scholarly journals Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

2021 ◽  
Author(s):  
Nguyen A. Tuan ◽  
D. Akila ◽  
Souvik Pal ◽  
Bikramjit Sarkar ◽  
Thien Khai Tran ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Stochastic Optimization ◽  
Performance Ratio ◽  
Data Allocation ◽  
Cloud Platform ◽  
Dynamic Data ◽  
Average Performance ◽  
Coverage Ratio ◽  
Dynamic Stochastic ◽  
Data Optimization

Abstract This article presents a new scheme for data optimization in IoT assister 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 (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 performance ratio, reliability ratio, coverage ratio and sensing error. 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.

scholarly journals Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

2021 ◽  
Author(s):  
G. Suseendran ◽  
D. Akila ◽  
Souvik Pal ◽  
Bikramjit Sarkar ◽  
Ayman A. Aly ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Stochastic Optimization ◽  
Performance Ratio ◽  
Data Allocation ◽  
Cloud Platform ◽  
Dynamic Data ◽  
Average Performance ◽  
Coverage Ratio ◽  
Dynamic Stochastic ◽  
Data Optimization

Abstract This article presents a new scheme for data optimization in IoT assister 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 (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 performance ratio, reliability ratio, coverage ratio and sensing error. 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.

Multi-AUV Aided Cooperative 3D-localization for Underwater Sensor Networks

2020 ◽  
Vol 13 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Meiyan Zhang ◽  
Wenyu Cai
Keyword(s):  
Sensor Networks ◽  
Mean Squared Error ◽  
Autonomous Underwater Vehicles ◽  
Estimation Method ◽  
Location Estimation ◽  
Underwater Sensor Networks ◽  
Coverage Ratio ◽  
Minimum Mean Squared Error ◽  
3D Localization ◽  
Localization Scheme

Background: Effective 3D-localization in mobile underwater sensor networks is still an active research topic. Due to the sparse characteristic of underwater sensor networks, AUVs (Autonomous Underwater Vehicles) with precise positioning abilities will benefit cooperative localization. It has important significance to study accurate localization methods. Methods: In this paper, a cooperative and distributed 3D-localization algorithm for sparse underwater sensor networks is proposed. The proposed algorithm combines with the advantages of both recursive location estimation of reference nodes and the outstanding self-positioning ability of mobile AUV. Moreover, our design utilizes MMSE (Minimum Mean Squared Error) based recursive location estimation method in 2D horizontal plane projected from 3D region and then revises positions of un-localized sensor nodes through multiple measurements of Time of Arrival (ToA) with mobile AUVs. Results: Simulation results verify that the proposed cooperative 3D-localization scheme can improve performance in terms of localization coverage ratio, average localization error and localization confidence level. Conclusion: The research can improve localization accuracy and coverage ratio for whole underwater sensor networks.

Dynamic stochastic optimization

2014 ◽  
Author(s):  
Craig Wilson ◽  
Venugopal Veeravalli ◽  
Angelia Nedic
Keyword(s):  
Stochastic Optimization ◽  
Dynamic Stochastic

scholarly journals STOCHASTIC OPTIMIZATION OF HEURISTIC METHOD RULE TO DETERMINE ASSET ALLOCATION TO RETIREMENT PORTFOLIO / STOCHASTINIS EURISTINIO METODO TAISYKLĖS PENSIJOS PORTFELIO SUDĖČIAI NUSTATYTI OPTIMIZAVIMAS

2011 ◽  
Vol 12 (1) ◽  
pp. 92-98
Author(s):  
Aušra Klimavičienė
Keyword(s):  
Stochastic Optimization ◽  
Stochastic Simulation ◽  
Asset Allocation ◽  
Heuristic Method ◽  
Simulation Models ◽  
Optimization Techniques ◽  
Human Lifespan ◽  
Stochastic Returns ◽  
Dynamic Stochastic ◽  
Population Mortality

The article examines the problem of determining asset allocation to sustainable retirement portfolio. The article attempts to apply heuristic method – 100 minus age in stocks rule – to determine asset allocation to sustainable retirement portfolio. Using dynamic stochastic simulation and stochastic optimization techniques the optimization of heuristic method rule is presented and the optimal alternative to „100“ is found. Seeking to reflect the stochastic nature of stock and bond returns and the human lifespan, the dynamic stochastic simulation models incorporate both the stochastic returns and the probability of living another year based on Lithuania‘s population mortality tables. The article presents the new method – adjusted heuristic method – to be used to determine asset allocation to retirement portfolio and highlights its advantages.

scholarly journals A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 10197
Author(s):  
Wenbo Zhu ◽  
Chia-Ling Huang ◽  
Wei-Chang Yeh ◽  
Yunzhi Jiang ◽  
Shi-Yi Tan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Smart Grids ◽  
Wireless Sensor ◽  
Coverage Problem ◽  
Swarm Optimization ◽  
Coverage Ratio ◽  
Sensing Coverage ◽  
Limited Sensing ◽  
Novel Concept

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.

Optimization of the Virtual Potential Field Based on Coverage-Enhancing Algorithm for Directional Sensor Networks

2014 ◽  
Vol 651-653 ◽  
pp. 1882-1887
Author(s):  
Jun Zhu ◽  
Chen Shi ◽  
Shan Shan Zhu ◽  
Jun Zhang
Keyword(s):  
Sensor Networks ◽  
Potential Field ◽  
Boundary Region ◽  
Sensor Nodes ◽  
Target Area ◽  
Virtual Node ◽  
Directional Sensor Networks ◽  
Coverage Ratio ◽  
Coverage Holes ◽  
Directional Sensor

After directional sensor nodes are randomly thrown into target area, coverage ratio often less than the anticipant value, in order to improve the coverage, sensor nodes should turn from overlapping regions to coverage holes by a much faster way. In this paper, we improved the existing potential field based coverage-enhancing algorithm (PFCEA), presented a optimization of the virtual potential field based on coverage-enhancing algorithm for directional sensor networks (OPFCEA), we introducing a new-style virtual node to enhance the coverage of boundary region and a new-style control for the rotation angle. By these ways, we can improve network’s performance. This algorithm enhanced the coverage ratio of the network, the simulation results show the effectiveness of the algorithm.

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