Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Control and Automation

Quantum Boson Data Aggregation Scheduling in UOSN

S. Madhavi

Published Apr 30, 2015• 1 citation
Publisher

Abstract

Data Aggregation Scheduling in Ubiquitous sensor networks is a major research interest for many researchers with the objective of reducing energy consumption and maximizing the network lifetime.Also the capacity of a channel for transmitting quantum states called the quantum capacity should be maximized,The sensor nodes should periodically forward data to the base station.Since battery power and bandwidth are the two resources to be effectively utilized the sensor nodes forwards data to the nearest neighbor node which inturn forwards the data to its neighbors and this repeats until all the sensors data aggregates at the base station.In a large network scheduling the sensor nodes for forwarding the data is very much essential and since the topology of the USN changes dynamically , finding such a schedule is also very difficult.In [1] the authors proposed a bosonic network model for communication.In this paper we present a quantum bosonic data aggregation method for constructing the optimal schedule of nodes for data aggregation called QBDAS (Quantum Boson Data aggregation scheduling) protocol.The main idea is to search a optimal schedule through utilizing the observable or measurable physical properties the network topology.

DOI
10.14257/ijca.2015.8.4.22

Related papers

Power Optimized Quantum Data Aggregation Scheduling

S. Madhavi

Communication Networks have limited resources like bandwidth and battery power.Hence efficient node scheduling protocols and communication protocols are needed to maximize the network throughput.In this paper, we discussed an optimal data aggregation scheduling problem for quantum networks.To optimize the utilization of the network bandwidth much research is done to find schedules with minimum frame length.In this paper we proposed a method for finding efficient quantum node scheduling.The schedule contains a set of slots where each slot is allotted to one or more quantum nodes for transmitting at the same time.Adjacent Quantum nodes can create entanglement for transmitting the data.The quantum nodes entangles with its neighbor quantum nodes during the slot allotted to them.Loss and distortion are the two major problems in free space channels transmission.The optical noise produced when two or more nodes are transmitting data concurrently interferes with the actual data.Hence the optical signal to noise ratio OSNR ie Square root of the signal power divided the square root of the noise power is used as a threshold on concurrent transmitters.In this proposed method we used OSNR for finding a set of nodes that can entangle concurrently for aggregating the data to the base station.Entanglement rate of each node is used as a metric for finding a schedule that maximizes the quantum network throughput.We derive a Power Optimized Maximal Link Entanglement Rate based Scheduling Method which maximizes the quantum network throughput and also increases the network life time.

International Journal of Grid and Distributed Computing•Jul 31, 2018•0 citations

A Reinforcement Learning Model for Quantum Network Data Aggregation and Analysis

Quantum Entanglement and Quantum swapping are major research areas nowadays.Remote quantum entanglement is used in many applications like secure communication, secret sharing, data aggregation, and precision sensing.In data aggregation applications, every sensor node captures data and communicates to the central node.Efficient Data aggregation depends on whether the local information or global quantum network information is used for constructing the aggregation schedules.In addition, Quantum networks suffer from lossy optical links and with limited resources such as quantum memories, edge capacities.Computation of optimal schedules deals with large quantities of data and complex time-consuming calculations.However, the quantum memories cannot hold the qubits for a longer time as the stored qubit completely decoheres an infinite amount of time.Hence, there is a necessity for finding new data aggregation scheduling protocols, which use optimal channel capacity and optimal size of memory for improving the network throughput.This paper uses a reinforcement-learning technique that considers entanglement pairing and swapping the success probability of nodes with their neighbors while finding an optimal scheduling policy.The proposed method uses local network information for constructing optimal data aggregation schedules by prior sharing the maximally entangled qubit pairs between the nodes through optimal usage of the processes, channel capacity, and memory at the intermediate nodes.Experiments show that our proposed method can maximize the network throughput.

Journal of System and Management Sciences•Feb 20, 2022•5 citations

Secured Data Aggregation Scheduling in Ubiquitous

S. Madhavi

Secured Data Aggregation Scheduling in Ubiquitous sensor networks is a major research problem nowadays. Since the sensors communicate wirelessly, they are exposed to a high level of vulnerability from the intruders. Quantum key distribution is a technique that is used nowadays for providing security. Though the secure keys are used for short distances only, it offers many advantages over traditional methods in providing security. Quantum key distribution adopts many methods like entanglement swapping and quantum teleportation. Reversible logic gates like CNOT, Toffoli, and Fredkin are proved to offer many advantages in quantum computing. Also a very little research is carried to provide security to the nodes in ubiquitous sensor networks using quantum cryptography so as to maximize the throughput and utilize the hardware resources effectively. Hence in this paper, we propose an intrusion detection policy for security to sensor nodes using quantum cryptography. The proposed protocol monitors the communication between sensor nodes and uses a simple policy to detect the malicious nodes. In our detection policy we adopted an EPR-pair allocation scheme in terms of the quantum gates like Toffoli and Feynman called SwapGate.

