A Generic Mobile Agent Framework towards Ambient Intelligence

Abstract

Recent advances of computing and networking technology have shifted computing convention from stationary to mobile. A forecast conducted by Gartner indicates that global mobile handset sales will increase exponentially in 2008 by 10% from 1.3 billion units in 2007 [13]. Furthermore, the popularity of wireless networking topology including both Wi-Fi access point and cellular mobile telecommunications enables users with constant access to online connection and further intensifies the demand of mobile devices. This provides the fundamental elements for creating ubiquitous environments of computing, networking, and interfacing that is both aware of and reactive to the presence of people. Such an environment is defined as Ambient Intelligence (AmI). Existing approaches that attempt to understand AmI environment mainly focus on how to seamlessly integrate hardware, i.e. mobile device and sensors, into human society and intelligently provide personalized knowledge and services. This has, however, left many essential issues unanswered, especially in regards to the integrity and performance of such dynamically distributed environments. In this chapter, we formulate a generic framework in which an AmI environment is generalized to consist only of users with devices, hosts where services are provided, and directory servers that act as information desks to users and hosts. In the proposed framework, the mobile agent notion is utilized to provide autonomous reasoning, learning, mobility, and collaboration features to construct AmI systems. Performance issues of load balancing and communication overhead in such a framework are then examined and analyzed against existing AmI techniques. Because of the dynamicity posed by AmI environments and the complexity of migrations and communications among agents, hosts and directory servers, it is necessary to provide a mechanism to warrant the accuracy and enhance the reliability of such an environment. Software verification employs formal methods of mathematically provable formulations to perform program analysis and model checking. Thus, general formulation using π-calculus is included to model the proposed approach to provide verification mechanism. In conclusion, the implementation of the proposed framework simulation using NetLogo is currently ongoing to visually demonstrate its feasibility. Some implementation details are included to stimulate comprehension of this framework.KeywordsMobile DeviceMobile AgentUbiquitous ComputingSoftware AgentAmbient IntelligenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.