Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Improve Quality of Service for the Internet of Things using Blockchain & Machine Learning Algorithms.

[EN] The quality of service (QoS) parameters in IoT applications plays a prominent role in determining the performance of an application. Considering the significance and popularity of IoT systems, it can be predicted that the number of users and IoT devices are going to increase exponentially shortly. Therefore, it is extremely important to improve the QoS provided by IoT applications to increase their adaptability. Majority of the IoT systems are characterized by their heterogeneous and diverse nature. It is challenging for these systems to provide high-quality access to all the connecting devices with uninterrupted connectivity. Considering their heterogeneity, it is equally difficult to achieve better QoS parameters. Artificial intelligence-based machine learning (ML) tools are considered a potential tool for improving the QoS parameters in IoT applications. This research proposes a novel approach for enhancing QoS parameters in IoT using ML and Blockchain techniques. The IoT network with Blockchain technology is simulated using an NS2 simulator. Different QoS parameters such as delay, throughput, packet delivery ratio, and packet drop are analyzed. The obtained QoS values are classified using different ML models such as Naive Bayes (NB), Decision Tree (DT), and Ensemble, learning techniques. Results show that the Ensemble classifier achieves the highest classification accuracy of 83.74% compared to NB and DT classifiers.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.Comment: Please use MobiSys version when referencing this work: http://dl.acm.org/citation.cfm?id=2906392. 18 pages, recon.meddle.mob

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

An optimized context-aware mobile computing model to filter inappropriate incoming calls in smartphone

Requests for communication via mobile devices can be disruptive to the receiver in certain social situation. For example, unsuitable incoming calls may put the receiver in a dangerous condition, as in the case of receiving calls while driving. Therefore, designers of mobile computing interfaces require plans for minimizing annoying calls. To reduce the frequency of these calls, one promising approach is to provide an intelligent and accurate system, based on context awareness with cues of a callee's context allowing informed decisions of when to answer a call. The processing capabilities and advantages of mobile devices equipped with portable sensors provide the basis for new context-awareness services and applications. However, contextawareness mobile computing systems are needed to manage the difficulty of multiple sources of context that affects the accuracy of the systems, and the challenge of energy hungry GPS sensor that affects the battery consumption of mobile phone. Hence, reducing the cost of GPS sensor and increasing the accuracy of current contextawareness call filtering systems are two main motivations of this study. Therefore, this study proposes a new localization mechanism named Improved Battery Life in Context Awareness System (IBCS) to deal with the energy-hungry GPS sensor and optimize the battery consumption of GPS sensor in smartphone for more than four hours. Finally, this study investigates the context-awareness models in smartphone and develops an alternative intelligent model structure to improve the accuracy rate. Hence, a new optimized context-awareness mobile computing model named Optimized Context Filtering (OCF) is developed to filter unsuitable incoming calls based on context information of call receiver. In this regard, a new extended Naive Bayesian classifier was proposed based on the Naive Bayesian classifier by combining the incremental learning strategy with appropriate weight on the new training data. This new classifier is utilized as an inference engine to the proposed model to increase its accuracy rate. The results indicated that 7% improvement was seen in the accuracy rate of the proposed extended naive Bayesian classifier. On the other hand, the proposed model result showed that the OCF model improved the accuracy rate by 14%. These results indicated that the proposed model is a hopeful approach to provide an intelligent call filtering system based on context information for smartphones

Improving the security level of the FUSION@ multi-agent architecture

The use of architectures based on services and multi-agent systems has become an increasingly important part of the solution set used for the development of distributed systems. Nevertheless, these models pose a variety of problems with regards to security. This article presents the Adaptive Intrusion Detection Multi-agent System (AIDeMaS), a mechanism that has been designed to detect and block malicious SOAP messages within distributed systems built by service based architectures. AIDeMaS has been implemented as part of FUSION@, a multi-agent architecture that facilitates the integration of distributed services and applications to optimize the construction of highly-dynamic multi-agent systems. One of the main features of AIDeMaS is that is employs case-based reasoning mechanisms, which provide it with great learning and adaptation capabilities that can be used for classifying SOAP messages. This research presents a case study that uses the ALZ-MAS system, a multi-agent system built around FUSION@, in order to confirm the effectiveness of AIDeMaS. The preliminary results are presented in this paper