266,732 research outputs found
An Ada framework for QoS-Aware applications
In this paper we present a framework for managing QoS-aware
applications in a dynamic, ad-hoc, distributed environment. This framework
considers an available set of wireless/mobile and fixed nodes, which may
temporally form groups in order to process a set of related services, and where
there is the need to support different levels of service and different
combinations of quality requirements. This framework is being developed both
for testing and validating an approach, based on multidimensional QoS
properties, which provides service negotiation and proposal evaluation
algorithms, and for assessing the suitability of the Ada language to be used in
the context of dynamic, QoS-aware systems
Dynamic, interactive and visual analysis of population distribution and mobility dynamics in an urban environment using the mobility explorer framework
© 2017 by the authors. This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application's usefulness and critically reflects on the evaluation results of the citizens' motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data
Multisite adaptive computation offloading for mobile cloud applications
The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints.
The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others.
To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo
Hadoop MapReduce for Mobile Cloud
The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile net- work enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The goal of this thesis is to bring Hadoop MapReduce framework to a mobile cloud environment such that it solves these bottlenecks involved in big data processing. The Mobile Distributed File System(MDFS) addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have evaluated its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment
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Integrated mobility and resource management for cross-network resource sharing in heterogeneous wireless networks using traffic offload policies
The problem of efficient use of resources in wireless access networks becomes critical today with users expecting continuous high-speed network access. While access network capacity continues to increase, simultaneous operation of multiple wireless access networks presents an opportunity to increase the data rates available to end-users even further using intelligent cross-network resource sharing. This paper introduces a new integrated mobility and resource management (IMRM) framework for automatic execution of policies for cross-network resource sharing using traffic offload and pre-emptive resource reservation algorithms. The presented framework enables both mobile-initiated and network-initiated resource sharing policies to be executed. This paper presents the framework in detail and analyses its performance using extensive ns-2 simulations of the operation of a set of static policies based on measured signal strength, and dynamic pre-emptive network-initiated policies in a WiFi/WiMAX scenario. The detailed evaluation of the static policies clearly shows that the quality of voice applications shows large deviation, mostly due to very different levels of delay in access networks. Based on these conclusions, this paper presents a design of two new dynamic policies and shows that such policies, when efficiently implemented using the new IMRM framework can greatly improve the capacity of the network to serve voice traffic with a minimal impact on the data traffic and with a very low signalling overhead
ANANAS - A Framework For Analyzing Android Applications
Android is an open software platform for mobile devices with a large market
share in the smartphone sector. The openness of the system as well as its wide
adoption lead to an increasing amount of malware developed for this platform.
ANANAS is an expandable and modular framework for analyzing Android
applications. It takes care of common needs for dynamic malware analysis and
provides an interface for the development of plugins. Adaptability and
expandability have been main design goals during the development process. An
abstraction layer for simple user interaction and phone event simulation is
also part of the framework. It allows an analyst to script the required user
simulation or phone events on demand or adjust the simulation to his needs. Six
plugins have been developed for ANANAS. They represent well known techniques
for malware analysis, such as system call hooking and network traffic analysis.
The focus clearly lies on dynamic analysis, as five of the six plugins are
dynamic analysis methods.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
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