1,654 research outputs found
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Fine-Grained Access Control with Attribute Based Cache Coherency for IoT with application to Healthcare
The Internet of Things (IoT) is getting popular everyday around the world. Given the endless opportunities it promises to provide, IoT is adopted by various organizations belonging to diverse domains. However, IoT’s “access by anybody from anywhere” concept makes it prone to numerous security challenges. Although data security is studied at various levels of IoT architecture, breach of data security due to internal parties has not received as much attention as that caused by external parties. When an organization with people spread across multiple levels of hierarchies with multiple roles adopts IoT, it is not fair to provide uniform access of the data to everyone. Past research has extensively investigated various Access Control techniques like Role Based Access Control (RBAC), Identity Based Access Control (IBAC), Attribute Based Access Control (ABAC) and other variations to address the above issue. While ABAC meets the needs of the growing amount of subjects and objects in an IoT environment, when implemented as an encryption algorithm (ABE) it does not cater to the IoT RDBMS applications. Also, given the query processing over huge encrypted data-set on the Cloud and the distance between the Cloud and the end-user, latency issues are highly prevalent in IoT applications. Various Client side caching and Server side caching techniques have been proposed to meet the latency issues in a Client-Server environment. Client side caching is more appropriate for an IoT environment given the dynamic connections and the large volume of requests to the Cloud per unit time. However, an IoT Cloud has mixed critical data to every user and conventional Client side caching techniques do not exploit this property of IoT data.
In this work, we develop (i) an Attribute Based Access Control (ABAC) mechanism for the IoT data on the Cloud in order to provide a fine-grained access control in an organization and (ii) an Attribute Based Cache Consistency (ABCC) technique that tailors Cache Invalidation according to the users’ attributes to cater to the latency as well as criticality needs of different users. We implement and study these models on a Healthcare application comprising of a million Electronic Health Record (EHR) Cloud and a variety of end-users within a hospital trying to access various fields of the EHR from their Smart devices (such as Android phones). ABAC is evaluated with and without ABCC and we shall observe that ABAC with ABCC provides a lower average latency but a higher staleness percentage than the one without ABCC. However, the staleness percentage is negligible since we can see that much of the data that contributes to the staleness percentage are the non-critical data, thus making ABAC with ABCC an efficient approach for IoT based Cloud applications
Blockchain and Reinforcement Neural Network for Trusted Cloud-Enabled IoT Network
The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAWMOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency
Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration
As the integration of Internet of Things devices with cloud computing
proliferates, the paramount importance of privacy preservation comes to the
forefront. This survey paper meticulously explores the landscape of privacy
issues in the dynamic intersection of IoT and cloud systems. The comprehensive
literature review synthesizes existing research, illuminating key challenges
and discerning emerging trends in privacy preserving techniques. The
categorization of diverse approaches unveils a nuanced understanding of
encryption techniques, anonymization strategies, access control mechanisms, and
the burgeoning integration of artificial intelligence. Notable trends include
the infusion of machine learning for dynamic anonymization, homomorphic
encryption for secure computation, and AI-driven access control systems. The
culmination of this survey contributes a holistic view, laying the groundwork
for understanding the multifaceted strategies employed in securing sensitive
data within IoT-based cloud environments. The insights garnered from this
survey provide a valuable resource for researchers, practitioners, and
policymakers navigating the complex terrain of privacy preservation in the
evolving landscape of IoT and cloud computingComment: 33 page
Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective
The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance
NebulaStream: Complex Analytics Beyond the Cloud
The arising Internet of Things (IoT) will require significant changes to current stream processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present challenges and opportunities for an IoT data management system to enable complex analytics beyond the cloud. As one of the most important upcoming IoT applications, we focus on the vision of a smart city. The goal of this paper is to bridge the gap between the requirements of upcoming IoT applications and the supported features of an IoT data management system. To this end, we outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system, NebulaStream. This paper lays the foundation for a new type of systems that leverages the IoT to enable large-scale applications over millions of IoT devices in highly dynamic and geo-distributed environments
A Study on Sanctuary and Seclusion Issues in Internet-of-Things
Internet-of-Things (IoT) are everywhere in our daily life. They are used in our homes, in hospitals, deployed outside to control and report the changes in environment, prevent fires, and many more beneficial functionality. However, all those benefits can come of huge risks of seclusion loss and sanctuary issues. To secure the IoT devices, many research works have been con-ducted to countermeasure those problems and find a better way to eliminate those risks, or at least minimize their effects on the user�s seclusion and sanctuary requirements. The study consists of four segments. The first segment will explore the most relevant limitations of IoT devices and their solutions. The second one will present the classification of IoT attacks. The next segment will focus on the mechanisms and architectures for authentication and access control. The last segment will analyze the sanctuary issues in different layers
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