A real time technique for targeted advertising using location-based services — For GPS enabled device: A review

2017 ◽  
Author(s):  
Nilima B. Umate ◽  
V. G. Bhujade
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Targeted Advertising

scholarly journals IoT Notary : Attestable Sensor Data Capture in IoT Environments

2022 ◽  
Vol 3 (1) ◽  
pp. 1-30
Author(s):  
Nisha Panwar ◽  
Shantanu Sharma ◽  
Guoxi Wang ◽  
Sharad Mehrotra ◽  
Nalini Venkatasubramanian ◽  
...  
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Third Party ◽  
Sensor Data ◽  
Published Data ◽  
Resource Limited ◽  
User Data ◽  
Data Capturing ◽  
Iot Devices ◽  
The University

Contemporary IoT environments, such as smart buildings, require end-users to trust data-capturing rules published by the systems. There are several reasons why such a trust is misplaced—IoT systems may violate the rules deliberately or IoT devices may transfer user data to a malicious third-party due to cyberattacks, leading to the loss of individuals’ privacy or service integrity. To address such concerns, we propose IoT Notary , a framework to ensure trust in IoT systems and applications. IoT Notary provides secure log sealing on live sensor data to produce a verifiable “proof-of-integrity,” based on which a verifier can attest that captured sensor data adhere to the published data-capturing rules. IoT Notary is an integral part of TIPPERS, a smart space system that has been deployed at the University of California, Irvine to provide various real-time location-based services on the campus. We present extensive experiments over real-time WiFi connectivity data to evaluate IoT Notary , and the results show that IoT Notary imposes nominal overheads. The secure logs only take 21% more storage, while users can verify their one day’s data in less than 2 s even using a resource-limited device.

scholarly journals Real-Time Route Search by Locations

2020 ◽  
Vol 34 (01) ◽  
pp. 574-581
Author(s):  
Lisi Chen ◽  
Shuo Shang ◽  
Tao Guo
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Efficiency ◽  
Route Planning ◽  
Real Data ◽  
Location Based Services ◽  
Good Time ◽  
Efficient Route ◽  
Route Search ◽  
Insight Into

With the proliferation of GPS-based data (e.g., routes and trajectories), it is of great importance to enable the functionality of real-time route search and recommendations. We define and study a novel Continuous Route-Search-by-Location (C-RSL) problem to enable real-time route search by locations for a large number of users over route data streams. Given a set of C-RSL queries where each query q contains a set of places q.O to visit and a threshold q.θ, we continuously feed each query q with routes that has similarity to q.O no less than q.θ. We also extend our proposal to support top-k C-RSL problem where each query continuously maintains k most similar routes. The C-RSL problem targets a variety of applications, including real-time route planning, ridesharing, and other location-based services that have real-time demand. To enable efficient route matching on a large number of C-RSL queries, we develop novel parallel route matching algorithms with good time complexity. Extensive experiments with real data offer insight into the performance of our algorithms, indicating that our proposal is capable of achieving high efficiency and scalability.

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

scholarly journals Smart Device-Supported BDS/GNSS Real-Time Kinematic Positioning for Sub-Meter-Level Accuracy in Urban Location-Based Services

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2201 ◽  
Author(s):  
Liang Wang ◽  
Zishen Li ◽  
Jiaojiao Zhao ◽  
Kai Zhou ◽  
Zhiyu Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Location Based Services ◽  
Smart Device ◽  
Kinematic Positioning ◽  
Urban Location ◽  
Real Time Kinematic

scholarly journals Evaluating the Acceptability and Usability of EASEL: A Mobile Application that Supports Guided Reflection for Experiential Learning Activities

10.28945/3791 ◽  
2017 ◽  
Vol 16 ◽  
pp. 195-214 ◽  
Author(s):  
Jerry C Schnepp ◽  
Christian B Rogers
Keyword(s):  
Experiential Learning ◽  
Real Time ◽  
Interaction Design ◽  
Mobile Application ◽  
Network Connectivity ◽  
Evaluation Study ◽  
Location Based Services ◽  
Needs Analysis ◽  
Learning Activity ◽  
Future Research

Aim/Purpose: To examine the early perceptions (acceptability) and usability of EASEL (Education through Application-Supported Experiential Learning), a mobile platform that delivers reflection prompts and content before, during, and after an experiential learning activity. Background: Experiential learning is an active learning approach in which students learn by doing and by reflecting on the experience. This approach to teaching is often used in disciplines such as humanities, business, and medicine. Reflection before, during, and after an experience allows the student to analyze what they learn and why it is important, which is vital in helping them to understand the relevance of the experience. A just-in-time tool (EASEL) was needed to facilitate this. Methodology: To inform the development of a mobile application that facilitates real-time guided reflection and to determine the relevant feature set, we conducted a needs analysis with both students and faculty members. Data collected during this stage of the evaluation helped guide the creation of a prototype. The user experience of the prototype and interface interactions were evaluated during the usability phase of the evaluation study. Contribution: Both the needs analysis and usability assessment provided justification for continued development of EASEL as well as insight that guides current development. Findings: The interaction design of EASEL is understandable and usable. Both students and teachers value an application that facilitates real-time guided reflection. Recommendations for Practitioners: The use of a system such as EASEL can leverage time and location-based services to support students in field experiences. This technology aligns with evidence that guided reflection provides opportunities for metacognition. Recommendation for Researchers: Iterative prototyping, testing, and refinement can lead to a deliberate and effective app development process. Impact on Society: The EASEL platform leverages inherent functionality of mobile devices, such as GPS and persistent network connectivity, to adapt reflection tasks based on lo-cation or time. Students using EASEL will engage in guided reflection, which leads to metacognition and can help instructors scaffold learning Future Research: We will continue to advance the application through iterative testing and development. When ready, the application will be vetted in larger studies across varied disciplines and contexts.

Sign in / Sign up

Export Citation Format

Share Document