Name: Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit (Video) | JoVE Journal
Uploaded: 2023-09-08T14:35:00.000+00:00
Duration: 5 min 30 s
Description: Explore quantum processor routing for large-scale sensor networks using Ocean tools and hybrid algorithms. This method outperforms legacy protocols in energy efficiency and extends network lifetime significantly.

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5 minutes

The work is supported by the Engineering and Physical Sciences Research Council of the UK (EPSRC) Grant number EP/W032643/1.

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The current state-of-the-art commercial quantum processor can be used in computational problems of any network topology1. Quantum processor application is not constrained by the number of physical qbits any of the quantum processors has been able to implement. 
In sensor network lifetime prolongation design, the results show an advancement in method to achieve even longer network life by using a quantum processor. The results imply that quantum advantage is ready to be ...

Energy conservation in sensor networks has been a very critical issue in design1. Classical methods normally tackle the problem using an ad hoc approach2,3,4,5,6. That said, these methods emulate the sensor nodes as individually managed intelligent assets that could also cooperate to serve both the interests of the individual and the community. Due to the volatile environment where sensors work, in some works, random algorithms are introduced in order to capture the environmental uncertainties, while in others, bio-intelligence is borrowed to devise heuristic algorithms that could achieve common sense acceptable results7. To illustrate further, for those random algorithms, for one hand, environmental uncertainties might not be as random as the random sequence generated by a classical CPU, for the other hand, even if the environmental uncertainties are absolutely random, they could not be captured by the random process simulator generated by classical CPU; for those bio-intelligence algorithms, first of all, no rigorous mathematical analysis has been derived to make a conceptual proof work, secondly, the convergence to truth or the error tolerance boundary can only be configured given an informed ground truth - though a significant amount of works in literature have demonstrated to some extent these heuristic algorithms work, for one thing, these algorithms are analyzed (not simulated) against well-defined use case scenarios, they stops at certain criteria that are still worth pondering in further research, for another, as said before, a majority of the algorithms have not been validated against software simulation that can be more readily deployed in the microprocessors that make a sensor into its being8.
We do not consider machine learning (ML) here because it needs to employ data analytics which requires a relatively large volume of computational power that is not portable in sensor devices9.
To address the above-mentioned concerns, we provide a hybrid quantum algorithm. The algorithm is hybrid in that the cluster head selection mechanism is implemented using a classical random algorithm during the routing computations conducted using a quantum processor once the network topology is set up. The method is justified as follows: (1) As discussed in the first paragraph regarding the environmental uncertainties, we do not want to endeavor further to apply a quantum sequence generator to capture the environmental dynamic because it might be historically traceable. The environmental dynamic that can be historically traceable has been justified by various machine learning research works in network science. For the current stage, we stay with the classical approach. (2) The exact method that relies on abstract mathematical analysis guarantees to arrive at ground truth. Quantum experimental physics has so far been sophisticatedly supported by physical mathematics. Moreover, algorithm applications like the Shor algorithm10 have existed to prove this rounded theory.
An adequate amount of literature survey is provided below for comparison. The HEESR protocol proposed11 has demonstrable merits in results, but the authors have specified the simulation configuration parameters well, for example, the exact random distribution function of the node position, the proper justification of the cluster head percentage p (0.2%), and the scaling parameter for distribution of energy level (1-2 joules) among nodes a_i. It prohibited the author from proceeding further to duplicate the experiments and conduct the comparison. The Power routing mechanism12 employs the curve fitting method to approximate converged continuous functions from discrete data sets obtained from unspecified sample space for determinants that impact the decision process of the optimal network routing. The curve fitting method13 requires prior information on the network topology. Real circumstances might not have prior information readily available. Even when prior information exists, network topology might not be regular enough to be able to be mapped onto fitting curves that are able to facilitate derivable computation. Following the same logic, DORAF protocol14 has not justified how and why to borrow the Boltzmann function and Logistic Function to approximate the network determinants. Ismail et al.15 have provided a sound reference for future research endeavors into energy-efficient routing protocol design in the underwater network.

<table><tbody><tr><td>Dell Laptop</td><td>Dell</td><td>N/A</td><td></td></tr><tr><td>Ubuntu 18.04.6 LTS</td><td>Canonical Ltd</td><td>18.04.6 LTS</td><td></td></tr><tr><td>Python3.8</td><td>Python Software Foundation</td><td>3.8.0</td><td></td></tr><tr><td>Dwave QPU</td><td>Dwave</td><td>https://docs.ocean.dwavesys.com/en/stable/overview/install.html</td><td></td></tr></tbody></table>

large scale energy efficient sensor network routing using a quantum processor unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than the heuristic algorithm using a classical computer. In this manuscript, the technical context for the significance of the method is presented and justified. Then the experimental steps are demonstrated in an operational sequence with illustrations if ever needed. The method has been validated by positive results across a randomly generated sample set of network topologies. The successful experimental results of this method have provided a better approach for sensor network lifetime maximization problems and demonstrated that the current state of art quantum processor has been able to solve large practical engineering problems with merits that override current methods in the literature. In other words, quantum advantage can be exploited to best efforts. It has gone beyond the stage of proof of concept to proof of feasibility.

Results from one run sample are shown in Table 2, Table 3, and Table 4. The detailed datasets for the three batches of data are available in the Supplementary Data 1 folder.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="3">Dataset 1</td>
		</tr>
		<tr>
			<td>198 nodes in a circular area with a radius of 50m</td>
			<td>Hybrid Quantum Algorithm</td>
			<td>A...

This study provides a method to use a quantum processor unit to compute the routes for various traffic dynamics that work to outperform classical methods in literature to maximize network lifetime.

The sensor network energy conservation method, which is a usage hybrid of a classical computer and quantum processor, has proved to perform better than ...

large-scale-energy-efficient-sensor-network-routing-using-quantum

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Explore quantum processor routing for large-scale sensor networks using Ocean tools and hybrid algorithms. This method outperforms legacy protocols in energy efficiency and extends network lifetime significantly.

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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