Abstract. Wireless Sensor and Actuator Networks (WSAN), composed by small sensing nodes for acquisition, collection and analysis of data, are often employed for communication between Internet objects. However the WSAN have some problems such as sensors' energetic consumption and CPU load. The massive storage capacity, large processing speeds and the rapid elasticity makes Cloud Computing a very good solution to these problems. To efficiently manage devices' resources, and achieve efficient communication with various platforms (cloud, mobile), this paper proposes a middleware that allows flows of AI applications' execution to be transferred between a device and the cloud.
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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