Internet Of Things Middleware Research Articles

A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness

In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

Machine-Learning based IoT Data Caching

Abstract—The Internet of Things (IoT) continuously produces big amounts of data. Data-centric middleware can therefore help reducing the complexity when orchestrating distributed Things. With its heterogeneity and resource limitations, IoT applications can lack performance, scalability, or resilience. Caching can help overcoming the limitations. We are currently working on establishing data caching within IoT middleware. The paper presents fundamentals of caching, major challenges, relevant state of the art, and a description of our current approaches. We show directions of using machine learning for caching in the IoT. Index Terms—Data-centric, Internet of Things, caching, ma- chine learning

A Generic Internet of Things (IoT) Middleware for Smart City Applications

The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA.

Energy awareness and energy efficiency in internet of things middleware: a systematic literature review

Energy awareness and energy efficiency in internet of things middleware: a systematic literature review

A pluggable IoT middleware for integrating data of wearable medical devices

Internet of Things (IoT) is growing rapidly, producing various internet connected devices. It is applied in multiple domains, having become a milestone advancement, especially in healthcare. The conjunction of these domains foresees the formulation of a global healthcare system where everyone will be connected through IoT wearable medical devices, whereas every datum will be recorded without being affected by given locations and circumstances. However, nowadays IoT is limited to managing all these devices, as emerging technologies must manage their dramatic increase and heterogeneity. Consequently, it is essential to create and adopt new techniques for automatically distinguishing the devices' nature and characteristics, towards collecting and analyzing their data. This manuscript proposes a data integration IoT middleware for addressing this gap, by automatically connecting and integrating IoT heterogeneous wearable medical devices with various healthcare platforms, whilst interacting only with the reliable and relevant ones. Moreover, the middleware enables a plug'n’play functionality for each platform exploiting it, since they neither need to have further knowledge of the connected devices' interfaces nor to be parameterized for collecting their data through the middleware. The middleware is evaluated through diverse healthcare scenarios, producing results of high accuracy and reliability, verifying its applicability in the healthcare domain.

Internet of Things Middleware: Evaluating the Costs of Service-Based Solutions Under Different Application Scenarios

The rapid evolution of technology has paved the way for the many applications of the Internet of Things (IoT). As IoT systems become increasingly complex, new technologies that simplify the development of the IoT are emerging, for example, IoT cloud platforms have gained popularity as tools that reduce development time and cut costs. Recent surveys indicate that only a few IoT projects have had successful outcomes. Among the main reasons for this, there is the lack of knowledge regarding IoT design and architecture. Numerous research studies have tried to address the problem of choosing the right cloud platform based on specific requirements, but none of the reviewed studies has proposed a cost-benefit analysis of the current available cloud solutions for the IoT. Here, the IoT has been studied from a decision-maker’s perspective, giving a brief but concise introduction to modern IoT architecture and IoT middleware and evaluating the costs of four popular IoT cloud middleware solutions under different project requirements and design scenarios.

Osmotic Message-Oriented Middleware for Internet of Things

In this paper will be addressing the managed operation of entities at various infrastructures to assist the automated orchestration of Internet of Things services which is a major challenge connected to the practical implementation of IoT. This concept is described to as “osmotic provisioning orchestration” which is basically the monitoring, automation, and orchestration principles so it can oversee the osmotic ecosystem. we will show in this paper the architectural design of an osmotic orchestrator tackling this challenge depending on trending protocols and technologies. the solution proposed will integrate will trending technologies like the cloud computing, with other more recent technologies like edge computing, osmotic computing, and osmotic provisioning. In addition, we will propose a prototype of the architecture that has been created using an open-source application and microservices technologies, taking into consideration the cloud and edge domains.

Key Technology of Internet of Things Middleware and Computer Event Matching Algorithm

In order to solve the problem of complex event pattern in big data and strengthen research on key technologies of the Internet of Things and computer time matching algorithms, this paper studies the problem based on Hadoop clustering algorithm. Firstly, based on the subtype attribute of event type, the maximum value is selected as the final attribute value of the event after weighting the event. Secondly, cluster analysis is conducted on the Internet of Things flow dataset through the relationship between complex events. Finally, the simulation test is carried out on the simulation dataset of complex event relationship, and the simulation test of clustering algorithm comparison is carried out. The experimental results show that the clustering accuracy of different datasets is above 85%, and the clustering accuracy of causality reaches 96.07% when the dataset is 5G. Therefore, the algorithm proposed has high feasibility, good stability, and high speed and effectiveness for complex event processing. This study has certain practical significance for solving the problem of complex event pattern in big data.

