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A Survey of Middleware for Internet of Things
Abstract
This paper provides a survey of middleware system for Internet of Things (IoT). IoT is considered as a part of future internet
and ubiquitous computing, and it creates a true ubiquitous or smart environment. The middleware for IoT acts as a bond joining
the heterogeneous domains of applications communicating over heterogeneous interfaces. Comprehensive review of the existing
middleware systems for IoT is provided here to achieve the better understanding of the current gaps and future directions
in this field. Fundamental functional blocks are proposed for this middleware system, and based on that a feature wise classification
is performed on the existing IoT-middleware. Open issues are analyzed and our vision on the research scope in this area is
presented.
KeywordsInternet of Things–middleware–semantic model–context-awareness–ubiquitous computing
... In practice, every device in a distributed system broadcasts its information to other neighbors. In this mechanism, IoT devices must be well-scaled Bandyopadhyay et al. [2011], Razzaque et al. [2016], da Cruz et al. [2018]. Resource Management: The IoT-middleware is responsible for managing resources to ensure they could handle users' requests to provide acceptable responses. ...
... In fact, a principal task of IoT-middleware is managing of data generated by IoT devices. This data, gathered by sensors/actuators or user applications, is often grouped in sets of identification data, descriptive data, and environmental data Bandyopadhyay et al. [2011]. Therefore, the IoT-middleware should provide data management services to take control of data flows including data acquisition, processing, and storage Razzaque et al. [2016]. ...
... In syntactic interoperability, format and structure of encoding exchanged data is defined and semantic interoperability deals with meaning of exchanged data across middleware. In fact, some rules are defined in semantic interoperability for understanding meaning of information Razzaque et al. [2016], Bandyopadhyay et al. [2011]. An IoT-middleware must declare all above-mentioned interoperability classes to ensure the correctness of exchanging information via IoT components. ...
Advancements of the Web technology provide this opportunity for Internet of Things (IoT) to take steps towards Web of Things (WoT). By increasing trend of reusing Web techniques to create a monolithic environment to control, monitor, and compose the smart objects, a mature WoT architecture is finally emerged in four layers to be a solution for IoT-middleware. Although WoT architecture facilitates addressing requirements of IoT in architectural or service aspects, but the effectiveness of this solution is indeterminate to meet IoT-middleware objectives. The most surveys and related reviews in this field just investigate IoT-middleware and WoT separately and thereby, report some new technologies or protocols on various middlewares or WoT models. In this paper a comprehensive survey is proposed on common area of IoT and WoT disciplines by leveraging Systematic Literature Review (SLR) as research methodology. This survey classifies variant types of IoT-middleware and WoT architecture to specifies their requirements and characteristics, respectively. Hence, WoT requirements could be categorized by comparing and analyzing IoT-middleware requirements and WoT characteristics. This research heavily reviews existing academic and industrial contributions to select potential platforms (or frameworks) and assess them against proposed WoT requirements. As a result of this survey, strengths and weaknesses of WoT architecture, as a IoT-middleware, are presented. Finally, this research attempts to open new horizon for the WoT architecture to enable researchers to dig role of WoT technologies in the IoT.
... • In the IoT, the middleware layer serves as an abstraction layer. High-performance computing and storage can also be provided through middleware [19]. This layer is in charge of service management and includes a database connection. ...
... It processes data and performs ubiquitous computation before making an automatic judgment based on the results [14]. Interoperability, device management, context awareness, security, and privacy are some of the properties of different IoT-middleware [19]. • The application layer is in charge of several different functional applications that are tailored to the demands of the users [20]. ...
... Bandyopadhyay et al. [25,24] review a number of middleware systems designed for IoT systems. While they look at security in passing, there is no detailed analysis of the security of each middleware system. ...
... This set was identified through a combination of the existing literature reviews on IoT middleware [25,45,146] together with our own search for middleware systems that explicitly target IoT scenarios. Some of the systems that were included in these papers we excluded from our list on the basis that they were not middleware. ...
The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area.
... The paper includes the current research till 2012. Bandyopadhyay et al. [27], proposed a survey of middleware systems for IoT. Since, in IoT, heterogeneous domains applications communicate over heterogeneous interfaces, developing middle-ware solutions is an important aspect to consider. ...
... In this section, we have discussed the existing survey papers on IoT. As in Fig. 10, most of the surveys on IoT described different aspects, such as, IoT applications and enabling technologies [8,9,12,27,30], research challenges [16,23], IoT architecture [11,14,24], security aspects [17,19,29], general aspects on IoT [10,26,28] and some other specific areas [15,18,20,22,25], etc. However, none of them provided on a comprehensive study on IoT covering all the aspects, such as architecture, protocols, security and privacy, scalability and energy efficiency, etc. ...
Internet of Things (IoT) is an emerging paradigm which aims to inter-connect all smart physical devices, so that the devices together can provide smart services to the users. Some of the IoT applications include smart homes, smart cities, smart grids, smart retail, etc. Since IoT systems are built up with heterogeneous hardware and networking technologies, connecting them to the software/application level to extract information from large amounts of data is a complex task. In this paper, we have surveyed various architecture and protocols used in IoT systems and proposed suitable taxonomies for classifying them. We have also discussed the technical challenges, such as security and privacy, interoperability, scalability, and energy efficiency. We have provided an in-depth coverage of recent research works for every mentioned challenge. The objective of this survey is to help future researchers to identify IoT specific challenges and to adopt appropriate technology depending on the application requirements.
... The middleware layer works as the 'brain' of IoT to process the numerous data received from lower layers. To cope with the interoperability of the heterogeneous (a) Active collection (b) Passive collection Fig. 2: IoT privacy protection framework [10], [13], [34], [45] physical devices [53], [54], the device abstraction component semantically describes the resources with a consistent language such as the eXtensible Markup Language (XML), Resource Description Framework (RDF) or Web Ontology Language (OWL) [55], [56], [57]. Based on that, resources are made discoverable through the resource discovery component by using Semantic Annotations for WSDL and XML Schema (SAWSDL) [56] or simply key words [58]. ...
Ubiquitous deployment of low-cost smart devices and widespread use of high-speed wireless networks have led to the rapid development of the Internet of Things (IoT). IoT embraces countless physical objects that have not been involved in the traditional Internet and enables their interaction and cooperation to provide a wide range of IoT applications. Many services in the IoT may require a comprehensive understanding and analysis of data collected through a large number of physical devices that challenges both personal information privacy and the development of IoT. Information privacy in IoT is a broad and complex concept as its understanding and perception differ among individuals and its enforcement requires efforts from both legislation as well as technologies. In this paper, we review the state-of-the-art principles of privacy laws, the architectures for IoT and the representative privacy enhancing technologies (PETs). We analyze how legal principles can be supported through a careful implementation of privacy enhancing technologies (PETs) at various layers of a layered IoT architecture model to meet the privacy requirements of the individuals interacting with IoT systems. We demonstrate how privacy legislation maps to privacy principles which in turn drives the design of necessary privacy enhancing technologies to be employed in the IoT architecture stack.
