# IoT-Based Solid Waste Management Solutions: A Survey
## 3 Solid Waste Management
The proposal is based on an Arduino IDE and an 8051 microcontroller that reads the data from an infrared sensor used to measure the depth of waste inside the container, processes and transmits via wireless communication to a central system based on a microcontroller Intel Galileo. The proposal presented by these authors approaches the sensors used in the solution very well and the used communication as well. They have another model based on a better collection route. A literature review for smart waste management and a comparison of the different methodologies is surveyed in Reference [47]. It focuses on IoT, considering its elements (identification, sensing, communication, computation, semantics, and services) and to its characteristics (anything communicates, anything identifies, and anything interacts). An algorithm for decision making during waste collection is presented in Reference [48]. Some algorithm models are compared, considering several performance metrics, such as receiving data in motion, multiobjective, loss of data during transmission, and increased data reception. It concludes that not all algorithms that are capable of receiving data in motion also serve the multi-objective or are still able to cope with increased data reception, but none of them can address data loss. Based on this information, some case studies are proposed and some objectives, such as reducing pollution through collection on time, reduce the operational cost, using trucks of appropriate size for demand, and using a better collection route, are considered. The algorithm should also take into account the speed and volume of data input as well as data generated by similar sensors. Thus, an algorithm model to optimize the collection decisions is proposed in this work. Moreover, in Reference [49], the proposed model uses information received from the compartments to define effective routes for each truck during collection. Within the model, some constraints are considered as maximum bin capacity and a Poisson distribution models the waste arrival rate. When a dump reaches a filling threshold, a garbage collection alarm is triggered and sent to the base station which, in turn, communicates with the cloud to process the data and find the best collection route. In the end, the trucks head to the emptying area where the amount of visited dumps is verified and establish the optimal path of collection with the objective of optimization based on lower cost for the waste collection. The solution presents a waste management approach based on three heuristics, considering the nearest vehicle first, a collection based on the upper limit, and a collection based on the upper and lower threshold. MATLAB was used to solve the optimization models based on cost and delay, and a Java-based simulator for heuristic methods. The proposals presented by the authors specifically deal with search algorithms for an optimal path for waste collection with a focus on reducing the collection time and cost. In Reference [50], a municipal waste management system for domestic use is presented that focuses on the application of biological and physicochemical methodologies that can eliminate or significantly reduce the stage of waste collection and transportation. The summary of the comparative assessment of the most promising solutions performed in this survey is presented in Section 7. A total of fifteen research efforts were reviewed and their strengths and weaknesses are analyzed, and each model is classified to represent essential parts of the proposed systems.