A Synergy of Distinct Contexts in AIoT‐Based Smart Environments for Waste Collection in Urban Centers

The widescale design, development, and deployment of connected, sensor‐based devices have paved the way for Industry 4.0. This is characterized as sensor‐based devices that gather data within the Fog and rely on the Cloud for processing and is termed the internet of things (IoT). IoT suffers from latency as it is dependent on the Cloud. Artificial intelligence of things (AIoT) enables low latency by processing sensor data within the Fog. This makes it a smart environment that exhibits context awareness, which is the ability of a system to acquire sensor data and process it using machine learning algorithms. Context awareness envisages that multiple smart personal spaces of different types and having distinct contexts should be able to interact with each other, thus enabling a synergy of distinct contexts. This paper presents a mathematical model of the synergy of distinct contexts. The contexts belong to distinct personal spaces, and each space gathers the contextual data and processes it using Bayesian classification. The participating personal spaces include garbage cans and garbage trucks, which share their contextual information to establish a synergy of distinct contexts. Furthermore, the system generates a priority list to schedule garbage removal. The results show that distinct contexts can interact with each other with low failure probability in the domain of solid waste management.