Non-GPS Positioning Systems

Tariq, Zain Bin; Cheema, Dost Muhammad; Kamran, M.; Naqvi, Ijaz Haider

doi:10.1145/3098207

Cited by 61 publications

(73 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Google location services on Android-based mobile devices, for example, make it possible to share the location of one's smartphone with other individuals over the Internet [72]. Positioning of devices in the built environment where cellular coverage is available can be done with high accuracy due to cellular tower trilateration, map-matching, and other methods integrated into phones or apps that improve on or substitute for GNSS-based positioning [73][74][75][76][77]. However, forestry and wildland fire management activities often occur in areas where cellular coverage is poor; thus, methods that rely on cellular connectivity are primarily useful in portions of the Wildland Urban Interface (WUI).…”

Section: Gnss With Two-way Satellite Communicationmentioning

confidence: 99%

Positioning Methods and the Use of Location and Activity Data in Forests

Keefe

Wempe

Becker

et al. 2019

Forests

View full text Add to dashboard Cite

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

show abstract

Section: Gnss With Two-way Satellite Communicationmentioning

confidence: 99%

Positioning Methods and the Use of Location and Activity Data in Forests

Keefe

Wempe

Becker

et al. 2019

Forests

View full text Add to dashboard Cite

show abstract

“…The localization error represents the closeness between the estimated target location b x k ; b y k ð Þand actual location(x k , y k ) to be estimated and is given in Equation (11). Whereas, the average localization error can be computed by taking an…”

Section: Performance Metricsmentioning

confidence: 99%

“…[9][10][11] Therefore, the research trend is to develop innovative, low-cost, and low-power target localization system, which can overcome the limitations of GPS-based systems. Though GPS can be used to locate the deployed nodes in the wheat field, the major challenge is the requirement of the line of sight (LOS) with satellites as well as the cost involved.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, maintaining LOS with satellite is not simple in wheat farmland as well attaching GPS module to every sensor node is not at all costeffective option. [9][10][11] Therefore, the research trend is to develop innovative, low-cost, and low-power target localization system, which can overcome the limitations of GPS-based systems. The features such as easy deployment and lower maintenance costs, unattended working, and self-organizing capability make WSN important alternative for various target Localization and Tracking (L&T) applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient localization of target in large scale farmland using generalized regression neural network

Jondhale¹,

Deshpande²

2019

Int J Communication

View full text Add to dashboard Cite

Although simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real-time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range-based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)-based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN-based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds. KEYWORDS general regression neural network (GRNN), optimal fitted parametric exponential decay model (OFPEDM), received signal strength Indicator (RSSI), wireless sensor network (WSN)

show abstract

“…Location-based services (LBS) have drawn great attention from academia and industry in recent years as they have become more frequently used and demanded. Positioning is at the core of LBS, and it remains a technical challenge for dense urban environments, indoors, and underground [1], despite the many efforts devoted to alternatives to GNSS-based positioning [2], and in particular to alternatives readily applicable to smartphones [3]. Among those alternatives are magnetic signals [4,5], RFID [6], LED light [7,8], vision-based techniques [9], and Wi-Fi [10] and Bluetooth Low Energy (BLE) [11] signals, all of them often combined with Pedestrian Dead Reckoning (PDR) and Map-Matching [3,12].…”

Section: Introductionmentioning

confidence: 99%

BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

Mendoza-Silva

Matey-Sanz

Torres-Sospedra

et al. 2019

Data

View full text Add to dashboard Cite

RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at −12 dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the −4 dBm, −12 dBm and −20 dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.

show abstract

Non-GPS Positioning Systems

Cited by 61 publications

References 107 publications

Positioning Methods and the Use of Location and Activity Data in Forests

Positioning Methods and the Use of Location and Activity Data in Forests

Efficient localization of target in large scale farmland using generalized regression neural network

BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning

Contact Info

Product

Resources

About