In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

The past decade has seen a rapid adoption of Artificial Intelligence (AI), specifically the deep learning networks, in Internet of Medical Things (IoMT) ecosystem. However, it has been shown recently that the deep learning networks can be exploited by adversarial attacks that not only make IoMT vulnerable to the data theft but also to the manipulation of medical diagnosis. The existing studies consider adding noise to the raw IoMT data or model parameters which not only reduces the overall performance concerning medical inferences but also is ineffective to the likes of deep leakage from gradients method. In this work, we propose proximal gradient split learning (PSGL) method for defense against the model inversion attacks. The proposed method intentionally attacks the IoMT data when undergoing the deep neural network training process at client side. We propose the use of proximal gradient method to recover gradient maps and a decision-level fusion strategy to improve the recognition performance. Extensive analysis show that the PGSL not only provides effective defense mechanism against the model inversion attacks but also helps in improving the recognition performance on publicly available datasets. We report 14.0 $\%$ , 17.9 $\%$ , and 36.9 $\%$ gains in accuracy over reconstructed and adversarial attacked images, respectively.

In the ubiquitous Internet of Things (IoT) environment, reusing objects instead of creating new one has become important in academics and industries. The situation becomes complex due to the availability of a huge number of connected IoT objects, and each individual service creates a new object instead of reusing the existing one to fulfill a requirement. A well-standard mechanism not only improves the reusability of objects but also improves service modularity and extensibility, and reduces cost. Web Objects enabled IoT environment applies the principle of reusability of objects in multiple IoT application domains through central objects repository and microservices. To reuse objects with microservices and to maintain a relationship with them, this study presents an architecture of Web of Objects platform. In the case of a similar request for an object, the already instantiated object that exists in the same or from other domain can be reused. Reuse of objects through microservices avoids duplications, and reduces time to search and instantiate them from their registries. Further, this article presents an algorithm for microservices and related objects discovery that considers the reusability of objects through the central objects repository. To support the reusability of objects, the necessary algorithm for objects matching is also presented. To realize the reusability of objects in Web Objects enabled IoT environment, a prototype has been designed and implemented based on a use case scenario. Finally, the results of the prototype have been analyzed and discussed to validate the proposed approach.

The Internet of Things (IoT) is evolving with the connected objects at an unprecedented rate, bringing about enormous opportunities for the future IoT applications as well as challenges. One of the major challenges is to handle the complexity generated by the interconnection of billions of objects. However, Social Internet of Things (SIoT), emerging from the conglomeration of IoT and social networks, has realized an efficient way to facilitate the development of complex future IoT applications. Nevertheless, to fully utilize the benefits of SIoT, a platform that can provide efficient services using social relations among heterogeneous objects is highly required. The web objects enabled IoT environment promotes SIoT features by enabling virtualization using virtual objects and supporting the modularity with microservices. To realize SIoT services, this article proposes an architecture that provides a foundation for the development of lightweight microservices based on socially connected web objects. To efficiently discover web objects and reduce the complexity of service provisioning processes, a social relationship model is presented. To realize the interoperable service operations, a semantic ontology model has been developed. Finally, to evaluate the proposed design, a prototype has been implemented based on a use case scenario.

The vision of internet of things is to connect every object beyond the boundaries of anywhere and whenever for the provision of services from the smart home user to fourth industrial revolution. The objects in the internet of things will be more heterogeneous because of their specialty and accuracy in their domain. Now a days in the Internet of things the data and its machine understanding gets more importance and remain the focal point rather than the objects that generate that data changes frequently due to innovations and their wears and tears. To make the machine understanding, exchanging and sharing information and knowledge; these objects needs a light weight and novel platform for the provision of IoT services in the future. The Web of Objects is a platform for IoT services because of its service modularity with the support of interoperable microservices and virtualization and granularity of heterogeneous objects through composite virtual objects and virtual objects. To realize the cross domain IoT applications we proposed WoO enabled interoperable microservices architecture and present a reference use case for the implementation.

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.