예술인문사회융합멀티미디어논문지•Jun 30, 2014•0 citations

Secured Data Aggregation Scheduling in Ubiquitous Quantum Sensor Networks

S. Madhavi

Asia-pacific Journal of Multimedia services convergent with Art Humanities and Sociology•Jun 30, 2014•7 citations

Quantum Data Aggregation Using Secret Sharing and Genetic Algorithm

Tai-hoon Kim, S. Madhavi

In the process of data aggregation, intruder nodes and compromised nodes may decrease the system performance. Hence, security plays an important role. Secret sharing is a method to find the compromised nodes. The previous researches in secure data aggregation concentrated on key authentication process and little research is done in N-party secret sharing methods. In this article, we proposed quantum data aggregation using secret sharing and genetic algorithm, where a node forwards data to its most trusted neighbor only. In the proposed method, a node selects n 1-hop neighbors from a list of its m 1-hop neighbors, which well behaved over a period and forwards data to the most trusted amongst them. We simulated quantum teleportation using SimulaQron tool for establishing secure communication. However secured is the communication channel the compromised nodes may decrease the system performance by exposing network to malicious activities. Each node creates n shares of a secret and forwards a share on a secured quantum channel to the n 1-hop neighbors in such a way that from any t neighbors share(t<; n) the secret is reconstructed. The data aggregation security increases since a group of t users can reconstruct the key but no single user has the complete authority on the secret. The proposed method ensures that each node will be able to aggregate the captured message securely without any interventions from the compromised/intruder nodes. The proposed system applies genetic method to improve the efficiency of the process of detection of the compromised nodes based on the behavioral history of the node. While detecting the malicious nodes the proposed method accuracy rate is 98.8 and false rate is 0.15, which is almost equal to secure and energy-efficient data aggregation method based on an access control method. However, in terms of the size of the network considered, the percentage of malicious nodes assumed and from the basic principles of quantum teleportation, the proposed method proves to be a better protocol for implementing secure data aggregation.

IEEE Access•Jan 1, 2020•4 citations

ESDI: Entanglement Scheduling and Distribution in the Quantum Internet

Huayue Gu, Ruozhou Yu, Zhouyu Li +2 more

Quantum entanglement distribution between remote nodes is key to many promising quantum applications. Existing mechanisms have mainly focused on improving throughput and fidelity via entanglement routing or single-node scheduling. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Two practical scenarios are considered. When requests do not have deadlines, we seek to minimize the average completion time of the communication requests. If deadlines are specified, we seek to maximize the number of requests whose deadlines are met. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI, and develop a probabilistic protocol to implement the optimized solutions in a general buffered quantum network. We develop a discrete-time quantum network simulator for evaluation. Results show the superior performance of ESDI compared to existing solutions.

