Efficient Location Based Services for Groups of Mobile Users

2013 ◽  
Author(s):  
Christos Anagnostopoulos ◽  
Kostas Kolomvatsos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Location Based Services ◽  
Mobile Users

SVM-based Models for Mobile Users' Initial Position Determination

2014 ◽  
Vol 67 (6) ◽  
pp. 950-966 ◽  
Author(s):  
Majda Petric ◽  
Aleksandar Neskovic ◽  
Natasa Neskovic ◽  
Milos Borenovic
Keyword(s):  
Mobile Networks ◽  
Mobile Communications ◽  
Field Measurements ◽  
Initial Position ◽  
Location Based Services ◽  
Base Stations ◽  
Mobile Users ◽  
Support Vector ◽  
Position Determination ◽  
Median Error

A large interest in developing commercial Location-Based Services (LBS) and the necessity of implementing emergency call services, have led to the intensive development of techniques for mobile users' localisation. In this paper, a Public Land Mobile Networks (PLMN) -based technique for initial position determination is proposed as an alternative to satellite-based methods in environments with obstructed satellite signals. Two positioning models, based on handset available Received Signal Strength (RSS) measurements from Global System for Mobile Communications (GSM) base stations and the use of Support Vector Machine (SVM) algorithms, are proposed. Performances of proposed models are verified using field measurements, collected in a suburban environment. Models are analysed in terms of positioning accuracy, complexity and latency, and compared to some other promising PLMN-based techniques. Using proposed SVM-based positioning models a median error of 4·3 m–6·2 m and latency of less than a second can be achieved.

scholarly journals An Efficient Spatial Temporal Provenance Mechanism for Adhoc Mobile Users

2019 ◽  
Vol 9 (1) ◽  
pp. 5282-8285
Keyword(s):  
Ad Hoc ◽  
Low Cost ◽  
Trust Model ◽  
Location Based Services ◽  
Wireless Access ◽  
Mobile Users ◽  
Certification Authority ◽  
Simulation Experiments ◽  
Current Location ◽  
Place Of Origin

Location-based services rectangle measures quickly changing into vastly in different style. Additionally to services supported users' current location, several potential services believe users' currentlocation history, or their spatial-temporal place of origin.Malicious users might idle their spatial-temporal place of originwhile not a rigorously designed security system for users toprove their past locations. during this system, we tends to gift theSpatial-Temporal place of origin Assurance with Mutual Proofs theme. STAMP is meant for ad-hoc mobile usersgenerating location system proofs for every different in an exceedinglydistributed settings. However, it will simply accommodate trustyworthy mobile users and wireless access points. STAMP ensures theintegrity and non-transferability of the placement proofs andprotects users' privacy. A semi-trusted Certification Authority isemployed to distribute specific keys in addition as guard users against collusion by a light-weight entropy-based trust analysisapproach. Our image implementation is based on the Andriod platformshows that STAMP is low cost in terms of procedure and storageresources. Intensive simulation experiments show that ourentropy-based trust model is in a position to attains high collusion to detects the accuracy.

Mobile Caching for Location-Based Services

2009 ◽  
pp. 3031-3039
Author(s):  
Jianliang Xu
Keyword(s):  
Mobile Computing ◽  
Information Access ◽  
Data Access ◽  
Mobile User ◽  
Location Based Services ◽  
Dependent Data ◽  
Mobile Users ◽  
Computing Environment ◽  
Mobile Environment ◽  
Mobile Computing Environment

