ccurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. Low-power wireless sensors may be many hops away from any other sensors with a priori location information.In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then form a map of the network. Various application requirements (such as scalability, energy efficiency, and accuracy) will influence the design of sensor localization systems. In this article, we describe measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, we show how to calculate a Cramér-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. This is a useful tool to help system designers and researchers select measurement technologies and evaluate localization algorithms. We also briefly survey a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research. INTRODUCTIONDramatic advances in RF and MEMS IC design have made possible the use of large networks of wireless sensors for a variety of new monitoring and control applications [1]- [5]. For example, smart structures will actively respond to earthquakes and make buildings safer; precision agriculture will reduce costs and environmental impact by watering and fertilizing only where necessary and will improve quality by monitoring storage conditions after harvesting; condition-basedA maintenance will direct equipment servicing exactly when and where it is needed based on data from wireless sensors; traffic monitoring systems will better control stoplights and inform motorists of alternate routes in the case of traffic jams; and environmental monitoring networks will sense air, water, and soil quality and identify the source of pollutants in real time.Automatic localization of the sensors in these wireless networks is a key enabling technology. The overwhelming reason is that a sensor's location must be known for its data to be meaningful. As an additional motivation, sensor location information (if it is accurate enough) can be extremely useful for scalable, "geographic" routing algorithms. Note also that location itself is often the data that needs to be sensed; localization can be the driving force for wireless sensor networks in applications such as warehousing and manufacturing logistics.To make these applications viable with possibly vast numbers of sensors, device costs will need to be low (from a few dollars to a few cents depending on the application), sensors will need to last for years or even decades ...

This paper examines the performance of cooperative the energy estimation and detection circuit of Figure 1. The spectrum sensing, using energy detection, in Suzuki fading decision is made by comparing the decision statistic Y, which channels. Sub-optimal centralized detection approaches are corresponds to energy collected in the observation time T, to an examined where decisions are made based on identical tests appropriate threshold that is traditionally selected to satisfy the performed at the individual radios. Decisions are performed at a false alarm rate specification of the detector. fusion center using a counting rule that encompasses the OR, AND, and majority rules as special cases. Analytical and simulation results are presented for Rayleigh, Log-normal and _ 1 T H Suzuki distributions.To

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