IoT Middleware Platforms for Smart Energy Systems: An Empirical Expert Survey

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances.

Osmotic Message-Oriented Middleware for Internet of Things

IoT is a trending computational concept that converts almost everything in modern life into a smart thing in various innovational and outstanding approaches. Smart homes, connected cities, autonomous vehicles, industrial automation, and smart healthcare that allows doctors to perform a patient examination and enable executing a remote surgery are now applicable through the smart connected things. Moreover, the recent IoT analytics report expects the universal number of connected IoT things to grow by 9%, to 12.3 billion operating terminals. It is expected that there will be more than 27 billion IoT live connections by 2025. In this paper, we present osmotic message-oriented middleware, introducing an end-to-end IoT platform to federate the dynamic orchestration process of resources across different heterogeneous types of devices belonging to physical and virtual infrastructures (e.g., edge, fog, and cloud layers); the orchestration process follows the osmotic computing concepts represented as the self-adaptive MAPE-K model, which maintains/adopts itself on the runtime through feedback loops from the provisioning engine, which collects the node’s hardware and software performance matrices. Accordingly, the orchestration process utilizes the optimized dynamic Hungarian algorithm to solve the MELs’ assignment problem based on the vibrant runtime provisioning data. The implemented middleware prototype is tested on both simulated and real-life environments to validate the architecture hypothesis of running an efficient, robust, elastic, and cost-efficient end-to-end osmotic IoT ecosystem, which unlocks a new implementation model for the IoT numerous domains.

A unified ontology-based data integration approach for the internet of things

<span lang="EN-US">Data integration enables combining data from various data sources in a standard format. Internet of things (IoT) applications use ontology approaches to provide a machine-understandable conceptualization of a domain. We propose a unified ontology schema approach to solve all IoT integration problems at once. The data unification layer maps data from different formats to data patterns based on the unified ontology model. This paper proposes a middleware consisting of an ontology-based approach that collects data from different devices. IoT middleware requires an additional semantic layer for cloud-based IoT platforms to build a schema for data generated from diverse sources. We tested the proposed model on real data consisting of approximately 160,000 readings from various sources in different formats like CSV, JSON, raw data, and XML. The data were collected through the file transfer protocol (FTP) and generated 960,000 resource description framework (RDF) triples. We evaluated the proposed approach by running different queries on different machines on SPARQL protocol and RDF query language (SPARQL) endpoints to check query processing time, validation of integration, and performance of the unified ontology model. The average response time for query execution on generated RDF triples on the three servers were approximately 0.144 seconds, 0.070 seconds, 0.062 seconds, respectively.</span>

Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility

Integrated farm management (IFM) is promoted as a whole farm approach toward Agriculture 4.0, incorporating smart farming technologies for attempting to limit livestock production’s negative impacts on the environment while increasing productivity with regard to the economic viability of rural communities. The Internet of Things (IoT) may serve as an enabler to ensure key properties—such as interconnectivity, scalability, agility, and interoperability—in IFM systems so that they could provide object-based services while adapting to dynamic changes. This paper focuses on the problem of facilitating the management, processing, and sharing of the vast and heterogeneous data points generated in livestock facilities by introducing distributed IoT middleware as the core of a responsive and adaptive service-oriented IFM system, specifically targeted to enable smart livestock farming in view of its unique requirements. The proposed IoT middleware encompasses the context-awareness approach via the integration of a flexible ontology-based structure for modeling and reasoning. The IoT middleware was assessed in actual conditions on the grounds of a case study for smart control of the thermal environment in a medium-sized pig farming facility. As derived from the obtained evaluation results, the system appears to perform quite satisfactorily in terms of computational performance as well as ontology coherence, consistency, and efficiency.

Middleware for the Internet of Things: a systematic literature review

The Internet of Things (IoT) emerged to describe a network of connected things on a large scale to offer services to a large number of applications in different environments and domains. Middleware is software that seeks to facilitate the management and communication of all these things, providing the necessary functionalities to manage things, to discover, to compose services, and perform communication. For this reason, several proposals for middleware solutions for IoT have been developed. In this article, we conducted a systematic review of the literature to bring together middleware solutions for IoT, identifying the requirements and communication protocols used. In addition, we present some gaps and directions for future research in the development of IoT middleware.

