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As expected, the devices resources decrease as the number of connections increases, up to a critical threshold that is clearly visible in Figure 6.
The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources.
Participatory Sensing (PS) systems rely essentially on users' willingness to dedicate their devices' resources (energy, processing time..) to contribute high-quality data about various phenomena.
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A similar framework has been provided by [83] for IoT devices resource management in micro data-centres.
Smaller SE is more efficient in device resources; however, it has limited capabilities of configuring various SE shapes.
Nowadays, managing device resources from energy efficiency perspective without affecting the user experience is one of the most difficult tasks.
The total design concept aims at balancing the functional performance, the computational burden, and the usage of the device resources.
The results show that the module fits into 4 BRAMs, thus reducing on device resources by 50%.
It has the advantage of creating LBS applications that are light on the wireless device resources and are OS independent.
The matching between the large-scale device collaboration demand and device resources is the basis of the realization of WMSN applications.
The experiments are conducted in three desktop computers: the first one simulates the requests of device collaborative tasks, the second one deploys a large-scale device collaboration system which uses our proposed matching method to assign the device resource with collaborative tasks, and the last one deploys a device simulator which can produce a lot of device resources with different abilities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com