From production to consumption, ensuring food quality and traceability depends on reliable monitoring of environmental conditions across the supply chain. Ambient sensing devices can collect relevant data such as temperature and humidity, but ensuring its integrity among stakeholders remains a challenge. This work presents AmBox, a system that enables device-to-blockchain ambient sensing for food traceability. AmBox connects sensors to a blockchain, ensuring secure, verifiable, and tamper-resistant data collection with minimal intermediaries. It manages sensor commissioning and operation with the adequate business context. AmBox can operate with standalone nodes or within a distributed node-mote architecture, allowing flexible deployment at different points along the supply chain. A prototype using Raspberry Pi and ESP32 hardware can record sensor data directly on Hyperledger Fabric. Experimental results show that AmBox provides timely and reliable data that can increase transparency and trust between the supply chain stakeholders.

Sensor technologies have evolved to a point where it is now practical to monitor products along the supply chain. The collected data can be stored in a decentralized way using blockchain technology. However, ensuring the reliability of the sensed data is a critical challenge. In other words, we need to trust the data that we write to the blockchain. In this work, we propose ChainGuards, a decentralized system that uses product-specific rules to verify data collected across the supply chain, with particular focus on sensor-derived information, issuing warnings and triggering audits when anomalies are detected. We evaluated ChainGuards using data from a real cherry supply chain deployment. The result shows that the implemented solution provides reliable verification of supply chain data with low performance overhead, able to correctly detect data discrepancies and inconsistencies.

Enterprise adoption of permissioned blockchains remains limited due to the complexity and cost of integrating legacy systems. We present a modular adapter architecture that bridges enterprise applications with blockchain networks, designed to support small and medium-sized enterprises with limited technical resources. The architecture provides five key modules: (1) configurable data extractors supporting diverse interfaces such as APIs and file uploads, (2) data transformers that can convert to standard formats, (3) messaging middleware to ensure operations can tolerate lack of connectivity and traffic spikes, (4) blockchain loader to commit transactions to the blockchain, and (5) status visibility to collect and expose runtime metrics that support operational transparency. We validated the adapters through a pilot deployment in a real-world fruit supply chain, involving three distinct enterprises. The pilot achieved blockchain integration with minimal workflow disruption, demonstrating the usefulness of these adapters for practical interoperability of existing systems with the blockchain.

Blockchain technology is increasingly studied as a means to replace trusted intermediaries in consortium settings. While permissioned blockchains offer better control and security, they face the challenge of balancing decentralization with local governance. Each organization must define and enforce its own access rules, which complicates collaboration. In particular, ensuring data confidentiality is challenging, as data owners are often very strict about what to share and with whom.This paper presents the design of mechanism for secure and confidential data sharing between separate blockchain organization groups. It leverages channels in Hyperledger Fabric to achieve data isolation but also includes the ability to filter and transform the data to be shared. A case study in fruit traceability demonstrates how the proposal meets important requirements of real-world supply chains.

Traditional databases have been important for supply chain businesses, helping them store and manage data for logistic operations. However, growing demands for transparency and traceability, especially in food products, have exposed the risk of data manipulation by dishonest participants in the supply chain. Blockchain technology has the potential to solve these issues by providing a decentralized system to store and share product data, ensuring integrity without any single organization having unchecked control. This work explores the integration of traditional database systems with blockchain technology, focusing on a supply chain scenario, to achieve dependable food traceability data. The integration leverages specific design patterns to efficiently structure and organize system implementation. To evaluate the effectiveness of this integration, a food traceability application was developed based on a real-world use case. The results show that integrating databases with blockchain, using design patterns, can protect data from tampering and make the overall system dependable.

Blockchain technology supports decentralized, consensus-driven data storage and processing, ensuring integrity and auditability. It is increasingly adopted for use cases with multiple stakeholders with shared ownership scenarios like digital identity and supply chain management. However, real-world deployments face challenges with mistakes and intrusions. This article presents EvoChain, a chaincode framework extension introducing controlled mutability for data redaction and recovery under time-limited or specific conditions. This mechanism allows corrections during a grace period before immutability takes effect. We validated our approach using WineTracker, a Hyperledger Fabric-based supply chain application. It enables some users to cancel unwanted operations while preserving the blockchain security and maintaining data consistency. Performance evaluations showed minimal overhead with functional benefits.

