In old times, castles were surrounded by moats (deep trenches filled with water, and even alligators) to thwart or discourage intrusion attempts. One can now replace such barriers with stealthy and wireless sensors. In this paper, we develop theoretical foundations for laying barriers of wireless sensors. We define the notion of k-barrier coverage of a belt region using wireless sensors. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether a region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. We introduce two notions of probabilistic barrier coverage in a belt region -- weak and strong barrier coverage. While weak barrier-coverage with high probability guarantees the detection of intruders as they cross a barrier of stealthy sensors, a sensor network providing strong barrier-coverage with high probability (at the expense of more sensors) guarantees the detection of all intruders crossing a barrier of sensors, even when the sensors are not stealthy. Both types of barrier coverage require significantly less number of sensors than full-coverage, where every point in the region needs to be covered. We derive critical conditions for weak k-barrier coverage, using which one can compute the minimum number of sensors needed to provide weak k-barrier coverage with high probability in a given belt region. Deriving critical conditions for strong k-barrier coverage for a belt region is still an open problem.

/pdf/barrier-coverage-with-wireless-sensors-snuzrnbyt0.pdf

Barrier coverage with wireless sensors

Applications of wireless sensor networks for urban areas

It is well-known that placing disks in the triangular lattice pattern is optimal for achieving full coverage on a plane. With the emergence of wireless sensor networks, however, it is now no longer enough to consider coverage alone when deploying a wireless sensor network; connectivity must also be con-sidered. While moderate loss in coverage can be tolerated by applications of wireless sensor networks, loss in connectivity can be fatal. Moreover, since sensors are subject to unanticipated failures after deployment, it is not enough to have a wireless sensor network just connected, it should be k-connected (for k > 1 ). In this paper, we propose an optimal deployment pattern to achieve both full coverage and 2-connectivity, and prove its optimality for all values of rc/rs, where rc is the communication radius, and rs is the sensing radius. We also prove the optimality of a previously proposed deployment pattern for achieving both full coverage and 1-connectivity, when rc/rs

Deploying wireless sensors to achieve both coverage and connectivity

The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and specialized applications (e.g., environmental monitoring, etc.) that are built on low-powered sensor devices that self-organize to form application-specific multihop wireless networks. We believe that sensor networks have reached an important crossroads in their development. The question we address in this paper is how to propel sensor networks from their small-scale application-specific network origins, into the commercial mainstream of people's every day lives; the challenge being: how do we develop large-scale general-purpose sensor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban settings (e.g., enterprises, hospitals, recreational areas, towns, cities, and the metropolis). We propose MetroSense, a new people-centric paradigm for urban sensing at the edge of the Internet, at very large scale. We discuss a number of challenges, interactions and characteristics in urban sensing applications, and then present the MetroSense architecture which is based fundamentally on three design principles: network symbiosis, asymmetric design, and localized interaction. The ability of MetroSense to scale to very large areas is based on the use of an opportunistic sensor networking approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination between people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, opportunistic tasking, and opportunistic data collection. We discuss architectural challenges including providing sensing coverage with sparse mobile sensors, how to hand off roles and responsibilities between sensors, improving network performance and connectivity using adaptive multihop, and importantly, providing security and privacy for people-centric sensors and data.

http://sensorlab.cs.dartmouth.edu/pubs/wicon06.pdf

People-centric urban sensing

Ubiquitous smart environments, equipped with low-cost and easy-deployable wireless sensor networks (WSNs) and widespread mobile ad hoc networks (MANETs), are opening brand new opportunities in wide-scale urban monitoring. Indeed, MANET and WSN convergence paves the way for the development of brand new Internet of Things (IoT) communication platforms with a high potential for a wide range of applications in different domains. Urban data collection, i.e., the harvesting of monitoring data sensed by a large number of collaborating sensors, is a challenging task because of many open technical issues, from typical WSN limitations (bandwidth, energy, delivery time, etc.) to the lack of widespread WSN data collection standards, needed for practical deployment in existing and upcoming IoT scenarios. In particular, effective collection is crucial for classes of smart city services that require a timely delivery of urgent data such as environmental monitoring, homeland security, and city surveillance. After surveying the existing WSN interoperability efforts for urban sensing, this paper proposes an original solution to integrate and opportunistically exploit MANET overlays, impromptu, and collaboratively formed over WSNs, to boost urban data harvesting in IoT. Overlays are used to dynamically differentiate and fasten the delivery of urgent sensed data over low-latency MANET paths by integrating with latest emergent standards/specifications for WSN data collection. The reported experimental results show the feasibility and effectiveness (e.g., limited coordination overhead) of the proposed solution.

Convergence of MANET and WSN in IoT Urban Scenarios

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

/pdf/exscal-elements-of-an-extreme-scale-wireless-sensor-network-2u04tlmxny.pdf

ExScal: elements of an extreme scale wireless sensor network

Project ExScal (for Extreme Scale) fielded a 1000+ node wireless sensor network and a 200+ node ad hoc network of 802.11 devices in a 1.3km by 300m remote area in Florida during December 2004. In several respects, these networks are likely the largest deployed networks of either type to date. We overview here the key requirements of the project, describe briefly how they were met and experimentally tested, and provide a pointer to our experimental results.

Bill Ferriera

Papers

ExScal: elements of an extreme scale wireless sensor network

Project ExScal (Short Abstract).

Project exscal