IoT assisted Hierarchical Computation Strategic Making (HCSM) and Dynamic Stochastic Optimization Technique (DSOT) for energy optimization in wireless sensor networks for smart city monitoring

Sundhari, R. P. Meenaakshi; Jaikumar, K.

doi:10.1016/j.comcom.2019.11.032

Cited by 53 publications

(12 citation statements)

References 18 publications

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“…Smart Cities /Energy [5], [11]- [14] Monitoring and optimization of energy consumption towards cost-efficiency in cities and communities. This topic includes the implementation of sensors and the use of previous records to predict consumption requirements.…”

Section: Descriptionmentioning

confidence: 99%

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

García-Pérez¹,

Mesa²

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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Internet of Things-IoT appeared in the forecast of engineering three decades ago. Since then, a vast range of applications has been developed in industrial, urban, and rural matters, where one of the most relevant issues has been sustainability. This paper presents a retrospective analysis of the most relevant trends in sustainability regarding IoT and its applications, considering evolution trends and tangible results from the last decade (2011-2020). When the concept of sustainability in engineering gained importance. The study provides useful insights into the future development of IoT in human life and industrial activities as well as research opportunities in terms of sustainability based on IoT approaches.

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Section: Descriptionmentioning

confidence: 99%

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

García-Pérez¹,

Mesa²

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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show abstract

“…During the last decade, this topic has motivated extensive research endeavors that have resulted in several interesting localization algorithms [ 2 ]. As the demand for location-based services continues to grow, the accuracy of node localization significantly impacts various application areas, such as city surveillance, and smart homes [ 3 , 4 ]. So far, many localization algorithms have been proposed in the literature and mainly fall into two categories: range-based and range-free algorithms.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Esheh,

Affes

2024

Sensors

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Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

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“…The inherent characteristics of the node location make it an essential prerequisite for many functions. With the demand for location-based services developing rapidly, the accuracy of node localization affects numerous practical application areas, such as smart homes [ 2 ], city surveillance [ 3 ], fault diagnosis [ 4 ], etc.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

Zheng

Yuan

Xie

et al. 2023

Sensors

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Localization is one of the essential problems in internet of things (IoT) and wireless sensor network (WSN) applications. However, most traditional range-free localization algorithms cannot fulfill the practical demand for high localization accuracy. Therefore, a localization algorithm based on an enhanced flower pollination algorithm (FPA) with Gaussian perturbation (EFPA-G) and the DV-Hop method is proposed.FPA is widely applied, but premature convergence still cannot be avoided. How to balance its global exploration and local exploitation capabilities still remains an outstanding problem. Therefore, the following improvement schemes are introduced. A search strategy based on Gaussian perturbation is proposed to solve the imbalance between the global exploration and local exploitation search capabilities. Meanwhile, to fully exploit the variability of population information, an enhanced strategy is proposed based on optimal individual and Lévy flight. Finally, in the experiments with 26 benchmark functions and WSN simulations, the former verifies that the proposed algorithm outperforms other state-of-the-art algorithms in terms of convergence and search capability. In the simulation experiment, the best value for the normalized mean squared error obtained by the most advanced algorithm, RACS, is 20.2650%, and the best value for the mean distance error is 5.07E+00. However, EFPA-G reached 19.5182% and 4.88E+00, respectively. It is superior to existing algorithms in terms of positioning, accuracy, and robustness.

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IoT assisted Hierarchical Computation Strategic Making (HCSM) and Dynamic Stochastic Optimization Technique (DSOT) for energy optimization in wireless sensor networks for smart city monitoring

Cited by 53 publications

References 18 publications

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

Sustainable Engineering and Internet of Things (IoT): trends and perspectives

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

An Enhanced Flower Pollination Algorithm with Gaussian Perturbation for Node Location of a WSN

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