Quantum Computing-Inspired Network Optimization for IoT Applications

Bhatia, Munish; Sood, Sandeep K.

doi:10.1109/jiot.2020.2979887

Cited by 65 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…|110 − , respectively. In [5], quantum computing-inspired optimization methods have been designed to maximize the data accuracy by assessing the sensor characteristics such as sensor vicinity and sensor space. The performance is measured by resolving vehicular routing problems based on WiSense nodes, and quantum simulators.…”

Section: Quantum Theory Overviewmentioning

confidence: 99%

An Efficient Quantum based D2D Computation and Communication Approach for the Internet of Things

Srivast

Lin

et al. 2021

Preprint

View full text Add to dashboard Cite

We can say with some clarity that the Internet of Things (IoT) can be made up of a set of embedded devices such as light detection sensors, ranging (LiDAR) sensors, and millimetre-wave (mmWave) sensors. These sensors generate a massive amount of data in which limited communication capacity is available to share a massive amount of data to Fog-based Roadside Units (RSU) for data process and analysis service. Fog-based RSU has become an emerging paradigm in intelligent transportation but needs research attention to design intelligent decision-making methods for data communication and computation at Fog-based RSU. To address these issues, we design a two-level Quantum based D2D Computation, Communication ($QDC^{2}$) approach. First, design a bandwidth allocation strategy based on spatial importance score factors to resolve embedded devices' data transmission issues. Second, design an adaptive equilibrium service offloading strategy based on device-centric measurements to assess the computation capacity and performance rate for resolving Fog-node computation consistency issues. Additionally, Fog-based RSU is interconnected with LAN helps to optimize service latency. Simulation results show that our approach achieved a high service reliability rate (79.56\%), low error rate (0.9\%), and an execution delay of 22.5s for 15 devices than state-of-art approaches.

show abstract

Section: Quantum Theory Overviewmentioning

confidence: 99%

An Efficient Quantum based D2D Computation and Communication Approach for the Internet of Things

Srivast

Lin

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, inspired by the IoT concept, with millions of interconnected wireless devices acquiring data ubiquitously, ref. [ 4 ] presented a novel quantum computing-inspired (IoT-QciO) optimization technique for wireless networks. The approach is based on the maximization of data accuracy (DA) in a real-time environment of IoT applications.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

Urgelles

Picazo-Martinez

García‐Roger

et al. 2022

Sensors

View full text Add to dashboard Cite

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.

show abstract

“…This involves two systems namely tangible and intangible. Tangible systems include manufacturing defects and intangible systems include sensor defects [4]. IoT systems are powered using wireless technologies.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

Nguyen¹,

Akila²,

Pal³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio, coverage ratio and sensing error. Data optimization was performed based on the availability of cloud resource, sensor energy, data flow volume and the centroid of each state. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

show abstract

Quantum Computing-Inspired Network Optimization for IoT Applications

Cited by 65 publications

References 14 publications

An Efficient Quantum based D2D Computation and Communication Approach for the Internet of Things

An Efficient Quantum based D2D Computation and Communication Approach for the Internet of Things

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

Contact Info

Product

Resources

About