Cloud-based Internet Of Things Applications Research Articles

An Enhanced Authenticated Key Agreement Scheme for Cloud-Based IoT in Wireless Sensor Networks

Recent advancements in mobile and wireless technology have fundamentally impacted the underpinnings of cloud computing and IoEs. These changes have changed the way data is communicated across numerous channels, allowing for intelligent discovery and operation. The Internet of Things (IoT) is highly reliant on wireless sensor networks (WSNs), which have several applications in industries ranging from smart medicine to military operations to farming. The IoT's substantial reliance on these activities generates a large amount of data. All the above-specified data is transferred to a remote server for storage and processing. As a result, it is critical to enable safe data access in WSNs by authenticating individuals in altered states of awareness. Authenticating drug addicts in WSNs is still a topic that has not been fully addressed. This study describes a novel and improved authenticated key agreement mechanism for WSNs in cloud-based IoT applications. The technique suggested in this research provides a safe and effective solution for ensuring the confidentiality and integrity of the connection between sensor nodes and the cloud server. To enable a secure key exchange, the system implements a cryptographic method that combines symmetric and asymmetric encryption techniques. Furthermore, it employs a basic authentication approach to ensure that no data has been tampered with during transmission. In terms of security, communication overhead, and computing complexity, the simulation results show that the suggested solution outperforms the alternatives. The proposed methodology applies to a wide range of IoT application cases, including the previously described smart home, smart city, and industrial automation implementations. A comparison of related approaches supports the safety of our solution for WSNs.

Development, Service-Oriented Architecture, and Security of Blockchain Technology for Industry 4.0 IoT Application

The Internet of Things (IoT) paradigm is laying the groundwork for a world in which many of our everyday devices will be connected and will interact with their surroundings to gather data and automate some operations. Among other things, such a concept necessitates seamless authentication, data privacy, security, attack resilience, simplicity of deployment, and self-maintenance. Blockchain, a technology created with the Bitcoin cryptocurrency, can provide such advantages. To create blockchain-based IoT (BIoT) applications, a full discussion of how to modify blockchain to meet the unique requirements of IoT is offered in this paper. The most important BIoT applications are detailed after a brief introduction to blockchain, with the goal of highlighting how blockchain can affect conventional cloud-based IoT applications. Then, several factors that have an impact on the design, development, and deployment of a BIoT application are covered, along with present obstacles and potential improvements. Lastly, a list of recommendations is provided to help future BIoT researchers and developers understand some of the problems that need to be solved before deploying the upcoming generation of BIoT applications. Doi: 10.28991/HIJ-2023-04-01-010 Full Text: PDF

Toward Secure Data Computation and Outsource for Multi-User Cloud-Based IoT

Cloud computing has promoted the success of Internet of Things (IoT) with offering abundant storage and computation resources where the data from IoT sensors can be remotely outsourced to the cloud servers, whereas storing, exchanging and processing data collected through IoT sensors via centralised or decentralised cloud servers make cloud-based IoT systems prone to internal or external attacks. To protect IoT data against potential malicious users and adversaries, some cryptographic schemes have been applied to ensure confidentiality and integrity of IoT data. It is however a challenging task to perform any arithmetical computations once data items are encrypted. Fully-homomorphic encryption which is based on lattices can, in principle, provide a solution, but it is unfortunately inefficient in computation and hence cannot be applied to IoT. Fully-homomorphic encryption is feasible when we allow an involvement of semi-trusted server. However, it is challenging to provide such a system in the situation of distributed environments for shared IoT data. We solve this problem and provide a fully-homomorphic encryption scheme for cloud-based IoT applications. We introduce a new method with the aid of semi-trusted server who can help in the computation of the homomorphic multiplications without gaining any useful information of the encrypted data.

A Privacy-Aware Approach for Managing the Energy of Cloud-Based IoT Resources Using an Improved Optimization Algorithm

Cloud-based Internet of Things (IoT) is a network of physical things controlled and monitored via the Internet to develop numerous smart systems. In this network, numerous physical things with different types of sensors are created that can be linked to the Internet. IoT devices need to work with limited energy sources for a long time, and wireless communications are the primary source of energy usage. As a result, the lifespan of IoT devices can be extended by minimizing radio communications. Because of the great capability of the IoT, the issue of resource energy consumption is raised. On the other hand, privacy concerns must be considered when designing cloud-based IoT applications. The current article introduces programming formulations of the mentioned issues using an improved ant colony optimization (IACO) algorithm. The method is combined with a new kind of local search algorithm to obtain higher-quality responses. Also, for responding to privacy concerns, a privacy-preserving method has been suggested. The suggested approach has been tested widely, and the outcomes have been compared to the genetic and particle swarm optimization (PSO) algorithms. The results indicated the good ability of the proposed method in terms of network lifespan and energy consumption.

