IoT Cloud Systems Research Articles

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

Online/Offline Attribute-Based Boolean Keyword Search For Internet Of Things

Abstract With the rapid development of Internet of Things (IoT) and cloud computing, a large amount of IoT data has been stored and shared in cloud servers. However, sensitive IoT data may be leaked by untrusted cloud servers, which is a key problem hindering the development of IoT-cloud systems. To solve this problem, searchable encryption has been presented, which enables IoT devices to encrypt the collected data before uploading them to the cloud and make retrievals over the encrypted data by keyword queries. In this paper, we will study attribute-based keyword search (ABKS), which relieves us from cumbersome key management and provides a fine-grained access control. However, since the existing ABKS schemes are all constructed by bilinear pairings, which incurs high computational costs, they are not suitable for resource-constrained IoT devices. In this article, we propose an online/offline attribute-based boolean keyword search scheme, which can significantly reduce the online computing costs of IoT devices. In addition, our scheme can support boolean keyword search for data users, which has more flexible keyword expressivity than the existing online/offline ABKS schemes. We simulate our scheme and the experimental results show that it is both efficient and practical for the real IoT-cloud systems.

Various Cloud platforms in IOT

(IoT) being the widespread merging of several “things” simultaneously as using the internet due to the backbone of the communicating devices, installation of an intelligent interaction among the human beings and surrounding objects. As part of the Internet of Things, the cloud is a significant problem that delivers valuable application-specific services across a wide range of application domains. Many IoT cloud providers have already entered the market to utilize proper and unique IoT-based fully unquestionably offerings. Despite the significant potential participation of these IoT clouds, no well-known or comparable analytical studies have been conducted to taken an observation has been determined in the course of the databases. This article examines well-known IoT cloud systems in jointly solving numerous service areas with software program development, device administration. An evaluation is available for the general distribution of IoT clouds based on their applicability.

A Secure IoT Service Architecture With an Efficient Balance Dynamics Based on Cloud and Edge Computing

The Internet of Things (IoT)-Cloud combines the IoT and cloud computing, which not only enhances the IoT's capability but also expands the scope of its applications. However, it exhibits significant security and efficiency problems that must be solved. Internal attacks account for a large fraction of the associated security problems, however, traditional security strategies are not capable of addressing these attacks effectively. Moreover, as repeated/similar service requirements become greater in number, the efficiency of IoT-Cloud services is seriously affected. In this paper, a novel architecture that integrates a trust evaluation mechanism and service template with a balance dynamics based on cloud and edge computing is proposed to overcome these problems. In this architecture, the edge network and the edge platform are designed in such a way as to reduce resource consumption and ensure the extensibility of trust evaluation mechanism, respectively. To improve the efficiency of IoT-Cloud services, the service parameter template is established in the cloud and the service parsing template is established in the edge platform. Moreover, the edge network can assist the edge platform in establishing service parsing templates based on the trust evaluation mechanism and meet special service requirements. The experimental results illustrate that this edge-based architecture can improve both the security and efficiency of IoT-Cloud systems.

Multi-Cloud Management Strategies for Simulating IoT Applications

The Internet of Things (IoT) paradigm is closely coupled with cloud technologies, and the support for managing sensor data is one of the primary concerns of Cloud Computing. IoT-Cloud systems are widely used to manage sensors and different smart devices connected to the cloud, hence a large amount of data is generated by these things that need to be efficiently stored and processed. Simulation platforms have the advantage of enabling the investigation of complex systems without the need of purchasing and installing physical resources. In our previous work, we chose the DISSECT-CF simulator to model IoT-Cloud systems, and we also introduced provider policies to enable cost-aware policies resource management. The aim of this paper is to further extend the simulation capabilities of this tool by enabling multi-cloud management. In this paper we introduce four cloud selection strategies aimed to reduce application execution time and utilization costs. We detail our proposed method towards multi-cloud extension, and evaluate defined strategies through a meteorological application.

The framework and algorithm for preserving user trajectory while using location-based services in IoT-cloud systems

Internet of things (IoT) based location-based services (LBS) are playing an increasingly important role in our daily lives. However, since the LBS server may be hacked, malicious or not credible, there is a good chance that interacting with the LBS server may result in loss of privacy.As per journal instruction, author photo and biography are mandatory for this article. Please provide. Thus, protecting user privacy such as the privacy of user location and trajectory is an important issue to be addressed while using LBS. To address this problem, we first construct three kinds of attack models that may expose a user's trajectory or path while the user is sending continuous queries to a LBS server. Then we construct a novel LBS system model for preserving privacy, and propose the k-anonymity trajectory (KAT) algorithm which is suitable for both single query and continuous queries. Different from existing works, the KAT algorithm selects $$k-1$$k-1 dummy locations using the sliding window based k-anonymity mechanism when the user is making single query, and selects $$k-1$$k-1 dummy trajectories using the trajectory select mechanism for continuous queries. We evaluate the effectiveness of our proposed algorithm by conducting simulations for the single-query and continuous-query scenarios. The simulation results show that our proposed algorithm can protect privacy of users better than existing approaches, while incurring a lower time complexity than those approaches.

SDG-Pro: a programming framework for software-defined IoT cloud gateways

Recently, emerging IoT cloud systems create numerous opportunities for a variety of stakeholders in terms of optimizing their existing business processes, as well as developing novel cross-organization and cross-domain applications. However, developers of such systems face a plethora of challenges, mainly due to complex dependencies between the application business logic and the underlying IoT cloud infrastructure, as well as difficulties to provision and govern vast, geographically distributed IoT cloud resources. In this paper, we introduce SDG-Pro – a novel programming framework for software-defined IoT cloud systems. The main features of our framework include programming abstractions: Software-Defined Gateways, Intents, Intent Scopes, and Data and Control Points, as well as provisioning and governance APIs that allow for programmatic management of software-defined gateways throughout their entire lifecycle. The SDG-Pro framework enables easier, efficient and more intuitive development of IoT cloud applications. It promotes the everything-as-code paradigm for IoT cloud applications in order to provide a uniform, programmatic view on the entire development process. To illustrate the feasibility of our framework to support development of IoT cloud applications, we evaluate it using a real-world case study on managing fleets of electric vehicles.