High Capacity Trucks Serving as Mobile Depots for Waste Collection in IoT-Enabled Smart Cities

2015 ◽  
pp. 80-94 ◽  
Author(s):  
Theodoros Anagnostopoulos ◽  
Arkady Zaslavsky ◽  
Stefanos Georgiou ◽  
Sergey Khoruzhnikov
Keyword(s):  
Smart Cities ◽  
High Capacity ◽  
Waste Collection

ROBUST WASTE COLLECTION: EXPLOITING IoT POTENTIALITY IN SMART CITIES

2017 ◽  
Vol 11 (3) ◽  
pp. 10 ◽  
Author(s):  
GOENKA SAKSHI ◽  
MANGRULKAR R.S. ◽  
◽  
Keyword(s):  
Smart Cities ◽  
Waste Collection

scholarly journals City with Smart Waste Management: Introducing Waste Bin Technologies

2021 ◽  
Vol 34 (02) ◽  
pp. 1032-1038
Author(s):  
Arya Majidi
Keyword(s):  
Internet Of Things ◽  
Adverse Effects ◽  
Waste Management ◽  
Smart Cities ◽  
Human Life ◽  
The Other ◽  
The Internet ◽  
Waste Collection ◽  
Waste Production ◽  
The Internet Of Things

Population growth and urbanization have led to an increase in the rate of waste production, the lack of timely and proper management of which will have adverse effects on human life and the environment. Since most of the waste management costs are spent on waste collection and transportation, it is necessary to find solutions to control the huge costs of this sector. On the other hand, today, intelligent technologies are used globally as solutions to meet challenges in various fields such as agriculture to improve agro-industrial production, transportation, and waste management, which creates a concept called smart cities. One of the categories that has changed the concept of cities and made them have easier and smarter answers to various events and needs is the "Internet of Things", in which many cases and infrastructures with new hardware technologies and Software are integrated. Waste collection is no exception to this rule and efforts have been made to make it smarter. In this research, some of the latest innovations presented globally in order to make trash smarter have been examined.

scholarly journals Development of Solar-Powered of Sensors Based Smart Waste Monitoring System

2021 ◽  
Vol 12 (5) ◽  
pp. 580-586
Author(s):  
Polaiah Bojja, Pamula Raja Kumari, A.Nagavardhan N.Dinesh, M.Gopla D Anirudh
Keyword(s):  
Waste Management ◽  
Monitoring System ◽  
Smart City ◽  
Waste Treatment ◽  
Smart Cities ◽  
Municipal Waste ◽  
Waste Collection ◽  
Solar Powered ◽  
Analyze Data

Dustbins (or Garbage Bins, Trash Cans, whatever you name them) are small containers of plastic or metal used on a temporary basis to store trash (or waste). They are also used for the collection of waste in houses, workplaces, highways, parks, etc. Littering is a major crime in some countries, and public waste bins are also the only way to dispose of small waste. Usually, using different bins for handling wet or dry, recyclable or non-recyclable waste is a common practice. From an ETS perspective, smart waste collection can help municipalities and private waste management companies avoid the need for collection sites, waste disposal facilities and waste treatment plants. As communities increasingly rely on smart city technology to improve, among other things, the quality of life of their residents and the environment, city leaders recognize that smart waste management can also help them achieve sustainability goals such as zero waste and improve services to residents, while improving service to residents. As an example, Development of Some solar-powered bins and recycling bins are already equipped with sensors that analyze data on what is disposed of or recycled and notify collectors when the bin is too full and needs to be picked up. These developed Smart waste management solutions use sensors placed in waste bins to measure levels, notify municipal waste collection services, when the bins are ready to be emptied, and also notify municipal waste collection with a ton has been emptied. Therefore, the solar-powered of sensors based smart waste monitoring system is more and more useful to the current smart cities policies under the smart city project works.

Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

2022 ◽  
pp. 34-46
Author(s):  
Amtul Waheed ◽  
Jana Shafi ◽  
Saritha V.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
High Capacity ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Smart Transportation ◽  
Data Propagation ◽  
Machine Learning Models

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

scholarly journals Performance Evaluation of the M2M Communication in the 4G Network (LTE/LTE-A) With Real Background Traffics: تقييم أداء نظم الاتصال M2M ضمن شبكات الجيل الرابع LTE/LTE-A المتضمنة حمل الشبكة الفعلي

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hala Adnan Fadel, Yasser Emleh, Ali Diab
Keyword(s):  
Mobile Communications ◽  
Large Scale ◽  
Smart Cities ◽  
High Capacity ◽  
The Elderly ◽  
Work Place ◽  
The Internet ◽  
Average Delay ◽  
Machine To Machine ◽  
Self Driving Cars

The applications of the fifth generation in 5G communications depend on the Internet Of Things (IOT), meaning that every person and everything will be connected to the Internet, so any tool or device in the house or in the street or any work place will be connected to the internet, and this leads us to the term Smart cities, i.e. data is formed everywhere by any person or any machine and is analyzed in a short time to obtain useful information in a timely manner such as monitoring the health status of patients and the elderly, and monitoring devices and tools at home and determining whether there is a malfunction or a lack of a substance, As well as analyzing the traffic situation in the streets and assisting and warning drivers Non-visual risks, which pave the way towards self-driving cars. Here, machine-to-machine (M2M) mobile communications play a pivotal role in enabling the effective and safe transfer of this information from machine to machine without human intervention at full speed and with minimal delay. This poses more challenges for the future network that must accommodate mobile data and the huge number of devices and sensors deployed everywhere in order to be a large-scale network with high capacity and efficiency [16, RODRIGUEZ.J]. In this article, several scenarios have been tested to evaluate the performance of M2M technology within 4Generation LTE / LTE-A networks by adding an external simulated network load. The results showed that the amount of data sent by the MMS sensors is fully received by the remote host, so we get 100% productivity. As for other applications, the productivity is around 99% and the average delay is relatively small as long as the network operates within the available transfer rates. In addition, the process of exchanging packages takes place almost completely (a small amount of losses).

scholarly journals Waste generation prediction under uncertainty in smart cities through deep neuroevolution

2019 ◽  
pp. 128-138 ◽  
Author(s):  
Andrés Camero ◽  
Jamal Toutouh ◽  
Javier Ferrer ◽  
Enrique Alba
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Smart Cities ◽  
Uncertain Data ◽  
Recurrent Network ◽  
The State ◽  
Waste Generation ◽  
Waste Collection ◽  
The Way

The unsustainable development of countries has created a problem due to the unstoppable waste generation. Moreover, waste collection is carried out following a pre-defined route that does not take into account the actual level of the containers collected. Therefore, optimizing the way the waste is collected presents an interesting opportunity. In this study, we tackle the problem of predicting the waste generation ratio in real-world conditions, i.e., under uncertainty. Particularly, we use a deep neuroevolutionary technique to automatically design a recurrent network that captures the filling level of all waste containers in a city at once, and we study the suitability of our proposal when faced to noisy and faulty data. We validate our proposal using a real-world case study, consisting of more than two hundred waste containers located in a city in Spain, and we compare our results to the state-of-the-art. The results show that our approach exceeds all its competitors and that its accuracy in a real-world scenario, i.e., under uncertain data, is good enough for optimizing the waste collection planning.

scholarly journals Increasing Efficiency of IoT-enabled Waste Collectionwith Controlled Mobility of Robotic Bins

2020 ◽  
Vol Volume 14 ◽  
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Waste Collection ◽  
Robot Assisted ◽  
Controlled Mobility ◽  
Sustainable Solutions ◽  
Saint Petersburg ◽  
Model Combining ◽  
The Impact ◽  
Line Following

Smart Cities cater for ever increasing population, which needs sustainable solutions for efficient wellbeing. Waste collection is significant for providing a green ecosystem in such cities. IoT-enabled waste collection solutions assist such a green ecosystem. Waste collection used to be performed by humans or via human intervention.However, contemporary research incorporates robots to perform waste collection. In this paper we describethe real case of a line following robot bin that assists waste collection in the Smart City of Saint Petersburg, Russia. Evaluation is performed through a model combining the distance covered by the actor, the time passed for the collection and the bins emptied. The results show the superiority of robot bins, compared to human workers, highlighting the impact of IoT-enabled robot assisted waste collection as part of a green ecosystem

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