... 10 For this reason, a middleware for IoT is essential for several reasons: (1) it allows interoperability between different heterogeneous things belonging to different IoT domains; (2) it acts as an abstraction layer for data communication and representation, allowing heterogeneous applications to communicate transparently, and (3) provides an API (Application Programming Interface) for communication between the physical layer and the services required for the applications, hiding the diversities of things and services. 29 IoT middleware systems are versatile, functioning across various layers of the IoT application architecture (see Section 2.1). At the Application layer, they can offer mechanisms that support efficient and secure processing of streaming data from many sensors. ...
Middleware has become an essential element in the construction of distributed Internet of Things (IoT) applications. While it plays a central role in hiding the complexities of distribution, middleware systems have also been responsible for dealing with the uncertainties in IoT environments, such as changes during operation (e.g., inaccuracies in sensor data collection) and fluctuations in resource availability, for example, the battery. These uncertainties demand attention as they can result in application failures or, even worse, jeopardize the safety of applications. Existing middleware systems are being enhanced with self‐adaptive capabilities to address these uncertainties. It means they can make runtime adjustments to the middleware and applications (built atop them) without complete shutdowns. Despite the variety of available adaptive solutions, IoT applications often face uncertainties, each requiring a distinct adaptive action. For instance, the need to fine‐tune a thing's workload due to battery consumption is a common challenge. Furthermore, these applications are susceptible to changes occurring at various layers, presenting a complex challenge of managing them simultaneously. This paper introduces Middleware Extendify (MEx), a solution for building and executing IoT adaptive middleware systems. MEx simplifies the implementation of middleware and provides an underlying environment that executes the middleware and supports a range of adaptation mechanisms. This approach ensures that the middleware meets the evolving demands of applications and copes with changes at runtime. The evaluation of MEx encompasses different adaptive middleware implementations to measure the impact of the proposed adaptation mechanisms. The results indicate that adaptation comes with acceptable performance costs while offering the ability to fine‐tune middleware functionality or align IoT applications more effectively.
... The support layer's function in the Internet of Things is to serve as a link between the network layer and the application layer. Bandyopadhyay.et.al [30] mentions that the support layer can also provide strong processing and storage Potential. This layer offers API interfaces to meet the needs of the application layer. ...
IoT is a very fast-growing technology. IoT can be defined as interconnected small smart devices linked over the Internet to communicate with each other to perform meaningful action.There are major concerns regarding the security of data being produced from millions of devices in the IoT system. Different security concerns in various IoT system tiers have been covered in this study. IoT security concerns can be reduced by using Blockchain Technology, which is a decentralized distributed ledger with several Blockchain potentials, including persistence, transparency, verifiability, encryption, and operationaly strong The paper reviews whether they make a good fit along with certain challenges of Blockchain that should be examined while integrating it with IoT for resolving various security issues.
... Middleware, a layer between the network and application layers, plays a critical role in the Internet of Things. Computing and storage functionalities can be provided by middleware [50]. To meet the application layer's demands, this layer provides APIs. ...
One way to define the Internet of Things is as a network of objects, data, and the internet. Things can be referred to as objects, whether an appliance, a car, a human, an animal, or a plant. Connected devices, manufacturers, and operators can exchange data over the Internet of Things to monitor and control their functions. According to analysts, thousands of things are predicted to be connected to the Internet of Things. Consequently, these devices generate a great deal of data. This enormous amount of data is described as Big Data. In addition to its volume and velocity, this data is diverse and varied. This data is at risk of being compromised. Firewalls are security devices that monitor, and control network traffic flow based on a set of predefined rules. More proactive firewalls are needed to block current and emerging threats such as botnets and targeted attacks. This paper provides a comprehensive overview of the information security issues and demonstrates how firewalls can mitigate these challenges in IoT applications.
... The goal of this paper is to improve the performance of the IoT platform. IoT platform supports interoperation, device management, reusability, security, and data management [28]. ...
... The middleware in IoT is responsible for creating an abstraction layer between the application and network layers. It is also possible for middleware to offer substantial compute and storage capabilities [325]. The APIs provided by this layer are used to meet the needs of the application layer. ...
The integration of the Internet of Things (IoT) connects a number of intelligent devices with a minimum of human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems were posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/ machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT systems protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT Securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach's benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.
... The middleware layer is just an abstraction between the network and application layers. Moreover, it enhances both layers computing and storage resources [83]. It also comprehends persistent data storages, queuing systems, machine learning techniques, etc. ...
Internet of Things (IoT) is the paramount virtual network that enables remote users to access connected multimedia devices. It has dragged the attention of the community because it encompasses real-world scenarios with implicit environs. Despite several beneficial aspects, IoT is surrounded by provocations for successful implementation, as data travels in different layers. One of the critical challenges is the security of the data in these layers. Researchers conducted numerous studies focused on the level of security at a single technique, creating loopholes to address the entire scenario of securing an IoT network. This study aims to comprehensively review current security issues, wireless communication techniques, and technologies for securing IoT. This work’s utmost significance is addressing all the security perspectives at a glance. For this purpose, research contributions from the previous years are investigated for better understanding. Some countermeasures and snags from security perspectives have also been analyzed in detail concerning the current industry trends. Blockchain, machine learning, fog, and edge computing are possible solutions to secure IoT. After studying these techniques and their immunity to attacks, machine learning can become a hope if incorporated with end-to-end security. This comprehensive review will provide adequate understanding and knowledge in defining security lines of action for the successful implementation of IoT.
... e middleware in IoT is responsible for creating an abstraction layer between the application and network layers. It is also possible for middleware to offer substantial compute and storage capabilities [96]. e APIs provided by this layer are used to meet the needs of the application layer. ...
The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach’s benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.
... IoT middleware is a software/script/API or software glue that interfaces IoT device components and system applications and enables communication between them. It helps to resolve the issues of physical-layer communications, application service requirements, and diversity/heterogeneity in communications [5]. The drawbacks of device on-boarding process is that it may cause substantial risk to the IoT network infrastructure. ...
The Internet of Things (IoT) are becoming a prevalent part of our society offering operational flexibility and convenience. However, insecure provisioning makes the IoT devices susceptible to various cyberattacks. For instance, mal-provisioned devices may leak sensitive information allowing the attackers to eavesdrop or disrupt communication infrastructures. Furthermore, compromised devices can act as zombies to intensify the scale of the attack. Hence, we need secure device provisioning services which can counteract such attacks and adverse circumstances. This paper proposes a Secure Smart Device Provisioning and Monitoring Service Architecture (SDPM) for smart network infrastructures such as IoT-enabled smart home or office and Industrial IoT infrastructures. Our architecture allows the provisioning of devices in such a way that the malicious devices can be controlled and their activities using a dynamic policy-based approach. SDPM introduces an IoT device ontology for device registration and authentication and uses the ontology to construct device category and service-specific policies. SDPM provides a fine granular pre and post condition-based policies to provision securely the IoT devices and control their runtime operations. Furthermore, SDPM utilises the digital twin concept, to monitor dynamically the security status of IoT devices at runtime. The policies associated with a device’s twin enables the SDPM to automate security capabilities such as device firmware updating and patching for security vulnerabilities.
