The fast growth of cloud applications highlights the requirement of appropriate security controls to restrict access to shared resources limited to authorized users. Existing authorization systems are not primarily designed for cloud environments and do not provide the required flexibility, adaptability, elasticity, scalability, or fine-grainedness of cloud applications. This paper outlines an ongoing effort in development of a flexible fine-grained access control system for modern cloud-based applications. Modern cloud applications are distinctive in that the required authorization rules are defined by the organizations owning data and resources, before the application logic can be developed by their programmers. Although this simplifies cloud application development and provides flexibility and adaptability to potential future policy changes, it highlights the need for an adaptive flexible authorization system.

Saving energy is one of the principal challenges in wireless sensor networks. Dynamic voltage and frequency scaling (DVFS) is often used to reduce energy consumption in systems where sleep is not an option. We show that changing the CPU frequency introduces sudden changes in clock behavior, thereby affecting the time-keeping functionality. This anomalous phenomenon is observed in different sensor platforms and causes a loss of time synchronization among nodes that is unacceptable to applications such as structural health monitoring that require tightly synchronized clocks over extended periods. The paper provides experimental measurements of the clock value shift anomaly in three widely used wireless sensor platforms and its impact on clock synchronization costs. A general framework balancing the need to save energy against the need to keep clocks synchronized is developed. Our system is implemented and evaluated on a network of sensors deployed on a truss bridge and running a high-fidelity structural health monitoring application. Experimental measurements confirm the efficacy of the solution in saving energy while maintaining an acceptable level of synchronization.

The process of determining the physical locations of nodes in a wireless sensor network is known as localization . Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35 m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1% (33 cm). The localization algorithm is based on least-squares scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor network deployments.

The process of computing the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments.

We consider the use of binary proximity sensors for tracking targets. Such sensors provide only 1-bit information regarding a target's presence or absence in their vicinity, albeit with less than 100% reliability. A novel tracking method employing such binary sensors is proposed and its performance in different deployment scenarios evaluated. For a given target, the method utilizes the sensor outputs to estimate individual positions in the path of the target in the near past and finds the line which best fits the path points. This line is then used to estimate the target's current position. A performance study has been conducted through comprehensive simulation using parameters collected from a prototype deployment consisting of wireless micro-sensors with binary acoustic detectors.

We present a novel method for tracking the movement of people or vehicles in open outdoor environments using sensor networks. Unlike other sensor network-based methods, which depend on determining distance to the target or the angle of arrival of the signal, our cooperative tracking approach requires only that a sensor be able to determine if an object is somewhere within the maximum detection range of the sensor. We propose cooperative tracking as a method for tracking moving objects and extrapolating their paths in the short term. By combining data from neighboring sensors, this approach enables tracking with a resolution higher than that of the individual sensors being used. We employ statistical estimation and approximation techniques to further increase the tracking precision, and to enable the system to exploit the tradeoff between accuracy and timeliness of the results. We analyze the behavior of the cooperative tracking algorithm through simulation, focusing on the effects of approximation techniques on the quality of estimates achieved. This work focuses on acoustic tracking, however the presented methodology is applicable to any sensing modality where the sensing range is relatively uniform.