Urban sanitation management faces growing challenges due to increasing waste production, limited resources, and inefficiencies in traditional handling methods. The integration of Internet of Things (IoT) technologies offers a transformative solution to these issues. This study focuses on the design, development, and deployment of Smart Bins equipped with IoT sensors for real-time waste monitoring. The Smart Bin Management System, implemented at Mulungushi University, incorporates advanced features, including ultrasonic sensors for waste level detection, edge computing with Arduino devices, and a user-friendly mobile application to ensure seamless data transmission, monitoring, and accessibility. Results from this study demonstrate that Smart Bins significantly enhance waste collection efficiency by enabling dynamic scheduling, reducing operational costs, and optimizing resource allocation. The integration of IoT enables data-driven decision-making and improved scalability. Beyond waste management, the study highlights the potential of Smart Bins as a sustainable solution for urban sanitation challenges, contributing to cleaner environments and improved public health. This research establishes a replicable model that aligns with global sustainability goals.

This study aimed to assess the effectiveness of an Artificial Intelligence (AI)-based deterrent system in reducing monkey incursions at a hospitality establishment in Livingstone, Zambia. The research sought to understand not only the behavioural changes in the monkeys but also the perceptions of hospitality personnel regarding the system's impact on their work environment and guest experiences. The target population included free-ranging monkeys regularly intruding on the premises and 30 hospitality staff members employed at the establishment. The staff represented diverse roles, genders, and experience levels, and all had been employed for at least six months prior to the intervention. A mixed-methods approach was adopted. Quantitative data were gathered through systematic behavioural observations of monkey activity before and after system implementation. Qualitative data were collected through 30 in-depth semi-structured interviews with staff to explore their perceptions of the system’s effectiveness. Data were analysed using statistical techniques and thematic content analysis, respectively. Findings revealed a substantial decrease in both the frequency and severity of monkey incursions following the installation of the AI-based deterrent. Observable monkey behaviours shifted significantly from habituated and aggressive patterns to avoidance and flight responses. Interview data indicated improved staff morale, reduced workplace stress, enhanced guest satisfaction, and a more professional atmosphere. While some participants expressed concerns about potential long-term monkey adaptation, the overall sentiment remained strongly positive. The AI-based deterrent system proved effective in mitigating human-wildlife conflict within a hospitality setting. It created a safer, more controlled work environment and contributed positively to both operational efficiency and guest experiences. The study demonstrates that intelligent, context-sensitive technologies can yield meaningful behavioural changes in non-human species while supporting human-centred hospitality operations. This contribution extends prior Zambian AI/IoT field deployments for wildlife and pest monitoring by demonstrating effective, real-time deterrence in a hospitality context near Mosi-oa-Tunya.