Location-aware Computing Research Articles

Assessment Framework Modeling using Location Aware Computing for Fertilizer Management and Crop Recommendation

Indian economy mainly depends on the agriculture, which contributes a major part in the growth and development of the nation. This is directly connected to the standard of life of farmers which covers more than 40 percent of the country population. Looking at the current situation of agriculture in India, agricultural productivity in India is not competitive to the world standards. For increasing the crop productivity farmers are forced to use more fertilizers which will end in health risk for the consumers. Lack of a proper knowledge of the effective usage of fertilizers and the changing soil nutrient values are the major problems for a farmer apart from the loss due to climate change and so many other factors. To address the problem , we propose to build an application model 'Prediction On Fertilizer Management for Crop Productivity' which enables the farmers to understand and effectively utilize their money with effective methods of seasonal crops production by providing recent and intimate information about which has to be produced, soil suitability, its nutrient values and soil moisture level which pasteurization management methods, how to apply the fertilizers in larger areas, what amount of water must be given, how to develop a model maps to the current scenario of farm areas. Crop Yield Production is basically an aggregation of crop and fertilizer recommendation, soil analysis, and crop yield calculations based on recent market scenario. Through our model we are revising and correcting the existing system with predictive analytics of the usage of effective fertilizers with less health risk.

Localization in Wireless Sensor Networks Using Rigid Graphs: A Review

The applications of wireless senor networks (WSN) vary in diversified field, at different geographic locations. Location aware computing is the key for the success of such applications. Henceforth, there is a need of efficiently estimating the location of individual WSN nodes deployed in the remote geographic locations. Manually estimating the locations of these densely deployed nodes is impossible, therefore the WSN nodes must be able to localize themselves collecting local information from its neighboring nodes, which is called as the localization technique. The information used for localization is generally distance and bearing information obtained from the ranging techniques which are not accurate and are prone to error. Therefore there is a need for techniques which can cope up with this perturbed distance information. Rigid graphs have the property of sustaining various kind of deformations due to translation, rotation and reflection. Hence, it is more fruitful using the concepts of rigid graphs, for estimating accurate location coordinates from error prone distance measurements. In this paper we will scrutinize the sound theoretical background which defines the need of rigid graph based localization and different localization techniques, with associated algorithms which uses the concepts of rigid graphs.

Context-Aware Security using Gimbal based Sensor Control System

Objectives: In order to provide efficient knowledge for location aware computing system in mobile applications, this paper proposes an experimental analysis considering user's location information. Methods/Analysis: These systems can entail information from array of sensors. Context aware management system combining central, distributed management scheme along with sensors, which can be utilized for location application. In order to provide better context/location aware management system, sensors (magnetometers) are mounted on Gimbal platform. This sensor platform synchronizes the device direction in all three axes. Findings: Magnetometer sensor can detect ground changes with respect to orientation angle with maximum accuracy. These possessions are validated with magnetic field intensity&rotation headings towards the north direction. Calibration of magnetometer reduces the noise and drift variations in the output. Conclusion :The proposed analysis offers displacement of the device and ensures the quality of service.

Route Pattern Mining From Personal Trajectory Data

The discovery of route patterns from trajectory data generated by moving objects is an essential problem for location-aware computing. However, the high degree of uncertainty of personal trajectory data significantly disturbs the existing route pattern mining approaches, and results in finding only short and incomplete patterns with high computational complexity. In this paper, we propose a personal trajectory data mining framework, which includes a group-and-partition trajectory abstraction technique and a frequent pattern mining algorithm called SCPM (Spatial Continuity based Pattern Mining). The group-and-partition technique can discover common sub-segments which are used to abstract the original trajectory data. The SCPM algorithm can efficiently derive longer and more complete route patterns from the abstracted personal trajectory data by tolerating various kinds of disturbances during the trips. Based on the real-world personal trajectory data, we conducted a number of experiments to evaluate the performance of our framework. The experimental results demonstrate that our framework is more efficient and effective as compared with the existing route pattern mining approaches, and the extracted route patterns can be effectively utilized to predict users' future route.

GPS-less indoor construction location sensing

Abstract Location aware computing in construction is an emerging area where the location of materials, workforce, and equipment can be used to derive knowledge about the state of the project. This paper aims to provide a low-cost location sensing solution for indoor construction and underground facilities, where satellite navigation systems cannot be used. In this solution a set of low-cost passive Radio Frequency Identification (RFID) tags is deployed using a predefined topology to provide a good spatial coverage of the project-active area. The locations are estimated and represented by zones and coordinates. The zone detection approach uses K-Nearest Neighbor classification method to assign a zone to the target. The coordinates of the target's location are also acquired by weighted average method using the Received Signal Strength Indicator (RSSI) as weight. The approach has been experimented on a construction project in Montreal as well as in a controlled lab environment. The experimental results show an average of 0.17 as error rate for detecting the target's zone and an average of 1.3 m accuracy in detecting the coordinates. The promising results presented in this paper demonstrate the potential for a high-accuracy and low-lost location estimation method for indoor construction job sites.

