scholarly journals Exploration of Location based Services using AI for Mobile Cloud Services

2020 ◽  
Vol 9 (6) ◽  
pp. 564-567
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Mobile User ◽  
Cloud Services ◽  
Location Based Services ◽  
Cloud System ◽  
Mobile Users ◽  
Mobile Cloud ◽  
Major Work

Mobile devices have several sensors, including GPS that can capture information about the location of a mobile user. The use of certain devices will, therefore, simplify services and make it simpler for operators to respond to the demands of mobile users. The main aim of this analysis is to incorporate middleware to pick suitable cloud services that leverage from mobile device position and cost preferences. If the number of small activities within a meta feature exceeds the number of major work, the Max min algorithm device operations are conducted in addition to big tasks, where the design of the process is dependent on how many functions it does. The model is wide since tasks cannot be conducted simultaneously. A new amendment to the computation system is used to overcome the drawbacks of the Max-Min algorithm. It encompasses the positives of Max-Min and eliminates drawbacks. This study focuses specifically on the number of resources and incidents. The work can be further expanded with the algorithm suggested for the cloud system and several other parameters such as scalability, performance, reliability, and others can be taken into account.

Mobile Cloud Computing

2018 ◽  
pp. 105-123
Author(s):  
Parkavi R ◽  
Priyanka C ◽  
Sujitha S. ◽  
Sheik Abdullah A
Keyword(s):  
Cloud Computing ◽  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Mobile Service ◽  
Service Users ◽  
Security Issues ◽  
Mobile Computing Environment

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry ring words and a major conversation thread in the IT world with an explosive development of the mobile applications and emerging of cloud computing idea, the MCC has become a possible technology for the mobile service users. The concepts of Cloud computing are naturally meshed with mobile devices to allow on-the-go functionalities and benefits. The mobile cloud computing is emerging as one of the most important branches of cloud computing and it is expected to expand the mobile ecosystems. As more mobile devices enter the market and evolve, certainly security issues will grow as well. Also, enormous growth in the variety of devices connected to the Internet will further drive security needs. MCC provides a platform where mobile users make use of cloud services on mobile devices. The use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing environment.

A Mobile Cloud Middleware to Support Mobility and Cloud Interoperability

2016 ◽  
Vol 7 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Khadija Akherfi ◽  
Hamid Harroud ◽  
Michael Gerndt
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Relevant Information ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Computing Power ◽  
Cloud Interoperability

With the recent advances in cloud computing and the improvement in the capabilities of mobile devices in terms of speed, storage, and computing power, Mobile Cloud Computing (MCC) is emerging as one of important branches of cloud computing. MCC is an extension of cloud computing with the support of mobility. In this paper, the authors first present the specific concerns and key challenges in mobile cloud computing. They then discuss the different approaches to tackle the main issues in MCC that have been introduced so far, and finally focus on describing the proposed overall architecture of a middleware that will contribute to providing mobile users data storage and processing services based on their mobile devices capabilities, availability, and usage. A prototype of the middleware is developed and three scenarios are described to demonstrate how the middleware performs in adapting the provision of cloud web services by transforming SOAP messages to REST and XML format to JSON, in optimizing the results by extracting relevant information, and in improving the availability by caching. Initial analysis shows that the mobile cloud middleware improves the quality of service for mobiles, and provides lightweight responses for mobile cloud services.

Mobile HCI

2014 ◽  
Vol 6 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Robin Deegan
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Location Based Services ◽  
Battery Life ◽  
Complex Environments ◽  
Processing Power ◽  
Communal Areas ◽  
And Performance ◽  
The Relationship ◽  
Mental Resources

Humans are approaching a new and intriguing time with regards to Mobile Human Computer Interaction. For years we have observed the processing power, memory capabilities and battery life of the mobile device increase exponentially. While at the same time mobile devices were converging with additional technologies such as increased connectivity, external peripherals, GPS and location based services etc. But what are the cognitive costs associated with these advancements? The software used on mobile devices is also becoming more sophisticated, demanding more from our limited mental resources. Furthermore, this complex software is being used in distracting environments such as in cars, busses, trains and noisy communal areas. These environments, themselves, have steadily become increasingly more complex and cognitively demanding. Increasingly complex software, installed on increasingly complex mobile devices, being used in increasing complex environments is presenting Mobile HCI with serious challenges. This paper presents a brief overview of five experiments before presenting a final experiment in detail. These experiments attempt to understand the relationship between cognition, distraction, usability and performance. The research determines that some distractions affect usability and not performance while others affect performance but not usability. This paper concludes with a reinforced argument for the development of a cognitive load aware system.

