Nowadays, the coronavirus pandemic has and is still causing large numbers of deaths and infected people. Although governments all over the world have taken severe measurements to slow down the virus spreading (e.g., travel restrictions, suspending all sportive, social, and economic activities, quarantines, social distancing, etc.), a lot of persons have died and a lot more are still in danger. Indeed, a recently conducted study [1] has reported that 79% of the confirmed infections in China were caused by undocumented patients who had no symptoms. In the same context, in numerous other countries, since coronavirus takes several days before the emergence of symptoms, it has also been reported that the known number of infections is not representative of the real number of infected people (the actual number is expected to be much higher). That is to say, asymptomatic patients are the main factor behind the large quick spreading of coronavirus and are also the major reason that caused governments to lose control over this critical situation. To contribute to remedying this global pandemic, in this paper, we propose an IoTa investigation system that was specifically designed to spot both undocumented patients and infectious places. The goal is to help the authorities to disinfect high-contamination sites and confine persons even if they have no apparent symptoms. The proposed system also allows determining all persons who had close contact with infected or suspected patients. Consequently, rapid isolation of suspicious cases and more efficient control over any pandemic propagation can be achieved. aIoT: Internet of Things.

In this paper, we focus on the usage of MQTT (Message Queuing Telemetry Transport) within Connected Vehicles (CVs). Indeed, in the original version of MQTT protocol, the broker is responsible "only" for sending received data to subscribers; abstracting then the underlying mechanism of data exchange. However, within CVs context, subscribers (i.e., the processing infrastructure) may be overloaded with irrelevant data, in particular when the requirement is real or near real-time processing. To overcome this issue, we propose MQTT-CV; a new variant of MQTT protocol, in which the broker is able to perform local processing in order to reduce the workload at the infrastructure; i.e., filtering data before sending them. In this paper, we first validate formally the correctness of MQTT-CV protocol (i.e., the three components of the proposed protocol are correctly interacting), through the use of Promela language and its system verification tool; the model checker SPIN. Secondly, using real-world data provided by our car manufacturer partner, we have conducted real implementation and experiments. The obtained results show the effectiveness of our approach in term of data workload reduction at the processing infrastructure. The mean improvement, besides the fact that it is dependent of the target application, was in general about 10 times less in comparison to native MQTT protocol.