Facilitating Internet of Things on the Edge

The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved

A Survey of the Security Challenges and Requirements for IoT Operating Systems

The Internet of Things (IoT) is becoming an integral part of our modern lives as we converge towards a world surrounded by ubiquitous connectivity. The inherent complexity presented by the vast IoT ecosystem ends up in an insufficient understanding of individual system components and their interactions, leading to numerous security challenges. In order to create a secure IoT platform from the ground up, there is a need for a unifying operating system (OS) that can act as a cornerstone regulating the development of stable and secure solutions. In this paper, we present a classification of the security challenges stemming from the manifold aspects of IoT development. We also specify security requirements to direct the secure development of an unifying IoT OS to resolve many of those ensuing challenges. Survey of several modern IoT OSs confirm that while the developers of the OSs have taken many alternative approaches to implement security, we are far from engineering an adequately secure and unified architecture. More broadly, the study presented in this paper can help address the growing need for a secure and unified platform to base IoT development on and assure the safe, secure, and reliable operation of IoT in critical domains.Comment: 13 pages, 2 figure

Effects of crowding and attention on high-levels of motion processing and motion adaptation

The motion after-effect (MAE) persists in crowding conditions, i.e., when the adaptation direction cannot be reliably perceived. The MAE originating from complex moving patterns spreads into non-adapted sectors of a multi-sector adapting display (i.e., phantom MAE). In the present study we used global rotating patterns to measure the strength of the conventional and phantom MAEs in crowded and non-crowded conditions, and when attention was directed to the adapting stimulus and when it was diverted away from the adapting stimulus. The results show that: (i) the phantom MAE is weaker than the conventional MAE, for both non-crowded and crowded conditions, and when attention was focused on the adapting stimulus and when it was diverted from it, (ii) conventional and phantom MAEs in the crowded condition are weaker than in the non-crowded condition. Analysis conducted to assess the effect of crowding on high-level of motion adaptation suggests that crowding is likely to affect the awareness of the adapting stimulus rather than degrading its sensory representation, (iii) for high-level of motion processing the attentional manipulation does not affect the strength of either conventional or phantom MAEs, neither in the non-crowded nor in the crowded conditions. These results suggest that high-level MAEs do not depend on attention and that at high-level of motion adaptation the effects of crowding are not modulated by attention

Security and Privacy for IoT Ecosystems

Smart devices have become an integral part of our everyday life. In contrast to smartphones and laptops, Internet of Things (IoT) devices are typically managed by the vendor. They allow little or no user-driven customization. Users need to use and trust IoT devices as they are, including the ecosystems involved in the processing and sharing of personal data. Ensuring that an IoT device does not leak private data is imperative. This thesis analyzes security practices in popular IoT ecosystems across several price segments. Our results show a gap between real-world implementations and state-of-the-art security measures. The process of responsible disclosure with the vendors revealed further practical challenges. Do they want to support backward compatibility with the same app and infrastructure over multiple IoT device generations? To which extent can they trust their supply chains in rolling out keys? Mature vendors have a budget for security and are aware of its demands. Despite this goodwill, developers sometimes fail at securing the concrete implementations in those complex ecosystems. Our analysis of real-world products reveals the actual efforts made by vendors to secure their products. Our responsible disclosure processes and publications of design recommendations not only increase security in existing products but also help connected ecosystem manufacturers to develop secure products. Moreover, we enable users to take control of their connected devices with firmware binary patching. If a vendor decides to no longer offer cloud services, bootstrapping a vendor-independent ecosystem is the only way to revive bricked devices. Binary patching is not only useful in the IoT context but also opens up these devices as research platforms. We are the first to publish tools for Bluetooth firmware and lower-layer analysis and uncover a security issue in Broadcom chips affecting hundreds of millions of devices manufactured by Apple, Samsung, Google, and more. Although we informed Broadcom and customers of their technologies of the weaknesses identified, some of these devices no longer receive official updates. For these, our binary patching framework is capable of building vendor-independent patches and retrofit security. Connected device vendors depend on standards; they rarely implement lower-layer communication schemes from scratch. Standards enable communication between devices of different vendors, which is crucial in many IoT setups. Secure standards help making products secure by design and, thus, need to be analyzed as early as possible. One possibility to integrate security into a lower-layer standard is Physical-Layer Security (PLS). PLS establishes security on the Physical Layer (PHY) of wireless transmissions. With new wireless technologies emerging, physical properties change. We analyze how suitable PLS techniques are in the domain of mmWave and Visible Light Communication (VLC). Despite VLC being commonly believed to be very secure due to its limited range, we show that using VLC instead for PLS is less secure than using it with Radio Frequency (RF) communication. The work in this thesis is applied to mature products as well as upcoming standards. We consider security for the whole product life cycle to make connected devices and IoT ecosystems more secure in the long term

Demand-driven data acquisition for large scale fleets

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Proceedings of the 5th Baltic Mechatronics Symposium - Espoo April 17, 2020

The Baltic Mechatronics Symposium is annual symposium with the objective to provide a forum for young scientists from Baltic countries to exchange knowledge, experience, results and information in large variety of fields in mechatronics. The symposium was organized in co-operation with Taltech and Aalto University. Due to Coronavirus COVID-19 the symposium was organized as a virtual conference. The content of the proceedings1. Monitoring Cleanliness of Public Transportation with Computer Vision2. Device for Bending and Cutting Coaxial Wires for Cryostat in Quantum Computing3. Inertial Measurement Method and Application for Bowling Performance Metrics4. Mechatronics Escape Room5. Hardware-In-the-Loop Test Setup for Tuning Semi-Active Hydraulic Suspension Systems6. Newtonian Telescope Design for Stand-off Laser Induced Breakdown Spectroscopy7. Simulation and Testing of Temperature Behavior in Flat Type Linear Motor Carrier8. Powder Removal Device for Metal Additive Manufacturing9. Self-Leveling Spreader Beam for Adjusting the Orientation of an Overhead Crane Loa