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.