EFFICIENT AND SECURE ALGORITHMS FOR MOBILE CROWDSENSING THROUGH PERSONAL SMART DEVICES.

The success of the modern pervasive sensing strategies, such as the Social Sensing, strongly depends on the diffusion of smart mobile devices. Smartwatches, smart- phones, and tablets are devices capable of capturing and analyzing data about the user’s context, and can be exploited to infer high-level knowledge about the user himself, and/or the surrounding environment. In this sense, one of the most relevant applications of the Social Sensing paradigm concerns distributed Human Activity Recognition (HAR) in scenarios ranging from health care to urban mobility management, ambient intelligence, and assisted living. Even though some simple HAR techniques can be directly implemented on mo- bile devices, in some cases, such as when complex activities need to be analyzed timely, users’ smart devices should be able to operate as part of a more complex architecture, paving the way to the definition of new distributed computing paradigms. The general idea behind these approaches is to move early analysis to- wards the edge of the network, while relying on other intermediate (fog) or remote (cloud) devices for computations of increasing complexity. This logic represents the main core of the fog computing paradigm, and this thesis investigates its adoption in distributed sensing frameworks. Specifically, the conducted analysis focused on the design of a novel distributed HAR framework in which the heavy computation from the sensing layer is moved to intermediate devices and then to the cloud. Smart personal devices are used as processing units in order to guarantee real-time recognition, whereas the cloud is responsible for maintaining an overall, consistent view of the whole activity set. As compared to traditional cloud-based solutions, this choice allows to overcome processing and storage limitations of wearable devices while also reducing the overall bandwidth consumption. Then, the fog-based architecture allowed the design and definition of a novel HAR technique that combines three machine learning algorithms, namely k-means clustering, Support Vector Machines (SVMs), and Hidden Markov Models (HMMs), to recognize complex activities modeled as sequences of simple micro- activities. The capability to distribute the computation over the different entities in the network, allowing the use of complex HAR algorithms, is definitely one of the most significant advantages provided by the fog architecture. However, because both of its intrinsic nature and high degree of modularity, the fog-based system is particularly prone to cyber security attacks that can be performed against every element of the infrastructure. This aspect plays a main role with respect to social sensing since the users’ private data must be preserved from malicious purposes. Security issues are generally addressed by introducing cryptographic mechanisms that improve the system defenses against cyber attackers while, at the same time, causing an increase of the computational overhead for devices with limited resources. With the goal to find a trade-off between security and computation cost, the de- sign and definition of a secure lightweight protocol for social-based applications are discussed and then integrated into the distributed framework. The protocol covers all tasks commonly required by a general fog-based crowdsensing application, making it applicable not only in a distributed HAR scenario, discussed as a case study, but also in other application contexts. Experimental analysis aims to assess the performance of the solutions described so far. After highlighting the benefits the distributed HAR framework might bring in smart environments, an evaluation in terms of both recognition accuracy and complexity of data exchanged between network devices is conducted. Then, the effectiveness of the secure protocol is demonstrated by showing the low impact it causes on the total computational overhead. Moreover, a comparison with other state-of-art protocols is made to prove its effectiveness in terms of the provided security mechanisms

Investigation into scalable energy and performance models for many-core systems

PhD ThesisIt is likely that many-core processor systems will continue to penetrate emerging embedded and high-performance applications. Scalable energy and performance models are two critical aspects that provide insights into the conflicting trade-offs between them with growing hardware and software complexity. Traditional performance models, such as Amdahl’s Law, Gustafson’s and Sun-Ni’s, have helped the research community and industry to better understand the system performance bounds with given processing resources, which is otherwise known as speedup. However, these models and their existing extensions have limited applicability for energy and/or performance-driven system optimization in practical systems. For instance, these are typically based on software characteristics, assuming ideal and homogeneous hardware platforms or limited forms of processor heterogeneity. In addition, the measurement of speedup and parallelization factors of an application running on a specific hardware platform require instrumenting the original software codes. Indeed, practical speedup and parallelizability models of application workloads running on modern heterogeneous hardware are critical for energy and performance models, as they can be used to inform design and control decisions with an aim to improve system throughput and energy efficiency. This thesis addresses the limitations by firstly developing novel and scalable speedup and energy consumption models based on a more general representation of heterogeneity, referred to as the normal form heterogeneity. A method is developed whereby standard performance counters found in modern many-core platforms can be used to derive speedup, and therefore the parallelizability of the software, without instrumenting applications. This extends the usability of the new models to scenarios where the parallelizability of software is unknown, leading to potentially Run-Time Management (RTM) speedup and/or energy efficiency optimization. The models and optimization methods presented in this thesis are validated through extensive experimentation, by running a number of different applications in wide-ranging concurrency scenarios on a number of different homogeneous and heterogeneous Multi/Many Core Processor (M/MCP) systems. These include homogeneous and heterogeneous architectures and viii range from existing off-the-shelf platforms to potential future system extensions. The practical use of these models and methods is demonstrated through real examples such as studying the effectiveness of the system load balancer. The models and methodologies proposed in this thesis provide guidance to a new opportunities for improving the energy efficiency of M/MCP systemsHigher Committee of Education Development (HCED) in Ira

