Abstract. When different wireless networks come in close proximity there is often a need for them to logically combine, or compose. We focus on a known research problem particularly in Ambient Networks (ANs), where heterogeneous Distributed Hash Tables (DHTs) contained in these wireless networks need to merge or divide as a result of these dynamic (de)composition processes, respectively. We present two novel DHT (de)composition models for ANs, known as absorption and gatewaying, that are designed to handle (de)composition of DHTs in different AN network environments, with minimal disturbance to existing member nodes.
Ambient Networks (ANs) are dynamically changing and heterogeneous as they consist of potentially large numbers of independent, heterogeneous mobile nodes, with spontaneous topologies that can logically interact with each other to share a common control space, known as the Ambient Control Space. ANs are also flexible i.e. they can compose and decompose dynamically and automatically, for supporting the deployment of cross-domain (new) services. Thus, the AN architecture must be sophisticatedly designed to support such high level of dynamicity, heterogeneity and flexibility. We advocate the use of service specific overlay networks in ANs, that are created on-demand according to specific service requirements, to deliver, and to automatically adapt services to the dynamically changing user and network context. This paper presents a self-management approach to create, configure, adapt, contextualise, and finally teardown service specific overlay networks.
This paper presents a novel network context monitoring system, known as the Context Monitoring System (CMS), that is designed to accommodate the rapidly changing network context requirements and network context availability in dynamically (de)composing Ambient Networks (ANs). CMS is designed to support dynamic deployment, activation, and (re)configuration of Context Sensors in ANs in an efficient and scalable way, and to locate available distributed network context in a scalable and distributed manner once Context Sensors are deployed, in order to support subsequent efficient and scalable network context retrieval.
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