... IoT scenario and, at the same time, the promotion of their autonomy. The autonomous interaction between smart objects occurs through mechanisms allowing each of them to understand what features/services can be provided by the smart objects it is in contact with [10]. The increasing of autonomy poses important challenges in terms of smart objects reliability. ...
In recent years, the Internet of Things paradigm has become pervasive in everyday life attracting the interest of the research community. Two of the most important challenges to be addressed concern the protection of smart objects and the need to guarantee them a great autonomy. For this purpose, the definition of trust and reputation mechanisms appears crucial. At the same time, several researchers have started to adopt a common distributed ledger, such as a Blockchain, for building advanced solutions in the IoT. However, due to the high dimensionality of this problem, enabling a trust and reputation mechanism by leveraging a Blockchain-based technology could give rise to several performance issues in the IoT. In this paper, we propose a two-tier Blockchain framework to increase the security and autonomy of smart objects in the IoT by implementing a trust-based protection mechanism. In this framework, smart objects are suitably grouped into communities. To reduce the complexity of the solution, the first-tier Blockchain is local and is used only to record probing transactions performed to evaluate the trust of an object in another one of the same community or of a different community. Periodically, after a time window, these transactions are aggregated and the obtained values are stored in the second-tier Blockchain. Specifically, stored values are the reputation of each object inside its community and the trust of each community in the other ones of the framework. In this paper, we describe in detail our framework, its behavior, the security model associated with it and the tests carried out to evaluate its correctness and performance.
... This makes the industrialization of the IoT services difficult as the PoC phase is expensive due to this heterogeneity and the lack of common shared standards. As a result, for widespread adoption of IoT based systems and services an intermediate software/service layer is needed to hide the details of various heterogeneous technologies underlying the IoT device ecosystem [9,10,11,12]. ...
L'Internet des objets (IoT) combine de nombreuses technologies et s'est étendu à des domaines d'application divers et multidisciplinaires. Chaque domaine a son propre ensemble d'exigences d'application en termes de matériel, de communication, de logiciel, de source d'énergie, etc. Cela empêche l'utilisation de modèles de programmation conventionnels de l'informatique distribuée qui suppose que les systèmes sont toujours connectés, disposant de ressources de calcul abondantes et d'accès à l'énergie électrique infinie. De plus, l'IoT englobe une large gamme d'appareils IoT embarqués hétérogènes (unités de traitement, capteurs, actionneurs, émetteurs-récepteurs, etc.) fournis par divers fabricants, chacun avec une architecture d'appareil différente, par conséquent, les logiciels d'application développés pour ces appareils ne sont pas compatibles avec chacun. autre. Cette hétérogénéité des appareils pose de sérieux problèmes pour l'interopérabilité des appareils et également pour les outils de développement IoT harmonisés sur une large gamme d'appareils IoT hétérogènes. Un défi important non seulement pour les experts du domaine, mais aussi pour les professionnels est de réaliser une preuve de concept (PoC) lors de l'industrialisation des services IoT, ce qui implique - le développement, le déploiement et la maintenance de services d'application IoT de bout en bout nécessitant différents types et La principale contribution de cette thèse est d'introduire un nouveau framework nommé PrIoT (Prototyping Internet of Things) qui permet une programmation simple et rapide des appareils IoT, de la conception au déploiement, qui gère mieux l'hétérogénéité de l'architecture des appareils IoT. Plus précisément, le framework PrIoT est basé sur le concept selon lequel les applications IoT possèdent diverses caractéristiques invariantes que nous avons étudiées et rassemblées à partir de diverses architectures et applications IoT présentées dans la littérature. Nous avons ensuite développé un langage de programmation minimaliste de haut niveau et des API pour montrer la composabilité facile de nos fonctionnalités en variante dans le développement d'applications IoT. D'un point de vue matériel, afin de mieux contrôler l'hétérogénéité des appareils, nous proposons deux nouveaux systèmes modulaires nommés R-Bus et P-Bus pour concevoir des systèmes embarqués sous la forme d'un ensemble de modules matériels pouvant être montés et démontés en fonction des besoins des applications IoT. . Cela résout l'hétérogénéité de l'interface des périphériques et prend en charge diverses classes de périphériques de contrainte, ainsi qu'une configuration système avancée et des fonctionnalités plug-and-play pour faciliter le prototypage matériel IoT. Cette approche complète notre proposition de cadre PrIoT car elle offre une nouvelle façon de créer des prototypes d'applications IoT de bout en bout avec une flexibilité à la fois matérielle et logicielle des appareils IoT. En fait, notre objectif est de permettre un prototypage rapide de l'IoT de bout en bout en mettant en œuvre une couche d'abstraction de haut niveau qui cache les détails de diverses technologies sous-jacentes à l'IoT et en mettant en œuvre des systèmes modulaires pour une intégration flexible des appareils ciblés pour la conception de systèmes IoT. notre cadre PrIoT à travers l'implémentation de référence et aussi le développement de l'implémentation prototype de divers scénarios IoT en utilisant notre framework et en le comparant à diverses solutions existantes. Pour nos systèmes modulaires, nous avons défini deux métriques - adéquation et ratio de couverture qui mesurent la compatibilité des systèmes modulaires embarqués en ce qui concerne les unités de traitement. Nous avons utilisé ces métriques pour comparer notre solution avec les systèmes modulaires existants.
... Nesta seção serão apresentados os referenciais teóricos necessários para um entendimento acerca das tecnologias utilizadas durante o desenvolvimento do trabalho. A primeira delaś e a Internet das Coisas (Internet of Things -IoT) [Bandyopadhyay et al. 2011] que pode ser definida como uma rede que interliga objetos físicos, possibilitando o controle e o compartilhamento de informações a distância. Para estabelecer esta redeé necessário utilizar um middleware para IoT, uma vez que este possibilita a comunicação entre meios heterogêneos, nesse caso, entre camadas diferentes em uma mesma arquitetura. ...
The Resource Management Architecture (RMA) is an architecture that allows the management of multi-agent systems in an IoT network. However, the architecture still cannot be applied on a large scale as it supports a low number of concurrent connections. Therefore, this work presents the reengineering of RMA using a Golang programming language to allow a more significant number of references.
... This layer provides power computing and storage facilities. 61 This layer is also susceptible to different attacks. The middleware layer is vulnerable to the database and cloud security problems. ...
The blockchain has emerged as an innovative and powerful technology that shows the tremendous potential to enhance the smart industrial frameworks by providing encryption, immutable storage, and decentralization. In the past few years, several applications in the industrial Internet of Things (IIoT) have emerged and blockchain technologies gained great attention from academia and industry. To discover the great potential of blockchain technology for the IIoT, we present a comprehensive survey on security issues, blockchain architec-tures, and applications from the industrial perspective. This article starts with a comparison of exiting state-of-the-art surveys of blockchain technologies for IoT/IIoT applications. A four-layer reference architecture of IIoT is presented along with the functionalities and security issues of each layer. To address these challenges, we assess the potential of blockchain technology by considering the key characteristics, architectures, consensus algorithms, and implementation platforms. Furthermore, we also discussed some use cases of blockchain for the IIoT frameworks. Finally, this survey is concluded by highlighting some open issues and future research directions.
