This document proposes a framework called ALADIN for data management in mobile ad hoc networks (MANETs). It utilizes cross-layer communication, multi-level ontologies, and relationships between data items. ALADIN includes components for data discovery, representation, caching, and consistency management. It exploits data correlations computed from usage logs to improve discovery and caching. Evaluation shows ALADIN reduces latency and increases hit ratio compared to existing solutions. The framework represents a comprehensive approach for MANET data management.

1. 1
Dr. Noman Islam
https://sites.google.com/a/nu.edu.pk/noman-islam/
http://facebook.com/sir.noman.islam
Data Management in MANET
1

2. 2
Introduction
 We are living in the era of computing where data has
become a critical and most valuable asset
 The progression of technology and the emergence of
mobile and ad hoc networking have further enabled
the creation and consumption of information on move
 Mobile Ad Network (MANET) is defined as a network
erected among a number of self organized mobile
nodes, on the fly
 These nodes interact and exchange data with each
other in P2P style to solve complex user problems

3. 3
Introduction – contd…
3
 Several useful applications are thus possible
 However, to truly realize these applications requires
the need for addressing an array of new issues and
challenges related to management of data

4. Mobile Ad hoc Network Vs Conventional Networks4
Mobile, resource
limited, unreliable and
heterogeneous nodes
Low capacity,
asymmetric and
instable links
Dynamic,
infrastructure-less and
data intensive
environment

5. 5
Introduction – contd…
5
 Unlike conventional client-server computing, the
P2P communication generates massive volume of
data
 Despite the immense need, there has been
shortage of a comprehensive data management
framework for MANET

6. 6
Introduction – contd…
6
 Hence, this work presents ALADIN. The framework
utilizes:
 Inter-layer communication across layers of protocol stack
to curtail network dynamism
 i.e. Coordination, interaction and joint operation of protocols
across different layers
 Fractional views of semantic information to ensure
scalability
 Maintaining ontology documents as needed
 Correlation among data items to optimize various data
management operations
 There are inherent associations among data items that can
be computed based on usage log

7. 7
Research Objectives
7
To propose a comprehensive framework for MANET that
addresses key issues of data management by utilizing:
1. Cross-layer communication
2. Multi-level ontologies
3. Inherent relationship among data
This work doesn’t provide:
Detailed discussions and evaluations of other issues of data
management (replication, security management etc.). But it
does sketch solutions for these issues

8. 8
Problem Area
8

9. 9
1. Introduction to Data Management
2. Data Management Issues
3. Data Management Frameworks
Background and Literature Review

10. 10
What is Data Management?
 We can define data management as:
“a set of actions performed on the data considering the end
user’s preferences and requirements”
 Based on the literature review, we can identify following
major steps/issues in data management (Perich, Joshi et al.
2006):
 Data representation
 Data discovery
 Cache Management
 Query Processing and Optimization
 Data Replication
 Naming
 Transaction Management

11. 11
Data Management Issues
 Data Representation - the mechanism to describe
the data such that it can be identified, discovered
and accessed by any local or remote consumer
 Challenges
 Heterogeneity
 Proprietary data formats, Semantic differences
 Absence of universal schema
 Unbounded/open nature, Resource limitations, Size of schema
 Solution
 On-demand discovery and maintenance of fractional
schema

12. 12
Data Management Issues (contd…)
12
 Data Discovery - the actions performed for locating the
owner of data considering the preferences specified by
the consumer
 Challenges
 Lack of dedicated directory servers
 Spatiotemporal variations
 Frequent disconnections
 Solution
 Directory-less approaches
 Cross-layer operation
 Exploit data correlations to store potentially useful data in
advance

13. 13
Data Management Issues (contd…)
13
 Cache Management – To locally save a data item for
future use after it has been discovered
 Challenges
 Lack of prior information about nature of data. Locality of
reference proposed for conventional systems will not work
 Frequent disconnections
 Frequent state changes
 Solution
 Exploit data correlations to compute relationships among data
items
 Cross-layer operations i.e. utilizing network events for managing
consistency of data

14. A summary of existing solutions for data management issues

15. 16
Data Management frameworks for
Mobile Ad hoc Network
 Alfanao et al. (2012) – a method for managing operations and data in
MANET
 CDMAN (Martin and Demeure 2008) – a framework for improved data
sharing by structured, segmented data
 DRIVE (Zhong, Xu et al. 2008) – a system for opportunistic information
sharing in vehicular networks
 Ad-hoc InfoWare (Sanderson, Skjelsvik et al. 2007) – a framework for
knowledge management in rescue scenarios
 AmbientDB (Fontijn and Boncz 2004) – a relational database abstraction
over ad hoc networks of heterogeneous peers
 MoGATU (Perich, Joshi et al. 2006) – a P2P data management framework
based on cross-layer operations, context management etc.
 CHaMeLeoN (Shahram Ghandeharizadeh 2006) – a framework to
facilitate exchange of text and continuous media over ad hoc networks

