Data Collection and Aggregation in Mobile Sensing

Nowadays, smartphones have become ubiquitous and are playing a critical role in key aspects of people\u27s daily life such as communication, entertainment and social activities. Most smartphones are equipped with multiple embedded sensors such as GPS (Global Positioning System), accelerometer, camera, etc, and have diverse sensing capacity. Moreover, the emergence of wearable devices also enhances the sensing capabilities of smartphones since most wearable devices can exchange sensory data with smartphones via network interfaces. Therefore, mobile sensing have led to numerous innovative applications in various fields including environmental monitoring, transportation, healthcare, safety and so on. While all these applications are based on two critical techniques in mobile sensing, which are data collection and data aggregation, respectively. Data collection is to collect all the sensory data in the network while data aggregation is any process in which information is gathered and expressed in a summary form such as SUM or AVERAGE. Obviously, the above two problems can be solved by simply collect all the sensory data in the whole network. But that will lead to huge communication cost. This dissertation is to reduce the huge communication cost in data collection and data aggregation in mobile sensing where the following two technical routes are applied. The first technical route is to use sampling techniques such as uniform sampling or Bernoulli sampling. In this way, an aggregation result with acceptable error can be can be calculate while only a small part of mobile phones need to submit their sensory data. The second technical rout is location-based sensing in which every mobile phone submits its geographical position and the mobile sensing platform will use the submitted positions to filter useless sensory data. The experiment results indicate the proposed methods have high performance

Secure Communication in wise Homes using IoT

The advancement of the Internet of Things has madeextraordinary progress in recent years in academic as well as industrial fields. There are quite a few wise home systems (WHSs) that have been developed by major companies to achieve home automation. However, the nature of wise homesinescapable raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving com-munication protocol for the WHSs. In our proposed scheme, data transmissions within the WHS are secured by a symmetric encryption scheme with secret keys being generated by anarchicsystems. Meanwhile, we incorporate message authentication codes to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead

Characterizing smart environments as interactive and collective platforms: A review of the key behaviors of responsive architecture

Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces

Game Theory Based Privacy Protection for Context-Aware Services

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information