Recent work in wireless embedded networked systems has followed heterogeneous designs, incorporating a mixture of elements from extremely constrained 8- or 16-bit “Motes ” to less resourceconstrained 32-bit embedded “Microservers.” Emstar is a software environment for developing and deploying complex applications on such heterogeneous networks. Emstar is designed to leverage the additional resources of Microservers by trading off some performance for system robustness in sensor network applications. It enables fault isolation, fault tolerance, system visiblity, in-field debugging, and resource sharing across multiple applications. In order to accomplish these objectives, Emstar is designed to run as a multiprocess system and consists of libraries that implement message-passing IPC primitives, services that support networking, sensing, and time synchronization, and tools that support simulation, emulation, and visualization of live systems, both real and simulated. We evaluate this work by discussing the Acoustic ENSBox, a platform for distributed acoustic sensing that we built using Emstar. We show that by leveraging existing Emstar services, we are able to significantly reduce development time This work was made possible with support from The Center for Embedded Networked Sensing (CENS) under the NSF Cooperative Agreement CCR-0120778, and the UC MICRO program (grant

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 considered. 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 1connectivity, when rc/rs < √ 3. Finally, we compare the efficiency of some popular regular deployment patterns such as the square grid and triangular lattice, in terms of the number of sensors needed to provide coverage and connectivity.

When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least £ distinct sensors before it crosses the barrier of wireless sensors, we say the network provides £-barrier coverage. In this paper, we develop theoretical foundations for £-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is £-barrier covered. Next, we establish the optimal deployment pattern to achieve £-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for £-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak £-barrier coverage.

We present the philosophy, design, and initial evaluation of the Trio Testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

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 smallscale 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.

to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation. (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources. (iii) High fidelity sensor data generation and real-time data and event injection. (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines. I.

We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox’s integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20–30 m distances between neighboring nodes. Categories and Subject Descriptors C.3 [Computer Systems Organization]: Special-Purpose and Application-Based Systems—Signal processing

There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect; that is, the many-to-one multi-hop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multi-radio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon, a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a

Early experiences in the WSN community, including our own, have reinforced the difficulties of developing large-scale applications. Extant analytical/formal models of sensor network hardware and simulation environments often lack the fidelity to adequately capture sensor and network behaviors of WSN applications when deployed in the field. As a result, the community has gravitated towards experimentation with current hardware and software platforms, not only to demonstrate applicability in real environments, but also to validate prototypes. Experimentation via at-scale field deployments has however been time-consuming and, in several respects, tedious. As a result, hardware and software testbeds are becoming a preferred basis for experimentation; they yield substantial efficiency in instrumenting potentially longlived experiments as compared to field deployments, which is valuable in the debugging, validation, and integration phases. They also enable research aimed at developing sound models and simulation tools, which if successful will in turn further expedite the development process. Some testbeds are sensing application- or domain-specific [1, 2, 3, 4]. Early adopter applications include security (of perimeters, buildings, etc.), monitoring (of agricultural lands, animal farms, forests, rivers, earthquakes, volcanoes, natural habitats, endangered species, factory

Target tracking systems need to meet certain real-time constraints in response to transient events, such as fast-moving targets. While the real-time performance is a major concern in these applications, it should be compatible with other important system properties such as energy consumption and accuracy. This work presents the real-time design and analysis of VigilNet, a large-scale sensor network system which tracks, detects and classifies targets in a timely and energy efficient manner. Based on a deadline partition method and theoretical derivations to guarantee each sub-deadline, we are able to make guided engineering decisions to meet the endto-end tracking deadline. The results from 10,000-node simulation and 200 mote field test reveal the effectiveness of our design.