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However, our design provides high sensitivity feature, which provides a long radio range and reduces deployment density.
Although our design's power consumption is larger than other wake-up radios, it reduces deployment density, and is still acceptable and more suitable to sensor networks within a long radio range.
In contrast, our wake-up radio provides most reliable performance, reduces deployment density, and has the total power consumption of 115.3 mW that is approximating to other low power wake-up radios' power consumption at high PR of 1 packet/s.
It also can achieve more approximate power dissipation and a similar latency as other wake-up radios, provides up to 16 times radio range, reduces deployment density, and is more suitable and reliable to sensor networks than other wake-up radios.
Although our design has a larger latency than other low power wake-up radios, it increases the radio range by up to 4 times, reduces deployment density, and is more suitable to sensor networks with less deployment density within a long radio range than other short range wake-up radios.
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Finally, this article has shown the relevance of mobile relaying to reduce deployment costs.
Then, a clustering algorithm groups several radio heads of different buildings in a single pooled baseband unit, reducing deployment costs.
This points to the need for management frameworks that exhibit agility and flexibility and reduce deployment, integration, testing time.
To reduce deployment and maintenance costs, a novel wave energy converter (WEC) is proposed for mooring-less sensor buoys.
For these cases, a clustering algorithm can reduce deployment costs by assigning the radio heads of different buildings into a single baseband unit.
Intelligent Networks (IN) separate the control and call processing from the switches, leading to fast, vendor and network-independent services, with reduced deployment costs.
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