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

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.