Collaborative Edge Computing in Mobile Internet of Things

The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportunities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision systems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool instead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abundance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative computing, collaborative security and collaborative privacy intelligence. The first framework, Opportunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spatial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third frame- work, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user’s privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration

The Forensic Swing of Things: The Current Legal and Technical Challenges of IoT Forensics

The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data is stored on the devices and them being always connected makes them extremely volatile. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality)

Joint optimisation of privacy and cost of in-app mobile user profiling and targeted ads

Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process and trade rich amount of consumer's personal data and carries out interests-based ads targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address user's privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns and interest-based ads targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve equivalent problem using Lyapunov optimisation and to overcome difficulty of solving nonlinear programming by decomposing it into various cases and achieve trade-off between user privacy, cost and targeted ads. We carry out extensive experimentations and demonstrate proposed framework's applicability by implementing its critical components using POC `System App'. We compare proposed framework with other privacy protecting approaches and investigate that it achieves better privacy and functionality for various performance parameters

Hawkes-modeled telecommunication patterns reveal relationship dynamics and personality traits

It is not news that our mobile phones contain a wealth of private information about us, and that is why we try to keep them secure. But even the traces of how we communicate can also tell quite a bit about us. In this work, we start from the calling and texting history of 200 students enrolled in the Netsense study, and we link it to the type of relationships that students have with their peers, and even with their personality profiles. First, we show that a Hawkes point process with a power-law decaying kernel can accurately model the calling activity between peers. Second, we show that the fitted parameters of the Hawkes model are predictive of the type of relationship and that the generalization error of the Hawkes process can be leveraged to detect changes in the relation types as they are happening. Last, we build descriptors for the students in the study by jointly modeling the communication series initiated by them. We find that Hawkes-modeled telecommunication patterns can predict the students' Big5 psychometric traits almost as accurate as the user-filled surveys pertaining to hobbies, activities, well-being, grades obtained, health condition and the number of books they read. These results are significant, as they indicate that information that usually resides outside the control of individuals (such as call and text logs) reveal information about the relationship they have, and even their personality traits

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

Energy-Aware Development and Labeling for Mobile Applications

Today, mobile devices such as smart phones and tablets have become ubiquitous and are used everywhere. Millions of software applications can be purchased and installed on these devices, customizing them to personal interests and needs. However, the frequent use of mobile devices has let a new problem become omnipresent: their limited operation time, due to their limited energy capacities. Although energy consumption can be considered as being a hardware problem, the amount of energy required by today’s mobile devices highly depends on their current workloads, being highly influenced by the software running on them. Thus, although only hardware modules are consuming energy, operating systems, middleware services, and mobile applications highly influence the energy consumption of mobile devices, depending on how efficient they use and control hardware modules. Nevertheless, most of today’s mobile applications totally ignore their influence on the devices’ energy consumption, leading to energy wastes, shorter operation times, and thus, frustrated application users. A major reason for this energy-unawareness is the lack for appropriate tooling for the development of energy-aware mobile applications. As many mobile applications are today behaving energy-unaware and various mobile applications providing similar services exist, mobile application users aim to optimize their devices by installing applications being known as energy-saving or energy-aware; meaning that they consume less energy while providing the same services as their competitors. However, scarce information on the applications’ energy usage is available and, thus, users are forced to install and try many applications manually, before finding the applications fulfilling their personal functional, non-functional, and energy requirements. This thesis addresses the lack of tooling for the development of energy-aware mobile applications and the lack of comparability of mobile applications in terms of energy-awareness with the following two contributions: First, it proposes JouleUnit, an energy profiling and testing framework using unit-tests for the execution of application workloads while profiling their energy consumption in parallel. By extending a well-known testing concept and providing tooling integrated into the development environment Eclipse, JouleUnit requires a low learning curve for the integration into existing development and testing processes. Second, for the comparability of mobile applications in terms of energy efficiency, this thesis proposes an energy benchmarking and labeling service. Mobile applications belonging to the same usage domain are energy-profiled while executing a usage-domain specific benchmark in parallel. Thus, their energy consumption for specific use cases can be evaluated and compared afterwards. To abstract and summarize the profiling results, energy labels are derived that summarize the applications’ energy consumption over all evaluated use cases as a simple energy grade, ranging from A to G. Besides, users can decide how to weigh specific use cases for the computation of energy grades, as it is likely that different users use the same applications differently. The energy labeling service has been implemented for Android applications and evaluated for three different usage domains (being web browsers, email clients, and live wallpapers), showing that different mobile applications indeed differ in their energy consumption for the same services and, thus, their comparison is both possible and sensible. To the best of my knowledge, this is the first approach providing mobile application users comparable energy consumption information on mobile applications without installing and testing them on their own mobile devices

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Voice-For-Blind: An Utilizable Email Client for Visually Impaired Users

For people who are sighted, visually impaired, or blind, electronic mail has evolved into a vital tool for collaboration and communication. However, the current email-related activities on smartphones cause a number of problems due to insufficient mapping of haptic feedback, complex text-entry layouts, a variety of screen sizes and orientations, illogical ordering of navigational items, and inconsistent interface design. The Components on touch-screen interfaces that can't be seen can be difficult for blind people to precisely access, making it difficult for them to carry out common mailing tasks such as receiving, sending, organising, managing spam, deleting, searching, and filtering. Due to these issues, blind people are having trouble using smartphones and completing a number of tasks related to email. Junk and Spam email frustration and cognitive overload are additional effects. We proposed Voice-For-Blind an utilizable email client that is friendly to visully imapired individuals to get around the obstacles relating to the usability and accessibility of smartphone-related mailing activities. 38 blind participants in an empirical study who carried out 14 email-related tasks are used to evaluate the proposed email client. The outcomes of this prototype's use demonstrate an elevated accuracy in complettion, improved user experience, and improved touchscreen interface control for basic tasks like email management. The findings show that Voice-For-Blind is an email client that is inclusive of accessibility, giving blind individuals an enhanced user - interface experience and reducing cognitive load when managing emails