Name: large scale energy efficient sensor network routing using a quantum processor unit
Uploaded: 2023-09-08T14:35:00
Description: Watch this Scientific Journal Video about Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit at JoVE.com

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

1. Setting up Dwave Ocean Environment
<ol>
	<li>Download and install the ocean tools from the link: https://docs.ocean.dwavesys.com/en/stable/overview/install.html
	<ol>
		<li>At the terminal, type python -m venv ocean.</li>
		<li>At the terminal, type . ocean/bin/activate, as shown in Figure 1.</li>
		<li>At the terminal, type git clone https://github.com/dwavesystems/dwave-ocean-sdk.git 
		Next, type&#160;cd...

<ol>
	<li>Chen, J., Date, P., Chancellor, N., Atiquazzaman, M., Cormac, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Controller-based+energy-aware+wireless+sensor+network+routing+using+quantum+algorithms.">Controller-based energy-aware wireless sensor network routing using quantum algorithms.</a> IEEE Transactions on Quantum Engineering. 3, 1-12 (2022).</li><li>Lin, H., Uster, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exact+and+heuristic+algorithms+for+data-gathering+cluster-based+wireless+sensor+network+design+problem.">Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem.</a> IEEE/ACM Transactions on Networking. 22 (3), 903-916 (2014).</li><li>Zhou, Y., Wang, N., Xiang, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clustering+hierarchy+protocol+in+wireless+sensor+networks+using+an+improved+PSO+algorithm.">Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm.</a> IEEE Access. 5, 2241-2253 (2017).</li><li>Seah, W. K. G., Mak, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+long+is+the+lifetime+of+a+wireless+sensor+network.">How long is the lifetime of a wireless sensor network.</a> , 763-770 (2009).</li><li>Salahud din, M., Rehman, M. A. U., Ullah, R., Park, C., Kim, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+network+lifetime+enhancement+of+resource+constrained+IoT+devices+in+heterogeneous+wireless+sensor+networks+Sensors.">Towards network lifetime enhancement of resource constrained IoT devices in heterogeneous wireless sensor networks Sensors.</a> 20, 4156 (2020).</li><li>Wu, W., Xiong, N., Wu, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+clustering+algorithm+based+on+energy+consumption+in+wireless+sensor+networks.">Improved clustering algorithm based on energy consumption in wireless sensor networks.</a> IET Network. 6 (3), 7-53 (2017).</li><li>Kumar, N., Kumar, V., Ali, T., Ayaz, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prolong+network+lifetime+in+the+wireless+sensor+networks:+An+improved+approach.">Prolong network lifetime in the wireless sensor networks: An improved approach.</a> Arabian Journal for Science and Engineering. 46, 3631-3651 (2021).</li><li>Faris, H., Aljarah, I., Al-Betar, M. A., Mirjalili, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Grey+wolf+optimizer:+A+review+of+recent+variants+and+applications.">Grey wolf optimizer: A review of recent variants and applications.</a> Neural Computing and Applications. 30, 413-435 (2018).</li><li>Kaur, J., Arifkhan, M., Iftikhar, M., Imran, M., Haq, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Machine+learning+techniques+for+5G+and+beyond.">Machine learning techniques for 5G and beyond.</a> IEEEAccess. 9, 23472-23488 (2021).</li><li>Shor, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polynomial-time+algorithms+for+prime+factorization+and+discrete+logarithms+on+a+quantum+computer.">Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer.</a> SIAMJournalonComputing. 26 (5), 1484-1509 (1997).</li><li>Qabouche, H., Sahel, A., Badri, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hybrid+energy+efficient+static+routing+protocol+for+homogeneous+and+heterogeneous+large+scale+WSN.">Hybrid energy efficient static routing protocol for homogeneous and heterogeneous large scale WSN.</a> Wireless Networks. 27, 575-587 (2021).</li><li>Farooq, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=POWER:+probabilistic+weight-based+energy-efficient+cluster+routing+for+large-scale+wireless+sensor+networks.">POWER: probabilistic weight-based energy-efficient cluster routing for large-scale wireless sensor networks.</a> The Journal of Supercomputing. 78, 12765-12791 (2022).</li><li>Maddams, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+scope+and+limitations+of+curve+fitting.">The scope and limitations of curve fitting.</a> Applied Spectroscopy. 34 (3), 245-267 (1980).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+dynamic+opportunistic+routing+protocol+for+asynchronous+duty-cycled+WSNs.">A dynamic opportunistic routing protocol for asynchronous duty-cycled WSNs.</a> IEEE Transactions on Sustainable Computing. , (2023).</li><li>Ismail, A. S., Wang, X., Hawbani, A., Alsamhi, S., Aziz, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Routing+protocols+classification+for+underwater+wireless+sensor+networks+based+on+localization+and+mobility.">Routing protocols classification for underwater wireless sensor networks based on localization and mobility.</a> Wireless Networks. 28, 797-826 (2022).</li><li>Garcia-Martin, E., Rodrigues, C. F., Riley, G., Grahn, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimation+of+energy+consumption+in+machine+learning.">Estimation of energy consumption in machine learning.</a> Journal of Parallel Distributed Computing. 134, 75-88 (2019).</li><li>Egger, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantum+computing+for+finance:+State-of-the-art+and+future+prospects.">Quantum computing for finance: State-of-the-art and future prospects.</a> IEEE Transactions on Quantum Engineering. 1, 1-24 (2020).</li><li>Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., Qadir, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantum+computing+for+healthcare+:+A+review.">Quantum computing for healthcare : A review.</a> TechRxiv. , (2021).</li></ol>

