FTT-MA: A Flexible Time-Triggered Middleware Architecture for Time Sensitive, Resource-Aware AmI Systems

Abstract

There is an increasing number of Ambient Intelligence (AmI) systems that are time-sensitive and resource-aware. From healthcare to building and even home/office automation, it is now common to find systems combining interactive and sensing multimedia traffic with relatively simple sensors and actuators (door locks, presence detectors, RFIDs, HVAC, information panels, etc.). Many of these are today known as Cyber-Physical Systems (CPS). Quite frequently, these systems must be capable of (1) prioritizing different traffic flows (process data, alarms, non-critical data, etc.), (2) synchronizing actions in several distributed devices and, to certain degree, (3) easing resource management (e.g., detecting faulty nodes, managing battery levels, handling overloads, etc.). This work presents FTT-MA, a high-level middleware architecture aimed at easing the design, deployment and operation of such AmI systems. FTT-MA ensures that both functional and non-functional aspects of the applications are met even during reconfiguration stages. The paper also proposes a methodology, together with a design tool, to create this kind of systems. Finally, a sample case study is presented that illustrates the use of the middleware and the methodology proposed in the paper.

Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications

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“…Some works provide programming-based solutions [ 60 , 61 , 62 ], so application definition is subject to application availability. On the contrary, others propose recovery approaches transparent to application definition [ 43 , 54 , 63 , 64 ]. In the case of stateful components, many works have focused on determining the best instant to perform the state transfer [ 65 , 66 , 67 ], which might involve blocking the application execution.…”

Implementation of middleware for Internet of Things in asset tracking applications: In-lining approach

“…Given the similarities between WSNs and the traditional distributed systems, it would seem that the already established middleware solutions such as CORBA, ICE, SOGI, OMG and web services could be used; however, they have been found to be inadequate in meeting IoT and WSNs' requirements [14] Consequently, tens of middleware solutions specific to WSNs have been developed; these can be categorized into three: (1) in-network middleware that is uploaded on the nodes; (2) server-side middleware that runs on a server; and (3) hybrid that combines both 1 and 2 [1]. Another categorization that is based on programming models that the architecture uses, produces two broad classes: programming support and programming abstractions [9].…”

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