As the number of Internet of Things (IoT) devices proliferates, the magnitude and velocity of data continues to increase rapidly. IoT systems rely primarily on using messaging protocols for exchanging IoT data and there exists several protocols or frameworks that support distinct types of messaging patterns. Given that IoT devices typically have limited computational resources and processing power, choosing a lightweight, reliable, scalable, interoperable, extensible and secure messaging protocol becomes a very challenging task. As a result, it is not uncommon that IoT systems may employ multiple messaging protocols for supporting device heterogeneity and different message exchange patterns. In addition, basic similarities among existing several messaging protocols or frameworks that exist today for exchanging IoT data within IoT systems suggest the potential of interoperability. Given that IoT systems help facilitate the interconnectivity among distributed, heterogeneous entities, interoperability among existing messaging protocols will play an increasingly important role in simplifying the development and deployment of IoT systems. In this paper, we present a comprehensive review of the existing messaging protocols that can be used in deploying IoT systems. Throughout this paper, we highlight the protocols’ distinctive approaches and applicability of using them across various IoT environments. In addition, we highlight challenges, strengths and weaknesses of these messaging protocols in the context of IoT.

Integrating hardware prototyping platforms such as Arduino, Raspberry Pi or BeagleBone Board in education is becoming more prevalent as the number of courses utilizing such platforms is continuously increasing. In this work, we conduct an analytical investigation on the plurality of courses that utilize or integrate hardware prototyping platforms. We examine curriculum and instructional material (e.g. course syllabi or outlines) through publicly available web informational resources (e.g. search engines). We use this data to determine the degree to which these platforms are used as effective learning technologies in existing courses. We further use this data to determine hardware platforms integration statistics and distribution based on the number of courses, types of platforms employed and institutions using them as learning technologies. This statistical data can be used to help determine the current status of the utilization and adoption rate of hardware prototyping platforms into courses. In this paper, we present evidence that hardware prototyping technologies are employed as tools for teaching and learning. Based on examining forty five universities worldwide, we determine that there are on average nine unique courses per university which utilize or integrate a variety of these platforms into courses. We also determine that 75% of these courses are Science, Technology, Engineering, and Math- (STEM-) based while 25% are Non-STEM. We further use our findings to provide insights on the extent to which educational institutions are utilizing these platforms as learning technologies and applying project-based or experiential learning approaches as part of their curriculum development.

The Social Internet of Things (S-IoT) paradigm is focused on topic of the Internet of Things (IoT), which accelerates the object issues by working with the concept of social networks. Searching and finding a new object in the community are considered to manage the number of friends and complex relationships between them and affect the ability to navigate at the cloud-edge layer, and resources, such as battery lifetime of S-IoT devices and energy resources, are important challenges in this field. In the processing of social messages of remote devices, increasing the battery life of devices that require such requirements plays the most important role. In this research, a collaboration scenario is presented to consider object attributes, friend’s functions and intelligent friend selection among objects for group messaging. First, a general reference model is designed and presented to select a friend to access group message remote processing services and minimize cloud-edge resources. The simulation results show that, for the correct communication of friends at the edge of the network and in each service discovery, according to the length of the path in the network, it is possible to establish stable communication and make better service with the least possible. The results show that if we want to develop a method for friendship between objects in communication in cloud computing, the proposed method can greatly improve the effectiveness of providing reliable message processing types.

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

In order for Web services to truly become a standard approach for just-in-time application integration, we need to enhance the discovery mechanism by assisting clients to select relevant Web services of interest. In recent years, there have been several standards that regulate how services can be published, discovered, or used (i.e. UDDI, WSDL, SOAP). Many of these standards have the potential of enhancing the discovery process, however, there are major technical challenges associated with these standards. One of these challenges is the client's ability to control and manage the discovery process for finding Web services of interest. Clients should be able to find relevant services much more efficiently. To address this issue, we propose a Web service ranking (WSR) algorithm for measuring the relevancy of Web services to particular clients' requirements. This paper presents experimental validation, results and analysis of the presented ideas.