The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

The coexistence of the Internet of Things and Edge Computing aims to offer a processing infrastructure close to end users that will improve the performance of applications and limit the latency in the provision of services. Services are adopted to assist in the execution of tasks imposed by the requests of end users/applications. The implementation of an effective framework for services management in the distributed edge nodes is necessary to achieve the aforementioned goals. The discussed framework ought to address the trade-off between overheads related to services migration/replication and data transmission. In this paper, we propose a proactive statistical model for allocating the available services upon the observed demand and supporting edge nodes to decide when and where it is necessary to migrate/replicate them. Our aim is to place services at locations where an increased demand is observed, however, under the uncertainty about the future evolution of the incoming requests. We elaborate on the evaluation of the proposed model and provide a comparative assessment with relevant schemes adopting real datasets. Our experimental validation demonstrates that our approach reinforces the heterogeneous engaged edge nodes to correctly infer the time instance and the location when/where services should be migrated/replicated to meet the dynamics of their demand. The interesting is that the proposed model achieves encouraging outcomes when it is adopted to cope with the mobility of users.

Currently, there is a great attention of the research community for the intelligent management of data in a context-aware manner at the intersection of the Internet of Things (IoT) and Edge Computing (EC). In this paper, we propose a strategy to be adopted by autonomous edge nodes related to their decision on what data should be migrated to specific locations of the infrastructure and support the desired requests for processing. Our intention is to arm nodes with the ability of learning the access patterns of offloaded data-driven tasks and predict which data should be migrated to the original ‘owners’ of tasks. Naturally, these tasks are linked to the processing of data that are absent at the original hosting nodes indicating the required data assets that need to be accessed directly. To identify these data intervals, we employ an ensemble scheme that combines a statistically oriented model and a machine learning scheme. Hence, we are able not only to detect the density of the requests but also to learn and infer the ‘strong’ data assets. The proposed approach is analyzed in detail by presenting the corresponding formulations being also evaluated and compared against baselines and models found in the respective literature.