Energy Efficient Computing, Distributed Mobile Trusted Computing & Clouds, Crowd
Sourcing and Computing
your project should have an abstract, an introduction, a body of paper (literature review , previous work
done bi others, technical and economical analysis, application and services, etc….) and your own
contribution and engineering/technical opinion with a conclusion and of course a list of your references
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Topic: Energy efficient computing, distributed mobile trusted computing & clouds, crowd sourcing and
computing
MOBILE CLOUD COMPUTING SERVICE MODELS
Abstract
Efficient energy computing also known as green computing initiates the maximization
and use of computing reserves with the aim of lessening the consumption of energy among other
scarce resources. With the proliferation of an increasing powerful mobile devices, applications
have been developed that can enable a user to collaborate through a mobile cloud with the aim of
providing pervasive services such as; computing, data collection, and processing through
efficient energy systems (Cătinean, & Cândea, 2013). Mobile users also have the advantage of
taking over the outsourced tasks through the mobile cloud and crowd outsourcing that emerges
as a service paradigm. By leveraging the capacity of a mobile device that integrates the
intelligence of human beings and machine computation, the mobile crowd gives the ability to
revolutionize the approaches involved in the processing and collection of data.
Introduction
The genesis of the mobile devices and mobile computing has turned out to be an
irresistible trend in the IT technology sector. With this, it is significant to mention that the mobile
devices face some challenges and limitations including; memory, energy, and computation. In
order to overcome these constraints, the mobile cloud computing approaches have proved to be
the solution in enabling the mobile device users consumes a varied cloud resource through a
wireless network (Cătinean, & Cândea, 2013).
This clouding computing method can, therefore, improve the computational ability
through an energy efficient mobile device by offloading the computational tasks into the cloud
services (Cătinean, & Cândea, 2013). The current and new-fashioned mobile devices have been
embedded with versatile sensors that provide a novel paradigm with the capacity to collect a
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wider array of data about the society, the environment, and other important aspects. The intent of
this exposition is to analyze the elements of an energy efficient computing, distributed mobile
trusted computing and clouds, crowd sourcing and computing through a review of the literature.
Energy Efficient Computing, Distributed Mobile Trusted Computing & Clouds,
Crowd Sourcing and Computing
Through the empowered efforts and capabilities, mobile devices have shifted today from
just the ordinary service providers that offer and make communication easier into a new service
model that incorporates the elements of mobile cloud computing (Qi, Jianxin, & Yufei, 2014).
The emergence of this technology and service model has led to the connection of mobile devices
with each other through wireless networks, a factor that has resulted in the formation of a
powerful mobile cloud that provides a persuasive approach to data collection, processing and
computing through efficient energy consumption. It is important to mention that the evolution of
the mobile cloud has given the mobile crowd sourcing a feasible solution for solving problems
on a large scale. The literature review segment provides an overview into how these elements
function.
Literature Review
The mobile crowd sourcing is a commercial electronic service in which the mobile users
within a mobile cloud can sell their services and resources for service consumers (Qi, et. al).
Through an outstanding task of the mobile cloud, a cost, and energy efficient pervasive cloud
service is possible to attain through the use of massive numbers of mobile users working together
in a distributive manner. The central idea behind a mobile crowd sourcing structure includes the
involvement of a variety of applications that are differently utilized in a business model.
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For instance, the author of this material gives an analogy of an Open Street Map that
depicts a crowded map of the world that was developed by worldwide mobile users through the
use of their knowledge, some donated sources and a GPS trajectory (Qi, et. al). The development
of this application clearly indicates the fact that this mobile crowd sourcing has the capacity to
revolutionize the traditional data collection and processing methods. I as much as the computing
paradigm seems promising and poses a tremendous advantage, the mobile crowding sourcing is
in its infancy stages since it is facing numerous challenges.
This approach is varied from the traditional cloud computing method that only relies on
the internet connection since through the crowd sourcing; it is able to access a pervasive cloud
service for both the local and online terminals (Qi, et. al). According to the author of this
material, the main difference between the two mobile crowd sourcing approaches is the fact that
all these models gives an interconnected mobile user the potential to be a service provider
through an internet-based mobile crowd sourcing, while the other approach only allows mobile
users within a specified vicinity to provide cloud services through a local-based mobile crowd
sourcing service. In order to achieve the end-results of these elements, there are essential
components of cellular crowd sourcing;
Service Consumers;
The service consumers, in this case, refers to the local and online users who from time to
time require cloud services through a mobile crowd sourcing system and who utilize the cloud
services by sourcing out tasks to mobile users.
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Mobile Users;
Mobile users with the enablement of their mobile devices can perform a mobile cloud by
providing cloud services to the online service consumers through a cellular Wi-Fi network or to
the local consumers through a process where communication is made to the local servers by
using Bluetooth and NFC techniques (Qi, et. al). In the event that a mobile user engages in an
outsourced task that incorporates the local computing mobile cloud approach to executing the
tasks required.