... In [63], a generic IoT trust architecture is proposed that integrates trust into all these layers as an integral component to manage security. IoT faces several security challenges [36], e.g., authentication [3,31,37,38], access control [47,60], trust management in cross domain along with smart edge nodes [6,7,9,11,33], security management in IoT equipped with VANET nodes [10,27,35,45], policy enforcement [54], secure middleware [13], and confidentiality [50]. ...
Internet of Things (IoT) is bringing revolution into today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous as it provides autonomy to nodes that they can communicate among other nodes and can also exchange information at any period. Due to the heterogeneous environment, IoT faces numerous security and privacy challenges, and one of the most significant challenges is the identification of malicious and compromised nodes. In this article, we have proposed a Machine Learning-based trust management approach for edge nodes. The proposed approach is a lightweight process to evaluate trust because edge nodes cannot perform complex computations. To evaluate trust, the proposed mechanism utilizes the knowledge and experience component of trust where knowledge is further based on several parameters. To eliminate the triumphant execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit where only those that satisfy the threshold value can participate in the network. To validate the performance of a proposed approach we have performed an extensive simulation in comparison with the existing approaches and the result shows the effectiveness of the proposed approach against several potential attacks.
... These proposals come from different fields, such as smart spaces, Internet of Things (IoT) and ambient intelligence (AmI), among others. Such fields have in common that they are built upon concepts and technologies such as ubiquitous computing, context awareness, embedded systems and human-centric computer interaction design [4,5]. These kind of solutions are typically employed in home environments, but may also be employed for other kinds of purposes, such as healthcare [6], cognitive robotics [7], education [8] and public safety [1]. ...
Pervasive service composition is useful in many scenarios, for instance, in urban planning or controlled harvest. Currently, there is no standard to develop solutions using pervasive service composition. However, big companies propose their frameworks to develop complex services, but their frameworks are appropriate in specific applications, such as home automation and agriculture. On the other hand, there are different very well-grounded academic proposals for pervasive service composition. However, these do not solve the problems of traditional approaches that are appropriate to specific areas of application, and adaptation is needed to deal with the dynamism of the environment. This article presents a cognitive approach for pervasive service composition where InfoCom devices and the implementation of cognitive functions interact to create pervasive composite services. Our central hypothesis is that cognitive theory can help solve actual problems requiring pervasive service composition, as it addresses the above-mentioned problems. To test our approach, in this article we present a case of urban insecurity. Specifically, in different countries, street robbery using firearms is one of the problems with a high impact because of its frequency. This article proposes to compose a pervasive service for deterring criminals from committing their crimes. The results obtained by simulating our proposal in our case study are promising. However, more research needs to be achieved before applying the proposed approach to actual problems. The research needed ought to address various problems, some of which are discussed in this article.
... This knowledge might need to be stored or action needs to be performed. To support IoT, middleware should handle the following challenges: interoperability between heterogeneous devices [46] [47], context awareness [48], managing data [49], privacy and security [50], and device discovery [51]. The current proposed IoT system has many challenges. ...
The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.
... This knowledge might need to be stored or action needs to be performed. To support IoT, middleware should handle the following challenges: interoperability between heterogeneous devices [46] [47], context awareness [48], managing data [49], privacy and security [50], and device discovery [51]. The current proposed IoT system has many challenges. ...
The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.
... The middleware and application layer, with the main objectives of functional abstraction and device communication [59] plays a key role due to the heterogeneous objects , limited storage, and processing capabilities [60]. The middleware is categorized in layers such as object abstraction, service management, service composition and application [61]. ...
Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.
... The middleware and application layer, with the main objectives of functional abstraction and device communication [59] plays a key role due to the heterogeneous objects , limited storage, and processing capabilities [60]. The middleware is categorized in layers such as object abstraction, service management, service composition and application [61]. ...
Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.
... The middleware and application layer, with the main objectives of functional abstraction and device communication [59] plays a key role due to the heterogeneous objects , limited storage, and processing capabilities [60]. The middleware is categorized in layers such as object abstraction, service management, service composition and application [61]. ...
Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from sensors’ raw data. In the current cloud computing architecture, all the IoT raw data is transmitted to the cloud for processing, storage, and control things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by Providing IoT Gateway at the edge layer with the required intelligence to gain the Knowledge from raw data to decide to actuate or offload complicated tasks to the cloud. This collaboration between cloud and edge called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between cloud and edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.
... En la actualidad, los fabricantes de automatismos ofrecen soluciones que conectan sistemas MES (Manufacturing Execution System) con ERP (Enterprise Resource Planning) consiguiendo simplificar la interacción entre el equipo humano y el sistema de producción mediante el uso exhaustivo de las tecnologías de la información. En esta evolución se encuentran paralelismos con los sistemas de "Internet de las cosas" [1] con los que comparte desafíos relacionados con el procesamiento masivo de la información para la búsqueda de patrones, sistemas de ejecución en la nube, coexistencia de diferentes niveles de criticidad, inestabilidad de las comunicaciones y cuestiones relacionadas con la ciberseguridad. ...
... The middleware and application layer, with the main objectives of functional abstraction and device communication [59] plays a key role due to the heterogeneous objects , limited storage, and processing capabilities [60]. The middleware is categorized in layers such as object abstraction, service management, service composition and application [61]. ...
Increasing the implication of growing data generated by the Internet of Things (IoT) brings the focus toward extracting knowledge from the raw data derived from sensors. In the current cloud computing architecture, all the IoT raw data are transmitted to the cloud for processing, storage, and controlling things. Nevertheless, the scenario of sending all raw data to the cloud is inefficient as it wastes the bandwidth and increases the network load. This problem can be solved by providing IoT Gateway at the edge layer with the required intelligence to gain the knowledge from raw data to decide whether to actuate or offload complicated tasks to the cloud. This collaboration between the cloud and the edge is called distributed intelligence. This work highlights the distributed intelligence concept in IoT. It presents a deep investigation of distributed intelligence between the cloud and the edge layers under IoT architecture, with an emphasis on its vision, applications, and research challenges. This work aims to bring the attention of IoT specialists to distributed intelligence and its role to deduce current IoT challenges such as availability, mobility, energy efficiency, security, scalability, interoperability, and reliability.
... According to Gartner Report envisage, by 2020 there will 50 billion number of such interconnected IoT objects across the globe. It is expected that heterogeneous connected things of IoT generate voluminous data every moment [1][2][3]. In addition, a deluge of IoT applications using data analytics are developed that involves control and optimize at real time through cognitive decision making mechanism based on voluminous data gathered from IoT sensors. ...
... the first layer in this scope is the IoT platform itself. the IoT platform is the most important part of the architecture and the key to supporting some important features such as device management, interoperation, security, reusability and the management of huge volumes of data [28]. ...