Towards Fast and Accurate Federated Learning with Non-IID Data for Cloud-Based IoT Applications

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in the Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of the vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. Comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data.

Cloud-Based IoT Applications and Their Roles in Smart Cities

A smart city is an urbanization region that collects data using several digital and physical devices. The information collected from such devices is used efficiently to manage revenues, resources, and assets, etc., while the information obtained from such devices is utilized to boost performance throughout the city. Cloud-based Internet of Things (IoT) applications could help smart cities that contain information gathered from citizens, devices, homes, and other things. This information is processed and analyzed to monitor and manage transportation networks, electric utilities, resources management, water supply systems, waste management, crime detection, security mechanisms, proficiency, digital library, healthcare facilities, and other opportunities. A cloud service provider offers public cloud services that can update the IoT environment, enabling third-party activities to embed IoT data within electronic devices executing on the IoT. In this paper, the author explored cloud-based IoT applications and their roles in smart cities.

Formal Verification of a Trusted Execution Environment-Based Architecture for IoT Applications

The Internet-of-Things (IoT) scenarios commonly present security and privacy concerns, either due to the processing constraints of devices or the employment of external servers to process and store data, for instance, in cloud-based IoT applications. In this sense, to protect data and decrease user distrust in external entities, security technologies are of utmost importance. Trusted execution environments (TEEs), which process data in an isolated and protected region of memory, are among these technologies. We focus on a trusted architecture solution based on the use of TEEs and the application of authentication, authorization, and encryption mechanisms to protect data in IoT applications. We specified the trusted IoT architecture (TIoTA) using hierarchical colored Petri nets, and performed simulations and model checking of key security properties related to desired and prohibited behaviors, enabling model-based testing. This article enhances the state of the art by providing project artifacts (e.g., executable and parametric models) for correctly implementing the TIoTA and by presenting evidence that the usage of the architecture can improve security and privacy.

Adaptive virtual machine migration based on performance-to-power ratio in fog-enabled cloud data centers

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement’s performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications ”E-PRWA” in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing.

Design of An Authentication Scheme for Cloud-Based IoT Applications*

In recent times, the Internet of Things (IoT) becomes one of the most popular innovations in the field of information and Communication Technology (ICT), and it connects billions or even trillions of devices. Meanwhile, security and privacy stand as a vital issue in the IoT environment, which generates the need for an efficient authentication protocol. In this context, we proposed a lightweight authentication protocol for cloud-based IoT applications. Further, formal security analysis using Burrows-Abadi-Needham (BAN) logic and informal security analysis of the proposed scheme are discussed. A comparative study of the proposed protocol and some existing methods is also demonstrated considering computation, communication, and storage cost. Finally, the proposed scheme is verified using a widely accepted AVISPA tool. The comparative study and verification support the efficiency of the proposed protocol for cloud-based IoT applications.

Lightweight authentication for IoT/Cloud-based forensics in intelligent data computing

Numerous application domains in practice related to target- tracking, monitoring, and transportation have utilized the wide usage of Wireless Sensor Networks (WSN’s) technology. These domains use physical networking objects connecting over the Internet to collect and exchange the data. Using the advancement of cloud computing technologies and aggravation of big-data growth caused by the incorporation of the Internet of Things (IoT), secure user authentication for remote access is playing a crucial role. Since it has limited authorization and authentication privileges for mobility users, an approach of mobile-sink has been instigated for the improvisation of remote user authentication i.e. cloud-based IoT applications. As a result, this article proposes a lightweight smartcard based secure authentication (LS-BSA) approach using the mathematical assumption of bilinear-pairing/mapping, elliptic-curve cryptosystems, and fuzzy verifier. An extensive security investigation demonstrates that the proposed LS-BSA not only guarantees the AKA security properties but also prevents significant vulnerabilities. Furthermore, the proposed LS-BSA uses lightweight operations to establish a seamless data connectivity over a secure network. It maintains the compatibility standards including low-cost and low-power to mitigate the computation and communication cost of cloud-based intelligent data computing. Formal security verification of BAN-logic is introduced to show that LS-BSA offers proper mutual user authentication and secret secure-session key agreement between the real-time entities. In addition, a network scenario has been set up using the NS-3 simulator to prove that the proposed LS-BSA is more efficient than other existing schemes in terms of packet delivery ratio, end-to-end delay, and throughput rate.