arXiv:2303.17540•International Conference on Computer Communications and Networks (ICCCN)•Jul 1, 2023•14 citations
Data Aggregation Scheduling in Ubiquitous sensor networks is a major research interest for many researchers with the objective of reducing energy consumption and maximizing the network lifetime.Also the capacity of a channel for transmitting quantum states called the quantum capacity should be maximized,The sensor nodes should periodically forward data to the base station.Since battery power and bandwidth are the two resources to be effectively utilized the sensor nodes forwards data to the nearest neighbor node which inturn forwards the data to its neighbors and this repeats until all the sensors data aggregates at the base station.In a large network scheduling the sensor nodes for forwarding the data is very much essential and since the topology of the USN changes dynamically , finding such a schedule is also very difficult.In [1] the authors proposed a bosonic network model for communication.In this paper we present a quantum bosonic data aggregation method for constructing the optimal schedule of nodes for data aggregation called QBDAS (Quantum Boson Data aggregation scheduling) protocol.The main idea is to search a optimal schedule through utilizing the observable or measurable physical properties the network topology.
Communication Networks have limited resources like bandwidth and battery power.Hence efficient node scheduling protocols and communication protocols are needed to maximize the network throughput.In this paper, we discussed an optimal data aggregation scheduling problem for quantum networks.To optimize the utilization of the network bandwidth much research is done to find schedules with minimum frame length.In this paper we proposed a method for finding efficient quantum node scheduling.The schedule contains a set of slots where each slot is allotted to one or more quantum nodes for transmitting at the same time.Adjacent Quantum nodes can create entanglement for transmitting the data.The quantum nodes entangles with its neighbor quantum nodes during the slot allotted to them.Loss and distortion are the two major problems in free space channels transmission.The optical noise produced when two or more nodes are transmitting data concurrently interferes with the actual data.Hence the optical signal to noise ratio OSNR ie Square root of the signal power divided the square root of the noise power is used as a threshold on concurrent transmitters.In this proposed method we used OSNR for finding a set of nodes that can entangle concurrently for aggregating the data to the base station.Entanglement rate of each node is used as a metric for finding a schedule that maximizes the quantum network throughput.We derive a Power Optimized Maximal Link Entanglement Rate based Scheduling Method which maximizes the quantum network throughput and also increases the network life time.
Quantum Entanglement and Quantum swapping are major research areas nowadays.Remote quantum entanglement is used in many applications like secure communication, secret sharing, data aggregation, and precision sensing.In data aggregation applications, every sensor node captures data and communicates to the central node.Efficient Data aggregation depends on whether the local information or global quantum network information is used for constructing the aggregation schedules.In addition, Quantum networks suffer from lossy optical links and with limited resources such as quantum memories, edge capacities.Computation of optimal schedules deals with large quantities of data and complex time-consuming calculations.However, the quantum memories cannot hold the qubits for a longer time as the stored qubit completely decoheres an infinite amount of time.Hence, there is a necessity for finding new data aggregation scheduling protocols, which use optimal channel capacity and optimal size of memory for improving the network throughput.This paper uses a reinforcement-learning technique that considers entanglement pairing and swapping the success probability of nodes with their neighbors while finding an optimal scheduling policy.The proposed method uses local network information for constructing optimal data aggregation schedules by prior sharing the maximally entangled qubit pairs between the nodes through optimal usage of the processes, channel capacity, and memory at the intermediate nodes.Experiments show that our proposed method can maximize the network throughput.
Secured Data Aggregation Scheduling in Ubiquitous sensor networks is a major research problem nowadays. Since the sensors communicate wirelessly, they are exposed to a high level of vulnerability from the intruders. Quantum key distribution is a technique that is used nowadays for providing security. Though the secure keys are used for short distances only, it offers many advantages over traditional methods in providing security. Quantum key distribution adopts many methods like entanglement swapping and quantum teleportation. Reversible logic gates like CNOT, Toffoli, and Fredkin are proved to offer many advantages in quantum computing. Also a very little research is carried to provide security to the nodes in ubiquitous sensor networks using quantum cryptography so as to maximize the throughput and utilize the hardware resources effectively. Hence in this paper, we propose an intrusion detection policy for security to sensor nodes using quantum cryptography. The proposed protocol monitors the communication between sensor nodes and uses a simple policy to detect the malicious nodes. In our detection policy we adopted an EPR-pair allocation scheme in terms of the quantum gates like Toffoli and Feynman called SwapGate.
In the process of data aggregation, intruder nodes and compromised nodes may decrease the system performance. Hence, security plays an important role. Secret sharing is a method to find the compromised nodes. The previous researches in secure data aggregation concentrated on key authentication process and little research is done in N-party secret sharing methods. In this article, we proposed quantum data aggregation using secret sharing and genetic algorithm, where a node forwards data to its most trusted neighbor only. In the proposed method, a node selects n 1-hop neighbors from a list of its m 1-hop neighbors, which well behaved over a period and forwards data to the most trusted amongst them. We simulated quantum teleportation using SimulaQron tool for establishing secure communication. However secured is the communication channel the compromised nodes may decrease the system performance by exposing network to malicious activities. Each node creates n shares of a secret and forwards a share on a secured quantum channel to the n 1-hop neighbors in such a way that from any t neighbors share(t<; n) the secret is reconstructed. The data aggregation security increases since a group of t users can reconstruct the key but no single user has the complete authority on the secret. The proposed method ensures that each node will be able to aggregate the captured message securely without any interventions from the compromised/intruder nodes. The proposed system applies genetic method to improve the efficiency of the process of detection of the compromised nodes based on the behavioral history of the node. While detecting the malicious nodes the proposed method accuracy rate is 98.8 and false rate is 0.15, which is almost equal to secure and energy-efficient data aggregation method based on an access control method. However, in terms of the size of the network considered, the percentage of malicious nodes assumed and from the basic principles of quantum teleportation, the proposed method proves to be a better protocol for implementing secure data aggregation.
Quantum entanglement distribution between remote nodes is key to many promising quantum applications. Existing mechanisms have mainly focused on improving throughput and fidelity via entanglement routing or single-node scheduling. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Two practical scenarios are considered. When requests do not have deadlines, we seek to minimize the average completion time of the communication requests. If deadlines are specified, we seek to maximize the number of requests whose deadlines are met. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI, and develop a probabilistic protocol to implement the optimized solutions in a general buffered quantum network. We develop a discrete-time quantum network simulator for evaluation. Results show the superior performance of ESDI compared to existing solutions.