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.
... According to Gartner Report envisage, by 2020 there will 50 billion number of such interconnected IoT objects across the globe. It is expected that heterogeneous connected things of IoT generate voluminous data every moment [1][2][3]. In addition, a deluge of IoT applications using data analytics are developed that involves control and optimize at real time through cognitive decision making mechanism based on voluminous data gathered from IoT sensors. ...
IoT is one of the key areas of future research due to its real time applications for industry. IoT Applications take more attention from industry and different projects are in progress for safety of end user in smart future. Security is one of hot research area in future applications of IoT and security and privacy playing a key role for implementation of applications in real time environment and ensure the end user privacy. In this paper, we will discuss the importance of security in different types of IoT applications and discussed the security on different levels like security issues on sensing layer, on network layer, middleware layer and application layer and then provide the best solution according to the recent research of different researchers IoT security using Blockchain and security on different Attacks. Smart Home and Smart City are two major applications of IoT and end users are more concern about their privacy of data and successful implementation of IoT applications require to develop the user trust in IoT applications.
As research in the Internet-of-Things area progresses, and a multitude of proposals exist to solve various problems, the need for a general principled software engineering approach for the systematic development of IoT systems and applications arises. In this chapter, by synthesizing from the state of the art, we frame the key concepts and abstractions that revolve around the design and development of IoT systems and applications and draft a software engineering methodology centred on these abstractions.
The current communication era is commonly called the Internet of Things (IoT). The IoT facilitates data generation, reception, and transmission among tangible entities. A multitude of IoT applications prioritize the automation of diverse processes to endow non-living entities with autonomous functionality. Thanks to current and future IoT applications, user comfort, effectiveness, and level of automation will probably increase. However, such a world needs high-security, private, authentic, and attack-recovery mechanisms. Implementing the necessary changes to the building blocks of IoT applications is essential to achieving secure end-to-end IoT environments. This chapter will comprehensively examine the obstacles related to security and the conceivable hazards presented by applications of the IoT. Following a discussion of the potential risks associated with the IoT, a look will be taken at the many different technologies currently in use and those still in the development stage. The enhancement of IoT security is the focus of current research into four distinct technologies: Edge computing, Fog computing, Blockchain, fog computing, Edge computing, and machine learning.
Background
With the advancements of ubiquitous computing, wireless sensor networks, and machine-to-machine communication, the Internet of Things (IoT) has become a constantly growing concept. The IoT is a new paradigm that interconnects all smart physical devices to provide smart services to users. It effectively delivers user-required services by utilising internet connectivity, sensors, and various technologies and protocols for the analysis and collection of data. IoT is predicted to permeate practically every facet of daily life, from smart cities to health care, smart agriculture, logistics and retail, and even smart living and smart ecosystems. Since IoT systems are comprised of heterogeneous hardware and networking technologies, integrating them to the software/application level to extract information from massive amounts of data is a difficult task.
Methods
In this survey, the definitions, elements, working, architecture, fundamental technologies, key challenges, and potential applications of IoT are systematically reviewed. Initially, the various definitions and elements of IoT are introduced, followed by an explanation of how an IoT works. Additionally, an outline of IoT in the context of the architecture is presented. The primary enabling technologies that will drive IoT research in the near future are examined in this paper. Furthermore, the major key challenges that the research community must address, as well as potential solutions, are investigated. Finally, the paper concludes with some potential IoT applications to demonstrate the concept's feasibility in real-world scenarios.
Conclusion
The goal of this survey is to assist future researchers in identifying IoT-specific challenges and selecting appropriate technology based on application requirements.
The Internet of Things (IoT) is a vast concept spreading rapidly throughout the world today. Due to their inherent nature, IoT devices are more vulnerable to attacks than other cyber infrastructure. In a typical IoT system, four different types of layers can be identified. Those layers can be specified as the application layer, data processing (software) layer, network layer, and sensing (physical) layer. According to this architecture, each layer operates under different technologies. Thus, various challenges and vulnerabilities related to security have emerged and exist. Thereby extant and forthcoming IoT applications must comply with standard cyber security guides and regulations to guarantee safety; otherwise, they would jeopardize the lives of people using these IoT applications resulting in chaos. To achieve this, IoT applications can create environments with end-to-end security by adding security measures and the required adjustment, guaranteeing safety and privacy. By bearing this in mind, this research reviews the different types of security challenges, such as access control attacks and physical security attacks found in each of the four layers of the IoT architecture, along with what countermeasures can be taken to mitigate these attacks. As the main objective of this research is to examine underlying security challenges in the standard IoT architecture, we examine and categorize IoT vulnerabilities and outline methods used to ensure such IoT systems safety. Further, we also present the future directions in terms of security and privacy of IoT as well.
Recent years have witnessed an increase in human life expectancy fueled by significant improvements in infrastructure, healthcare, and economies across the globe. Longer life spans have altered the world demographics resulting in a larger senior population compared with previous years. This trend has created the need for providing additional care and assistive services to support the ageing individuals. Innovative assistance techniques are especially necessary for elderly people who live on their own in their homes. Simultaneously, an explosive growth in IoT gadgets such as sensors and actuators have accelerated the development of smart homes which comprise various types of IoT systems that provide increased convenience to people with degenerating physical and cognitive abilities. Common examples of IoT systems that are being integrated into smart homes include home automation systems, home activity detectors, wearable sensor technologies for remote health management and so on. We identify the common needs of ageing and impaired individuals and then we review several IoT applications that can provide the required support. We further discuss some of the challenges that must be addressed to make these IoT systems more practical and reliable for everyday use.
The Internet of Things (IoT) enables humans and computers to learn from and interact with billions of devices such as sensors, actuators, services, and other Internet-connected gadgets. The implementation of IoT technologies leverages seamless integration of the cyber and physical worlds, radically altering and empowering human interaction. Middleware, commonly described as a software system designed to be the intermediary between IoT devices and applications, is a fundamental technology in developing IoT systems. The IoT middleware solutions must match the requirements of the IoT ecosystem to acquire the widespread adoption. Among various approaches to middleware, service-oriented approach (SOA) is most suitable. Extending advantages of SOA, the special case of service orientation paradigm called microservices approach that has created a hype in the domain of cloud and enterprise application business. Furthermore, the microservices model has several advantages, particularly in dynamic IoT applications, where it is highly straightforward to utilize microservices-based architectures. This paper provides an overview of the current state-of-the-art and practice regarding the usage of microservice architectures by IoT. More specifically, we examine the requirements of a typical IoT middleware and presents an in-depth investigation of microservice-based IoT middlewares to address the middleware requirements and their implementation.KeywordsInternet of ThingsCyber-physical systemsMiddlewareMicroservices
Due to the large variety of Internet of Things (IoT) platforms, selecting the right one to implement an IoT solution is a tough task. To mitigate right selection by the developer, this paper presents a Systematic Multivocal Mapping Study on IoT platforms and its main software elements, to define their anatomy considering how they were studied by the market analysts and academia. By using a precise protocol defined on this work, it was possible to select 50 academic articles and industry reports that perform IoT platform descriptions, evaluations and comparisons. As results, this paper identified the most important IoT platforms are AWS IoT, Azure IoT, Watson IoT, PTC ThingWorx and Google IoT. Its main capabilities are Interoperability, Security & Privacy, Developer Support, Data Management, Device Management and Services Management. It was also defined an architectural model with the main platform components highlighted according to their relevance, the main communication models (Publish/Subscribe and REST APIs) and the common API that should be implemented by the IoT platforms.
In recent years, the Internet of Things paradigm has become pervasive in everyday life attracting the interest of the research community. Two of the most important challenges to be addressed concern the protection of smart objects and the need to guarantee them a great autonomy. For this purpose, the definition of trust and reputation mechanisms appears crucial. At the same time, several researchers have started to adopt a common distributed ledger, such as a Blockchain, for building advanced solutions in the IoT. However, due to the high dimensionality of this problem, enabling a trust and reputation mechanism by leveraging a Blockchain-based technology could give rise to several performance issues in the IoT. In this paper, we propose a two-tier Blockchain framework to increase the security and autonomy of smart objects in the IoT by implementing a trust-based protection mechanism. In this framework, smart objects are suitably grouped into communities. To reduce the complexity of the solution, the first-tier Blockchain is local and is used only to record probing transactions performed to evaluate the trust of an object in another one of the same community or of a different community. Periodically, after a time window, these transactions are aggregated and the obtained values are stored in the second-tier Blockchain. Specifically, stored values are the reputation of each object inside its community and the trust of each community in the other ones of the framework. In this paper, we describe in detail our framework, its behavior, the security model associated with it and the tests carried out to evaluate its correctness and performance.
Internet of Things (IoT) is a platform governed by information and communication technologies that facilitates affordable data communication among heterogeneous devices in large scale. However, computation-rich applications running on IoT need specific considerations as user response plays key role in critical situations particularly in transportation, health care, and smart cities. In this view, this chapter explains in detail characteristics of various software components that are in use in IoT over existing communication networks and presents various problem-solving techniques for IoT applications in intelligent transport systems. Further, the role of algorithms and computational structures for development of efficient IoT application is illustrated in detail with three real-time case studies in transportation domain.
The Internet of Things (IoT) is a framework that attends industry and people on a daily basis. It applies to the networking of daily basis physical objects that are tending to be smart with intelligent features. It intends not only to improve internet ubiquity but also to contribute to a well-distributed network of devices which are able to communicate with both human and with each other. IoT provides useful possibilities for a wide variety of new technologies due to the
rapid developments in fundamental methods, which aim to enhance human living efficiency and
promote the sharing of resources. So, IoT can be considered as a theatrical concept of vision
that aims to provide the connection of everything with the Internet.
In such cases, IoT devices are operating in vulnerable environments. This later factor leads to
a number of security challenges that should be taken into consideration. Cyber attacks have
targeted the IoT and numerous threats and attacks may cause serious network problems without
the required security solutions. Authentication is considered an important feature for ensuring
a reliable and secure communication between devices in an IoT environment for making the
system quite relevant.
The main goal of this PhD thesis concerns the design and the implementation of secure and
distributed authentication schemes for IoT application domains. The new schemes should cope
with the challenges and improve the performance of the existing authentication schemes.
In this thesis, first, a security analysis of the well-known attacks applied to the different layers
of IoT architecture is done. Then, the existing authentication schemes provided in the literature
are analyzed. Next, a benchmark is presented using a multi-criteria classification emphasizing
the main strengths and weaknesses of such approaches.
Second, the traditional cryptographic protocols are implemented on IoT hardware platform
based on the use of the famous Raspberry PI3. The main aim was the study of the energy
performance regarding the power consumption and the throughput.
Third, we introduced the use of Distributed Ledger Technology (DLT) to achieve lightweight
security solutions for resource constrained IoT devices. A distributed scheme based on Ethereum
Blockchain is proposed by generating a Pre-shared key between the entities wishing to communicate
after verifying the device by asking the Ethereum Blockchain for its public key stored inside the
smart contract. Another novel authentication scheme is proposed based on IOTA distributed
ledger. The main idea was to create a virtual zone in which the devices identify and authenticate
each other. Besides, devices for different zones are identified and considered trusted also.
Finally, a new authentication scheme was proposed based on hardware security solutions. Physically Unclonable Functions (PUFs) was used to define a lightweight authentication scheme for IoT devices. The security analysis of the new approach has shown its safety against machine learning attacks.
As future work, the thesis presents several research areas where this thesis can be used as a basis. Deeper analysis of particular mechanisms, new proposals to try different methods, and further implementation of the proposed solutions.
As the core layer of the Internet of Things (IoT), middleware bridges the gap between applications and devices to resolve many common IoT issues and enhancing application development. Consequently, developing suitable middleware is the main challenge that covers functionality and required quality to combine heterogeneous hardware and software as the integrated system in the IoT. This survey discusses IoT middleware requirements and challenges, and presents the current state of research in this domain. A technical taxonomy is presented for the IoT middleware according to the abstract and processing approach of data. We focus on discovering similarities and differences by making comparisons and appropriateness studies. Besides, this survey discusses three enabling techniques in detail to present analytically the current research trends on the IoT middleware. In the end, we summarize open issues in IoT middleware. This survey aims to provide guidance for the development and research of middleware in the IoT paradigm.
Logistics is a driver of countries’ and firms’ competitiveness, and plays a vital role in economic growth. However, the current logistics industry still faces high costs and low efficiency. The development of smart logistics brings opportunities to solve these problems. As one of the important technologies of modern information and communication technology (ICT), the Internet of Things (IoT) can create oceans of data and explore the complex relationships between the transactions represented by these data with the help of various mathematical analysis technologies. These features are helpful to promote the development of smart logistics. In this paper, we provide a comprehensive survey on the literature involving IoT technologies applied to smart logistics. First, the related work and background knowledge of smart logistics are introduced. Then, we highlight the enabling technologies for IoT in smart logistics. Furthermore, we review how IoT technologies are applied in the realm of smart logistics from the perspectives of logistics transportation, warehousing, loading/unloading, carrying, distribution processing, distribution, and information processing. Finally, some challenges and future directions are discussed.
This book stems from the CyberBRICS project, which is the first initiative to develop a comparative analysis of the digital policies developed by BRICS (Brazil, Russia, India, China and South Africa) countries. BRICS have been chosen as a focus not only because their digital policies are affecting more than 40% of the global population – i.e. roughly 3.2 billion individuals living in such countries – but also all the individuals and businesses willing to use technologies developed in the BRICS or trading digital goods and services with these countries. Given the complexity of digital policies in general and cybersecurity in particular – not to mention the specificities of BRICS countries – this work aims at laying the foundation on which further research on cybersecurity and digital policy in the BRICS can and will be developed.
Much of the recent innovation and development in technology is geared towards the integration of communication networks among systems and devices. Various applications of technology are witnessing a shift to internet-linked components and integrating cyber and physical systems together; such phenomenon is often referred to as Cyber Physical Systems (CPS). CPS is used in many applications including industrial control systems and critical infrastructure such as health-care and power generation. The increased integration of CPS and internet networks raises security concerns and vulnerabilities. This book delves into some of the security challenges associated with CPS as well as intelligent methods used to secure CPS in various applications. The book also discusses various AI-based methods for enhanced CPS security and performance and presents case studies and proof of concepts in simulated environments.
Increased Distributed Energy Resource (DER) penetrations are causing a shift in the electricity system operations paradigm. Therefore, it is indispensable to enhance the current data acquisition systems and communications infrastructures to improve the economic efficiency and reliability of power systems. Since dedicated communications investments are capital cost prohibitive, in our work we leverage the existing communication solutions for Supervisory Control And Data Acquisition (SCADA) and Advanced Metering Infrastructure (AMI) and exploit real-time communications middleware technologies to develop an attack-resilient LTE/WiFi-based communication infrastructure. In our communication infrastructure, we propose an intelligent communication middleware architecture that utilizes the Quality of Experience (QoE) of operators of the power system to complement the traditional Quality of Service (QoS) evaluation. Furthermore, our communication infrastructure enhances IEC62351 security standard by employing deep learning techniques to detect and defend against potential cyber attacks including False Data Injection (FDI). The simulation results illustrate the effectiveness of our proposed middleware-based communication infrastructure.
The development in technology and the convergence of digital electronics, wireless communication, and micro-electromechanical systems have paved the way for the emergence of Internet of Things (IoT). The interconnection layer is the third layer that allows the data generated by the sensors to be transmitted to data center. Secure communication presently requires several levels of configuration and algorithms, encourages users to prefer functionality over security, and discourages them staying away from protection-based implementation. Communications between subsystems are enabled by the session layer. Apart from these protocols, various security- and management-related protocols are defined for IoT. Data distribution service standard, developed by the Object Management Group, uses publish–subscribe architecture used for machine-to-machine communication. Security is the major concern and is highly challenging in the big data environment since it involves billions of devices and different technologies that claim solutions for various categories of security threats.
In the Hydra Middleware project context awareness, among many other aspects plays an important role. Context is not only defined by the user's presence but also by identifying available devices and services that are offered by the environment or the devices themselves. Inside the architecture, special components handle contextual information by also separating between core-functionality and the possibility to extend the built-in concept by new components providing context information. This work presents a first step towards ubiquitous and context aware applications in the healthcare and home automation sector. In the scope of the Hydra Middleware project, applications taken from different domains are derived from the current project's state and knowledge. The inclusion of different application domains supports the development of a domain-independent middleware and a wide spectrum of interests for application developers.
This paper studies the state-of-art of Internet of Things (IoT). By enabling new
forms of communication between people
and things, and between things themselves, IoT would add a new dimension
to the world of information and communication just as Internet once did. In this
paper, IoT definitions from different perspective in academic communities are described and compared. The main enabling
technologies in IoT are summarized such
like RFID systems, sensor networks, and
intelligence in smart objects, etc. The effects of their potential applications are reviewed. Finally the major research issues
remaining open for academic communities
are analyzed.
The ìInternet of Thingsî intends to enhance items of our everyday life with information and/or processing and interconnect them, so that computers can sense, integrate, present, and react on all kinds of aspects of the physical world. As this implies that enormous numbers of data sources need to be connected and related to each other, e xible and dynamic middleware support essentially providing zero-programming deployment is a key requirement to cope with the sheer scale of this task and the heterogeneity of available technologies. In this pa- per we briey overview our Global Sensor Networks (GSN) middleware which supports these tasks and offers a e xible, zero-programming deployment and in- tegration infrastructure. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descrip- tors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. The GSN imple- mentation is available from http://globalsn.sourceforge.net/.
The development of next generation Embed- ded Peer-To-Peer Systems raises a number of challenging issues for pervasive computing. In this paper we overview the objectives of the ongoing European SMEPP (Secure Middleware for Embedded Peer-To-Peer)project. In par- ticular we discuss different types of requirements that have been identified in SMEPP.
The main characteristic of devices in a pervasive (or ubiquitous) computing system is their context awareness which allows them to provide proactively adapted services to user and to applications according to the global context. In order to support the development and to ease the implementation of context-aware systems, many architectures were proposed with characteristics related to the application domain and techniques used. A survey of such architectures that makes comparison between them and evaluates them is strongly recommended. Proposed surveys are either restricted to a limited number of architectures or do not offer a good comparison or their evaluation is not based on appropriate criteria which keep them as simple descriptions. Our aim is to make a survey of relevant architectures which mark the evolution of context-aware systems based on criteria related to pervasive computing. This survey will serve as a guide to developers of context-aware systems and help them to make architectural choices.
As ubiquitous systems become increasingly complex, traditional solutions to manage and control them reach their limits and pose a need for self-manageability. Also, heterogeneity of the ubiquitous components, standards, data formats, etc, creates significant obstacles for interoperability in such complex systems. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. This paper describes our vision of a middleware for the Internet of Things, which will allow creation of self-managed complex systems, in particular industrial ones, consisting of distributed and heterogeneous components of different nature. We also present an analysis of issues to be resolved to realize such a middleware.
Wireless Sensor Networks (WSNs) have found more and more applications in a variety of pervasive computing environments. However,
how to support the development, maintenance, deployment and execution of applications over WSNs remains to be a nontrivial
and challenging task, mainly because of the gap between the high level requirements from pervasive computing applications
and the underlying operation of WSNs. Middleware for WSN can help bridge the gap and remove impediments. In recent years,
research has been carried out on WSN middleware from different aspects and for different purposes. In this paper, we provide
a comprehensive review of the existing work on WSN middleware, seeking for a better understanding of the current issues and
future directions in this field. We propose a reference framework to analyze the functionalities of WSN middleware in terms
of the system abstractions and the services provided. We review the approaches and techniques for implementing the services.
On the basis of the analysis and by using a feature tree, we provide taxonomy of the features of WSN middleware and their
relationships, and use the taxonomy to classify and evaluate existing work. We also discuss open problems in this important
area of research.
Advances in the areas of embedded systems, computing, and networking are leading to an infrastructure composed of millions of heterogeneous devices. These devices will not simply convey information but process it in transit, connect peer to peer, and form advanced collaborations. This "Internet of Things" infrastructure will be strongly integrated with the environment, and its integration with the enterprise systems will not only further blur the line between business IT systems and the real world, but will change the way we design, deploy, and use services. New opportunities can emerge for businesses, which can now closely collaborate with the real world. The work presented here proposes an architecture for an effective integration of the Internet of Things in enterprise services
The computing and networking capacities of today's wireless portable devices allow for ubiquitous services, which are seamlessly networked. Indeed, wireless handheld devices now embed the necessary resources to act as both service clients and providers. However, the ubiquitous networking of services remains challenged by the inherent mobility and resource constraints of the devices, which make services a priori highly volatile. This paper discusses the design, implementation and experimentation of the ubiSOAP service-oriented middleware, which leverages wireless networking capacities to effectively enable the ubiquitous networking of services. ubiSOAP specifically defines a layered communication middleware that underlies standard SOAP-based middleware, hence supporting legacy Web Services while exploiting nowadays ubiquitous connectivity.