A performance-aware dynamic scheduling algorithm for cloud-based IoT applications

Cloud computing has been employed for supporting storage and handling of Internet of Things (IoT) data. There is an increasing demand for IoT framework to provide services with fast processing time and less delay to offer latency sensitivity real-time applications like disaster management in smart homes. IoT process is mostly comprised of scheduling techniques that makes it hard to self-adapt, self-configure to respond with performance aware on environment changes. Existing scheduling techniques of IoT applications are not based on allocating tasks through sleep modes, which unavoidably lead to more power consumption and longer time delays. Consenting sensor devices and applying separate queueing to a sensor device that varies differently in their capabilities are increasingly significant. In this work, a dynamic management framework for IoT devices in cloud (DMFIC) algorithm is proposed to evaluate and schedule requests and sensor data, which allow coordinating huge data with high time-based resolution in a cost-effectual manner through anticipating various queues for sending an appropriate notification to users. A smart home application was used to demonstrate the proposed framework. The experimental result shows that the DMFIC algorithm gives an average of 5% higher processing time and 0.2% less delay compared to other IoT services and can efficiently manage sensor data in cloud.

EC(DH)<SUP align="right">2</SUP>: an effective secured data storage mechanism for cloud based IoT applications using elliptic curve and Diffie-Hellman

In the rapid growth of cloud computing with internet of things (IoT) technology utilisation in this fast world, security is an essential issue in cloud data storage and access the encrypted data. In the past, several researchers have worked on data storage mechanisms and came up with different ideas to fulfil the cloud user's expectation. Even though, they have failed to provide the sufficient security on cloud. For providing better security over the cloud, we propose a new elliptic curve and Diffie-Hellman based data storage mechanism called EC(DH)2. Here, the standard Diffie-Hellman algorithm is used twice as DH2 and it is combined with the standard elliptic curve cryptographic algorithm for performing secured storage in cloud based IoT applications. The experiments have been conducted by using the various sizes of documents and files for evaluating the proposed EC(DH)2 algorithm and is proven that it provides better protection and increased key size.

Attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications

With exploding growth in information technology (IT), numerous services and applications having enhanced capabilities are coming into picture with an aim to serve the users. Internet of things (IoT) along with its enabling cutting-edge technologies is establishing a scenario where these services can be utilised effectively. However, with large number of users and applications, it becomes challenging to safeguard the identifying information being transmitted to provide access to these services. This paper presents a refined version of an integrated attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications. System-wide attributes not only restrict the users to access the remote cloud services, but also ensure user anonymity. We also implement the proposed mechanism on ACPT and AVISPA tool for its validation and to verify its correctness. Moreover, we present an analysis of its security and performance efficiency on the basis of different parameters.

Security Assertion of IoT Devices Using Cloud of Things Perception

Adoption of Internet of Things (IoT) and Cloud of Things (CoT) in the current developing technology era are expected to be more and more invasive, making them important mechanism of the future Internet-based communication systems. Cloud of Things and Internet of Things (IoT) are two emerging as well as diversified advanced domains that are diversified in current technological scenario. Paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios. Due to the adoption of the Cloud and IoT paradigm a number of applications are gaining important technical attention. In the future, it is going to be more complicated a setup to handle security in technology. Information till now will severely get changed and it will be very tough to keep up with varying technology. Organisations will have to repeatedly switch over to new skill-based technology with respect to higher expenditure. Latest tools, methods and enough expertise are highly essential to control threats and vulnerability to computing systems. Keeping in view the integration of Cloud computing and IoT in the new domain of Cloud of things, the said article provides an up-to-date eminence of Cloud-based IoT applications and Cloud of Things with a focus on their security and application-oriented challenges. These challenges are then synthesized in detail to present a technical survey on various issues related to IoT security, concerns, adopted mechanisms and their positive security assurance using Cloud of Things.

ElGamal cryptosystem‐based secure authentication system for cloud‐based IoT applications

Life in modern society becomes easier due to the rapid growth of different technologies like real-time analytic, ubiquitous wireless communication, commodity sensors, machine learning and embedded systems. Nowadays, there seems to be a need to merge these technologies in the form of Internet of Things (IoT) so that smart systems can be achieved. On the other hand, cloud computing is a pillar in IoT by which end users get connected through the cloud servers for getting different services. However, to recognise the legitimacy of communicators during communication sessions through insecure channels like the Internet, serious issues in cloud-based IoT applications need to be addressed. Thus, authentication procedure is highly desirable to remove the unapproved access in IoT applications. This study presents an ElGamal cryptosystem and biometric information along with a user's password-based authentication scheme for cloud-based IoT applications refereed as SAS-Cloud. Security of the proposed scheme has been analysed by well popular random oracle model and it is found that SAS-Cloud has the ability to defend all the possible attacks. Furthermore, the performance of SAS-Cloud has been evaluated and it was found that SAS-Cloud has better efficiency than other existing competing ElGamal cryptosystem-based authentication schemes.

