José Manuel Torres, Luis P. Mota, Rui S. Moreira, Christophe Soares · 5 authors
Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts.
Augusto Magalhães Pinto de Mendonça, Filipe Pessôa Sousa, fellipe souza pessanha, Igor Machado Coelho
Fitness apps help users track their exercises and health. Wearable sensors provide increasing amounts of data, bringing motivation but also challenges to privacy. Data shared by users’ devices may exceed what they want to share for the desired results. Health insurers, corporate wellness programs, and gamified apps all want proof of exercise, but current systems require users to disclose raw activity logs, heart rate, and location data. This paper presents a system that lets users prove they met exercise goals without revealing raw sensor data. We embed a Decision Tree classifier inside a zero-knowledge proof circuit using the Groth16 protocol. The classifier distinguishes rest, moderate, and vigorous activity from accelerometer and gyroscope readings. The model reaches 97.19% holdout accuracy on the MHEALTH dataset, validated through $\mathbf{1 0}$-fold crossvalidation. The circuit uses 10,447 constraints, small enough for smartphones and wearables. Proofs take about 500 ms on desktop and 1.1 seconds on mobile, with only 800 bytes each. Unlike systems where classification runs outside the proof, our approach makes the classification itself verifiable, so label injection attacks become computationally infeasible. We tested 30 cases covering all intensity classes and boundary conditions, and all proofs were generated and verified correctly. The system provides cryptographic guarantees while remaining practical for real-world deployment.
Buildings are shifting from static, environment centric control schemes toward dynamic systems that adaptively respond to individual occupants. To clarify how this transition affects building operation, this paper reviews recent advances in occupant centric control from three dimensions: physical control systems, data and network infrastructure, and occupant interaction mechanisms. The review draws from research in building science and computing and organizes key developments using the trinity of transformation, resilience, and sustainability. The results show that the shift to occupant centric requires high-density sensing, distributed data processing, interoperable protocols, and feedback models that integrate occupant behavior. The paper also discusses how concepts introduced from Web3 can support decentralized data management and long-term trust in occupant participation. This work provides a technical and conceptual basis for researchers and practitioners involved in the digital transformation of smart building operation.
Smart contracts, as self-executing code on blockchain platforms, are transforming digital agreements across multiple industries. This paper reviews the technical foundations, applications, security challenges, and emerging directions of smart contract technology through an analysis of recent academic literature and real-world implementations. While smart contracts demonstrate significant potential in decentralized finance, supply chain management, and healthcare, they face critical challenges, including security vulnerabilities, ecosystem centralization risks, and legal uncertainties. Layer-2 scaling solutions, cross-chain interoperability protocols, and AI-assisted security auditing represent promising directions for addressing these challenges. Our analysis reveals that despite technological advances, fundamental issues in security verification and regulatory frameworks require continued research attention.
This paper presents CommitFit, a blockchain-based fitness platform designed to enhance user engagement and data security through financial incentives and decentralized attestation protocols. The architecture integrates modular layers comprising a secure frontend, AI-driven backend verification, and Ethereum smart contracts for automated staking and non-fungible token (NFT) rewards. Empirical evaluation demonstrates significant improvements in user retention and goal completion rates compared to traditional systems, with notable accuracy in AI-based activity verification. The platform addresses critical challenges such as privacy, scalability, and integration with wearable devices. Comparative analysis with existing fitness solutions highlights CommitFit's advancements and future potential for broader applications in secure, decentralized health data management.
Move to Earn (MtoE) initiatives emerged during the COVID-19 pandemic. STEPN, the leading MtoE walking applications (app.) using various artificial intelligence (AI) techniques, allows users to earn rewards by walking, jogging, or running with their mobile devices while wearing Non-Fungible Token (NFT) shoes. Although MtoE games have huge potentials to improve our quality of life (QOL), there is no scientific papers to point out the possibilities. This work describes present situation of MtoE and importance of AI to establish MtoE projects, and proposes several potential applications of MtoE, including as an aid to support athletes, international students, and bedridden individuals. This is the primitive survey research of MtoE projects as a trigger to start scientific discussions.
This work explores the evolution and impact of IoT-enabled smart home systems, highlighting advancements in automation, energy management, security, and elderly care. A comprehensive review of 38 studies was conducted to identify research trends, opportunities, and challenges including interoperability, privacy, and standardization. A novel framework is proposed, integrating sensors, actuators, edge devices, cloud platforms, and AI engines to support intelligent decision-making and seamless operation. The study also suggests crowdsourced platforms and Decentralized Autonomous Organizations (DAOs) to enhance interoperability and user involvement. A detailed diagram and system architecture further support the proposed solution. The findings emphasize the need for continued innovation in smart home ecosystems to ensure secure, adaptive, and inclusive living environments.
