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Localization is a fundamental problem in wireless sensor networks. In many applications, sensor location information is critical for data processing and meaning. While the global positioning system (GPS) can be used to determine mote locations with meter precision, the high hardware cost and energy requirements of GPS receivers often prohibit the ubiquitous use of GPS for location estimates. This high cost (in terms of hardware price and energy consumption) of GPS has motivated researchers to develop localization protocols that determine mote locations based on cheap hardware and localization algorithms. The purpose of this paper is to present a comprehensive review of wireless sensor network localization techniques, and provide a detailed overview for several distance‐based localization algorithms.

To provide a detailed summary of wireless sensor network localization algorithms, the authors outline a tiered classification system in which they first classify algorithms as distributed, distributed‐centralized, or centralized. From this broad classification, the paper then further categorizes localization algorithms using their protocol techniques. By utilizing this classification system, the authors are able to provide a survey of several wireless sensor network localization algorithms and summarize relative algorithm performance based on the algorithms' classification.

There are numerous localization algorithms available and the performance of these algorithms is dependent on network configuration, environmental variables, and the ranging method implemented. When selecting a localization algorithm, it is important to understand basic algorithm operation and expected performance. This tier‐based algorithm classification system can be used to gain a high‐level understanding of algorithm performance and energy consumption based on known algorithm characteristics.

Localization is a widely researched field and given the quantity of localization algorithms that currently exist, it is impossible to present a complete review of every published algorithm. Instead, the paper presents a holistic view of the current state of localization research and a detailed review of ten representative distance‐based algorithms that have diverse characteristics and methods. This review presents a new classification structure that may help researchers understand, at a high‐level, the expected performance and energy consumption of algorithms not explicitly addressed by our work.

The pattern of racial segregation in U.S. elementary and secondary schools has changed significantly over the last 25 years. This chapter examines the relationship between the racial composition of schools and the choices white parents make concerning the schools their children attend. Restricted access files at the Bureau of the Census allow us to identify each household's Census block of residence and, in turn, suburban public school districts and urban public school attendance areas. We find that the racial composition of schools and neighborhoods are very important in the school and location decisions of white families.

Outdoor localization is an important issue for many applications, such as autonomous mobile robotics and augmented reality. The purpose of this paper is to propose a budgeted dynamic exclusion heuristic based on signal phase shifts from multiple base stations.

The authors also propose an outdoor localization technique based on the particle filter for data fusion and present an overview of a potential target application of the proposed outdoor localization approach for the blind and visually impaired (BVI).

The combination of multiple sensor data tends to overcome the drawbacks of using one sensor technology in the localization process.

The novelty of the proposed approach stems from its ability to fuse data collected from different sensor technologies to converge to more accurate position estimation.

The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.

Nowadays, WiFi indoor positioning based on received signal strength (RSS) becomes a research hotspot due to its low cost and ease of deployment characteristics. To further improve the performance of WiFi indoor positioning based on RSS, this paper aims to propose a novel position estimation strategy which is called radius-based domain clustering (RDC). This domain clustering technology aims to avoid the issue of access point (AP) selection.

The proposed positioning approach uses each individual AP of all available APs to estimate the position of target point. Then, according to circular error probable, the authors search the decision domain which has the 50 per cent of the intermediate position estimates and minimize the radius of a circle via a RDC algorithm. The final estimate of the position of target point is obtained by averaging intermediate position estimates in the decision domain.

Experiments are conducted, and comparison between the different position estimation strategies demonstrates that the new method has a better location estimation accuracy and reliability.

Weighted k nearest neighbor approach and Naive Bayes Classifier method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of the two strategies are affected by AP selection strategies and inappropriate selection of APs may degrade positioning performance considerably.

The RDC positioning approach can improve the performance of WiFi indoor positioning, and the issue of AP selection and related drawbacks is avoided.

The RSS-based effective WiFi indoor positioning system can makes up for the indoor positioning weaknesses of global navigation satellite system. Many indoor location-based services can be encouraged with the effective and low-cost positioning technology.

A novel position estimation strategy is introduced to avoid the AP selection problem in RSS-based WiFi indoor positioning technology, and the domain clustering technology is proposed to obtain a better accuracy and reliability.

Available literature claims that location is a key attribute in the housing market. However, the impact of this attribute is difficult to measure and the traditional hedonic approach using subjective assessments is problematic. This paper seeks to explore trend surface analysis technique, attempting to provide an alternative way to measure location values.

TSA works in a similar way to other response surface methods but it is implemented directly in regression models, using a set of combinations of the co‐ordinates of properties in several power degrees. It can also be implemented in artificial neural networks, taking advantage of the neural ability in non‐linear domains. This work presents a comparison between traditional regression approach, error modelling, response surfaces, and TSA. ANN is also used to estimate some models, comparing their results. The objective is to verify the behaviour of TSA in hedonic models. A case study was carried using data of over 30,000 sales tax data of apartments sold in Porto Alegre, a southern Brazilian town.

The results indicates that TSA is an effective tool for the spatial analysis of real estate, because TSA models are similar to other approaches, but are developed with less expert work.

This paper presents an application of TSA in real estate market, which is an interesting alternative to traditional measures of location attributes.

Examines one of the main problems of mobile robot navigation: determining exactly where the robot is at all times. Describes the most important algorithm in localization: the extended Kalman filter. Looks at the simplest type of navigation using a system of fixed beacons in conjunction with a special sensor located on the vehicle and also the use of “natural beacons”. Discuss the problems of building and maintaining a map for the vehicle. Concludes that a complete solution to mobile vehicle localization is a long way off.

In this paper, the authors examine the association between a firm's geographical location and the value of its cash holdings.

Following Loughran and Schultz (2005) and Nielsson and Wójcik (2016), the authors define firms as either geographically remote or geographically proximate based on their distance to areas that are either largely populated or concentrated in financial expertise. We also estimate the marginal value of cash using the model developed by Faulkender and Wang (2006).

The authors find that the marginal value of cash is $0.10–$0.16 lower in remotely located firms than in geographically proximate firms. The lower marginal value of cash is prominent among remotely located firms with greater severity of information asymmetry. Our findings support the view that the inability of shareholders to closely monitor how managers use of firm cash may increase the perceived conflicts of interest associated with managers' cash spending and decrease the value of cash.

Previous studies try to explain the cash holdings puzzle by attributing it to CEO overconfidence, external funding constraints, poor corporate governance, difference in corporate financial policy, poor investor protection, lack of firm diversification and large operating losses. This study contributes to the extant literature by offering new evidence of the role of geographic location on the value of cash holdings.

In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

The purpose of this paper is to estimate crop yield densities considering time trends in the first three moments and spatially varying coefficients.

Yield density parameters are assumed to be spatially correlated, through a Gaussian spatial process. This study spatially smooth multiple parameters using Bayesian Kriging.

Assuming that county yields follow skew normal distributions, the location parameter increased faster in the eastern and northwestern counties of Iowa, while the scale increased faster in southern counties and the shape parameter increased more (implying less left skewness) in southwestern counties. Over time, the mean has increased sharply, while the variance and left skewness increased modestly.

Bayesian Kriging can smooth time-varying yield distributions, handle unbalanced panel data and provide estimates when data are missing. Most past models used a two-stage estimation procedure, while our procedure estimates parameters jointly.