16. Summary of Data Management frameworks for MANET

17. 18
Gap Analysis
18
1. There has been shortage of a comprehensive data
management framework
2. Most of the solutions ruminated on a subset of issues
of data management. Key issues of data management
are:
 Data Representation
 Data Discovery
 Cache management
3. None of the solutions exploit correlation and multi-
level ontology for data management
4. Only few solutions are based on cross-layer
communication

18. 19
1. Introduction
2. Design Principles
3. Block Diagram
4. Description
Proposed Framework
19

19. 20
ALADIN
 In this work an Association Rules Based Network
Layer Data and Information Management System
(ALADIN) for MANET has been proposed
 It comprises of components for:
1. Network layer, correlation based data discovery
2. Multi-level, ontology based data
representation
3. Correlation-based cache replacement and
cross-layer consistency management

20. 21
Framework’s Design Principles
21
 ALADIN rests on following three points:
1. Exploits Correlation Information
 There are some inherent associations/correlation among data
items, as has been verified (Appendix 1)
 Two data items are correlated if they have been used together for
reasonable number of times. Correlated data items are anticipated
to be used together in future
 Correlation can be exploited for:
 Associative advertisements – proactively advertise data to neighbors.
Neighbors will store these advertisements
 Piggybacking of information – piggyback correlated information with
current data request
 Priority based cache replacement – keep those data items in cache
that are correlated with current request

21. 22
Framework’s Design Principles
(contd…)22
2. Exploits cross-layer operations
 Network layer data discovery – data items and routes are
discovered simultaneously
 Cache Consistency Management – exploiting network
layer events for reactively manage data states
3. Multi-level data representation
 On-demand discovery and maintenance of semantic
information – use two level ontology. Keep the core
ontology at nodes. Extended ontologies can be
discovered when needed

22. Proposed Data Management Framework

23. 24
Description
 Logging Component
 Maintains the fractional history of data requests in the
form of sessions
 Association Rules Mining Component
 Calculates the correlation among data items by running FP-
Growth algorithm (Han, J et al. 2004) on the log database
 FP-Growth is a tree-based data mining algorithm to
compute frequently used itemsets
 The output of FP-Growth algorithm is the set of frequent
itemsets representing what data requests have been
mostly consumed together in past

24. 25
Data Discovery Component
 Discovery component is responsible for discovery of data
items from other nodes on the network
 It comprises of two subcomponents:
 A proactive discovery component that periodically advertises its
data and route via SADV messages
 A reactive discovery component to discover the desired
information based on the delivery mechanism of any reactive
routing protocols
 The proposed discovery component discovers a data item
and corresponding routes to provider simultaneously
 This leads to reduced latency of the overall discovery
process

25. 26
Proactive Component
 Periodically advertises data items at regular intervals to neighbors
 Listener component at neighbors can accumulate the received
advertisements
 Advertisement (SADV) includes details about the data items along
with corresponding routes to access these data items
 The advertiser can operate in two modes:
 Correlated Mode
 Advertise the catalog entries that are correlated to each other
 Correlation is determined based on the itemsets generated by the mining
component
 Non-Correlated Mode
 Advertise the catalog entries in round robin style

26. Proactive Discovery Component

27. 28
Reactive Component
 Reactive component performs on-demand discovery of any
requested data item
 ALADIN presents two discovery algorithms based on following
reactive routing algorithms:
 AODV based Discovery
 DSR based Discovery
 Source node prepares a discovery request (SREQ) and sends to
adjacent nodes
 Intermediate nodes propagates the request until it is answered by
the destination node
 In case of AODV, a temporary reverse route is used
 In case of DSR, the traversed hop is appended with the original request
 A SREP is then generated. SREP is sent back through the same
route used for propagation of SREQ

28. Discovery Request
Discovery Response
AODV based Reactive Discovery

29. 30
Using Correlation Patterns to Improve
Discovery Process
 The correlation computed by mining component can be
exploited to prophesize about likely future requests and
answers of these guessed requests can be piggybacked/
attached with current request
 While generating a discovery response, the replying node
appends the details of data items related to request with
current response

30. Using correlation patterns for piggybacking31

31. 32
Data Representation
 MANET suffers from issues of heterogeneity due to its open
nature
 Ontology can be used by nodes to reach to a common
agreement of the domain terminologies
 However, the use of a global ontology is not viable due to:
 Limited resources
 Nodes of varied origins

32. 33
Data Representation (contd…)
33
 ALADIN recommends
maintaining two-level
ontology:
 Core Ontology
 Available at every node
 Ext Ontology
 Fetched as needed from the
network by invoking the discovery
component
 A General Purpose Software
Ontology for Ad hoc and
Vehicular Applications has
been proposed
ALADIN Multi-level ontology management approach

