The anatomy of a context-aware application

Harter, Andy; Hopper, Andy; Steggles, Pete; Ward, Andy; Webster, Paul

doi:10.1145/313451.313476

Cited by 915 publications

(583 citation statements)

References 11 publications

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“…In the past several years, a number of localization protocols have been proposed to reduce or completely remove the dependence on GPS in wireless sensor networks [1]- [4], [9], [10], [27]- [31], [33], [34].…”

Section: A Localization Problems and Schemesmentioning

confidence: 99%

“…Common techniques for distance/angle estimation include Time of Arrival (TOA) [11], Time Difference of Arrival (TDOA) [1], [9], [31], Angle of Arrival (AOA) [29], and Received Signal Strength (RSS) [1]. While producing fine-grained locations, range-based protocols remain costineffective due to the cost of hardware for radio, sound, or video signals, as well as the strict requirements on time synchronization and energy consumption.…”

Section: A Localization Problems and Schemesmentioning

confidence: 99%

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LAD: Localization anomaly detection for wireless sensor networks

Fang

Ningi

2006

Journal of Parallel and Distributed Computing

134

130

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Abstract-In wireless sensor networks (WSNs), sensors' locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery (localization) schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the reference information provided by these beacon nodes.Most of the beacon-based localization schemes assume a benign environment, where all beacon nodes are supposed to provide correct reference information. However, when the sensor networks are deployed in a hostile environment, where beacon nodes can be compromised, such an assumption does not hold anymore.In this paper, we propose a general scheme to detect localization anomalies that are caused by adversaries. Our scheme is independent from the localization schemes. We formulate the problem as an anomaly intrusion detection problem, and we propose a number of ways to detect localization anomalies. We have conducted simulations to evaluate the performance of our scheme, including the false positive rates, the detection rates, and the resilience to node compromises.

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Section: A Localization Problems and Schemesmentioning

confidence: 99%

Section: A Localization Problems and Schemesmentioning

confidence: 99%

LAD: Localization anomaly detection for wireless sensor networks

Fang

Ningi

2006

Journal of Parallel and Distributed Computing

134

130

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“…TDoA technique is normally used to measure distance from a transmitter to receivers or vice versa and then, target position is determined by using these distances and applying lateration methods. Active Bat of Andy Ward and his colleagues ( [8] and [9]), Cricket Indoor Localization of Priyantha and his team at MIT ( [36] and [37]) are good completed systems which pioneer this technique. Ultrasound-based positioning systems provide an accuracy up to centimeter range but require a stationary structure and line of sight (LoS) condition, it means user has to ensure that there is no obstacle between transmitter and receiver.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

Tan¹

2017

Preprint

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This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

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“…These include Parkinson's disease (PD), Alzheimer's disease (AD), Huntington disease (HD), and Amyotrophic Lateral Sclerosis (ALS) among others. Patients suffering with these kinds of disease, experience a cognitive decline over a long period and symptoms include gait abnormalities, problems with speech, and memory loss due to progressive cognitive deterioration [10]. Analysing gait signals to recognize relevant patterns and correlations with specific neurodegenerative diseases is one of the most challenging tasks that will be considered in this paper using various evaluation techniques.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Iram

Jumeily

Fergus

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple "accuracy rate" can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that "misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets", a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples.

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The anatomy of a context-aware application

Cited by 915 publications

References 11 publications

LAD: Localization anomaly detection for wireless sensor networks

LAD: Localization anomaly detection for wireless sensor networks

Improvement of Ultrasound-based System Using Sine Wave Detector and CAN Network

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

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