Location-based services (LBS) are services that answer queries based on the locations with which the queries are associate; normally the locations where the queries are issued. With a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurants) (Barbara, 1999; Ren & Dunham, 2000; D. L. Lee, Lee, Xu, & Zheng, 2002; W. C. Lee, Xu, & Zheng, 2004), LBS is emerging as an integral part of daily life. The greatest potential of LBS is met in a mobile computing environment, where users enjoy unrestricted mobility and ubiquitous information access. For example, a traveler could issue a query like “Find the nearest hotel with a room rate below $100” from a wireless portable device in the middle of a journey. To answer such a query, however, three major challenges have to be overcome: • Constrained Mobile Environments: Users in a mobile environment suffer from various constraints, such as scarce bandwidth, lowquality communication, frequent network disconnections, and limited local resources. These constraints pose a great challenge for the provision of LBS to mobile users. • Spatial Data: In LBS, the answers to a query associated with different locations may be different. That is, query results are dependent on spatial properties of queries. For a query bound with a certain query location, the query result should be relevant to the query as well as valid for the bound location. This requirement adds additional complexity to traditional data management techniques such as data placement, indexing, and query processing (D. L. Lee, 2002). • User Movement: The fact that a mobile user may change its location makes some tasks in LBS, such as query scheduling and cache management, particularly tough. For example, suppose that a mobile user issues a query “Find the nearest restaurant” at location A. If the query is not scheduled timely enough on the server, the user has moved to location B when he or she gets the answer R. However, R is no longer the nearest restaurant at location B. Caching has been a commonly used technique for improving data access performance in a mobile computing environment (Acharya, Alonso, Franklin, & Zdonik, 1995). There are several advantages for caching data on mobile clients: • It improves data access latency since a portion of queries, if not all, can be satisfied locally. • It helps save energy since wireless communication is required only for cache-miss queries. • It reduces contention on the narrow-bandwidth wireless channel and off-loads workload from the server; as such, the system throughput is improved. • It improves data availability in circumstances where clients are disconnected or weakly connected because cached data can be used to answer queries. However, as discussed above, the constrains of mobile computing environments, the spatial property of location-dependent data, and the mobility of mobile users have opened up many new research problems in client caching for LBS. This chapter discusses the research issues arising from caching of location-dependent data in a mobile environment and briefly describes several state-of-the-art solutions.

Mobile Caching for Location-Based Services

2011 ◽  
pp. 760-765
Author(s):  
Jianliang Xu
Keyword(s):  
Mobile Computing ◽  
Information Access ◽  
Data Access ◽  
Mobile User ◽  
Location Based Services ◽  
Dependent Data ◽  
Mobile Users ◽  
Computing Environment ◽  
Mobile Environment ◽  
Mobile Computing Environment

Location-based services (LBS) are services that answer queries based on the locations with which the queries are associate; normally the locations where the queries are issued. With a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurants) (Barbara, 1999; Ren & Dunham, 2000; D. L. Lee, Lee, Xu, & Zheng, 2002; W. C. Lee, Xu, & Zheng, 2004), LBS is emerging as an integral part of daily life. The greatest potential of LBS is met in a mobile computing environment, where users enjoy unrestricted mobility and ubiquitous information access. For example, a traveler could issue a query like “Find the nearest hotel with a room rate below $100” from a wireless portable device in the middle of a journey. To answer such a query, however, three major challenges have to be overcome: • Constrained Mobile Environments: Users in a mobile environment suffer from various constraints, such as scarce bandwidth, low-quality communication, frequent network disconnections, and limited local resources. These constraints pose a great challenge for the provision of LBS to mobile users. • Spatial Data: In LBS, the answers to a query associated with different locations may be different. That is, query results are dependent on spatial properties of queries. For a query bound with a certain query location, the query result should be relevant to the query as well as valid for the bound location. This requirement adds additional complexity to traditional data management techniques such as data placement, indexing, and query processing (D. L. Lee, 2002). • User Movement: The fact that a mobile user may change its location makes some tasks in LBS, such as query scheduling and cache management, particularly tough. For example, suppose that a mobile user issues a query “Find the nearest restaurant” at location A. If the query is not scheduled timely enough on the server, the user has moved to location B when he or she gets the answer R. However, R is no longer the nearest restaurant at location B. Caching has been a commonly used technique for improving data access performance in a mobile computing environment (Acharya, Alonso, Franklin, & Zdonik, 1995). There are several advantages for caching data on mobile clients: • It improves data access latency since a portion of queries, if not all, can be satisfied locally. • It helps save energy since wireless communication is required only for cache-miss queries. • It reduces contention on the narrow-bandwidth wireless channel and off-loads workload from the server; as such, the system throughput is improved. • It improves data availability in circumstances where clients are disconnected or weakly connected because cached data can be used to answer queries. However, as discussed above, the constrains of mobile computing environments, the spatial property of location-dependent data, and the mobility of mobile users have opened up many new research problems in client caching for LBS. This chapter discusses the research issues arising from caching of location-dependent data in a mobile environment and briefly describes several state-of-the-art solutions.