We present Free-Delete, a censorship-resistant group-chat protocol whose membership is validated by non-fungible-token (NFT) ownership while user privacy ranges across five selectable modes. A single Groth16 circuit, anchored in a sparse Merkle tree of verifiable commitments, realises (i) Fully Anonymous messaging, (ii) Linkable Anonymous reputation building, (iii) Publicly Identified disclosure, (iv) Confidential end-to-end encryption, and (v) Rate-Limited Accountability that revokes keys on spam—all without moderator involvement or economic deposits. A black-paper prototype written in TypeScript, Circom 2, and Solidity achieves 0.35 s for register and 1.38 s for postMessage on consumer hardware; on-chain verification costs 3–5.5×105 gas per proof on Polygon. These results demonstrate that NFT-gated, stake-free, privacy-preserving communication can be deployed today on any EVM chain.
Tracking and tracing of assets using decentralized systems demand optimized data formats to balance storage costs and retrieval performance. This paper proposes a cost-effective sensor data format tailored for decentralized storage of geographic point locations on XNO, a distributed ledger technology. The proposed format optimizes storage by encoding location data in a compact yet retrievable manner, leveraging spatial indexing techniques to facilitate efficient and transparent querying. This solution presents a promising approach for scalable and affordable asset tracking and tracing using the highly available and transparent decentralized environments, with potential applications across various industries.
The IOTA Extended UTXO (IOTA EUTXO) model extends the UTXO blockchain to include features like smart contracts and non-fungible tokens. In this work, we show that the IOTA EUTXO model maintains the base correctness properties of the UTXO model while extending it with extra functionality. We achieve this by specifying and verifying the essential concepts of the base UTXO model and the extensions proposed by IOTA using the Isabelle proof assistant. The specification is designed to be modular and extensible, meaning it can be used as a foundation for further research of the UTXO and IOTA EUTXO models.
Kiseok Kim, Sangmin Lee, Taehoon Yoo, Hwangnam Kim
In performing various missions using various types of vehicles or other moving objects, the positioning of each agent within the swarm is essential. In particular, for missions that require precise location estimation, the case of malicious attacks through data forgery cannot be excluded. In this paper, we propose a highly secure and accurate localization framework utilizing a Directed-Acyclic-Graph (DAG)-based distributed ledger as an intelligent vehicle network for an Ultra-Wideband (UWB) positioning system. When performing UWB positioning, the data generated from each node are used to calculate the Time of Flight (ToF), and if any of them are tampered with, the overall positioning performance is greatly reduced. We prevented positioning performance degradation by ensuring the safety and integrity of the data by applying a chain-based logical network between each node utilized for UWB positioning. The experimental results indicated that the proposed framework was effective at providing system stability and security without affecting the UWB positioning performance. In addition, the performance of the framework was verified by presenting defense indicators for various attack scenarios.
Falls among the elderly are a major health concern, frequently resulting in serious injuries and a reduced quality of life. In this paper, we propose "BlockTheFall," a wearable device-based fall detection framework which detects falls in real time by using sensor data from wearable devices. To accurately identify patterns and detect falls, the collected sensor data is analyzed using machine learning algorithms. To ensure data integrity and security, the framework stores and verifies fall event data using blockchain technology. The proposed framework aims to provide an efficient and dependable solution for fall detection with improved emergency response, and elderly individuals' overall well-being. Further experiments and evaluations are being carried out to validate the effectiveness and feasibility of the proposed framework, which has shown promising results in distinguishing genuine falls from simulated falls. By providing timely and accurate fall detection and response, this framework has the potential to substantially boost the quality of elderly care.
WebAssembly, or WASM for short, is a binary code format for a stack-based virtual machine, first published in 2018 and now becomes a main-steam technology for providing distributed serverless functions. Recently, the demand for privacy and trustless serverless functions has started to grow in cloud, edge, and grid computation, which poses a question for those serverless function providers: what feature they need to add to make WASM runtime more secure so that the application run on top it become trustless to their users. To address this, we leverage the technology ZKSNARK (zero-knowledge Succinct Non-interactive Argument of Knowledge), a powerful proof system that allows efficient verification of the evaluation problem of statements, to give WASM runtime the ability to provide trustless computation service. More precisely, we present ZAWA, a ZKSNARK backed virtual machine that emulates the execution of WASM bytecode and generates zero-knowledge-proofs for the emulation result. The proof generated by the ZAWA virtual machine can then be used to convince an entity, with no leakage of confidential information, that the result of the emulation enforces the semantic specification of WASM.
