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Intelligent Transportation Systems in the domain of vehicular networking, have recently been subject to rapid development. In vehicular ad hoc networks, data broadcast is one of the main communication types and its reliability is crucial for high performance applications. However, due to the lack of acknowledgment techniques in IEEE 802.11p standard, it is challenging to ensure communication reliability. In this work, we analytically model a receiver-oriented reliability mechanism, with the objective of on-demand error recovery for multi-hop broadcast vehicular communication. In particular, using absorbing Markov modeling and probabilistic graphical modeling, we analyze its performance in terms of relevant indicators, such as overhead and delivery ratio. Further, the model is validated using simulations. The results are useful in tuning the influential parameters and accordingly adjusting trade-offs and meet performance requirements in various circumstances.

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

A system using frequency offset based transmit- reference (TR) modulation allows multiple nodes to transmit simultaneously and asynchronously without any mutual timing coordination. Thus, such a system provides inherent capabilities for a multiple access in the medium access control (MAC) layer to coordinate the shared use of the common wireless medium among the nodes of the wireless sensor network (WSN). However, certain characteristics of a frequency offset based system limits its performance, for example, the number of available frequency offsets is limited as it depends on several system parameters, and the number of simultaneous communications using different frequency offsets is limited due to inter-user interference. In this paper, we introduce an extended version of the performance model of a basic slotted- Aloha system, that captures the basic phenomena of a multi- channel system with a limited set of channels and a limit to the number of simultaneously used channels. An analysis of this model reveals the potential of a MAC protocol for TR modulation with frequency offsets.

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity.

Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatio-temporal associations in the locational history both at stay-points and on the paths connecting them. We compare the accuracy of our model with a number of other spatio-temporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.

With ever-increasing advancements of body area networks and participatory sensing, various healthcare applications have emerged. These applications are used to collect physiological and activity data from people and to evaluate their health situation in an online manner. The ultimate goal here is to ensure health and well-being of participants. Although various researches have been performed on underlying system of these applications from the technical point of view, very few have focused on ways to encourage people to use them and to increase their acceptability. In this paper, we present a taxonomy for motivations and incentives used in body area network based healthcare systems. Furthermore, we identify incentives' features and output expectancies and use them in a model to determine important factors that persuade people to accept and use a health monitoring system. Finally, we evaluate each incentive using Analytical Hierarchy Process (AHP) technique to identify the most effective elements of each incentive having a specific goal in mind.

Wireless sensor networks are composed of sensors with low computational and energy resources. Transmission of data is one of the most energy consuming operations in such networks. In-network data aggregation is a popular technique which is performed to reduce data transmissions. However, by aggregating multiple data into one, the security of them is no longer guaranteed. While concealed data aggregation methods have been recently proposed to provide energy efficient, end-to-end confidentiality, not much work has been done to enhance them with aggregate integrity. In this work, we propose a scalable and energy efficient bihomomorphic method to preserve end-to-end confidentiality and aggregate integrity against outsider attacks. A distributed validation scheme is used to prevent blind rejection caused by outsiders. To provide better resilience, a key refreshment method is used to prevent analysis attacks.