Anish Arora

Barrier coverage with wireless sensors

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 k distinct sensors before it crosses the barrier of wireless sensors, we say the network provides k-barrier coverage. In this paper, we develop theoretical foundations for k-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment 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. 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 k-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak k-barrier coverage.

Design of a wireless sensor network platform for detecting rare, random, and ephemeral events

We present the design of the extreme scale mote, a new sensor network platform for reliably detecting and classifying, and quickly reporting, rare, random, and ephemeral events in a large-scale, long-lived, and ret askable manner. This new mote was designed for the ExScal project which seeks to demonstrate a 10,000 node network capable of discriminating civilians, soldiers and vehicles, spread out over a 10 km/sup 2/ area, with node lifetimes approaching 1,000 hours of continuous operation on two AA alkaline batteries. This application posed unique functional, usability, scalability, and robustness requirements which could not be met with existing hardware, and therefore motivated the design of a new platform. The detection and classification requirements are met using infrared, magnetic, and acoustic sensors. The infrared and acoustic sensors are designed for low-power continuous operation and include asynchronous processor wakeup circuitry. The usability and scalability requirements are met by minimizing the frequency and cost of human-in-the-loop operations during node deployment, activation, and verification through improvements in the user interface, packaging, and configurability of the platform. Recoverable retasking is addressed by using a grenade timer that periodically forces a system reset. The key contributions of this work are a specific design point and general design methods for building sensor network platforms to detect exceptional events.

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 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.

Closure and convergence: a foundation of fault-tolerant computing

The authors formally define what it means for a system to tolerate a class of faults. The definition consists of two conditions. The first is that if a fault occurs when the system state is within the set of legal states, the resulting state is within some larger set and, if faults continue to occur, the system state remains within that larger set (closure). The second is that if faults stop occurring, the system eventually reaches a state within the legal set (convergence). The applicability of the definition for specifying and verifying the fault-tolerance properties of a variety of digital and computer systems is demonstrated. Using the definition, the authors obtain a simple classification of fault-tolerant systems. Methods for the systematic design of such systems are discussed. >

Kansei: a testbed for sensing at scale

The Kansei testbed at the Ohio State University is designed 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

Automating the Addition of Fault-Tolerance

In this paper, we focus on automating the transformation of a given fault-intolerant program into a fault-tolerant program. We show how such a transformation can be done for three levels of fault-tolerance properties, failsafe, nonmasking and masking. For the high atomicity model where the program can read all the variables and write all the variables in one atomic step, we show that all three transformations can be performed in polynomial time in the state space of the fault-intolerant program. For the low atomicity model where restrictions are imposed on the ability of programs to read and write variables, we show that all three transformations can be performed in exponential time in the state space of the fault-intolerant program. We also show that the the problem of adding masking fault-tolerance is NP-hard and, hence, exponential complexity is inevitable unless P=NP.

Component based design of multitolerant systems

The concept of multitolerance abstracts problems in system dependability and provides a basis for improved design of dependable systems. In the abstraction, each source of undependability in the system is represented as a class of faults, and the corresponding ability of the system to deal with that undependability source is represented as a type of tolerance. Multitolerance thus refers to the ability of the system to tolerate multiple fault classes, each in a possibly different way. We present a component based method for designing multitolerance. Two types of components are employed by the method, namely detectors and correctors. A theory of detectors, correctors, and their interference free composition with intolerant programs is developed, which enables stepwise addition of components to provide tolerance to a new fault class while preserving the tolerances to the previously added fault classes. We illustrate the method by designing a fully distributed multitolerant program for a token ring.

Detectors and correctors: a theory of fault-tolerance components

Two primitive components, namely detectors and correctors, provide a basis for achieving the different types of fault tolerance properties required in computing systems. We develop the theory of these primitive tolerance components, characterizing precisely their role in achieving the different types of fault tolerance. Also, we illustrate how they can be used to formulate extant design methods and argue that they sometimes offer the potential for better designs than those obtained from extant methods.

Kansei: a high-fidelity sensing testbed

Hardware and software testbeds are becoming the preferred basis for experimenting with embedded wireless sensor network applications. The Kansei testbed at the Ohio State University features a heterogeneous hardware infrastructure, with dedicated node resources for local computation, storage, data retrieval, and back-channel communication. Kansei includes a time-accurate hybrid simulation engine that uses testbed hardware resources to simulate large arrays. It supports high-fidelity sensor data generation as well as real-time data and event injection. The testbed also includes software components and an associated job-control language for complex multi-tier experiments.

Sprinkler: a reliable and energy efficient data dissemination service for wireless embedded devices

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions, and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and an indoor environment. Our indoor evaluation is based on an implementation in the Kansei testbed that houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluations show that Sprinkler is not only energy efficient as compared to existing schemes but also have less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as network grows larger