Directed by Directionality: Benefiting from the Gain Pattern of Active RFID Badges

Tracking of people via active badges is important for location-aware computing and for security applications. However, the human body has a major effect on the antenna gain pattern of the device that the person is wearing. In this paper, the gain pattern due to the effect of the human body is experimentally measured and represented by a first-order directional gain pattern model. A method is presented to estimate the model parameters from multiple received signal strength (RSS) measurements. An alternating gain and position estimation (AGAPE) algorithm is proposed to jointly estimate the orientation and the position of the badge using RSS measurements at known-position anchor nodes. Lower bounds on mean squared error (MSE) and experimental results are presented that both show that the accuracy of position estimates can be greatly improved by including orientation estimates in the localization system. Next, we propose a new tracking filter that accepts orientation estimates as input, which we call the orientation-enhanced extended Kalman filter (OE-EKF), which improves tracking accuracy in active RFID tracking systems.

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

Analysis of WLAN’s received signal strength indication for indoor location fingerprinting

An indoor positioning system that uses a location fingerprinting technique based on the received signal strength of a wireless local area network is an enabler for indoor location-aware computing. Data analysis of the received signal strength indication is very essential for understanding the underlying location-dependent features and patterns of location fingerprints. This knowledge can assist a system designer in accurately modeling a positioning system, improving positioning performance, and efficiently designing such a system. This study investigates extensively through measurements, the features of the received signal strength indication reported by IEEE 802.11b/g wireless network interface cards. The results of the statistical data analysis help in identifying a number of phenomena that affect the precision and accuracy of indoor positioning systems.

Location-aware information services platform for mobile users in Nigeria

Ongoing advances in technology and proceeding miniaturization have led to growing popularity and widespread use of mobile devices. A modern research direction is location-aware computing, which aims at providing services to users taking into consideration the location of the user in space. Apart from communication features and basic functionality like address books or organizers, users expect today’s portable and handheld devices to provide more advanced features such as presenting relevant location information, a location plan or information about nearby facilities or services. The usage of location information to adapt the applications to the current location and provide the user information that is relevant to the current position is a fascinating concept. In this paper, we present a location-aware application that provides location-aware information services. Our approach presents an overview of location-aware services from a broad perspective involving application prospects. We provide here a platform for a location-aware information notification service that allows for personalized deployment of services that can be customized as regards mobile phone users in Nigeria. Key words: Location-aware computing, location-aware services, mobile devices, technology.

An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs

The recent advances of ubiquitous wireless infrastructures and requirements for high speed context-aware computing have created the opportunities to supply the high efficient location based service (LBS) in indoor wireless local area network (WLAN) environment. Because of the serious multi-path effect, unpredictable co-channel interference and inherent equipment noise, the measured signal strengths vary a lot in the real-world indoor environment. And this strength variation will also result in the performance deterioration of radio map-based neighboring matching algorithm. In response to this compelling problem, we propose the adoption of adaptive autocorrelation-based signal preprocessing method as a specific solution by effectively eliminating the singular strength from the original fingerprint set. Finally, the feasibility and effectiveness of autocorrelation-based preprocessing are also verified by decreasing about 33.4% and 32.9% of errors in k nearest neighbor (KNN) and weighted KNN (WKNN).

Location aware computing using RFID infrastructure

This paper focuses on the possibilities of utilising infrastructural RFIDs for location aware computing. A key to automated computer-aided work is location and situation awareness. This allows the system to operate independently from direct user input, minimising the impact on the original process. For demonstration purposes, a large area tracking system using existing RFID technology and infrastructure is developed and evaluated. As a first possible application, we embed the system into an aircraft cabin. The localisation procedure is based on the coordinates of the RFID transponders detected by the RFID reader, as well as the orientation information measured by a gyroscope. To increased robustness concerning environmental noise and interferences, we estimate a tags readability using modulated power output of the antenna. The average error of our demonstration set-up is below half a meter. Possible use cases range from maintenance assistance to way finding in commercial shops or underground parking lots.

A two-stage multimodal speaker location-aware approach in pervasive computing

Location-aware computing is important in pervasive computing and intelligent video surveillance. We propose a two-stage multimodal approach to locate the active speaker in intelligent environments. Firstly, human voice is captured as audio cue to find the approximate orientation of current speaker. Secondly, the colour feature of mouth region is extracted as visual cue to detect continuous mouth motion that identifies the active speaker. The speaking recognition is conducted by a well-trained Hidden Markov Model based on colour feature of mouth region during continuous motion. Experiments show that the proposed multimodal approach is effective for speaker localisation in intelligent indoor environments.

Location-Aware Computing, Virtual Networks

This issue includes four works in process on issues and applications in location-aware computing: letting users set and control privacy policies when they use location-aware applications, cold-starting recommender systems for mobile location-aware services, aggregating contextual information for location-based applications, and applying location-based services to public transportation environments. The department also includes a report on lightweight virtualization of low-power wireless personal area networks.

