Intelligent Multi-platform Middleware for Wireless Sensor and Actuator Networks

Francisco, Rui; Arsénio, Artur

doi:10.1007/978-3-319-19656-5_3

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…But still this middleware does not have the intelligence to decide whether it is necessary to run some code on the cloud or on a device. Francisco et al [14] presented such solution, which is further detailed in this paper.…”

Section: Discussionmentioning

confidence: 97%

“…After running the experiment, all the files that were in the folder were successfully stored in the cloud, showing that the management mechanism is reacting when the memory reaches is a critical point (Figure 8 shows the upload of one such file on the cloud). More recently [18], we have successfully integrated the proposed middleware with a WSAN platform, OpenHAB, for smart home automation (see Figure 9). Data storage is transferred between the OpenHAB server device and the cloud according to the monitored conditions.…”

Section: Cloud Storage For Wireless Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Switching Brains: Cloud-based Intelligent Resources Management for the Internet of Cognitive Things

Francisco¹,

Arsénio²

2015

EAI Endorsed Transactions on Cognitive Communications

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Cognitive technologies can bring important benefits to our everyday life, enabling connected devices to do tasks that in the past only humans could do, leading to the Cognitive Internet of Things. Wireless Sensor and Actuator Networks (WSAN) are often employed for communication between Internet objects. However, WSAN face some problems, namely sensors' energy and CPU load consumption, which are common to other networked devices, such as mobile devices or robotic platforms. Additionally, cognitive functionalities often require large processing power, for running machine learning algorithms, computer vision processing, or behavioral and emotional architectures. Cloud massive storage capacity, large processing speeds and elasticity are appropriate to address these problems. This paper proposes a middleware that transfers flows of execution between devices and the cloud for computationally demanding applications (such as those integrating a robotic brain), to efficiently manage devices' resources.

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