Essential Technologies for Location-Aware Mobile, Online Map Prefetching, and Caching

2012 ◽  
pp. 303-323
Author(s):  
Wen-Chen Hu ◽  
Naima Kaabouch ◽  
Lei Chen ◽  
Ming Yang
Keyword(s):  
Power Consumption ◽  
Location Based Services ◽  
Transmission Time ◽  
Mobile Users ◽  
Handheld Computing ◽  
Battery Power ◽  
Location Aware ◽  
Tile System ◽  
First In First Out

Map navigation is one of the most popular applications used by mobile users. At the same time, it is also one of the time- and resource-consuming applications. Various methods such as most-recently used and first-in, first-out algorithms are used to reduce the map transmission time and delay. One of the popular methods is online mobile map prefetching and caching. However, the mobility and location features of mobile users are usually left out by these methods. Caching and prefetching maps based on a mobile user’s location would greatly reduce the transmission time and hence the battery power consumption. For example, if a user is visiting a town, prefetching the maps of nearby interesting stores and caching the maps of the visited, neighboring landmarks would help the user’s visitation experience and save the transmission time. Online mobile map prefetching or caching is useful, but is not widely employed because it involves several different subjects and developers usually are not familiar with all of them. This chapter intends to relieve the problem by introducing essential technologies for online mobile map prefetching and caching so more developers can start working on this kind of project or research. It consists of four themes: (1) green handheld computing, (2) location-based services and programming, (3) map tile system, and (4) location-aware map prefetching and caching methods. A summary is given at the end of this chapter.

Protecting User Privacy Better with Query l-Diversity

2010 ◽  
Vol 4 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Fuyu Liu ◽  
Kien A. Hua
Keyword(s):  
Service Provider ◽  
Location Based Services ◽  
Mobile Users ◽  
Simulation Studies ◽  
User Privacy ◽  
Privacy Concerns ◽  
Extensive Simulation ◽  
Novel Concept

This paper examines major privacy concerns in location-based services. Most user privacy techniques are based on cloaking, which achieves location k-anonymity. The key is to reduce location resolution by ensuring that each cloaking area reported to a service provider contains at least k mobile users. However, maintaining location k-anonymity alone is inadequate when the majority of the k mobile users are interested in the same query subject. In this paper, the authors address this problem by defining a novel concept called query l-diversity, which requires diversified queries submitted from the k users. The authors propose two techniques: Expand Cloak and Hilbert Cloak to achieve query l-diversity. To show the effectiveness of the proposed techniques, they compare the improved Interval Cloak technique through extensive simulation studies. The results show that these techniques better protect user privacy.

Internet of Things and Location-Based Services

2020 ◽  
pp. 213-237
Author(s):  
Shaveta Bhatia
Keyword(s):  
Wireless Networks ◽  
Internet Of Things ◽  
Mobile Computing ◽  
Mobile Phones ◽  
New Technologies ◽  
Location Based Services ◽  
Mobile Users ◽  
Data Networks ◽  
Internet Applications ◽  
Geographic Locations

The progressive rise of mobile computing devices and wireless networks have created a lot of interest in location-based systems and services. The involvement of internet applications in almost every field has changed our lives. Location-based services are the services provided to mobile users according to their geographic locations. Each user wants to get the service according to his/her own interest. The general user's actions in location-based services are locating, searching, navigating, identification, and checking. The location identification has now become a critical attribute. Today, internet of things in the field of location-based services (LBS) provide services to the mobile users by explore the location depending on the geographical coordinates for their valuable needs. Mobile phones that are equipped with new technologies and supported by the presence and development of broadband mobile data networks have created new opportunities for the processing of location-based applications.

scholarly journals MapReduce Algorithm for Location Recommendation by Using Area Skyline Query

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 191 ◽  
Author(s):  
Chen Li ◽  
Annisa Annisa ◽  
Asif Zaman ◽  
Mahboob Qaosar ◽  
Saleh Ahmed ◽  
...  
Keyword(s):  
Parallel Algorithm ◽  
Mobile Applications ◽  
Large Scale ◽  
Real Data ◽  
Location Based Services ◽  
Mobile Users ◽  
Skyline Query ◽  
Location Recommendation ◽  
Large Scale Data ◽  
Scale Data

Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called “area skyline query”, to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real data confirm that our proposed algorithm is sufficiently efficient for large-scale data.

Privacy-preserving relative location based services for mobile users

2015 ◽  
Vol 12 (5) ◽  
pp. 152-161 ◽  
Author(s):  
Ning Fei ◽  
Yi Zhuang ◽  
Jingjing Gu ◽  
Jiannong Cao ◽  
Liang Yang
Keyword(s):  
Privacy Preserving ◽  
Location Based Services ◽  
Mobile Users ◽  
Relative Location
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