Study on Location Based Services Driven Mobile E-Commerce System

2013 ◽  
Vol 734-737 ◽  
pp. 3214-3219
Author(s):  
Hai Dong Zhong ◽  
Ping Li ◽  
Shao Zhong Zhang ◽  
Wen Ting Yuan ◽  
Xu Dong Zhao ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Location Based Services ◽  
Prototype System ◽  
Security Risks ◽  
Privacy And Security ◽  
Positioning Accuracy ◽  
Business Markets ◽  
Key Issues ◽  
Novel Concept

With the tremendous advances in mobile computing and communication capabilities, rapid proliferation of mobile devices, increasing powerful functions, and decreasing device costs, we are seeing a explosively growth in mobile e-commerce in various consumer and business markets. On the basis of analyzing demands of both buyers and seller in mobile e-commerce, the paper put forward a novel concept and technological framework of Location Based Services (LBS) driven mobile e-commerce. Some LBS related functions, in mobile device terminal, of the prototype system based on the architecture put forward are implemented. Also, some key issues of LBS based mobile e-commerce, such as positioning accuracy and new privacy and security risks, are discussed in detail.

scholarly journals COMPUTATIONAL OFFLOADING FOR PERFORMANCE IMPROVEMENT AND ENERGY SAVING IN MOBILE DEVICES

2019 ◽  
Vol 1 (04) ◽  
pp. 225-234
Author(s):  
Dr.Joy Iong Zong Chen
Keyword(s):  
Mobile Devices ◽  
Performance Improvement ◽  
Vital Role ◽  
Cloud Services ◽  
Battery Life ◽  
Mobile Cloud ◽  
External Device ◽  
Battery Lifetime ◽  
Computational Offloading ◽  
Storage Energy

The mobiles devices such as the smart phones and the wearables take a vital role in our daily life scenario as they are been used as alternative for many devices apart from communication. The mobile devices that are controlled in terms of dimensions and weightiness to make them easy and flexible for handling in turn limits the computational energy, storage and the lifetime of the battery. So this entails the need for the external device to support the mobile devices by providing a computational power, storage and energy, this is known as the computational offloading. So the paper puts forth the mobile cloud services as an external platform to offload the resource intensive computation tasks of the mobile devices to enhance the performance of the mobile devices in terms of storage, energy consumption and battery life. The performance evaluation of the mobile cloud based offloading in the mobile devices proves the efficiency of the proffered method in terms of storage, energy and battery lifetime.

Mobile and Wireless Computing towards Sustainable and Ubiquitous Clouds Services

2013 ◽  
Vol 5 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Ching-Hsien Hsu ◽  
Emmanuel Udoh
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Critical Role ◽  
Cloud Services ◽  
Future Research ◽  
Mobile Cloud ◽  
Extended Version ◽  
Special Issue ◽  
Energy Aware ◽  
Wireless Computing

The ever-growing cloud computing and services provide dynamic intelligence and play an increasingly critical role in all aspects of our lives. By taking advantage of virtualized resources, cloud computing services presents an attractive means to address the challenges while realizing the potential of Mobile and Wireless Computing (MWC). The MWC paradigm can be generalized to include mobile devices, which not only incorporate sophisticated methods for users to interact with the online world through numerous applications in their devices, but are endowed with multiple sensors that enable them to contribute data as nodes in the IoT. In this context, mobile cloud services that enable widespread data collection through mobile devices and collaborative use of mobile devices to enhance existing and realize new applications are very much of interest. As such, the MWC has come to the picture seeking solutions for computing and IT infrastructures to be energy efficient and environmentally friendly. This special issue is in response to the increasing convergence between MWC and cloud services, while different approaches exist, challenges and opportunities are numerous in this context. The research papers selected for this special issue represent recent progresses in the field, including works on services computing and modeling, mobile cloud, U-Care cloud, vehicle networks, energy-aware architectures, and wireless sensor network technologies and applications. This special issue includes four extended version of the selected paper originally presented at the 17th Mobile Computing Workshop (MC 2012) and the 8th Workshop on Wireless, Ad Hoc and Sensor Networks (WASN 2012), held at Taipei, Taiwan; one extended version of the selected paper originally presented at the 4th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom 2012), held at Taipei, Taiwan; and one regular submission with 20% average acceptance rate for 2012 submissions in IJGHPC. The papers selected for this issue not only contribute valuable insights and results but also have particular relevance to the mobile, wireless and cloud computing community. All of them present high quality results for tackling problems arising from the ever-growing mobile and cloud services. We believe that this special issue provides novel ideas and state-of-the-art techniques in the field, and stimulates future research in the mobile and wireless services in clouds.

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.