Centralized Servers;
A centralized server remains a mobile crowd sourcing avenue for the internet-based
service consumers in which the crowd sourcing information is stored such as historical service
records, users, and profiles that are used for service evaluation and task outsourcing (Qi, et. al).
Centralized servers have the ability to provide a trusted service for task publishing in which tasks
are allocated, reported, collected, and the feedback processing for the internet-connected mobile
users and consumers.
Local Servers;
The local servers have the capacity to provide a local crowd sourcing service that
involves an outsourced task broadcasting the result in a task result aggregation for the service ad
mobile consumers within particular vicinity (Qi, et. al). The local servers are in many cases
equipped with a dedicated mobile gateway that is tasked with the dissemination of functions and
information to the neighboring mobile users and, in the long run, collects user results. However,
a local server can only be deployed for commercial reasons and remains not trusted by the
mobile users.
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Previous Work Done By Others
Over a period now, many mobile devices have turned out to be the sensor and
information hubs in our day to day activities. Through the integration of mobile computing and
crowd sourcing approach, many applications have today emerged that have achieved efficiency
in energy, cost-effective data computation, collection, and the processing services (Qi, et. al).
The mobile crowd computing model is used to supply data computations and tasks to mobile
consumers who can locally execute a task and offload them to a cloud server based on an
individual data and computation resource.
The author states that human interventions have also played a significant role in mobile
crowd computing since their intelligence has helped in the handling of tasks that are only
suitable for human evaluation that the computation of a machine. These functions include image
annotation, sentimental analysis, and entity resolution (Qi, et. al). An instance of this is given of
Honeybee, which is a local-based mobile crowd computing application that enables users to
detect their faces through a photographic task and can be carried out through a mobile device.
The mobile users are required to use their devices to run face detection algorithms through
photos blended with an individual’s evaluation.
Technical and Economical Analysis
It is significant to mention that the technicality of these IT technologies remains the
primary challenge of security as one of the major concerns for the cloud service consumers,
while at the back of everything, a mobile crowd sourcing is meant to originate from an
assumption and perception that users should honestly provide accurate results. This, therefore,
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remains a significant challenge to the users since some malicious mobile users have developed
mechanisms of misbehaving to undermine the crowd sourcing.
These malicious users have the capacity to fabricate computations or maliciously
suspending an ongoing process. It is, however, crucial to realize that there are several approaches
that are being tried with the aim of mitigating the impacts of malice in the task reports and to
identify the irrational users (Shiraz, & Gani, 2014). Recently, a robust trajectory estimation
approach geared towards alleviating the negative influences of malice in a crowd sourced user
trajectory to identify fraudulent users. In relation to the economics of these technologies it is
important that responsibility is directed towards the environment, a factor that is emerging for
corporate IT entities. With the increasing pressure on efficient energy because of its conservative
nature, there are emerging technologies geared towards regulating the consumption
Application and Services
The crowd sourcing approach has assisted in big data application. According to my
opinion, there are contributions that can be engineered to enhance the mobile trusted computing
& clouds, crowd sourcing and computing technologies. This includes an approach that will help
this technology by integrating the mobile crowd sourcing with the big data analytics (Shiraz, &
Gani, 2014). This application can be beneficial since it would enable Smartphone user’s book
their train seats in advance based upon the mobility pattern model and the contributions of a
mobile data user.
It is however important to note that jelling the mobile crowd sourcing model and the big
data analytic can be challenging since the methods of data collection may prove and turn out to
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be huge on a mobile device, a factor that constrains the data velocity, and volume. Privacy
should also be a measure to include in the development of these systems.
Conclusion
The advent of an increasing powerful mobile devices applications have been developed
that can enable a user to collaborate through a mobile cloud with the aim of providing pervasive
services such as; computing, data collection, and processing through an energy efficient system
(Shiraz, & Gani, 2014). These systems have made it easier for users to use the clouding
computing methods to, therefore, improve the computational ability through energy efficient
mobile devices. The evolution of these technologies has therefore seen several inventions that
have impacted the society today.
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References
Cătinean, I., & Cândea, D. (2013). Characteristics of the Cloud Computing Model as a
Disruptive Innovation. Review of International Comparative Management / Revista De
Management Comparat International, 14(5), 783-803.
Qi, Q., Jianxin, L., & Yufei, C. (2014). Cloud service-aware location update in mobile cloud
computing. IET Communications, 8(8), 1417-1424.
Shiraz, M., & Gani, A. (2014). A lightweight active service migration framework for
computational offloading in mobile cloud computing. Journal of Supercomputing, 68(2),
978-995.