An IoT Edge Computing Framework Using Cordova Accessor Host

The Internet of Things (IoT) is a rapidly growing system of physical sensors and connected devices, enabling advanced information gathering, interpretation, and monitoring. The realization of a versatile IoT edge computing framework will accelerate seamless integration of the cyber-world with new physical IoT devices, and will fundamentally change and empower the way humans interact with the world. While there are many cloud-based IoT computing frameworks, they cannot support the needs of IoT applications that require local processing and guarantee of consumer’s privacy. This article presents experimentation with the opensource plug-and-play IoT middleware, called Cordova Accesor Host. We demonstrated that Cordova Accessor Host supports the essential ingredients of the composition and reusability of IoT services using the accessor as the basic building block and adopting an accessor-module-plugin design pattern. The portability is demonstrated by using the same accessor for collecting sensor data from radically different IoT devices such as, wearables (e.g., smartwatches) and microcontrollers (e.g., Arduino). Our energy profiling experiments show that IoT services deployed using the Cordova Accessor Host consume around 35% less battery power than the same IoT services deployed in the native Android operating system.

Design, Deployment and Performance Evaluation of an IoT Based Smart Energy Management System for Demand Side Management in Smart Grid

The Smart Grid (SG) is the technological development that incorporates digital technologies and advanced communication methods to determine and respond to variations in electricity consumption in order to revolutionize power distribution, transmission, and generation. In the conventional electrical grid, customers remain unaware to their energy usage patterns that not only results in energy loss but also money. The consumers’ usage and consumption standards need to be regulated in order to improve energy efficiency (EE). SG utilizes demand side management (DSM) for energy savings by use of various approaches like financial incentives, subsidized tariffs, and awareness to alter consumers’ energy demand. In smart environments (SE), Internet-of-Things (IoT) is evolving as a significant partner for resource and energy management. DSM in SG must take advantage of smart energy management system (SEMS) developed on smart meters (SMs) and modern technologies like IoT. Using SMs with IoT based technologies makes SEMS more effective in the SG. This paper offers design, deployment, implementation, and performance evaluation of an IoT based SEMS, including SMs as well as IoT middleware module and its related benefits. The proposed SEMS operates online and offers real-time (RT) load profiles (LPs) to customers and suppliers remotely. The customers’ LPs allow suppliers to disseminate and regulate their incentives as well as incite the customers to alter their energy consumption. Furthermore, these LPs serves as an input for developing numerous DSM approaches. The proposed solution is installed and evaluated at 4 different locations of Stylo Pvt. Ltd. Pakistan, which can communicate commands and observe the efficiency of electricity supplied by the utility. Moreover, the RT impact of using separate SM for automated control of heating, ventilation and air conditioning (HVAC) system is shown in terms of power consumption. The RT case study presents the efficacy of the proposed IoT-based-SEMS.

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

Internet of Things for Agricultural Applications: The State of the Art

The advent of the Internet of Things (IoT) inspired various new and enhanced sets of applications in multiple domains including agriculture. The recent drive in the adoption of IoT technologies offers a major enhancement for the agricultural sectors in terms of efficiency and scalability. In this article, we investigate the specific issues and challenges associated with IoT, and review various IoT architectures, communication, middleware, and information processing technologies. We, then, discuss few IoT applications for agriculture-presenting various case studies to thoroughly analyze the solutions along with their design and implementation related parameters. Consequently, we provide a comprehensive review of the available simulation tools, data sets, and testbeds which provisions experimentation with IoT in agriculture. We enumerate open issues and challenges present in enabling IoT for agriculture. Finally, this article concludes while giving directions for future research.

Middleware for the Internet of Things: A survey on requirements, enabling technologies, and 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.

In.IoT—A New Middleware for Internet of Things

The evolution of Internet of Things (IoT) led to the construction of many IoT middleware, a software that plays a key role since it supports the communication among devices, users, and applications. Although various solutions and studies were proposed, they rarely address crucial privacy and security considerations, especially regarding the message queuing telemetry transport (MQTT) protocol. Moreover, in the majority of the solutions, integrating new devices is a time-consuming task performed manually that cannot be accomplished in a scenario with thousands, maybe millions of devices. In this sense, this article proposes a new IoT middleware, called In.IoT, a scalable, secure, and innovative middleware solution that addresses the middleware concerns identified in this article. In.IoT architectural recommendations and requirements are detailed and can be replicated by new and available solutions. It supports MQTT, CoAP, and HTTP as application-layer protocols. Its performance is evaluated in comparison with the most promising solutions available in the literature and the results obtained by the proposed solution are extremely promising. In.IoT is evaluated, demonstrated, validated, and it is ready and available for use.