Geographical Position Information Research Articles

An Image Planar Positioning Method Base on Fusion of Dual-View Airborne SAR Data

Effective utilization of airborne synthetic-aperture (Airborne SAR) imagery often requires precise location of each image pixel. Historically, the positioning of airborne SAR imagery either relies on the use of reliable reference points to determine the relative position of the image, or requires the precise motion information of the aircraft and the characteristics of the SAR data collection system as input to determine the absolute position of the image. However, for many applications, the accuracy of traditional positioning methods is not high due to the challenge in obtaining the accurate geographic positions of reliable reference points and the inaccuracy of the recorded aircraft motion information. This study introduces an airborne SAR image planar positioning approach based on the premise that the systematic positioning error of the dual-view airborne SAR images are relatively consistent. The suggested planar positioning method applies the positioning auxiliary parameters of the initial ground-range airborne SAR image to ascertain the transformation relationship between the target’s initial geographic position and pixel position, and it then uses the equivalent equation for the position of the homologue point to assess the systematic positioning error of the SAR image and determine the geographic position of a pixel in a digital SAR image. This approach has advantages over previous techniques in that it requires no precise geographic position information of the ground reference points, and on the basis of using the RD model to accomplish coarse positioning of four corners of SAR image, it no longer needs aircraft trajectory data. Tests were conducted using two airborne SAR images actually captured, and the experimental results indicate that the proposed method can achieve high precision planar positioning of dual-view airborne SAR images. Error sources are analyzed and recommendations are given to improve image positioning accuracy in future airborne SARs.

Utilization of Location-Based Services Using Augmented Reality Perception Method For Android Based School Search

Data technology has become the main means for activities in various sectors of life, one of which is location-based services for SMA/SMK schools in the Bangka Belitung area. By using a location-based service application, it is expected to be able to overcome the problem of finding the position of SMA/SMK in the Bangka Belitung area. In this study, we would like to discuss the search for SMA/SMK positions. Through the push, Location-Based Service will be displayed in the smartphone, which will help identify the presence of the school's position on the smartphone. The result of the application that is formed is an Android-based application that can recognize the presence of SMA/SMK positions. This study aims to create a location-based service application that is combined with the concept of overlaying geographic position information from augmented reality perception, which is one type of augmented reality. The overall application quality test results include 1). Based on the Functionality aspect, the percentage of the actual score got a total score of 91.8%. 2). Based on the aspect of reliability, the percentage of the actual score got a total score of 97.6%. 3). Based on the Usability aspect, the percentage of the actual score gets a total score of 92.2%. 4). Based on the aspect of efficiency, the percentage of the actual score gets a total value of 76.5%. 5). From the calculation of the total score in percent is 89.5%. So based on the total score in percent, it can be concluded that the overall application quality test results are 89.5%, so the prototypes produced in this study are included in very good criteria.

Geographical position spoofing detection based on camera sensor fingerprint

Currently, position check-in on mobile devices has become a fashionable social activity. Meanwhile, criminals probably tamper the geographical position (geo-position) information to provide an alibi. Therefore, it is of importance to identify the authenticity of geo-position. To our knowledge, many current methods for geo-position spoofing detection mainly rely on geo-position information in the database. However, these methods possibly fail in the case of missing prior information or lacking rich training samples. To address that challenge, this paper proposes an alternative manner for detecting the geo-position spoofing via camera sensor fingerprint. In particular, the camera sensor fingerprint is first extracted through the images posted by an inquiry user based on the well-designed denoising filter. Second, the authenticity of the geo-position is verified by comparing the consistency of the residual noise from newly-posted images with position check-in and the unique camera sensor fingerprint from an inquiry user. Finally, the extensive experiments are conducted on the image database, that empirically indicates the relevance of our proposed simple but effective method.

Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

Geographic Position based Hopless Opportunistic Routing for UAV networks

Compared with traditional mobile Ad hoc networks (MANET) and vehicular Ad hoc networks (VANET), unmanned aerial vehicle (UAV) networks have two unique characteristics. First, the UAV network topology changes more frequently and unpredictably. Second, link qualities between UAVs vary significantly with time and space. Existing hop-by-hop routing protocols can neither adapt to the highly dynamic network environment nor fully utilize transmission opportunities in all links with various qualities in UAV networks. In this paper, we propose the Geographic Position based Hopless Opportunistic Routing (GPHLOR) Protocol for UAV networks to solve these problems. GPHLOR combines the advantages of the hopless protocols and the position-based protocols. Benefiting from the idea of hopless, compared with hop-by-hop routing protocols, GPHLOR can fully utilize the transmission opportunities of all links regardless of hop distances and link qualities. While different from existing hopless protocols, GPHLOR selects relays according to each node’s geographic position information without caring about the topology, so it can well adapt to the frequent topology change. Moreover, GPHLOR allows each node to calculate its forwarding priority in a distributed manner according to its local position information. It does not rely on frequent network information measurement and exchange, reducing the protocol overhead. Simulation results show that GPHLOR achieves better performance in packet delivery rate, end-to-end delay, throughput, and overhead compared with existing routing protocols in UAV networks.

An Optimization-Based Multi-Sensor Fusion Approach Towards Global Drift-Free Motion Estimation

Precise and drift-free motion estimation is an essential technology for autonomous driving. Single-sensor methods such as laser-based or vision-based have proven to be inadequate. To solve the problem, we proposed an optimization-based fusion approach that incorporates information from complementary sensors to achieve high accuracy and global drift-free. The core idea is to construct a globally unified pose graph through a dual-layer optimization strategy. The local estimation layer obtains the relative pose through LiDAR odometry and visual-inertial odometry. Subsequently, by introducing the absolute geographic position information of GPS, the accumulated drifts are corrected in the global optimization layer. The performance of our approach has been evaluated both in real-world environments and public datasets. The result demonstrates that our approach outperforms other state-of-the-art algorithms, with an average translation error of 0.8045% and an average rotation error of 0.0043deg/m.

Measuring user similarity using check-ins from LBSN: a mobile recommendation approach for e-commerce and security services

ABSTRACTSocial friendship and geographical position information often reflects individuals’ personal preferences and other types of knowledge that can be used to extract their similarity for recommendation systems. This paper finds that users are more likely to move around some specific centres or check in at some hotspots; a few individuals check in frequently, whereas most locations are rarely visited. Based on these findings, we propose a multi-centre clustering algorithm to capture users’ mobile patterns and develop a user similarity measurement method. Complexity analysis shows the method’s efficiency in handling large datasets and experimental results demonstrate its good applicability.

Efficient Position Estimation Based on GPU-Accelerated Content-based Image Retrieval

We propose an efficient position estimation method based on GPUaccelerated content-based image retrieval (CBIR). The idea is to use videos of first-person vision associated with geographical position information as the database. When a user sends a current subjective image, the system estimates the position using CBIR. Since features extracted from images are in general high-dimensional vectors, thousands of vectors are extracted even from a single image, resulting in high processing cost. On the other hand, GPUs (graphics processing unit) have contributed to accelerate various processing, while they are originally for graphics processing. Therefore, we utilize GPU to accelerate CBIR with appropriate data structures and algorithms. Moreover, our proposed method considers spatial locality of pedestrians in position estimation applications in order to improve accuracy. We demonstrate the efficiency and accuracy of the proposed method through experiments using a video dataset.

Heuristics of the algorithm: Big Data, user interpretation and institutional translation

Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification) build on large aggregates of information (Big Data) analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike), Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings), one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.

