1. 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*

2. Introduction
Service-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).

3. SYSTEM MODEL
Two roles: service provider (SP) and service requestor (SR)
Example:
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

4. SYSTEM MODEL
Service Quality Criteria: QoI, service Delay (as a QoS attribute), and Cost.
Normalization:
Max and Min are known a priori
Q,D,C
MOO => multi-objective maximization

5. SYSTEM MODEL Malicious 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 nodes
Packet dropping: drop packets

6. SERVICE COMPOSITION AND BINDING
Service Advertisement
Reply

7. SERVICE COMPOSITION AND BINDING
Service composition specification (SCS):
Constraints:

8. SERVICE COMPOSITION AND BINDING
Service Binding SP can only participate in one service request at a time to ensure its availability and commitment to a single service request.

9. PROBLEM DEFINITION AND METRICS
parallel structure
series structure

10. PROBLEM DEFINITION AND METRICS
MOO Problem Formulation system level:
Preferences of SR

11. 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 Om
Compare with USTm:
user satisfaction threshold

12. TRUST MANAGEMENT PROTOCOL
Trust management schemes:
BRS: single-trust beta reputation system
Multi-trust Protocol Design
threshold-based relationship model (TRM)
scaling relationship model (SRM)

13. TRUST MANAGEMENT PROTOCOL
Single-trust Baseline Protocol Design (BRS)
A positive evidence is observed when SRm is satisfied (USm exceeds USTm )

14. TRUST MANAGEMENT PROTOCOL
Multi-trust Protocol Design
Competence: intrinsic service capability, “true” Q, D, and C scores
Integrity: degree complies with the protocols

15. TRUST MANAGEMENT PROTOCOL
Multi-trust Protocol Design
Still
How to count ?
For competence: the same with BRS
For 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

16. TRUST MANAGEMENT PROTOCOL
Trust formation
Threshold-based relationship model (TRM) :
Scaling relationship model (SRM):
More strict

17. ALGORITHM DESCRIPTION
Four algorithms
Non-trust-based
BRS
TRM
SRM

18. ALGORITHM DESCRIPTION
Non-trust-based blacklist of SPs: randomly select

19. ALGORITHM DESCRIPTION
Trust-based Modify By multiplying to each single node in the bottom layer

20. 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 service
Optimal solution to be compared: Integer Linear Programming (exponential runtime complexity )

21. RESULTS AND ANALYSIS
Comparative Performance Analysis

22. RESULTS AND ANALYSIS
Effect of Service Quality Constraints and Opportunistic Service Attacks

23. RESULTS AND ANALYSIS
Effect of Q, D, C Score Distribution