## 4 Benefits, Limitations, and Future Work  
Regarding the certified quality of ozonation treatments for indoor environments, the proposed IoT-based system has several additional benefits. First, the _automation_ degree provided by the design choices and implementation technologies allow for higher effectiveness, also avoiding the need of a constant (and error-prone) human intervention. Then, by resorting to the AWS infrastructure, the system's resources are automatically optimized and secured, with obvious advantages in terms of _capital expenditures_, _time-to-market_, and _scalability_. Indeed, if an entirely locally built serverless platform would offer full control over the infrastructure as well as lower latency for lightweight local workloads and no vendor-lock issues, the fully-managed AWS Lambda solution offers a compelling choice to prioritize agility, operational efficiency, and interoperability. Indeed, it totally abstracts away operational overheads, reduces upfront investments (in hardware, maintenance, and power, etc. ), and introduces a limited delay, since cold starts have been notably mitigated \begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**Sensor Data Processing**} & \multicolumn{2}{c}{**Image Processing**} \\ \hline
**Operation** & **Time Range (ms)** & **Operation** & **Time Range (ms)** \\ \hline Parsing SQS message & 1–5 & S3 metadata retrieval & 10–20 \\ \hline DynamoDB write & 10–20 & Recognition API call & 500–1000 \\ \hline Anomaly check & 1–2 & Label analysis & 1–5 \\ \hline SNS notification (if needed) & 20–40 & SNS notification (if needed) & 20–40 \\ \hline
**Total (w/o notification)** & **12–27** & **Total (w/o notification)** & **511–1025** \\ \hline
**Total (with notification)** & **32–67** & **Total (with notification)** & **531–1065** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation. through recent AWS innovations like Provisioned Concurrency. Last but not least, the choice of a hybrid edge-cloud setting dictated by the EI paradigm provides significant _flexibility_ by allowing systems to be dynamically deployed case-by-case across the continuum based on real-time conditions, resource availability, and expected performance requirements. With respect to our scenario, this introduces a two-fold strength: on the one hand, the local analysis of sensor data and video pre-processing improves privacy, responsiveness, and bandwidth efficiency; the cloud, on the other data, brings relevant resources for a data store and intensive data tasks such as predictive maintenance. Some limitations, however, are detectable in the proposed system, and they pave the way for interesting future research directions. Indeed, at least in its current status, the system is not suitable for providing its functionalities in outdoor environments. While the outlined IoT infrastructure might remain mostly untouched, additional hardware components need to be considered for fostering the air purification [32]. In order to evaluate the system in additional deployments, instead, scalability tests are needed. Resorting to simulations for preliminary assessments of the performance in different settings is, however, a well-established methodology [33]. Even then, a key activity like the devices' calibration is still performed manually and resorts to operators' expertise, albeit its automation would bring relevant advantages: some contributions in such direction are already available [25], whereas it can be still considered an open challenge. Finally, additional machine learning models should be added to enable, over the gathered data and other contextual information, air quality forecast or predictive maintenance functionalities. Indeed, the latter are a key task in industrial contexts [34] in order to enforce the overall system reliability by reducing the downtime; but, as for the devices' calibration, the required human intervention is not negligible.