We demonstrate how to leverage Apple's Find My protocol, most well known as the underlying protocol of the AirTag, for arbitrary data-muling and location services. This provides a new "infrastructure-free" deployment, where areas with frequent human activity can take advantage of this zero-cost backhaul network. While there are severe limitations (e.g. no acknowledgement channel back to the sending device), Find My-based networking could still be a reliable backhaul with sufficient transmission redundancy and knowledge of deployment context. Towards that end, we develop TagAlong, a protocol for scalable, efficient data transmission on the Find My network. We implement a proof-of-concept and demonstrate throughput up to 12.5 bytes/sec and up to a 97% data reception rate.
With the rise in fog computing, users are no longer restricted to only accessing resources located in central and distant clouds and can request services from neighboring fog nodes distributed over networks. This can effectively reduce the network latency of service responses and the load of data centers. Furthermore, it can prevent the Internet’s bandwidth from being used up due to massive data flows from end users to clouds. However, fog-computing resources are distributed over multiple levels of networks and are managed by different owners. Consequently, the problem of service discovery becomes quite complicated. For resolving this problem, a decentralized service discovery method is required. Accordingly, this research proposes a service discovery framework based on the distributed ledger technology of IOTA. The proposed framework enables clients to directly search for service nodes through any node in the IOTA Mainnet to achieve the goals of public access and high availability and avoid network attacks to distributed hash tables that are popularly used for service discovery. Moreover, clients can obtain more comprehensive information by visiting known nodes and select a fog node able to provide services with the shortest latency. Our experimental results have shown that the proposed framework is cost-effective for distributed service discovery due to the advantages of IOTA. On the other hand, it can indeed enable clients to obtain higher service quality by automatic node selection.
Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an “Internet of Things (IoT).” These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient’s personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure “Smart Medical System (SMS)” framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.
This paper presents an architecture, based on Distributed Ledger Technologies\n(DLTs) and Decentralized File Storage (DFS) systems, to support the use of\nPersonal Information Management Systems (PIMS). DLT and DFS are used to manage\ndata sensed by mobile users equipped with devices with sensing capability. DLTs\nguarantee the immutability, traceability and verifiability of references to\npersonal data, that are stored in DFS. In fact, the inclusion of data digests\nin the DLT makes it possible to obtain an unalterable reference and a\ntamper-proof log, while remaining compliant with the regulations on personal\ndata, i.e. GDPR. We provide an experimental evaluation on the feasibility of\nthe use of DFS. Three different scenarios have been studied: i) a proprietary\nIPFS approach with a dedicated node interfacing with the data producers, ii) a\npublic IPFS service and iii) Sia Skynet. Results show that through proper\nconfiguration of the system infrastructure, it is viable to build a\ndecentralized Personal Data Storage (PDS).\n
Health Data Records Fragmentation across hospitals, labs, pharmacies, and general caregivers has been hurting medical services quality and patient outcome. The problem is getting worse as we have even more healthcare accesses from different sources such as mobile phones, wearable devices, etc. In an attempt to defragment health data, we are proposing the utilization of distributed transaction ledgers (DTL) technology by IOTA-Tangle which facilitates communication between patients and care providers. Through this technology the gap between health-care delivery and population health can be bridged so the quality of health services and patient outcomes may be improved. Our approach provides complete picture for each patient by sharing data between healthcare participants.
Paulo Bartolomeu, Emanuel Vieira, Joaquim Ferreira
Cyber-physical housing technology is going through a revolution, started the early 2000s and expected to continue in the years to come. In fact, according to market research reports, connected devices for smart homes will soon have the largest market share of all IoT devices. In contrast with siloed devices and applications, which until recently prevailed even in the most sophisticated dwellings, nowadays it is already possible to glimpse a complex blend of technologies seamlessly integrated to promote the residents' comfort. However, several challenges lie ahead before the technology can fully support such a visionary scenario. One of them is security, especially in scenarios encompassing low-end embedded devices. This paper describes a mechanism allowing limited resource embedded appliances to participate in a network of IoT devices secured by a distributed ledger technology named IOTA. The paper describes the devised proof-of-concept implementation and provides insight on preliminary performance results showing its feasibility.