Mobile Location Tracking

2017 ◽  
pp. 194-209
Author(s):  
Sima Nadler
Keyword(s):  
Mobile Devices ◽  
Use Cases ◽  
Location Tracking ◽  
Mobile Location ◽  
Different Types ◽  
Computing Platforms ◽  
Accuracy Performance

One of the key things that differentiate mobile devices from static computing platforms is the ability to provide information about the device user's location. While the raw location is often useful, it is the ability to understand the user's context that makes this capability so powerful. This chapter will review the technologies used today to provide location tracking of mobile devices and which are best for different types of use cases. It will also address challenges associated with location tracking, such as accuracy, performance and privacy.

Mobile Devices

2015 ◽  
pp. 8-29 ◽  
Author(s):  
Maurice Dawson ◽  
Jorja Wright ◽  
Marwan Omar
Keyword(s):  
National Security ◽  
Mobile Devices ◽  
Security Policy ◽  
Cyber Attacks ◽  
Driven Systems ◽  
Security Software ◽  
Desktop Computers ◽  
Confidential Data ◽  
Computing Platforms ◽  
Share Information

Mobile devices are becoming a method to provide an efficient and convenient way to access, find and share information; however, the availability of this information has caused an increase in cyber attacks. Currently, cyber threats range from Trojans and viruses to botnets and toolkits. Presently, 96% of mobile devices do not have pre-installed security software while approximately 65% of the vulnerabilities are found within the application layer. This lack in security and policy driven systems is an opportunity for malicious cyber attackers to hack into the various popular devices. Traditional security software found in desktop computing platforms, such as firewalls, antivirus, and encryption, is widely used by the general public in mobile devices. Moreover, mobile devices are even more vulnerable than personal desktop computers because more people are using mobile devices to do personal tasks. This review attempts to display the importance of developing a national security policy created for mobile devices in order to protect sensitive and confidential data.

Framework to Understand and Deploy AI-Based Smart CRM in the Airline Industry

2022 ◽  
pp. 154-165
Author(s):  
Vikram Bawa
Keyword(s):  
Mobile Devices ◽  
Airline Industry ◽  
Critical Analysis ◽  
Use Cases ◽  
Customer Acquisition ◽  
Customer Journey

This is the age of AI. Soon what customers think will be understood by the smart applications on their mobile devices and the information—most of which will be pre-processed based on the customer personas—will be available at the blink of an eye. In this chapter a critical analysis of how AI bolsters CRM capabilities in the airline industry is conducted. To understand that, AI capabilities are surveyed and its transformational effects on CRM and its impact on customer acquisition, retention, loyalty, and experience are explored in depth. In the end, a customer journey-based deployment framework is presented that supports the finding of the AI-CRM implementation use cases.

Session and Network Support for Autonomous Context-Aware Multiparty Communications in Heterogeneous Mobile Systems

2010 ◽  
Vol 1 (4) ◽  
pp. 1-24 ◽  
Author(s):  
Josephina Antoniou ◽  
Christophoros Christophorou ◽  
Augusto Neto ◽  
Susana Sargento ◽  
Filipe Pinto ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Performance Optimization ◽  
Network Performance ◽  
Future Internet ◽  
Mobile Systems ◽  
Context Aware ◽  
Network Support ◽  
Different Types ◽  
Optimization Schemes ◽  
Service Groups

The increase of networking complexity requires the design of new performance optimization schemes for delivering different types of sessions to users under different conditions. In this regard, special attention is given to multi-homed environments, where mobile devices cross areas with overlapping access technologies (Wi-Fi, 3G, WiMax). In such a scenario, efficient multiparty delivery depends upon the grouping operation, which must be done based on several parameters. In this paper, the authors propose context-aware sub-grouping of content-based service groups so that the same service session can be delivered using different codings of the same content, adapting to current network, users, session, and environment context. The context-aware information is used to improve the sub-grouping process. This paper aims to describe these sub-grouping techniques, and in particular how they improve network performance and user experience in the future Internet by focusing on the improved network-level and session-level mechanisms.

scholarly journals A Security Monitoring Framework for Mobile Devices

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1197 ◽  
Author(s):  
António Lima ◽  
Luis Rosa ◽  
Tiago Cruz ◽  
Paulo Simões
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Lessons Learned ◽  
Bring Your Own Device ◽  
Reference Architecture ◽  
Proof Of Concept ◽  
Security Monitoring ◽  
Detection Systems ◽  
Management Policies ◽  
Computing Platforms