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
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			<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>
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		<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>
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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...

Watch this Scientific Journal Video about Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit at JoVE.com

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

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 ...

This protocol proves to be more energy-wise than legacy protocols. From a content computing research perspective, this protocol shows that current bottleneck of noisy qubit issue does not hold ceiling for the commerciality of content computing technology. This techniques demonstrates the feasibility of applying current state art content computing methods to network problems.Furthermore, it presents the merits of applying content computing methods to network problems over legacy methods. To begin, download and install the Ocean tools from the given link. At the terminal, type python space minus m space venv space ocean then ocean/bin/activate.Next, type git space clone space https:github.com/dwavesystems/dwave-ocean-sdk. git then cd space dwave-ocean-sdk, followed by python space setup. py space install.After downloading and installing Cplex, at the terminal, type pip space install space cplex. Using Python programming notation script, set up the experiment configuration parameters. Once the script is executed, the underlying language will process to store the variables in RAM.Next, create the Python scripts to generate 198 sensor node 2D positions that are equally scattered into six sectors, and divide the circular area with a radius of 50 meters. Within each sector, ensure that the 33 sensor nodes are scattered randomly by a normal distribution. Save the 2D positions into text files by each sector under the name spelling rule as posdata with single quotation mark, plus sector underscore no plus txt with single quotation.Segment the circular area with a radius of 50 meters into six sectors. For sector index I, set the pole length for the jth sensor node by entering the indicated command. If the sector index is L, set the angular value for the jth sensor node.Then set the Cartesian coordinates of the jth sensor node in the ith sector. To prepare the initial energy levels for all 198 sensor nodes, divide them equally, allocating an initial energy of 0.5 joules to half of the sensor nodes and one joule to the other half. Proceed to create an array to store each node's energy level and use a loop to assign cells sequenced in even numbers the value of one and those sequenced in odd numbers the value of 0.5.Next, prepare a functional script to select the cluster head. For each sensor node, attain a random number between zero and one, threshold_rm equal to random. random bracket.If threshold_RM is less than t_n, select this sensor node as the cluster head. For each noncluster_head node, select the closest cluster head sensor node to it as its cluster head. Prepare the command lines to calculate the energy depletion process across the whole network for this round.Finally, calculate the required transmission round metrics. To prepare a hybrid quantum algorithm script, run the selection procedure in a loop to ensure that the amount of cluster heads is six. Following this, for each of the non-cluster_head_valid nodes, calculate the distance to each selected cluster head and assign it to the cluster head whose cluster size hasn't exceeded six and where the distance value was smallest.Next, prepare a sub-function script where the rooting optimization problem per cluster is formed and submitted to the Dwave API. Using Python script, calculate the energy depletion across the entire network to quantitatively evaluate the algorithm by network lifetime in terms of the number of transmission rounds. Then record the moment when the first node is drained out and when half the nodes are drained out.In this study, it was observed that the hybrid quantum algorithm has more efficiency than the advanced_leach algorithm. The time complexity of the hybrid quantum algorithm and the hybrid quantum algorithm that complies with the advanced_leach are also shown here. These methods can be applied to other systems in objective optimization.For instance, machine-to-machine communication in manufacturing industry. According to content physics, the in content theory has paved issues for each fixed game setting. It can lead to new research in social science and economy.

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