Fuzzy Logic Based Utility Function for Context-Aware Adaptation Planning

Context-aware applications require an adaptation phase to adapt to the user context. Utility functions or rules are most often used to make the adaptation planning or decision. In context-aware service based applications, context and Quality of Service (QoS) parameters should be compared to make adaptation decision. This comparison makes it difficult to create an analytical utility function. In this paper, we propose a fuzzy rules based utility function for adaptation planning. The large number of QoS and context parameters causes rule explosion problem. To reduce the number of rules and the processing time, a rules-utility function can be defined by a hierarchical fuzzy system. The proposed approach is validated by augmenting the MUSIC middleware with a fuzzy rules based utility function. Simulation results show the effectiveness of the proposed approach.

[1]  Frank Eliassen,et al.  Using architecture models for runtime adaptability , 2006, IEEE Software.

[2]  Chuen-Chien Lee,et al.  Fuzzy logic in control systems: fuzzy logic controller. II , 1990, IEEE Trans. Syst. Man Cybern..

[3]  Bradley R. Schmerl,et al.  Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure , 2004, Computer.

[4]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[5]  Kaitlyn Muller,et al.  Using a Trust Inference Model for Flexible and Controlled Information Sharing During Crises , 2010 .

[6]  Julie A. McCann,et al.  A survey of autonomic computing—degrees, models, and applications , 2008, CSUR.

[7]  Barbara Pernici,et al.  A Fuzzy Service Adaptation Based on QoS Satisfaction , 2011, CAiSE.

[8]  Nearchos Paspallis,et al.  A multi-dimensional model enabling autonomic reasoning for context-aware pervasive applications , 2008, Mobiquitous 2008.

[9]  Kurt Geihs,et al.  An Adaptation Reasoning Approach for Large Scale Component-based Applications , 2009, Electron. Commun. Eur. Assoc. Softw. Sci. Technol..

[10]  Frank Eliassen,et al.  A comprehensive solution for application-level adaptation , 2009 .

[11]  Frank Eliassen,et al.  MUSIC: Middleware Support for Self-Adaptation in Ubiquitous and Service-Oriented Environments , 2009, Software Engineering for Self-Adaptive Systems.

[12]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[13]  Frank Eliassen,et al.  The DigiHome Service‐Oriented Platform , 2013, Softw. Pract. Exp..

[14]  Brahim Bensaou,et al.  Fuzzy-based rate control for real-time MPEG video , 1998, IEEE Trans. Fuzzy Syst..

[15]  Chuen-Chien Lee FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[16]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

[17]  Ronald R. Yager,et al.  On the construction of hierarchical fuzzy systems models , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[18]  Ming-Ling Lee,et al.  Modeling of hierarchical fuzzy systems , 2003, Fuzzy Sets Syst..

[19]  George Angelos Papadopoulos,et al.  Please Scroll down for Article Enterprise Information Systems a Survey of Software Adaptation in Mobile and Ubiquitous Computing a Survey of Software Adaptation in Mobile and Ubiquitous Computing , 2022 .

[20]  Alvin T. S. Chan,et al.  Dynamic QoS Adaptation for Mobile Middleware , 2008, IEEE Transactions on Software Engineering.

[21]  David Garlan,et al.  Rainbow: architecture-based self-adaptation with reusable infrastructure , 2004 .

[22]  George Angelos Papadopoulos,et al.  Applying Utility Functions to Adaptation Planning for Home Automation Applications , 2008, ISD.