Internet of Things (IoT) Cloud Platform Using MQTT End-to-Cloud Architecture

IoT devices are constrained in computation and storage, therefore cannot store all long-term obtained data or perform complex computations. Shifting those jobs to cloud platform are feasible, yet rising heterogeneity and security issues. This study proposes an IoT cloud platform to facilitate communication among heterogeneous devices and the cloud while ensuring devices’ validity. It uses publish/subscribe paradigm with an end-to-cloud architecture and HTTP-based auth server. The proposed system has successfully addressed heterogeneity and security issues. Performance tests conclude that the fewer publishers publish data simultaneously, the smaller the delay. Moreover, the system performs better at up to 250 publishers as the average delay is under 1000 ms, compared to 500 publishers that has average delay above 1000 ms. On its scalability, in 250-concurrent-publishers experiment, the system affords 191 publishers responded in under one second with 100% success rate. In 500-concurrent-publishers one, 187 responded in under one second with 99% rate.

DESIGN OF A SERVERLESS OGC WPS BASED GEOPROCESSING SERVICE SOLUTION

Geospatial data and related technologies have increasingly become a crucial part of big data analysis processes and even a prominent player in most of them. Serverless architectures have become today's trending and widely used technology within the cloud computing paradigm. In this paper, we review the serverless paradigm advantages over traditional cloud architecture models and infrastructures. Moreover, we examined the deployment of Open Geospatial Consortium (OGC) Web Processing Service (WPS) specification based geoprocessing Application Programming Interface (API) with serverless architecture. In this context, we propose a system design and review it in detail together with the results discussed along with use cases.

Service-Based Hybrid Access Control Technology with Priority Level for the Internet of Vehicles under the Cloud Architecture

The communication process of devices in IoV under cloud architecture needs to be protected by access control models. However, existing access control models have difficulty establishing the appropriate granularity of permissions in the face of large amounts of data in IoV. Moreover, the access control model may need to temporarily change user privileges to accommodate the dynamic nature of IoV scenarios, a requirement that is difficult to implement for traditional access control models. The unstable connection status of devices in IoV also creates problems for access control. The service (composed of role and attribute) based access control model (in IoV) S-RABAC (V), under the Cloud computing architecture, introduces a formal theoretical model. The model uses attribute grouping and prioritization mechanisms to form a hierarchical structure. The permission combination pattern in the hierarchical structure can avoid duplicate permissions and reduce the number of permissions while ensuring fine-grained permissions. Different layers in the model have different priorities, and when a user’s permission requires temporary changes, it can be adjusted to the corresponding layers according to the user’s priority. In addition, users are allowed to keep their assigned privileges for a period to avoid frequent access control because of unstable connections. We have implemented the proposed access control model in Alibaba Cloud Computing and given six example demonstrations. The experiment shows that this is an access control model that can protect IoV security more effectively. Various unique mechanisms in the model enable S-RABAC(V) to improve the overall access control efficiency. The model adds some extra features compared to ABAC and RBAC and can generate more access control decisions using the priority mechanism.

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

Scheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan. Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan, monetary cost, speed up, SLR, and efficiency metrics, respectively.