This document summarizes research challenges in mobile computing. It discusses fields like data management, security, data mining, and distributed databases. For each area, it identifies challenges like limited resources, dynamic connectivity, and lack of centralization. It proposes solutions like context-aware and collaborative systems, distributed intrusion detection, and algorithms for trust and authentication in mobile environments. The goal is to develop intelligent systems that can adapt to challenges in mobile and pervasive computing domains.

1. Mobile Computing:
Research Survey
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- Joseph Hennawy
Research Advisor: Dr. Anupam Joshi

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Agenda
• Introduction
• Mobile Computing Fields of Challenge
• Data Management
• Security
• Data Mining
• Distributed Databases
• Conclusion & Potential Future Research

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Introduction
The 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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