Maintaining a reliable record of goods movement is essential to supply chain management systems. Ensuring this reliability becomes more challenging as supply chains expand and many stakeholders must collaborate to provide data. Blockchain technology enables a multi-owner system that ensures data integrity through its immutable, transparent, and decentralized features. However, in the supply chain, data is collected from sensor devices at business sites or during transport along the supply chain. This dependence on single-source data undermines the decentralization and security guarantees of blockchain systems. This work proposes ChainGuard, a system to verify data intake from single sources in blockchains using a decentralized verification process guided by stakeholder-defined rules. It detects errors to ensure compliance with quality criteria. A prototype focused on location sensors was developed and tested in a real-world traceability system monitoring for commercial fruit shipments. The prototype successfully identified three types of suspicious data patterns, achieving low performance impact compared to a system without data verification.

Users increasingly rely on identity providers for accessing online services and resources. However, centralized identity systems often compromise user privacy due to online activity tracking or data breaches. At the same time, many online services require digital copies of physical documents for validation in claims processes, such as providing proof of residence for opening a bank account or verifying medical images for health insurance claims. With centralized solutions, privacy depends entirely on the trusted party, but there are emerging decentralized approaches that offer greater transparency. This article introduces SoverClaim, a decentralized application prototype that empowers users to control their identity and also allows them to present digital documents with privacy. SoverClaim leverages Hyperledger Indy, a blockchain for issuing and presenting self-sovereign digital identities with transparent audit logs, and Storj, a decentralized peer-to-peer service, for secure and decentralized document storage and subsequent deletion. The prototype demonstrates the seamless integration of self-sovereign identities and document-based claims, achieving response times of under 750 ms, making it suitable for timely human interactions.

Modern cars are equipped with sensors that can detect other moving vehicles and obstacles on the road. However, their range is usually limited to line-of-sight and their accuracy is also limited. To provide information beyond the sensor range, each vehicle broadcasts Basic Safety Messages (BSMs) with its position and speed. For road awareness, it would be best if multiple vehicles could confirm the position (redundancy), using their on-board sensors for verification (diversity), and excluding position and speed errors (plausibility). This paper presents a decentralized solution that uses multiple vantage points to provide more trust in moving vehicle position data. It extends broadcast messages with sensor verification and plausibility filtering. It processes a stream of data from nearby vehicles and for short time periods, to achieve the safety benefits without the privacy risks of long-term data retention. The proposal was evaluated with detailed simulations with different levels of traffic and misbehavior. It provides good detection results with only a limited increase in network and computing resources.

Location-aware mobile applications are increasingly popular and useful. However, as more services rely on location, there are concerns that users may misreport their location to gain undue advantages. One way to prevent such location spoofing is to rely on location certification systems. For example, SureThing uses Wi-Fi or Bluetooth beacons and ad-hoc witnesses to allow a user to make proof of location at a specific time and place. This approach can be extended to smart spaces, such as smart buildings, managed by platforms like DS2OS. In this work, we present SureSpace, a new system that combines location certification with smart space management, to verify the location of users in rooms inside smart buildings. The new system relies on a prover mobile device and on existing infrastructure in the room to act as signal beacons and witnesses. The system is evaluated and shown to be effective using light and audio signals to achieve security by diversity and thwart location spoofing attacks.

Nowadays, tourists turn to digital platforms to discover new places to explore. CROSS City is a smart tourism mobile application that enhances the user experience of tourists visiting points of interest in a route by rewarding them in the end, if they actually visited all locations. From a technical standpoint, the user location is certified resorting to strategies that take advantage of both the diversity of the existing Wi-Fi network infrastructure throughout the city, as well as the presence of other users at the same site using Bluetooth. This work developed a new, peer-to-peer location certification strategy and added gamification elements to encourage users to keep the wireless radios turned on and use the app more. This work was evaluated both in laboratory experiments and with users in a real-world scenario which demonstrated that the new Bluetooth peer-based strategy is both feasible and resistant to collusion attacks.