Topology for Intelligent Mobile Computing

2009 ◽  
pp. 589-596
Author(s):  
Robert Statica ◽  
Fadi P. Deek
Keyword(s):  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Device ◽  
Rural Areas ◽  
Cell Phones ◽  
Mobile User ◽  
Base Stations ◽  
Global Networks ◽  
Mobile Phone System ◽  
Single Standard

We discuss an interconnectivity framework for data and content delivery to mobile devices that allows data of higher priority to reach the mobile unit in the shortest time possible. Two possible scenarios are presented; one that connects the servers in an N-cube configuration network, and another that shows the same N servers connected in a grid type network. The goal is to minimize the rate of data jumps from server to server until it reaches the mobile device. As the mobile user travels, the mobile device registers itself with the next server and the session is migrated from the old server to the new one without interruptions, in an analogous way, cell phones move from one cell to another. Starting with the idea that all data is not equal (in importance/priority), this article suggest a framework topology for intelligent mobile computing that guarantees data will reach the mobile device in a minimum amount of time, assuring at the same time the privacy of transmission. The integration of this type of technology into the 3r d Generation (3G), and 4t h Generation (4G) mobile computing is also discussed. Pervasive computing is rapidly emerging as the next generation of computing with the underlying premise of simplicity (of use), minimal technical expertise, reliability, and intuitive interactions. As technology continues to advance and mobile devices become more and more omnipresent, the aim towards achieving easier computing, more availability and prevalence is becoming a given. Through the clever use of advanced technologies, the new generation of intelligent mobile computing has the opportunity to serve user needs via prevalent computing devices that are ever more transportable and connected to an increasingly ubiquitous network structure. Mobile communication is changing as the trends of media convergence including the Internet and its related electronic communication technologies and satellite communications collide into one. A change is being ushered by the 3G (3r d Generation) mobile technology with the usability and usefulness of information delivered to mobile devices taking on added features. For example, multimedia messaging, as opposed to voice transmissions, being delivered to cell phones has rendered such mobile devices an integral part of people’s lives and a core part of how they conduct their daily business rather than an add on tool (Buckingham, 2001). The 3G mobile phone system aims at unifying the disparate standards of current second generation wireless systems. The idea is to eliminate the different types of global networks being adopted with a single standard network. This will allow for the delivery of multimedia content and propagation through the network without the need for conversion from one standard to another. 3G systems need smaller cells thus the need for more base stations (mostly due to their operating frequency, power requirements, and modulation) and in many cases will not be feasible to install them in areas where population is not so dense (i.e., rural areas) (Garber, 2002). Because of these requirements and conditions, a better way to deliver the communication must be established. However, global access to such mobile devices will create data delivery challenges and servers can become clogged with unwanted communication, like that of wired Internet access. The need for moving relevant data to mobile devices in the shortest time possible becomes of utmost importance.

Topology for Intelligent Mobile Computing

2011 ◽  
pp. 1070-1074
Author(s):  
Robert Statica ◽  
Fadi P. Deek
Keyword(s):  
Mobile Computing ◽  
Mobile Devices ◽  
Mobile Device ◽  
Rural Areas ◽  
Cell Phones ◽  
Mobile User ◽  
Base Stations ◽  
Global Networks ◽  
Mobile Phone System ◽  
Single Standard

We discuss an interconnectivity framework for data and content delivery to mobile devices that allows data of higher priority to reach the mobile unit in the shortest time possible. Two possible scenarios are presented; one that connects the servers in an N-cube configuration network, and another that shows the same N servers connected in a grid type network. The goal is to minimize the rate of data jumps from server to server until it reaches the mobile device. As the mobile user travels, the mobile device registers itself with the next server and the session is migrated from the old server to the new one without interruptions, in an analogous way, cell phones move from one cell to another. Starting with the idea that all data is not equal (in importance/priority), this article suggest a framework topology for intelligent mobile computing that guarantees data will reach the mobile device in a minimum amount of time, assuring at the same time the privacy of transmission. The integration of this type of technology into the 3rd Generation (3G), and 4th Generation (4G) mobile computing is also discussed. Pervasive computing is rapidly emerging as the next generation of computing with the underlying premise of simplicity (of use), minimal technical expertise, reliability, and intuitive interactions. As technology continues to advance and mobile devices become more and more omnipresent, the aim towards achieving easier computing, more availability and prevalence is becoming a given. Through the clever use of advanced technologies, the new generation of intelligent mobile computing has the opportunity to serve user needs via prevalent computing devices that are ever more transportable and connected to an increasingly ubiquitous network structure. Mobile communication is changing as the trends of media convergence including the Internet and its related electronic communication technologies and satellite communications collide into one. A change is being ushered by the 3G (3rd Generation) mobile technology with the usability and usefulness of information delivered to mobile devices taking on added features. For example, multimedia messaging, as opposed to voice transmissions, being delivered to cell phones has rendered such mobile devices an integral part of people’s lives and a core part of how they conduct their daily business rather than an add on tool (Buckingham, 2001). The 3G mobile phone system aims at unifying the disparate standards of current second generation wireless systems. The idea is to eliminate the different types of global networks being adopted with a single standard network. This will allow for the delivery of multimedia content and propagation through the network without the need for conversion from one standard to another. 3G systems need smaller cells thus the need for more base stations (mostly due to their operating frequency, power requirements, and modulation) and in many cases will not be feasible to install them in areas where population is not so dense (i.e., rural areas) (Garber, 2002). Because of these requirements and conditions, a better way to deliver the communication must be established. However, global access to such mobile devices will create data delivery challenges and servers can become clogged with unwanted communication, like that of wired Internet access. The need for moving relevant data to mobile devices in the shortest time possible becomes of utmost importance.

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.

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