Evaluation of position tracking technologies for user localization in indoor construction environments

Evolving technologies such as location-aware computing offer significant potential of improving important decision-making tasks on construction sites by providing support for tedious and time-consuming tasks associated with timely and accurate access to project information. For example, rapid and convenient access to contextual project information, through continuous position tracking of engineers, managers, and inspectors, can lead to significant cost and time savings due to the accuracy and immediacy with which relevant project information can be made available to field personnel. Considering the spatial expanse and dynamic nature of typical construction projects, mobile users need to be constantly tracked both outdoors as well as indoors. The Global Positioning System (GPS) is an attractive option for outdoor environments, but is not suitable for indoor applications because it needs a clear line-of-sight to orbital satellites in order to track position. As a result, alternate means of tracking users' location in indoor environments without relying on GPS is needed. This paper presents research that investigated the effectiveness of three wireless technologies for dynamic indoor user position tracking. In particular, Wireless Local Area Networks (WLAN), Ultra-Wide Band (UWB), and Indoor GPS positioning systems are evaluated and compared. Experimental results demonstrate the ability of Indoor GPS, in particular, to estimate a mobile user's location with relatively low uncertainty (1 to 2 cm).

Location awareness through trajectory prediction

Location-aware computing is a type of ubiquitous computing that uses user’s location information as an essential parameter for providing services and application-related optimization. Location management plays an important role in location-aware computing because the provision of services requires convenient access to dynamic location and location-dependent information. Many existing location management strategies are passive since they rely on system capability to periodically record current location information. In contrast, active strategies predict user movement through trajectories and locations. Trajectory prediction provides richer location and context information and facilitates the means for adapting to future locations. In this paper, we present two models for trajectory prediction, namely probability-based model and learning-based model. We analyze these two models and conduct experiments to test their performances in location-aware systems.

Mobility-Awareness: An Efficient Approach to Improve Energy Efficiency in Location-Aware Computing

There are two principal aspects of in location-aware computing: (1) how to support mobility and (2) how to exploit it. This paper considers the latter, while many existing works only concentrate on the former. This work is trying to prove that the performance of location-aware systems will be greatly improved by understanding the user's movement. In this paper, we propose a novel location update protocol called state-based location update protocol (SLUP), which significantly minimizes the energy consumption of mobile client by exploiting a syntactic information of a user's movement. This concept is called mobility-awareness which is a kind of context-awareness. Moreover, there are three variations of the proposed protocol in terms of how to choose the optimal state: SLUP/BS, SLUP/UITR, and SLUP/IUT $lt;Tiut$gt;. Experimental results show that the proposed protocol outperforms the well-known existing protocols such as dead-reckoning and distance-based protocol, and that the SLUP/IUT $lt;Tiut$gt; approach can achieve different performance tradeoffs between energy efficiency and location accuracy by fine-tuning its algorithmic parameter Tiut

A study of frequency interference and indoor location sensing with 802.11b and Bluetooth technologies

Location-aware computing is regarded as a key feature of many future mobile applications. Recently there has been an increase in the use of commodity wireless technologies like 802.11 or Bluetooth for indoor location positioning. Since Bluetooth and Wi-Fi use the same frequency band, this paper presents the experimental results of the interference effect of one technology on the other. These results help in determining which of the two technologies is more suited for location sensing applications. The paper presents two techniques for location sensing using Bluetooth and concludes by suggesting some changes to the Bluetooth architecture to improve its capabilities for location positioning.

Continuous Query Processing of Spatio-Temporal Data Streams in PLACE

The tremendous increase in the use of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. These environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor includes: (1) New incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extended semantics of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) A shared-execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Experimental evaluation shows promising performance of the continuous query processor of the PLACE server.

Pedestrian Tracking with Shoe-Mounted Inertial Sensors

A navigation system that tracks the location of a person on foot is useful for finding and rescuing firefighters or other emergency first responders, or for location-aware computing, personal navigation assistance, mobile 3D audio, and mixed or augmented reality applications. One of the main obstacles to the real-world deployment of location-sensitive wearable computing, including mixed reality (MR), is that current position-tracking technologies require an instrumented, marked, or premapped environment. At InterSense, we've developed a system called NavShoe, which uses a new approach to position tracking based on inertial sensing. Our wireless inertial sensor is small enough to easily tuck into the shoelaces, and sufficiently low power to run all day on a small battery. Although it can't be used alone for precise registration of close-range objects, in outdoor applications augmenting distant objects, a user would barely notice the NavShoe's meter-level error combined with any error in the head's assumed location relative to the foot. NavShoe can greatly reduce the database search space for computer vision, making it much simpler and more robust. The NavShoe device provides not only robust approximate position, but also an extremely accurate orientation tracker on the foot.

An integrated and layered architecture for location-aware computing

This paper gives an overall introduction to the basic concept of LAC (location-aware computing) and its development status, puts forward an integrated location-aware computing architecture which is useful for designing the reasonable logical model of LBS(location-based service). Finally, a brief introduction is conducted on a LAC experimental prototype, which acts as a mobile urban tourism assistant.