A Survey: Review of Cloud IoT Security Techniques, Issues and Challenges

The Internet of things (IoT) has become a new buzzword in the emerging field of computation and research as it integrates various objects (things) to exchange data with one another without human intervention. The need for the extensive deployment of the cloud based IoT are rapidly increasing with a major security related threats. This work basically outlines existing security threats and vulnerabilities along with some of the existing security mechanisms by a comprehensive survey of existing works in the area of cloud based IoT security. This paper also outlines a brief taxonomy of the current security threats in cloud based IoT applications, architecture, and communication. In this work, we also presented a comparative analysis of various security mechanisms for IoT environment as well as compared possible security threats in the IoT. And, at the end, we presented open research issues and security implementation challenges in IoT security. This work aims to provide detailed discussion on various security mechanisms proposed as well as a useful manual of existing security threats and vulnerabilities of the cloud based IoT heterogeneous environment and proposes guidelines for improving the cloud based IoT security.

An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications

In our rapidly-growing big-data area, often the big sensory data from Internet of Things (IoT) cannot be sent directly to the far data-center in an efficient way because of the limitation in the network infrastructure. Fog computing, which has increasingly gained popularity for real-time applications, offers the utilization of local mini data-centers near the sensors to release the burden from the main data-center, and to exploit the full potential of cloud-based IoT. In this paper, a high-performance approach based on the Max–Min Ant System (MMAS), which is an efficient variation in the family of ant colony optimization algorithms, is proposed to tackle the static task-graph scheduling in homogeneous multiprocessor environments, the predominant technology used as mini-servers in fog computing. The main duty of the proposed approach is to properly manipulate the priority values of tasks so that the most optimal task-order can be achieved. Leveraging background knowledge of the problem, as heuristic values, has made the proposed approach very robust and efficient. Different random task-graphs with different shape parameters have been utilized to evaluate the proposed approach, and the results show its efficiency and superiority versus traditional counterparts from the performance perspective.

An energy-aware service composition algorithm for multiple cloud-based IoT applications

There has been a shift in research towards the convergence of the Internet-of-Things (IoT) and cloud computing paradigms motivated by the need for IoT applications to leverage the unique characteristics of the cloud. IoT acts as an enabler to interconnect intelligent and self-configurable nodes “things” to establish an efficient and dynamic platform for communication and collaboration. IoT is becoming a major source of big data, contributing huge amounts of streamed information from a large number of interconnected nodes, which have to be stored, processed, and presented in an efficient, and easily interpretable form. Cloud computing can enable IoT to have the privilege of a virtual resources utilization infrastructure, which integrates storage devices, visualization platforms, resource monitoring, analytical tools, and client delivery. Given the number of things connected and the amount of data generated, a key challenge is the energy efficient composition and interoperability of heterogeneous things integrated with cloud resources and scattered across the globe, in order to create an on-demand energy efficient cloud based IoT application. In many cases, when a single service is not enough to complete the business requirement; a composition of web services is carried out. These composed web services are expected to collaborate towards a common goal with large amount of data exchange and various other operations. Massive data sets have to be exchanged between several geographically distributed and scattered services. The movement of mass data between services influences the whole application process in terms of energy consumption. One way to significantly reduce this massive data exchange is to use fewer services for a composition, which need to be created to complete a business requirement. Integrating fewer services can result in a reduction in data interchange, which in return helps in reducing the energy consumption and carbon footprint.This paper develops a novel multi-cloud IoT service composition algorithm called (E2C2) that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements. A formal user requirements translation and transformation modelling and analysis is adopted for the proposed algorithm. The algorithm was evaluated against four established service composition algorithms in multiple cloud environments (All clouds, Base cloud, Smart cloud, and COM2), with the results demonstrating the superior performance of our approach.

People-Centric Internet of Things

The articles in this special section focus on the Internet of things (IoT) as it relates to people. The focus is on state-of-the-art research and development activities contributing to all aspects of people-centric IoT, which include, but are not limited to, people-IoT interactions, social network applications to mobile computing, context-aware applications and services, human in the loop, big data analysis in people-centric IoT, cloud-based people-centric IoT applications and environments, security and privacy, and prototypes, field experiments, and testbeds.