Dynamic packet beaconing for GPSR mobile ad hoc position-based routing protocol using fuzzy logic

Greedy perimeter stateless routing (GPSR) is a well-known position-based routing protocol in mobile ad hoc network (MANET). In GPSR, nodes use periodic beaconing (PB) strategy in broadcasting beacon packets to maintain up-to-date information on the geographical position of their neighbor nodes within their transmission boundary range. The nodes that receive beacon packets save all known neighbor nodes, along with their geographical position information, in their neighbor list to make effective routing decisions. Most recent studies on position-based routing protocols assume that the position information in the neighbor list of a node is accurate, although only a rough estimate of such position information is actually available to the node. Node mobility causes frequent network topology changes in MANETs. Thus, neighbor-to-neighbor relationships change frequently. Using the PB strategy leads to inaccurate node position information in the neighbor list of a node. In addition, it may cause the routing protocol to make suboptimal decisions and not route packets through the best-located neighbor within the transmission range of the node. This study presents an analysis of the influence of position information inaccuracy caused by network parameters such as beacon packet interval time (BPIT) and node moving speed (NMS) on the performance of GPSR position-based routing protocols. To overcome the effect of position information inaccuracy in the neighbor list of a node, we proposed a fuzzy logic dynamic beaconing (FLDB) strategy to improve the reliability of the neighbor list of a node by optimizing time between transmissions of beacon packets in position-based routing protocols. Optimization is based on the correlation between NMS, number of neighboring nodes (NoNNs) and BPIT using fuzzy logic control (FLC) mechanism. The simulation experiment shows the effectiveness of the FLC mechanism in improving overall performance of GPSR position-based routing protocol in terms of beacon packet control overhead, end-to-end delay, non-optimal hop, and false node position.

Effect of mobility parameters on the inaccuracy of the position information of position-based MANET routing

Position-based routing protocols have become increasingly popular in mobile ad-hoc networks because they can use geographical node position information to route data packets to their destinations. Each node periodically sends its geographical position information to its neighbours by using beacon packets. Data packet routing in position-based routing protocols uses the neighbours' position information that is stored in the node's neighbour list to route the data packet from the source to the destination. Most recent studies on position-based routing protocols assume that the position information in the node's neighbour list is accurate, although only a rough estimate of this position information is actually available to the nodes. In general, inaccuracy occurs when inaccurate geographical position information is used. This study presents an analysis and a simulation to examine the influence of position information inaccuracy caused by beacon packet interval-time and node speed mobility parameters on the performance of position-based routing protocols.

Effect of network parameters on neighbor wireless link breaks in GPSR protocol and enhancement using mobility prediction model

The greedy perimeter stateless routing (GPSR) protocol is a well-known position-based routing protocol. Data packet routing in position-based routing protocols uses the neighbors' geographical position information, which is stored in the sender's neighbors list, and the destination's position information stored in the routing data packet header field to route the packet from source to destination. In the GPSR protocol, the sender routes the packets to a neighboring node, whose geographical position is the closest to the destination of all the sender's neighbors. However, the selected neighbor is closer to the edge of the maximum of the sender's transmission range and thus has a higher likelihood of leaving the transmission range of the sender. Thus, the wireless link between the sender node and its routing neighboring node may break down, which degrades the performance of the routing protocol. In this study, we identify and study the effects of network parameters (beacon packet interval-time, node speed, network density, transmission range, and network area size) on wireless link breakage, identified as the neighbor wireless link break (NWLB) problem, in the GPSR protocol. To overcome the NWLB problem, we propose a neighbor wireless link break prediction (NWLBP) model. The NWLBP model predicts the accurate position of a routing neighboring node in the sender's neighbors list before routing the data packet to that neighbor. The simulation results show the ability of the NWLBP model to overcome the observed problem and to improve the overall performance of the GPSR protocol.

Load Balancing for Greedy Forwarding of Geographic Routing in Wireless Networks

In this letter, we propose three algorithms to reduce congestion for greedy forwarding, which is one of common principles in geographic routing. The new algorithms take geographic position information and network congestion metrics to balance traffic. When these algorithms are combined with well-known GPSR protocol [1], packet delivery ratio is enhanced by reducing number of lost packets in a buffer. In addition, end-to-end delay is reduced by bypassing congested nodes. These features are evaluated and analyzed through several simulation results.

Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data

Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (fPAR), the light use efficiency (LUE) and ecosystem respiration (Re). The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI) obtained by the Vegetation instrument on board the Satellite Pour l’Observation de la Terra (SPOT) satellite. Gross Primary Production (GPP) of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR). The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The Re is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations.