The circular sensing model has been widely used to estimate performance of sensing applications in existing analyses and simulations. While this model provides valuable high-level guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the sensing irregularity introduced by existence of obstacles in real deployment areas and insufficient hardware calibration. In this paper, we design and implement two sensing area modeling (SAM) techniques useful in the real world. They complement each other in the design space. Physical sensing area modeling (P-SAM) provides accurate physical sensing area for individual nodes using controlled or monitored events, while virtual sensing area modeling (V-SAM) provides continuous sensing similarity between nodes using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in real-world settings, using testbeds consisting of 14 XSM motes. To study the performance at scale, we also provide an extensive 1,400-node simulation. Evaluation results reveal several serious issues concerning circular models and demonstrate significant improvements in several applications when SAM is used instead.

As a key approach to achieve energy efficiency in sensor networks, sensing coverage has been studied extensively. Researchers have designed many coverage protocols to provide various kinds of service guarantees on the network lifetime, coverage ratio and detection delay. While these protocols are effective, they are not flexible enough to meet multiple design goals simultaneously. In this paper, we propose a unified sensing coverage architecture, called uSense, which features three novel ideas: asymmetric architecture, generic switching and global scheduling. We propose asymmetric architecture based on the conceptual separation of switching from scheduling. Switching is efficiently supported in sensor nodes, while scheduling is done in a separated computational entity, where multiple scheduling algorithms are supported. As an instance, we propose a two-level global coverage algorithm, called uScan. At the first level, coverage is scheduled to activate different portions of an area. We propose an optimal scheduling algorithm to minimize area breach. At the second level, sets of nodes are selected to cover active portions. Importantly, we show the feasibility to obtain optimal set-cover results in linear time if the layout of areas satisfies certain conditions. We evaluate our architecture with a network of 30 MicaZ motes, an extensive simulation with 10,000 nodes, as well as theoretical analysis. The results indicate that uSense is a promising architecture to support flexible and efficient coverage in sensor networks.

Despite the well-known fact that sensing patterns in reality are highly irregular, researchers continue to develop protocols with simplifying assumptions about the sensing. For example, a circular 0/1 sensing model is widely used in most existing simulators and analysis. While this model provides high-level guidelines, it could cause wrong estimation of system performance in the real world. In this project, we design and implement a practical Sensing Area Modeling technique, called SAM. By injecting events through regular and hierarchical training, SAM estimates the sensing areas of individual sensor nodes accurately. Especially, this work is the first to investigate the impact of irregular sensing area on application performance, such as coverage scheduling. We evaluate SAM using outdoor experiments with XSM motes, indoor experiment with 40 MicaZ motes as well as an extensive 1000-node simulation. Our evaluation results reveal serious problems caused by circular sensing model, while demonstrating significant performance improvements in major applications when SAM is used.

In an adversarial environment, various kinds of security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located. In this paper, we propose a secure localization mechanism that detects the existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in quite strong results: even when the number of phantom nodes is greater than that of honest nodes, we can Alter out most phantom nodes. Our analysis and simulations under realistic noisy settings demonstrate our scheme is effective in the presence of a large number of phantom nodes.

The circular sensing model has been widely used to estimate performance of sensing applications in existing analysis and simulations. While this model provides valuable high-level guidelines, the quantitative results obtained may not reflect the true performance of these applications, due to the existence of obstacles and sensing irregularity introduced by insufficient hardware calibration. In this project, we design and implement two Sensing Area Modeling (SAM) techniques useful in the real world. They complement each other in the design space. P-SAM provides accurate sensing area models for individual nodes using controlled or monitored events, while V-SAM provides continuous sensing similarity models using natural events in an environment. With these two models, we pioneer an investigation of the impact of sensing irregularity on application performance, such as coverage scheduling. We evaluate SAM extensively in real-world settings, using three testbeds consisting of 40 MICAz motes and 14 XSM motes. To study the performance at scale, we also provide an extensive 1,400-node simulation. Evaluation results reveal several serious issues concerning circular models, and demonstrate significant improvements

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