This article discusses the challenges for Database Management in the Internet of Things. We provide -scenarios to illustrate the new world that will be produced by the Internet of Things, where physical objects are fully integrated into the information highway. We discuss the different types of data that will be part of the Internet of Things. These include identification, positional, environmental, historical, and descriptive data. We consider the challenges brought by the need to manage vast quantities of data across heterogeneous systems. In particular, we consider the areas of querying, indexing, process modeling, transaction handling, and integration of heterogeneous systems. We refer to the earlier work that might provide solutions for these challenges. Finally we discuss a road map for the Internet of Things and respective technical priorities.
The SIRENA project started in 2003 to leverage Service Oriented Architectures (SOA) to seamlessly interconnect (embedded) devices inside and between four distinct domains - the industrial, telecommunication, automotive and home automation domain. A framework was developed to achieve this aim as well as assuring interoperability with existing devices and extensibility of network based on SIRENA technology. The core of the framework is the Devices Profile for Web Services (DPWS) which will play an important role in the upcoming Microsoft Windows. The first DPWS stack worldwide developed by the SIRENA consortium is described and its operation evidenced in several demonstrators. This paper presents the results of the SIRENA project, provides insights into used technologies and presents tools, components and services for advanced development, deployment and maintenance of devices.
Developing distributed applications is a di#cult task due to three major problems: the complexity of programming interprocess communication, the need to support services across heterogeneous platforms, and the need to adapt to changing conditions.
A smart road environment is such a traffic environment that is equipped with all necessary facilities to enable seamless mobile service provisioning to the users. However, advanced sensors and network architectures deployed within the traffic environment are insufficient to make mobile service provisioning autonomous and proactive, thus minimizing drivers’ distraction during their presence in the environment. For that, an Intelligent Transportation System, which is operating on top of numerous sensor and access networks and governing the process of mobile services provisioning to the users in self-managed and proactive way, must be deployed. Specifically, such system should provide solutions to the following two interoperability problems: interoperability between the in-car and roadside devices produced and programmed by different vendors and/or providers, and the need for seamless and flexible collaboration (including discovery, coordination, conflict resolution and possibly even negotiation) amongst the smart road devices and services. To tackle these problems, in this paper we propose UbiRoad middleware intending utilization of semantic languages and semantic technologies for declarative specification of devices’ and services’ behavior, application of software agents as engines executing those specifications, and establishment of common ontologies to facilitate and govern seamless interoperation of devices, services and humans. The main contribution of the paper includes the requirements and the architecture of complex traffic management systems and shows how such systems may benefit from utilization of semantic and agent technologies.
This paper provides a survey of a chosen set of context-aware middleware systems, and categorises their properties and use according to a taxonomy. An overview of each system is provided, as well as descriptions of the different properties.
The HYDRA project develops middleware for networked embedded systems that allows developers to create ambient intelligence applications based on wireless devices and sensors. Through its unique combination of service-oriented architecture (SoA) and a semantic-based model driven architecture, HYDRA will enable the development of generic services based on open standards.
In future computing environments, networked sensors will play an increasingly important role in mediating between the physical and virtual worlds. However, programming sensor networks, and the applications that depend on the data they produce, is extremely challenging. The need for suitable middleware to address this problem is evident. In the last few years, various middleware solutions for sensor networks have emerged. These dier in terms of their mod- els for querying and data aggregation, and their assumptions about the topology and other characteristics of the network. Naturally, the assumptions made for each particular mid- dleware limit its potential applicability. Most of the current solutions provide relatively simple query abstractions, and therefore are not suitable for applications that have sophis- ticated requirements for processing of sensor data in the network. This paper presents a survey and analysis of the current state-of-the art in the field, highlighting the open re- search challenges. It also draws on the authors' experience with developing middleware for context-aware systems - that is, systems that rely on sensor-derived data to intelligently adapt their behaviour - to propose some future directions for the development of middleware for sensor networks.
Generative communication is the basis of a new distributed programming langauge that is intended for systems programming in distributed settings generally and on integrated network computers in particular. It differs from previous interprocess communication models in specifying that messages be added in tuple-structured form to the computation environment, where they exist as named, independent entities until some process chooses to receive them. Generative communication results in a number of distinguishing properties in the new language, Linda, that is built around it. Linda is fully distributed in space and distributed in time; it allows distributed sharing, continuation passing, and structured naming. We discuss these properties and their implications, then give a series of examples. Linda presents novel implementation problems that we discuss in Part II. We are particularly concerned with implementation of the dynamic global name space that the generative communication model requires.
In the Hydra Middleware project context awareness, among many other aspects plays an important role. Context is not only defined by the user's presence but also by identifying available devices and services that are offered by the environment or the devices themselves. Inside the architecture, special components handle contextual information by also separating between core-functionality and the possibility to extend the built-in concept by new components providing context information. This work presents a first step towards ubiquitous and context aware applications in the healthcare and home automation sector. In the scope of the Hydra Middleware project, applications taken from different domains are derived from the current project's state and knowledge. The inclusion of different application domains supports the development of a domain-independent middleware and a wide spectrum of interests for application developers.
Distributed applications and middleware services targeted for mobile devices must use device discovery service to provide any kind of service to other devices. Device discovery algorithms developed for wired networks are not suitable for mobile ad-hoc networks of pervasive computing environments. This research proposes a dependable device discovery mechanism for the middleware of the applications consisting of rapidly reconfiguring mobile devices. Our approach offers a comprehensive solution to potential problems that can arise in highly adaptive mobile ad-hoc networks of pervasive computing environments. The approach is robust enough to accommodate the device limitations and rapid changes in the resource strengths of each device in the network. We present three new device discovery algorithms in this paper: a window based broadcasting algorithm, a connectivity based dynamic algorithm, and a policy-based scalable algorithm. The algorithms vary in complexity and efficiency depending upon the pervasive computing applications. We identify the desirable dependability related characteristics of device discovery services and present how our algorithms realize those characteristics. Experimental results are presented to compare and contrast the algorithms.
Middleware Support for the Internet of Things Drahtlose Sensornetze
- K Aberer
- M Hauswirth
- A Salehi
Aberer, K., Hauswirth, M., Salehi, A.: Middleware Support for the Internet of Things. In: 5th GI/ITG KuVS Fachgespr˝ach " Drahtlose Sensornetze", pp. 15–21 (2006)
Hydra: Networked Embedded System Middleware for Heterogeneous Physical Devices in a Distributed Architecture
- A Badii
- J R Khan
- M Crouch
- S Zickau
Middleware Support for the Internet of Things In: 5th GI/ITG KuVS Fachgespr˝ach “Drahtlose Sensornetze
- K Aberer
- M Hauswirth
- A Salehi