Analysoimme lohkoketjualustojen potentiaalia palvella OpenIE:n hajautetun sopimisen ja tapahtumien käsittelyä ja näytämme, miten huutokauppamme voidaan toteuttaa lohkoketjuteknologia hyödyntäen. Edellä mainittujen OpenIE:n keskeisten kompponenttien avulla pyrimme luomaan yleisiä puitteita joiden avulla jokainen reunalaskennan kapasiteetin tarjoamisesta kiinnostunut taho voisi ryhtyä palveluntarjojaksi helposti. Riippumattomien reunapalveluntarjoajien mukaantulo tekisi reunalaskennan lupaamat hyödyt yleisesti saataviksi