Fourth Baltic Conference in Exercise and Sport Sciences, April 7-9, 2011, Tartu, Estonia

Abstrac

Acta kinesiologiae Universitatis Tartuensis. 16(Supplement)

http://www.ester.ee/record=b1227224*es

Secure Certificate Management and Device Enrollment at IoT Scale.

The Internet of Things (IoT) is expected to comprise of over 20 billion devices connected to the Internet by the year 2020, and support mission critical applications such as health care, road safety and emergency services to name a few. This massive scale of IoT device deployment, heterogeneity of devices and applications, and the autonomous nature of the decision making process introduces new security requirements and challenges. The devices must be securely bootstrapped in to the network to provide secure inter--device communication and also, the applications must be able to authenticate and authorize these devices to provide the relevant services. In today's Internet, Public Key Infrastructure (PKI) is widely used to provide authenticity, encryption and data integrity during network communication through the use of digital certificates. This thesis investigates the key aspects for deploying a PKI security solution in an IoT ecosystem, ranging from deploying certificates on new devices (bootstrapping) to complete life cycle management of these certificates. We believe that the current PKI can be, with suitable enhancements, used to provide the efficiency, scalability and flexibility needed for IoT security. This thesis provides a survey of key aspects for deploying PKI security solution in IoT ecosystem. We investigate different certificate management protocols and motivate the applicability of enhanced security over transport (EST) protocol for IoT PKI solution. In addition, we propose a PKI deployment model and the bootstrap mechanism to bring up an IoT device and provision it with a digital certificate. Furthermore, we provide a prototype implementation to demonstrate certificate enrollment procedure with an EST server

NIM: The Neutral Gas and Ion Mass Spectrometer to Explore the Galilean Ice Worlds

The JUpiter ICy moons Explorer (JUICE) of the European Space Agency (ESA) has the purpose to investigate Jupiter and its icy moons Europa, Ganymede and Callisto in great detail. Among other scientific goals, JUICE will investigate the Jupiter system as a potential habitable system because the three icy moons have subsurface oceans where life might be possible. On board of JUICE is the Particle Environment Package (PEP), which consists of six individual instruments measuring electrons, ions and neutral particles in an energy range from meV up to MeV. One of these six instruments is the Neutral gas and Ion Mass spectrometer (NIM) from the University of Bern. The NIM instrument is designed to measure the chemical and isotope composition of the icy moons’ exospheres during the flybys of JUICE of the icy moons and also during JUICE final destination in Ganymede’s orbit. Knowing the chemical and isotope composition allows to investigate the origin and evolution processes involved in the formation processes of the icy moons, Jupiter and our solar system. NIM is a time-of-flight mass spectrometer able to measure thermal neutral molecules and ionospheric ions. This thesis shows the journey from finalising the flight design of the NIM instrument to the actual testing, qualification, and calibration until delivery of the NIM Proto-Flight (PFM) instrument in December 2020 to the JUICE spacecraft. On this journey, different flight components were tested and analysed as they became available during the development and finalisation of the PFM and Flight-Spare (FS) instrument. From the foreseen scientific scope for the NIM instrument, a list of measurement requirements was delivered. This work shows that NIM PFM and NIM FS meet all these requirements

Bio-inspired robotic joint and manipulator : from biomechanical experimentation and modeling to human-like compliant finger design and control

textOne of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.Mechanical Engineerin