Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius. 1.

This paper describes a distributed, linear-time algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a two-dimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available. We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles real-world deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes.

Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.

Amorphous computing is the development of organizational principles and programming languages for obtaining coherent behavior from the cooperation of myriads of unreliable parts that are interconnected in unknown, irregular, and time-varying ways. The impetus for amorphous computing comes from developments in microfabrication and fundamental biology, each of which is the basis of a kernel technology that makes it possible to build or grow huge numbers of almost-identical information-processing units at almost no cost. This paper sets out a research agenda for realizing the potential of amorphous computing and surveys some initial progress, both in programming and in fabrication. We describe some approaches to programming amorphous systems, which are inspired by metaphors from biology and physics. We also present the basic ideas of cellular computing, an approach to constructing digital-logic circuits within living cells by representing logic levels by concentrations DNA-bin...

Self-config uration in wireless sensor networks is ag eneral class of estimation problems which we study via the Cramer-Rao bound (CRB).Specifically, we consider sensor location estimation when sensors measure received sig]P strengI (RSS) or time-of-arrival (TOA) between themselves and neig boring sensors.A small fraction of sensors in the network have known location while the remaining locations must be estimated.We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log -normal models for the TOA and RSS measurements, respectively.An extensive TOA and RSS measurement campaig in an indoor o#ce area illustrates MLE performance.Finally, relative location estimation alg orithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments.The measurements and testbed experiments demonstrate 1 m RMS location errorsusing TOA, and 1 m to 2 m RMS location errors using RSS. Index Terms sensor position location estimation, radio channel measurement, sig nal streng h, time-ofarrival, wireless sensor network testbed, self-config uration, Cramer-Rao bound I.

In many applications of wireless sensor networks (WSN), sensors are deployed un-tethered in hostile environments. For locationaware WSN applications, it is essential to ensure that sensors can determine their location, even in the presence of malicious adversaries. In this paper we address the problem of enabling sensors of WSN to determine their location in an un-trusted environment. Since localization schemes based on distance estimation are expensive for the resource constrained sensors, we propose a rangeindependent localization algorithm called SeRLoc. SeRLoc is distributed algorithm and does not require any communication among sensors. In addition, we show that SeRLoc is robust against severe WSN attacks, such as the wormhole attack, the sybil attack and compromised sensors. To the best of our knowledge, ours is the first work that provides a security-aware range-independent localization scheme for WSN. We present a threat analysis and comparison of the performance of SeRLoc with state-of-the-art range-independent localization schemes.

Sensor networks present a number of novel programming challenges for application developers. Their inherent limitations of computational power, communication bandwidth, and energy demand new approaches to programming that shield the developer from low-level details of resource management, concurrency, and in-network processing. We argue that sensor networks should be programmed at the global level, allowing the compiler to automatically generate nodal behaviors from a high-level specification of the network’s global behavior. This paper presents the design of a functional macroprogramming language for sensor networks, called Regiment. The essential data model in Regiment is based on region streams, which represent spatially distributed, time-varying collections of node state. A region stream might represent the set of sensor values across all nodes in an area or the aggregation of sensor values within that area. Regiment is a purely functional language, which gives the compiler considerable leeway in terms of realizing region stream operations across sensor nodes and exploiting redundancy within the network. We describe the initial design and implementation of Regiment, including a compiler that transforms a macroprogram into an efficient nodal program based on a token machine. We present a progresssion of simple programs that illustrate the power of Regiment to succinctly represent robust, adaptive sensor network applications.

We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building’s unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95 % of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of locationaware applications without requiring special-purpose hardware or complicated training and calibration procedures.

Many sensor network applications require that each node's sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don't scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure.We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.

We develop distributed algorithms for self-reconfiguring sensor networks that respond to directing a target through a region. The sensor network models the danger levels sensed across its area and has the ability to adapt to changes. It represents the danger areas as obstacles. A protocol that combines the artificial potential field of the sensors with the goal location for the moving object guides the object incrementally across the network to the goal, while maintaining the safest distance to the danger areas. We report on hardware experiments using a physical sensor network consisting of Mote sensors.