Optimal Path Planning for Selective Waste Collection in Smart Cities

Abstract

Waste collection is one of the targets of smart cities. It is a daily task in urban areas and it entails the planning of waste truck routes, taking into account environmental, economic and social factors. In this work, an optimal path planning algorithm has been developed together with a practical software platform for smart and sustainable cities that enables computing the optimal waste collection routes, minimizing the impact, both environmental (CO2 emissions and acoustic damage) and socioeconomic (number of trucks to be used and fuel consumption). The algorithm is executed in Net2Plan, an open-source planning tool, typically used for modeling and planning communication networks. Net2Plan facilitates the introduction of the city layout input information to the algorithm, automatically importing it from geographical information system (GIS) databases using the so-called Net2Plan-GIS library, which can also include positions of smart bins. The algorithm, Net2Plan tool and its extension are open-source, available in a public repository. A practical case in the city of Cartagena (Spain) is presented, where the optimal path planning for plastic waste collection is addressed. This work contributes to the urban mobility plans of smart cities and could be extended to other smart cities scenarios with requests of optimal path planning.

Waste Management System Using IoT-Based Machine Learning in University

“…On the other hand, some approaches have developed waste management strategies based on the optimization to achieve an efficient system. In [15], the authors presented a waste control and management stage to be applied in rural areas using LoRaWAN technology and route optimization. Additionally, an implementation based on IoT was set up, but the system did not provide clarity about communication and optimization for all trash bins in the system.…”

“…First, waste collection is a daily task in urban areas entailing the planning of waste truck routes, in which environmental, economic, and social factors must be considered. Second, the path length should be shortened, in order to avoid high fuel costs and reduce the work amount, by applying graph theory [14][15][16][17]. Some solutions have introduced the use of IoT devices to estimate the fill level of inboxes and send this data over the Internet to a server for decision-making [18][19][20][21][22][23].…”

Contributing to the planning and deployment of enabling technologies for Smart Cities and Smart Factories

“…The candidate was hired with funds of this project for 24 months (April 2017 to March 2019). The contributions the candidate developed in the framework of this project were: [Bueno17], [Romero17], [Bueno17b], [Romero18], [Romero18a], [Bueno19], [Romero20].…”

“…The academic contributions in which the candidate has participated within the context of the thesis have been [Romero18], [Romero18a], [Bueno19] , [Romero19], [Romero19a], [Troia19], [Garrich20], [Garrich20b].…”

“…This chapter gathers the contributions presented in the following works, where the PhD student contributed: [Romero18b]; [Romero18] and [Bueno19].…”