Guest editorial: IoT sensing, applications, and technologies for smart sustainable cities

Sensing applications and Internet of Things (IoT) are indispensable for the construction of smart cities and provision of improved public services. Sensors act as the nerves of a smart city, enabling the collection of information that provides for intelligent decisions to be made, both in terms of future planning and immediate actuation. Internet of Things technologies provide the platform for implementing sensing applications, covering everything from embedded software and connectivity for edge nodes to data ingestion and analytics, including embedded OS, sensor interoperability and interfacing, local sensor node networks, wide area networks (e.g., LORA/5G), middleware for data handling and node management, green sensing and wireless networks.

While the research community has not yet settled on a precise definition of what makes a city ‘smart’, it is clear that IoT Sensing and Technologies are key for a safe, efficient, and environmentally friendly city and crucial for the provision of services in major application areas, such as intelligent transport, smart buildings, utilities, environment, and health.

This paper outlines Taiwan's experience in developing smart cities, including visions, implementation strategies and application cases. To take global trends and local needs into account, Taiwan has applied a dual development model that combines top-down (theme-based)/bottom-up (needs-based) approaches for a synergy effect in balancing innovations and local needs. Furthermore, a PPP program has been adopted to prompt collaboration between central/local authorities with local businesses.

The trend towards cities and urbanisation, which increases the number of people living in urban areas, requires local authorities to provide services and natural resources more efficiently and effectively and to develop some strategies for a sustainable environment. The more effective use of resources, growing awareness of sustainable environment, climate confidence and motivation can make cities more liveable.

Biodiversity surveys are often required for development projects in cities that could affect protected species such as bats. Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report the performance of mitigation actions. Typically, sensors are used in the field to listen to the ultrasonic echolocation calls of bats, or the audio data is recorded for post processing to calculate the activity levels. Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field. This paper shows the results from a longitudinal study of 15 novel internet-connected bat sensors—Echo Boxes—in a large urban park. The study provided empirical evidence of how edge processing can reduce network traffic and storage demands by several orders of magnitude, making it possible to run continuous monitoring activities for many months including periods that traditionally would not be monitored. The results demonstrate how the combination of artificial intelligence techniques and low-cost sensor networks can be used to create novel insights for ecologists and conservation decision makers.