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Mobile CrowdSensing (MCS) is defined as a pervasive sensing paradigm where mobile devices gather data with the aim of performing a specific application. The major issues in MCS are the following: mobile devices are characterized by limited resources; scalability issues appear when the number of objects that could be potentially involved in the sensing increases together with the application requests; the MCS sensing tasks should be easily integrated in the variety of IoT applications that in a dynamic way requires the crowd wisdom through MCS tasks. This paper handles the analyzed issues by providing the following contributions. The Social IoT (SIoT) paradigm is adopted to address the scalability issues when searching for objects that can potentially take part to the MCS scenarios. Indeed in SIoT a social network is created among objects, which exhibits the typical scalability advantages of social networks when looking for peers in big communities. We integrate the MCS logic into the Lysis platform that implements the SIoT paradigm, making easy for applications developers to activate MCS tasks. We propose a new algorithm to address the resource management issue so that MCS tasks are fairly assigned to the objects, with the objectives of maximizing the lifetime of the task groups. Preliminary experimental results prove that the devices' lifetime values tend to a single value and multiple applications can use the same outcome with improvement in terms of latency and computing resources

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