Yating Wang Ing Ray Chen JinHee Cho Ananthram Swami and Kevin S Chan Introduction Serviceoriented mobile ad hoc network MANET is populated with service providers SPs and service requesters SRs ID: 550242
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Slide1
Trust-based Service Composition and Binding with Multiple Objective Optimization in Service- Oriented Mobile Ad Hoc Networks
Yating
Wang†,
Ing
-Ray Chen†,
Jin-Hee
Cho*,
Ananthram
Swami* and Kevin S. Chan* Slide2
IntroductionService-oriented mobile ad hoc network (MANET) is populated with service providers (SPs) and service requesters (SRs)
In this paper, the authors are concerned with:
satisfying
user service requests with multiple objectives including maximizing quality-of-service (QoS) and quality-of-information (QoI) while minimizing the service cost with user satisfaction (US) ultimately measuring success
multi-objective optimization (MOO).Slide3
SYSTEM MODELTwo roles
: service provider (SP) and service requestor (SR)
E
xample: A user in a smart city issues a service request “take me to a nice Thai restaurant nearby with drunken noodle on its menu” with a service quality specified in terms of QoI, QoS, and cost for the overall service
Service
composition
phase:
transportation
+ food
Service
binding
phase:
select the best SPs out of all SPs available to the user at the time the service request is issued Slide4
SYSTEM MODELService Quality
Criteria:
Q
oI, service Delay (as a QoS attribute), and Cost. Normalization:Max and Min are known a priori
MOO
=> multi-objective
maximization
Q,D,CSlide5
SYSTEM MODELM
alicious
behaviors:
Self-promotion: reporting false service quality information Opportunistic service: “just enough” service Bad-mouthing attack (BMA): providing bad recommendations Ballot stuffing attack (BSA): boost the reputation for bad nodesPacket dropping: drop packets Slide6
SERVICE COMPOSITION AND BINDINGService Advertisement
Reply Slide7
SERVICE COMPOSITION AND BINDINGService
Composition
S
ervice composition specification (SCS):Constraints: Slide8
SERVICE COMPOSITION AND BINDINGService Binding
SP can only participate in one service request at a time to ensure its availability and commitment to a single service request. Slide9
PROBLEM DEFINITION AND METRICS
parallel
structure
series structure Slide10
PROBLEM DEFINITION AND METRICS MOO Problem
Formulation
system level:
P
references of SRSlide11
PROBLEM DEFINITION AND METRICS User Satisfaction: different from MOO value
ratio of the actual service quality received to the best service quality available among SPs for executing O
m
Compare with USTm: user
satisfaction
thresholdSlide12
TRUST MANAGEMENT PROTOCOL T
rust
management
schemes:BRS: single-trust beta reputation systemMulti-trust Protocol Designthreshold-based relationship model (TRM)scaling relationship model (SRM)Slide13
TRUST MANAGEMENT PROTOCOL Single-trust Baseline Protocol
Design (BRS)
A positive evidence is observed when
SRm is satisfied (USm exceeds USTm )Slide14
TRUST MANAGEMENT PROTOCOL Multi-trust Protocol
Design
Competence:
intrinsic service capability, “true” Q, D, and C scores Integrity: degree complies with the protocols Slide15
TRUST MANAGEMENT PROTOCOL Multi-trust Protocol Design
Still
How to count ?
For competence: the same with BRSFor integrity: positive if SR sees node j’s observed Q, D and C scores are close to node j’s advertised scaled Q, D, and C scores
Compare with Slide16
TRUST MANAGEMENT PROTOCOL Trust formation
T
hreshold-based
relationship model (TRM) : Scaling relationship model (SRM):
More strictSlide17
ALGORITHM DESCRIPTION F
our
algorithms
Non-trust-basedBRSTRMSRM Slide18
ALGORITHM DESCRIPTION Non-trust-based
blacklist
of
SPs:randomly selectSlide19
ALGORITHM DESCRIPTION T
rust-based
Modify
By multiplying to each single node in the bottom layerSlide20
RESULTS AND ANALYSIS Experiment
Setup
Proposed method:
Heuristic-based solution (linear runtime complexity) SR ranks all eligible SPs for executing an abstract service and selects the highest ranked SP as the winner for executing that particular abstract serviceOptimal solution to be compared: Integer Linear Programming (
exponential runtime complexity
)Slide21
RESULTS AND ANALYSIS
Comparative
Performance AnalysisSlide22
RESULTS AND ANALYSIS
Effect of Service Quality Constraints
and Opportunistic
Service AttacksSlide23
RESULTS AND ANALYSIS Effect of Q, D, C Score Distribution