This section presents a comprehensive exploration of smart community infrastructure, focusing on integrating multi-access edge computing (MEC) to address privacy, latency, and service scalability challenges. Through extensive use of real-world implementations and standardization efforts, we detail the conceptual evolution, structural design, and operational execution of smart communities, emphasizing their role in enhancing local quality of life (QoL) through data-driven services. The concept of a smart community evolved to encompass broader smart infrastructure, including smart transportation, agriculture, government, and healthcare. A smart community thus functions as a regionalized ecosystem where cross-domain data, from energy usage to healthcare records, is collected and analyzed to develop services that address local societal needs. To support such services, this section introduces a multilayered smart community platform. It discusses the development of common service APIs, infrastructure optimized for real-time control, and service models that are robust, interoperable, and privacy conscious. A key focus is on data granularity and latency, showing how MEC enables services with varying temporal demands—from power grid stabilization to autonomous vehicle control—by executing tasks at appropriate network layers. This section elaborates further on the implementation of open standards and the evolution of data representation protocols, including XML-based formats and IEEE standards such as 1888, 1950.X, and 1451.1.6. Our group has contributed to these standardization efforts to ensure interoperability and data utility across smart infrastructures.
A central technical challenge discussed is anonymizing and securing personal data. The proposed architecture incorporates a hierarchical data handling mechanism where anonymization and data transformation occur as close to the data source as possible, ensuring regulatory compliance (e.g., GDPR) and minimizing risk. FPGA-based hardware accelerators and containerized applications further enhance the performance of anonymization and message processing (e.g., MQTT brokers), achieving over 100x improvements in throughput and latency under high-load conditions.
The concept of “Transparent Add-on” is introduced to enable network-transparent edge computing, allowing for low-latency processing and secure anonymization even under encrypted communications. A method for edge decryption without modifying IoT endpoints was established, along with a revocable key system to maintain data sovereignty.
Real-world deployments illustrate the utility of this framework. In the Urawa-Misono Smart Town project, various services were implemented using data from smart homes, commercial facilities, and public health devices. Services included thermal insulation evaluation, reinforcement learning for HVAC control, nutrition recommendation systems based on food purchases, and trajectory anonymization for location services using BLE and deep learning techniques.
In Kurihara City, the Green Society ICT Life Infrastructure project integrated agricultural, healthcare, meteorological, and municipal data to create predictive and preventive services. Examples include heatstroke risk prediction, optimized ambulance dispatch, and personalized healthcare guidance based on ambient environmental data. A social capital index was also used to evaluate the impact of social cohesion on health outcomes.
These services underscore the importance of dynamic data handling, secure service provisioning, and the need for modular design methodologies. A low-code development framework was proposed to support nonexpert stakeholders in service design, alongside new metrics to evaluate the trade-off between data utility and privacy. In conclusion, this section positions MEC as a pivotal enabler of smart community services by supporting flexible, low-latency, and privacy-preserving computation at the network edge. The proposed architecture balances performance, security, and usability, offering a blueprint for scalable and sustainable smart community development. This work contributes technical solutions and a comprehensive framework for designing and deploying community-centered services in an increasingly data-driven society.
