iCAP: An IoTbased Intelligent Liquid Waste Barrels Monitoring System This paper proposes an intelligent liquid waste barrels monitoring system, named iCAP system, which is based on the Internet of things (IoT) technologies for detecting abnormal opening and dumping of the liquid waste barrels. The proposed system is composed of an inboundoutbound areas management system, intelligent screw caps (named iCAP), and an online cloudbased platform. The inboundoutbound areas management system can provide inboundoutbound areas status management for the liquid waste barrels during transportation. If the intelligent screw cap mounted on the liquid barrel is unscrewed or closed during outdoor transportation, the related messages of such a situation will be uploaded to the proposed online could platform via the SigFox low power wide area networks (LPWAN) wireless communication technology to provide an effectively management and monitoring of liquid waste container transportation. As a result, the proposed system can avoid the problem of environmental pollution, which is caused by illegal waste dumping in the waste treatment process.

Heterogeneous Sensing Data Analysis for Commercial Waste Collection Waste collection has become a major issue all over the world, especially when it is offered as a service for businesses; unlike public waste collection where the parameters are relatively homogeneous. This industry can greatly benefit from new sensing technologies and advances in artificial intelligence that have been achieved over the last few years. However, in most situations waste management systems are based on obsolete technologies, with a low level of interoperability and thus offering static processes. The most advanced solutions are generally limited to statistical, nonpredictive approaches and have a limited view of reality, making them weakly effective in dealing with daytoday business issues (overflowing containers, poor quality of service, etc.). This paper presents a case study currently being developed in Luxembourg with a company offering a business waste collection service, which has a significant amount of constraints to consider. Our main objective is to investigate the use of multiple waste data sources to derive useful indicators for improving collection processes. We start with companyowned historical data and then investigate GPS information from tracking devices positioned on collection trucks. Furthermore, we analyze data collected from ultrasonic sensors deployed on almost 50 different containers to measure fill levels. We describe the deployment steps and show that this approach, combined with anomaly detection and prediction techniques, has the potential to change the way this business operates. We also discuss the interest of the different datasets presented and multiobjective optimization issues. To the best of our knowledge, this article is the first major work dedicated to the world of professional waste collection.