The Internet of Things (IoT) is becoming pervasive and immersive due to the recent advancements in communication and sensing technologies. The proliferation of smart devices and their sensing capabilities has opened new opportunities and business models. The billions of connected sensing devices are generating enormous amount of data. The sensing as a service concept has the potential to provide a wide variety of services to citizens, companies and public administrations. This paper presents a sensing as service vision for IoT in different domains such as agriculture, waste management, supply chain, traffic management and others. Moreover, different applications of sensing as service model is analyzed and discussed in detail. In this paper, we specifically propose a service oriented sensing as service architecture to realize the vision of sensing as a service. The proposed service oriented architecture has the potential to address the challenges of heterogeneity, integration and interoperability of a sensing as service concept and can open new business opportunities.

Due to the enormous growth of social media the potential of social media mining has increased exponentially. Individual users are producing data at unprecedented rate by sharing and interacting through social media. This user generated data provides opportunities to explore what people think and express on social media. Users exhibit different behaviors on social media towards individuals, a group, a topic or an activity. In this paper, we present a social media mining approach to perform behavior analytics. In this research study, we performed a descriptive analysis of user generated data such as users’ status, comments and replies to identify individual users or groups which can be a potential threat. Tokenization technique is used to estimate the polarity of the behavior of different users by considering their comments or feedbacks against different posts on Facebook. The proposed approach can help to identify possible threats reflected by the user’s behavior towards a specific event. To evaluate the approach, a data set was developed containing comments on the Facebook from different users in different groups. The dataset was divided into different groups such as political, religious and sports. Most negative users’ in different groups were identified successfully. In our research, we focused only on English content; however, it can be evaluated with other languages.