Quite often, organizations are confronted with the burden of managing mobile device assets, requiring control over installed applications, security, usage profiles or customization options. From this perspective, the emergence of the Bring Your Own Device (BYOD) trend has aggravated the situation, making it difficult to achieve an adequate balance between corporate regulations, freedom of usage and device heterogeneity. Moreover, device and information protection on mobile ecosystems are quite different from securing other device assets such as laptops or desktops, due to their specific characteristics and limitations—quite often, the resource overhead associated with specific security mechanisms is more important for mobile devices than conventional computing platforms, as the former frequently have comparatively less computing capabilities and more strict power management policies. This paper presents an intrusion and anomaly detection framework specifically designed for managed mobile device ecosystems, that is able to integrate into mobile device and management frameworks for complementing conventional intrusion detection systems. In addition to presenting the reference architecture for the proposed framework, several implementation aspects are also analyzed, based on the lessons learned from developing a proof-of-concept prototype that was used for validation purposes.

scholarly journals A weighting system to build physical layer measurements maps by crowdsourcing data from smartphones

2020 ◽  
Vol 9 (3) ◽  
pp. 211
Author(s):  
Hosam Alamleh ◽  
Ali Abdullah S. AlQahtani
Keyword(s):  
Mobile Devices ◽  
Access Point ◽  
Physical Layer ◽  
Network Coverage ◽  
Wide Range ◽  
Different Types ◽  
Radio Signals ◽  
Data Points ◽  
And Performance ◽  
Map Accuracy

<p>Mobile devices can sense different types of radio signals. For example, broadcast signals. These broadcasted signals allow the device to establish a connection to the access point broadcasting it. Moreover, mobile devices can record different physical layer measurements. These measurements are an indication of the service quality at the point they were collected. These measurements data can be aggregated to form physical layer measurement maps. These maps are useful for several applications such as location fixing, navigation, access control, and evaluating network coverage and performance. Crowdsourcing can be an efficient way to create such maps. However, users in a crowdsourcing application tend to have different devices with different capabilities, which might impact the overall accuracy of the generated maps. In this paper, we propose a method to build physical layer measurements maps by crowdsourcing physical layer measurements, GPS locations, from participating mobile devices. The proposed system gives different weights to each data point provided by the participating devices based on the data source’s trustworthiness. Our tests showed that the different models of mobile devices return GPS location with different location accuracies. Consequently, when building the physical layer measurements maps our algorithm assigns a higher weight to data points coming from devices with higher GPS location accuracy. This allows accommodating a wide range of mobile devices with different capabilities in crowdsourcing applications. An experiment and a simulation were performed to test the proposed method. The results showed improvement in crowdsourced map accuracy when the proposed method is implemented.</p>

scholarly journals PROPOSING A MULTI-CRITERIA PATH OPTIMIZATION METHOD IN ORDER TO PROVIDE A UBIQUITOUS PEDESTRIAN WAYFINDING SERVICE

2015 ◽  
Vol XL-1-W5 ◽  
pp. 639-644 ◽  
Author(s):  
M. Sahelgozin ◽  
A. Sadeghi-Niaraki ◽  
S. Dareshiri
Keyword(s):  
Mathematical Model ◽  
Mobile Devices ◽  
Environmental Conditions ◽  
Context Awareness ◽  
Optimization Method ◽  
Path Optimization ◽  
Navigation Systems ◽  
Different Types ◽  
Novel Applications

