A global generic architecture for the future Internet of Things

Abstract

The envisioned 6A Connectivity of the future Internet of things (IoT) aims to allow people and objects to be connected anytime, anyplace, with anything/anyone, using any path/network and any service. Due to diverse resources, incompatible standards and communication patterns, the current IoT is constrained to specific devices, platforms, networks and domains. As the standards have been accepted worldwide, most existing IoT platforms use Web Services to integrate heterogeneous devices. Humanreadable protocols of Web Services cause non-negligible overhead in object-to-object communication. Other issues, such as: lack of applications and modularized services, high cost of devices and software development also hinder common use of the IoT. In this paper, a Global Generic Architecture for the Future IoT (GGIoT) is proposed to meet the envisioned 6A Connectivity of the future IoT. GGIoT is independent of particular devices, platforms, networks, domains and applications, and it can minimize transmission message size to fit devices with minimal capabilities, such as passive RFID tags. As a result, lower physical size and cost are possible, and network overhead can be reduced. The proposed GGIoT is evaluated via performance analysis and proof-of-concept case studies.

Attention to online channels across the path to purchase: An eye-tracking study

“…For example, the Marketing Science Institute points to the omnichannel phenomenon as one of the five marketing research priorities for the 2018-2020 period (Marketing Science Institute, 2018). Another important rising trend is the data flow from the Internet of Things (IoT), which interconnects people and objects anytime and anyplace with anything and anyone, using any path/network and any service (Wang et al, 2017). Therefore, the understanding of how today's consumer purchasing process evolves is essential for firms confronted with the design and management of these channels and points of contact (Beck and Rygl, 2015).…”

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

“…In the future, more effective and efficient machine learning methods should be studied for analysing ever growing data streams in CPS, such as taking advantages of distributed and parallel environment provided by the Cloud and Fog computing [167], developing hierarchical and composable machine learning methods that are well suited to partitioned execution across the Cloud and the Edge, studying transfer learning and continual learning techniques to deal with the non-stationarity of data streams. In the meanwhile, studies should be carried out on the development of Cloud and Edge systems that facilitate the CPS data stream analytics by accommodating the discrepancy and the heterogeneity between the capabilities of edge devices and datacenter servers and among the edge devices themselves; providing uniformed APIs [168] and services [169] [170], and etc.…”

Fog Analytics - A Survey

“…The Machine learning algorithms that are used for analyzing growing data streams in the Cyber Physical System (CPS), for using as the advantage of parallel and distributed computing environment of the Cloud and Fog Computing [44] for composable and hierarchical machine learning algorithms, which portioned the execution among the Cloud and Edge Computing to know the continual and transfer learning's to deal on the non-movable data streams. In the future, there should be more studies to be carried on the Cloud and Edge computing systems that help the CPS for data stream analysis to accommodate heterogeneity and discrepancy among data centers and edge devices to provide APIs [45] and the services [46,47].…”