Wi-Fi hotspots are a valuable resource for people on the go, especially tourists, as they provide a means to connect personal devices to the Internet. This extra connectivity can be helpful in many situations, e.g., to enable map and chat applications to operate outdoors when cellular connectivity is unavailable or is expensive. Retail stores and many public services have recognized that hotspots have potential to attract and retain customers, so many of them offer free and open Wi-Fi. In busy cities, with many locals and visitors, the number of hotspots is very significant. Some of these hotspots are available for long periods of time, while others are short-lived. When we have many users with devices collecting hotspot observations, they can be used to detect the location -- using the long-lived hotspots -- and to prove the time when the location was visited -- using the short-lived hotspots observed by others users at the location. In this article, we present a dataset of collected Wi-Fi data from the most important tourist locations in the city of Lisbon, Portugal, over a period of months, that was used to show the feasibility of using hotspot data for location detection and proof. The obtained data and algorithms were assessed for a specific use case: smart tourism. We also present the data model used to store the observations and the algorithms developed to detect and prove location of a user device at a specific time. The Lisbon Hotspots dataset, LXspots, is made publicly available to the scientific community so that other researchers can also make use of it to develop new and innovative mobile and Internet of Things applications.

Location-aware mobile applications are increasingly popular and useful. However, as more services rely on location, there are concerns that users may misreport their location to gain undue advantages. One way to prevent such location spoofing is to rely on location certification systems. For example, SureThing uses Wi-Fi or Bluetooth beacons and ad-hoc witnesses to allow a user to make proof of location at a specific time and place. This approach can be extended to smart spaces, such as smart buildings, managed by platforms like DS2OS. In this work, we present SureSpace, a new system that combines location certification with smart space management, to verify the location of users in rooms inside smart buildings. The new system relies on a prover mobile device and on existing infrastructure in the room to act as signal beacons and witnesses. The system is evaluated and shown to be effective using light and audio signals to achieve security by diversity and thwart location spoofing attacks.

Millions of tourists each year use smartphone applications to discover points of interest. Despite relying heavily on location sensing, most of them are susceptible to location spoofing, but not all. CROSS City is a smart tourism application that rewards users for completing tourist itineraries and uses location certificates to prevent attacks. In this case, the location verification relies on the periodic collection of public Wi-Fi network observations by multiple users to make sure the travelers actually went to the points of interest.In this paper, we introduce the Location-Certification-as-a-Service (LoCaaS) approach, supported by a cloud-native and improved location certification system, capable of producing and validating time-bound location proofs using network data collected by tourists’ mobile devices. We show that the system can efficiently compute the stable and transient networks for a given location that are used, respectively, to validate the location of a tourist and to prove the time-of-visit. The system was deployed to the Google Cloud Platform and was validated with performance experiments and a real-world deployment.

Location is an important attribute for many mobile applications but it needs to be verified. For example, a user of a tourism application that gives out rewards can falsify his location to pretend that he has visited many attractions and thus receive benefits without deserving them. To counter these attacks, the system asks users to prove their location through witnesses, i.e., other devices that happen to be at the location at the same time and that can be partially trusted. However, for this approach to be effective, it is important to keep track of the witness behavior over time. Many crowdsourcing applications, like Waze, build up reputations for their users, and rely on user co-location and redundant inputs for data verification.In this work, we present SureRepute, a reputation system capable of withstanding reputation attacks while still maintaining user privacy. The results show that the system is able to protect itself and its configuration is flexible, allowing different trade-offs between security and usability, as required in real-world applications. The experiments show how the reputation system can be easily integrated into existing applications without producing a significant overhead in response times.

