The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to implement the following processes: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoTbased Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches. Index Terms-Social Internet of Things; Mobile CrowdSensing; resource allocation LIST OF MAIN SYMBOLS AND ACRONYMS D G Geofence size E i,k Energy consumption for task k in node i E res i Residual energy for node i f i,k Frequency at which node i performs task k F M SF k Minimum Sampling Frequency for task k G Reference geofence P drain i
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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