Mobile Computing:Research Survey

UMBC Spring 2012

05/05/2012 Mobile Computing: Research Survey 1

- Joseph HennawyResearch Advisor: Dr. Anupam Joshi

UMBC Spring 2012

05/05/2012 Mobile Computing: Research Survey 2

Agenda

• Introduction• Mobile Computing Fields of Challenge• Data Management• Security• Data Mining• Distributed Databases

• Conclusion & Potential Future Research

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IntroductionThe Uniqueness of the mobile environment

• Constrained Communications Capability• Limited Power Resources• Frequent Disconnects• Asymmetric Communications• Dynamic System Topology• Limited Node Capabilities

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Knowledge Discovery in ubiquitous environments

• Ubiquitous Technologies used • Resource Awareness• Ubiquities data collection• Ubiquities data computing

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Knowledge Discovery in ubiquitous environments

• Security and Privacy• Human Computer Interface (HCI)• Application Area

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Data Management challenges

In distributed computing three main architectures are defined for exchanging data between system elements:

• Client-server interaction model• Client-proxy-server interaction model• Peer-to-peer interaction model

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Data Management challenges

• Networking Challenges• Device discovery• Routing• RF and wireless links

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Data Management challenges

• Context Awareness Challenges • Service and Data Discovery• Mobile location maintenance of network nodes.• User context and profiling

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Data Management challenges• Distributed Systems Data Management

Challenges• Distributed Processing and threading • Distributed systems messaging and communication • Naming name spaces, and name resolution • Synchronization • Consistency and Replication • Fault tolerance

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Developing intelligent Mobile Data Management systems

• Cross layer intelligent collaboration• Collaborative intelligence• Context aware intelligence

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Security challenges• No prior knowledge of participating nodes due to the dynamic

mature of the system. • At any given point, the composition of the system can be

diverse enough that a centralized node identification, and authentication is not feasible.

• The possibly fragmentary nature of the system also makes it harder to perform security tasks as monitoring, intrusion detection, etc.

• Mobile Systems wireless communications technologies are broadcast in nature.

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Security challenges

• Wireless communications physical characteristics of such networks make them vulnerable to security threats such as intrusion, denial of service, masquerading, and tampering.

• Limited processing, memory, storage and power resources make it harder to implement resource intensive security processes and functions.

• The collaborative nature of these systems forces them to disseminate valuable information about system components and data, without central routing control, or monitoring.

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Intrusion Detection in Mobile Systems• Traditionally intrusion detection techniques fall short in such

environment due to:• Higher rates of false positives or vice versa due to the unreliable RF

communications environment• Lack of consistent tracking mechanisms due to the dynamic nature of

the system.• Lack of a solid definition of a misbehaving node, or fixing a reputation

associated with a given node, due to the heterogeneous nature of the system, its communications mechanisms and protocols.

• Lake of definition or the existence of a single administrative domain.• No global view of the networked system, and the reliance on

approximate and localized observations on making intrusion detection mechanisms.

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Intrusion Detection in Mobile Systems• There is a need for a Distributed Intrusion Detection Systems

(DIDS) that fits the mobile environment. • Security system is envisioned to use light weight collaborative

data mining of various network and distributed system communications stack.

• Mining activities will produce, enhance, and match patterns of attacks, intrusions, misbehavior of particulate nodes.

• These patterns will be fed into machine learning intelligent agents that will reason, adapt, and enhance, and ask for more mining activities for a system, or a system segment security.

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Node identification, authentication & privileges

• The assumption of an existing central authentication, and resource privilege granting entity that does not suffer from communication reachability issues is not valid.

• Research performed for devices to compute trust and beliefs about neighboring peers and share them to produce a global trust and belief picture.

• Research performed in implementing an authentication framework of where a delegation scheme of authentications, privileges and roles can be done in a mobile environment to accommodate nodes joining a system, or a system segment.

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Node identification, authentication & privileges

“ We believe that both trust modeling and authentication distribution activities can be rolled up in a coherent framework where both segments can interact to provide a back and forth credentials, and trust metrics feedback based on the ongoing member nodes dynamics. The intrusion detection activities described above can enhance the proposed framework to offer and intelligent and adaptable security solution based on mobile data mining, autonomous machine learning, and a distributed credentials and authentication engines.”

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Data Mining Challenges

• Data mining combines techniques from a variety of field that includes:• Statistics• Machine Learning• Pattern Recognition• Database Systems• Information Retrieval• World-Wide Web• Visualization• Application Specific Domains related to the data being mined

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Data Mining Challenges

• Data mining techniques includes:

• Summarization• Sampling• Approximation• Clustering• Pattern Recognition • Classification• Outlier Detection

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Data Mining Challenges

• Data Mining Fields related to distributed systems:

• Stream Data Mining• Moving Objects Mining • Web and Text Data Mining

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Web and Text Data Mining

• Web mining can be decomposed into three main styles of mining:

• Web Content Mining• Web Structure Mining• Web Usage Mining

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Web and Text Data Mining• For its textual contents, web mining algorithms uses the

common textual data mining techniques such as :• Information extraction• Topic tracking• Summarization• Categorization• Clustering• Concept linkage

• Multimedia web contents and its integration with web textual contents is still an open field for web mining research activities with lots of potential promises.

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Distributed Databases Challenge

The breakdown of mobile querying

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Distributed Databases Challenge

• Mobile databases performance measures in term of reliability, availability, correctness, and timeliness.

• The usage of heterogynous mobile communication infrastructures and combining it with using different query strategies. A layer of intelligence that detects available and changing infrastructure characteristics and adapt query strategies.

• Smart resource and data discovery, and fuses that with user profiles and policies to produced smart automated or semi-automated queries.

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Distributed Databases Challenge

• Sensor network and Mobile databases integration. Such an integrate can offer a local, or global sensor view based on database queries or processes or semi-processed sensor data.

• The definition of meta-data needed for multimedia database. Researching the way the methods of meta-data are being produced, the ways that meta-data can model multimedia databases, and expanding the usage of meta-data to be a source for upper level intelligence functions that builds semantics based on them is needed.

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Distributed Databases Challenge

• Sensor network and Mobile databases integration. Such an integrate can offer a local, or global sensor view based on database queries or processes or semi-processed sensor data.

• The definition of meta-data needed for multimedia database. Researching the way the methods of meta-data are being produced, the ways that meta-data can model multimedia databases, and expanding the usage of meta-data to be a source for upper level intelligence functions that builds semantics based on them is needed.

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