Wearable devices are further connecting people to the world, extending the reach of smartphones and the Internet. New applications are possible such as activity and location tracking that allows health monitoring and increased access to health services. Bluetooth Low-Energy (BLE) is a pivotal technology for this vision, as it allows power-efficient network connections to smartphones and to service infrastructure. However, there are design flaws and implementation vulnerabilities in BLE that affect the most widely used chipsets and operating systems. In this paper, we present POSE, an end-to-end security layer, that can mitigate attacks on BLE pairing and link-layer communications. POSE uses protocol buffers for efficient message data serialization/deserialization and, on top of them, provides message confidentiality and authenticity, including message freshness. POSE was implemented and its processing time, packet overhead, and CPU usage were evaluated. The results show that POSE is an efficient solution for secure communication with wearables and other constrained devices, especially when they already use protocol buffers.

Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors' data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user's mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder's care.

Maintaining consistent radio maps for WiFi fingerprinting-based indoor positioning systems is an essential step to improve the performance of the positioning engines. The radio maps consist of WiFi fingerprints collected at a predefined set of positions/places within a positioning area. Each fingerprint consists of the identification and radio signal level of the surrounding Access Points (APs). Due to the wide proliferation of WiFi networks, it is very common to observe 10 to 20 APs at a single position and more than 50 APs across a single building. However, in practical, not all of the detected APs are useful for the position estimation process. Some of them might have weak signals at certain positions or might have less significance for a position's fingerprint. Thus, those useless APs will add additional computational overheads during the position estimation, and consequently they will reduce the overall performance of the positioning engines. A similar phenomenon also occurs with some of the collected fingerprints. While it is widely accepted that the larger and more detailed the radio map is, the better is the accuracy of the positioning system, we found that some of the fingerprint samples on the radio maps do not contribute significantly to the estimation process. In this paper, we propose two methods for filtering the positioning radio maps: APs filtering and Fingerprints filtering. Then we report on the results of a set of experiments that have been done to evaluate the performance of a WiFipositioning radio map before and after applying the filtering approaches. The results show that there is possibility to simplify the radio maps of the positioning engines without significant degradation on the positioning precision and accuracy, and therefore to reduce the processing time for estimating the position of a tracked WiFi tag. This result has an important impact on increasing the number of tags a single instance of a WiFi positioning engine can handle at a time.

Indoor positioning is an exciting research area th at promises many new applications in ubiquitous computing area. Know i g the exact position of a person in a building becomes a common requirement f or many applications including person tracking, fire fighting, rescue, hea lthcare and patient monitoring. Several techniques and technologies have been devel oped for indoor positioning e.g. WiFi, RFID and Fingerprinting. Fingerprinti ng technique can be considered as a suitable candidate for many location-awa re applications due to the low-cost and high-accuracy localization features. H owever, existing fingerprinting technique does not always give accurate localiz ation especially in dynamic environments in which the measured signals are dist orted by unexpected environment changes or other obstacles that might lead to position outliers. Within my PhD, we would like to identify which kind of obs tacles cause position outliers in indoor environments and how these outliers can be handled in order to provide an effective indoor positioning method that c uses trivial amount of localization errors. Moreover, we believe that ubiqui to s computing environments bring new positioning requirements that are n ot supported by the existing positioning solutions and therefore, we would like to identify these new requirements.

Location and tracking services and technologies are becoming fundamental components for supporting healthcare solutions. They facilitate patients' tracking and monitoring processes and also allow for better and long-term daily activity recognition. Various location and tracking services have been developed, over the last years, to provide real time localization for different applications. However, most of these services are not designed particularly to comply with all the requirements of Ambient Assisted Living (AAL) and, as a result, they reduce the viability of adopting AAL services as an alternative for continuous healthcare services. In this paper we set out the general requirements for location and tracking services for AAL. The requirements are extracted from a typical scenario of AAL. From the scenario, we define the requirements and also we identify a set of metrics to be used as evaluation criteria. If the identified requirements and metrics are adopted widely, potential location and tracking services will fit the real needs of AAL, and thus will increase the accessibility to AAL services by a larger sector of people. Moreover, in the paper, we evaluate two of the existing location techniques through the use of the proposed metrics. The aim is to asses to which level these solutions fulfill the identified requirements.