IoT-Enabled Smart City Waste Management using Machine Learning Analytics

Bakhshi, Taimur; Ahmed, Muhammad

doi:10.1109/ece.2018.8554985

Cited by 37 publications

(18 citation statements)

References 7 publications

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“…It has employed several sensors to monitor the capacity, humidity, weight, chemical, and temperature of the bins storing solid waste. The study conducted in [16] has employed machine learning based analytics to dynamically schedule bin servicing by generating optimized routes to collect the bins. The above-mentioned studies have used centralized servers or cloud-based storage systems to store and process data collected by sensors.…”

Section: A Centralized Solutions For the Forward Supply Chain And Wamentioning

confidence: 99%

Blockchain-based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies

Ahmad¹,

Salah²,

Jayaraman³

et al. 2021

Preprint

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The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today's systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub.

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Section: A Centralized Solutions For the Forward Supply Chain And Wamentioning

confidence: 99%

Blockchain-based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies

Ahmad¹,

Salah²,

Jayaraman³

et al. 2021

Preprint

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show abstract

“…Penerapan di bidang lingkungan sangat membantu terwujudnya perlindungan lingkungan, misalnya prediksi kualitas udara atau air [29], atau kondisi atmosfer atau tanah [30]. Tidak hanya itu saja, analisis prediktif dengan IoT dapat dimanfaatkan dalam penanggulangan bencana semacam sistem peringatan dini [28].…”

Section: Tren Penelitian DI Analisis Prediktif Dengan Iotunclassified

Systematic Literature Review pada Analisis Prediktif dengan IoT: Tren Riset, Metode, dan Arsitektur

Rozi

2020

JSC

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Nowadays IoT researches on intelligent service systems is becoming a trend. IoT produces a variety of data from sensors or smart phones. Data generated from IoT can be more useful and can be followed up if data analysis is carried out. Predictive analytic with IoT is part of data analysis that aims to predict something solution. This analysis utilization produces innovative applications in various fields with diverse predictive analytic methods or techniques. This study uses Systematic Literature Review (SLR) to understand about research trends, methods and architecture used in predictive analytic with IoT. So the first step is to determine the research question (RQ) and then search is carried out on several literature published in popular journal databases namely IEEE Xplore, Scopus and ACM from 2015 - 2019. As a result of a review of thirty (30) selected articles, there are several research fields which are trends, namely Transportation, Agriculture, Health, Industry, Smart Home, and Environment. The most studied fields are agriculture. Predictive analytic with IoT use varied method according to the conditions of data used. There are five most widely used methods, namely Bayesian Network (BN), Artificial Neural Network (ANN), Recurrent Neural Networks (RNN), Neural Network (NN), and Support Vector Machines (SVM). Some studies also propose architectures that use predictive analytic with IoT.

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“…Selain itu terdapat penelitian yang dilakukan oleh Alhajri., dkk [5] yang menggunakan pendekatan machine learning untuk implementasi aplikasi IoT dalam smart home. Penerapan IoT dan machine learning dalam smart city dilakukan dalam penelitian Bakhsi [6] yang menggunakan Analisa machine learning untuk diterapkan pada aplikasi IoT untuk mengatur pembuangan pada suatu kota yang menerapkan konsep smart city.…”

Section: Pendahuluanunclassified

“…Layer ini penting dan sangat menentukan kualitas dari perangkat IoT, karena layer ini yang merupakan produk layanan cerdas yang diharapkan dapat memenuhi kebutuhan pengguna [7], [8]. Layer ini mencakup berbagai domain vertical seperti kesehatan, lingkungan, trasportasi dsb [2], [4]- [6], [11].…”

Section: B Arsitektur Internet Of Thingsunclassified

“…Sehingga diimplementasikanlah IoT dengan machine learning, dimana digunakan sensor untuk mendeteksi kepadatan dan ketinggian dari sampah yang kemudian data mengenai hal tersebut akan dianalisis menggunakan pendekatan machine learning sehingga akan menghasilkan keputusan langkah yang sebaiknya dilakukan untuk mengatasi sampah tersebut. Penelitian selanjutnya yang masih berhubungan dengan lingkungan dan manajemen sampah adalah penelitian yang dilakukan oleh Bakhshi dan Ahmed [6]. Penelitian tersebut mengintegrasikan IoT dengan machine learning untuk mengelola sampah pada suatu perkotaan demi mendukung dijalankannya konsep smart city pada kota tersebut.…”

Section: Gambar 8 Strukturunclassified

See 1 more Smart Citation

A Survey on Machine learning and IoT

Hardini

2019

ITEJ

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Internet of Things is an intelligence devices that can connect to any other devices. Based on that ability to connect and its intelligence, it makes people to create an intelligence device and capable to learn like human brain. To accomplish that goal, machine learning is the solution. Combination between machine learning and IoT generate a revolution in human life and application industry. The combination also generate new trend in market. Therefore, in this paper will be discuss about machine learning including machine learning technique and algorithm and also discuss about IoT including architecture and elements. Then in this paper will discuss about some researches that have already done that combine IoT with machine learning approach, including issue and challenge in those researches.

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IoT-Enabled Smart City Waste Management using Machine Learning Analytics

Cited by 37 publications

References 7 publications

Blockchain-based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies

Blockchain-based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies

Systematic Literature Review pada Analisis Prediktif dengan IoT: Tren Riset, Metode, dan Arsitektur

A Survey on Machine learning and IoT

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