33. Multilevel approach to maintain ontology

34. An example of Multilevel Ontology
Core ontology contains
generalized concepts for any
ad hoc and vehicular
applications
Ext ontology extends Core
Ontology and contains
concepts for specialized
applications

35. 36
Cache Management
 It contains sub-components for:
 Cache replacement
 Exploits the correlation patterns generated by the mining
component to place a newly arrived data item in to the
cache
 Victim slot is thus picked from those data items that are
not related to current request
 Consistency management
 Exploits the routing layer to determine the topological
changes in the network.
 These topological changes are used to maintain
consistency of cached data

36. 37
Cache Replacement
 C = {c1, c2, c3 …} denotes the cache, ρ denotes the
correlation function. Cache Items related to d i.e. Rd
can be expressed as:
 Residual Set Sd i.e. data items not related to d can be
written as:
 The victim slot is thus picked from those data items
that are not related to d i.e.

37. Proposed cache replacement algorithm38

38. 39
Consistency Manager
39
 The robustness of network layer can be used to ensure
the consistency of items maintained in data catalog
 ALADIN proposes the consistency management based
on the existing routing protocols (AODV and DSR etc.)
 In case of a link failure, the node (who senses the
failure) attempts to perform local route maintenance
 The upstream nodes are then notified depending upon
the status of local maintenance. A failure in local repair
leads to invalidation of routes and data table
 The upstream nodes notify their upstream nodes

39. 42
1. Experimental Setup
2. Data sets
3. Demonstration
Implementation Details
42

40. 43
Network Simulation
 All the experiments were conducted on JIST/SWANS Simulator
 The ontology has been developed in OWL language using Protégé
 JENA Toolkit has been employed for ontology processing tasks

41. 44
Mobility Models
44
P1
P2
P3
P4
P5
Random Way Point
Street Random Waypoint Mobility (Straw)
•A node chooses a random point
•The node moves towards the point with a
uniform but random speed
•Pauses for a constant time
•Repeats the whole process again
•Node moves according to streets
•Streets are defined by real maps of US cities
•Mobility is also restricted based on vehicular
congestion and other traffic control mechanisms
To model the movement patterns of mobile nodes during simulation, mobility models are used

42. 45
Datasets
 Three types of datasets have been used for generating
sessions.
 Random Dataset. Picks a data item randomly and
floats on the network
 Generating Correlated Data Sessions
 The YouTube Dataset: A dataset generated based on
YouTube video requests by users in a campus (Zink, M. et
al. 2008)
 The Correlated Data Generator(CDG) Dataset: A simulated
dataset generated based on a random correlation matrix

43. Demonstration of ALADIN’S Simulation49

44. 50
1. Discovery Component
2. Data Representation Component
3. Caching Component
RESULTS AND DISCUSSIONS50

45. 51
Analysis of Discovery Component
51
 A series of experiments were conducted to analyze the impact of individual sub-
components on latency and hit ratio
 Latency is the time difference when a discovery request is floated and the time
when a response is received
 Hit Ratio is the ratio of discovery request locally satisfied to the total discovery
request issued by a node
 Summary of the results are:
 Next slides compare ALADIN with MoGATU

46. Latency Analysis of Discovery Component52

47. Hit Ratio Analysis of Discovery Component53

48. 54
1. Discovery Component
2. Data Representation Component
3. Caching Component
RESULTS AND DISCUSSIONS
54

49. Scalability of Data Representation Component55
Growth of ontology size
during simulation is linear

50. Latency Analysis of Data Representation Component56

51. 57
1. Discovery Component
2. Data Representation Component
3. Caching Component
RESULTS AND DISCUSSIONS
57

52. Hit Ratio of Cache Replacement58

53. Hit Ratio of Consistency Manager59

54. 60
Research Contributions
60
 We present a scalable, network layer, association rules based data
and information management framework for MANET
 This research substantiates that:
 the use of a multi-level schema gives rise to improvement in scalability
of data management operations
 a network layer discovery approach leads to improvement in latency
and robustness of data management operations
 the correlation patterns present in the request sessions can be
exploited in predictive fashion to ameliorate the discovery and caching
process
 It is the first work(for MANET) exploring the use of :
 data correlations
 multi-level data representation

55. 61
Future Work
61
 Improvement in discovery component
 Proactive Component
 Smart broadcasting based on available battery, network congestion
 Reactive Component
 Implementation for other reactive routing protocols like TORA, ABR, OLSR etc.
 Association Rules Mining are inefficient in terms of memory consumption
 New approximate algorithms can be devised
 Caching Component
 Based on proposed correlation technique, extensions of other caching
algorithms (MFU, Profile Driven caching)
 Log database
 Can it be used for transaction management, query optimization etc.?
 Further work/evaluation of replication, query optimization and security

56. 62
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62
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