A myriad of novel applications have emerged nowadays for different types of navigation systems. One of their most frequent applications is <i>Wayfinding</i>. Since there are significant differences between the nature of the pedestrian wayfinding problems and of those of the vehicles, navigation services which are designed for vehicles are not appropriate for pedestrian wayfinding purposes. In addition, diversity in environmental conditions of the users and in their preferences affects the process of pedestrian wayfinding with mobile devices. Therefore, a method is necessary that performs an intelligent pedestrian routing with regard to this diversity. This intelligence can be achieved by the help of a <i>Ubiquitous</i> service that is adapted to the <i>Contexts</i>. Such a service possesses both the <i>Context-Awareness</i> and the <i>User-Awareness</i> capabilities. These capabilities are the main features of the ubiquitous services that make them flexible in response to any user in any situation. In this paper, it is attempted to propose a multi-criteria path optimization method that provides a Ubiquitous Pedestrian Way Finding Service (UPWFS). The proposed method considers four criteria that are summarized in <i>Length, Safety, Difficulty</i> and <i>Attraction</i> of the path. A conceptual framework is proposed to show the influencing factors that have effects on the criteria. Then, a mathematical model is developed on which the proposed path optimization method is based. Finally, data of a local district in Tehran is chosen as the case study in order to evaluate performance of the proposed method in real situations. Results of the study shows that the proposed method was successful to understand effects of the contexts in the wayfinding procedure. This demonstrates efficiency of the proposed method in providing a ubiquitous pedestrian wayfinding service.

scholarly journals Media File Jacking Threats and Protections

2020 ◽  
Vol 9 (4) ◽  
pp. 2021-2025
Keyword(s):  
Social Media ◽  
Mobile Devices ◽  
Security Threat ◽  
Different Types ◽  
The Media ◽  
Media Messaging ◽  
Precautionary Measures ◽  
Audio Files

Media File Jacking (MFJ) is one security threat that affects media file usages within apps. Media files include image files, voice/audio files, video files and document files like pdf, docs, excel sheets, text files, etc. and these files easily find its place within our devices. The primary symptom of a Media File Jacking attack is that it will manipulate the media files, on transferring between users or apps. This type of malwares mainly targets mobile devices. The researchers from Symantec reported that this vulnerability has already found its way into the two top social media messaging apps namely, WhatsApp and Telegram. Not just limited to this, media file jacking can easily target mobile functioning’s whilst managing affected media files and its managed media chat apps. This analysis in detail tries to understand the vulnerabilities that devices are left exposed to via Media file jacking and how can protect Android based mobile devise with the help of existing, upcoming, configurable or programmable features. We try to cover in detail on i.) What Media file jacking attack is? ii.) How this vulnerability is created? iii.) Under which scenario this will happen iv.) What are the different types of attacks? iv.) What are the implications of this attack? v.) what are precautionary measures and how we can mark safe our mobile devices from this attack. This study mainly help to Android media app users and app develops to get a glance about the precautionary measures from media file jacking attack

scholarly journals THE ACCURACY OF ALGORITHMS FOR WLAN INDOOR POSITIONING AND THE STANDARDIZATION OF SIGNAL RECEPTION FOR DIFFERENT MOBILE DEVICES

2014 ◽  
pp. 103-109
Author(s):  
Uwe Grossmann ◽  
Markus Schauch ◽  
Syuzanna Hakobyan
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Indoor Positioning ◽  
Base Station ◽  
Personal Digital Assistants ◽  
Strength Index ◽  
Average Accuracy ◽  
Different Types ◽  
Seminar Room

Often mobile devices like mobile phones or personal digital assistants (PDA) are equipped with IEEE 802.11 WLAN adapters. Furthermore, within many buildings a WLAN infrastructure is available. The aim of this work is to investigate the quality of different indoor positioning methods based on values of WLAN received signal strength index (RSSI) using commercially available devices (mobile phones, PDA). Three positioning algorithms were considered: minimal Euclidian distance, intersections of RSSI-Isolines and a stochastic model based on Bayes' theorem. Two measuring testbeds, a museum's exhibition room and an empty seminar room, four different types of PDA (Dell, Fujitsu, HP, T-Mobile) and two types of access points (Netgear, Lancom) were used. Results show that positioning can be achieved with an average accuracy of approx. 2-3 metres. WLAN capable mobile devices behave differently in receiving RSSI values of a base station. For reasons of standardization a linear correlation between different receiving levels of PDA was investigated.

scholarly journals Mobile Cloud Computing: Offloading Mobile Processing to the Cloud

2016 ◽  
Vol 9 (1) ◽  
pp. 90
Author(s):  
Sanjay P. Ahuja ◽  
Jesus Zambrano
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Response Times ◽  
Mobile Apps ◽  
Mobile Systems ◽  
Battery Life ◽  
Data Intensive ◽  
Computing Platforms ◽  
Computational Resources

<p class="zhengwen">The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.</p><p>Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.</p>

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