Internet Of Things Domain Research Articles

Analytics for the Internet of Things

The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation, and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective, and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches, and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.

A survey on application of machine learning for Internet of Things

Internet of Things (IoT) has become an important network paradigm and there are lots of smart devices connected by IoT. IoT systems are producing massive data and thus more and more IoT applications and services are emerging. Machine learning, as an another important area, has obtained a great success in several research fields such as computer vision, computer graphics, natural language processing, speech recognition, decision-making, and intelligent control. It has also been introduced in networking research. Many researches study how to utilize machine learning to solve networking problems, including routing, traffic engineering, resource allocation, and security. Recently, there has been a rising trend of employing machine learning to improve IoT applications and provide IoT services such as traffic engineering, network management, security, Internet traffic classification, and quality of service optimization. This survey paper focuses on providing an overview of the application of machine learning in the domain of IoT. We provide a comprehensive survey highlighting the recent progresses in machine learning techniques for IoT and describe various IoT applications. The application of machine learning for IoT enables users to obtain deep analytics and develop efficient intelligent IoT applications. This paper is different from the previously published survey papers in terms of focus, scope, and breadth; specifically, we have written this paper to emphasize the application of machine learning for IoT and the coverage of most recent advances. This paper has made an attempt to cover the major applications of machine learning for IoT and the relevant techniques, including traffic profiling, IoT device identification, security, edge computing infrastructure, network management and typical IoT applications. We also make a discussion on research challenges and open issues.

Proxy Cache Maintenance Using Multicasting in CoAP IoT Domains

In this paper, we consider Internet of Things (IoT) domain running multicasting constrained application protocol (CoAP) over IEEE 802.15.4 network ended by CoAP proxy/cache. We examine the features of CoAP multicasting in order to ensure freshness of data in the cache as a function of the leisure parameter which allows devices to reply in arbitrary (random) time periods after receiving multicast GET request. We also investigate communication delay in the IoT domain and daily energy consumption of devices under several leisure schemes which may be implemented at the application level or at the medium access control layer. The impact of the leisure parameter appears to be critical for congestion avoidance. We show that a combination of proactive and reactive cache update with appropriate multicast leisure scheme can achieve low probability of outdated data while limiting the energy expenditure of nodes to a satisfactory value. Furthermore, best performance with respect to delay is obtained when the leisure period is integrated in the CSMA/CA backoff process.

Big Data for Internet of Things: A Survey

With the rapid development of the Internet of Things (IoT), Big Data technologies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is extensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data approaches in each IoT domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT.In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.

QDaS: Quality driven data summarisation for effective storage management in Internet of Things

The proliferation of Internet of Things (IoT) has led to the emergence of enabling many interesting applications within the realm of several domains including smart cities. However, the accumulation of data from smart IoT devices poses significant challenges for data storage while there are needs to deliver relevant and high quality services to consumers. In this paper, we propose QDaS, a novel domain agnostic framework as a solution for effective data storage and management of IoT applications. The framework incorporates a novel data summarisation mechanism that uses an innovative data quality estimation technique. This proposed data quality estimation technique computes the quality of data (based on their utility) without requiring any feedback from users of this IoT data or domain awareness of the data. We evaluate the effectiveness of the proposed QDaS framework using real world datasets.

Architecture for IoT Domain With CoAP Observe Feature

The constrained application protocol (CoAP) observe feature has the potential to achieve fast communication and low energy consumption. We have analyzed a CoAP proxy which stores observed data from multiple Internet of Things (IoT) nodes that belong to single IoT domain. To maintain the freshness of cached records under random data arrivals, we have implemented the evaluation of Max-Age parameter at the proxy. We have also implemented congestion control at both IoT nodes and proxy, and show that it interacts with data freshness estimation at the proxy. Our results demonstrate the capacity limits of a single IoT domain against cluster size, interobservation time, proxy re-registration rate, ratio of confirmable/nonconfirmable observations, and presence of congestion control.

An efficient in‐network caching decision algorithm for Internet of things

SummaryRecently, content‐centric networking (CCN) has become one of the important technologies for enabling the future networks. Along with its recognized potentialities as a content retrieval and dissemination solution, CCN has been also recently considered as a promising architecture for the Internet of things (IoT), because of 2 main features such as named‐based routing and in‐network caching. However, IoT is characterized by challenging features: small storage capacity of resource‐constrained devices due to cost and limitation of energy and especially transient data that impose stringent requirements on the information freshness. As a consequence, the intrinsic caching mechanisms existing in CCN approach do not well suit IoT domains; hence, providing a specific caching policy at intermediate nodes is a very challenging task. This paper proposes an effective multiattribute in‐network caching decision algorithm that performs a caching strategy in CCN‐IoT network by considering a set of crucial attributes including the content store size, hop count, particularly key temporal properties like data freshness, and the node energy level. Simulation results proved that our proposed approach outperforms 2 cache management schemes (probabilistic least recently used and AlwaysCache–first in first out in terms of improving total hit rate, reducing data retrieval delay, and enhancing content reusability in IoT environment).

A visual programming framework for distributed Internet of Things centric complex event processing

Complex Event Processing (CEP) is a promising approach for real-time processing of big-data streams originating from Internet of Things (IoT) devices. Even though scalability and flexibility are key issues for IoT applications, current studies are mostly based on centralized solutions and restrictive query languages. Moreover, development, deployment and operation of big-data applications require significant amount of technical expertise. Hence, a framework that provides a higher abstraction level programming model and a streamlined execution environment is essential to facilitate widespread user adoption. In this paper, we propose a data-flow based visual programming model that enables development of big-data applications in IoT domain even by users who have limited or no experience in big-data technologies. A scalable CEP engine is also devised to execute the applications developed using this language. A prototype is implemented by using the state-of-the-art big-data and real-time stream processing technologies to evaluate the applicability and usability of the framework.

Adding Digital Forensic Readiness as a Security Component to the IoT Domain

The unique identities of remote sensing, monitoring, self-actuating, self–adapting and self-configuring “things” in Internet of Things (IoT) has come out as fundamental building blocks for the development of “smart environments”. This experience has begun to be felt across different IoT-based domains like healthcare, surveillance, energy systems, home appliances, industrial machines, smart grids and smart cities. These developments have, however, brought about a more complex and heterogeneous environment which is slowly becoming a home to cyber attackers. Digital Forensic Readiness (DFR) though can be employed as a mechanism for maximizing the potential use of digital evidence while minimizing the cost of conducting a digital forensic investigation process in IoT environments in case of an incidence. The problem addressed in this paper, therefore, is that at the time of writing this paper, there still exist no IoT architectures that have a DFR capability that is able to attain incident preparedness across IoT environments as a mechanism of preparing for post-event response process. It is on this premise, that the authors are proposing an architecture for incorporating DFR to IoT domain for proper planning and preparing in the case of security incidents. It is paramount to note that the DFR mechanism in IoT discussed in this paper complies with ISO/IEC 27043: 2015, 27030:2012 and 27017: 2015 international standards. It is the authors’ opinion that the architecture is holistic and very significant in IoT forensics.

Edge-Centric Distributed Discovery and Access in the Internet of Things

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things (IoT) will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this paper, we present an edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the Constrained Application Protocol (CoAP) to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a peer-to-peer overlay implemented by a distributed hash table, which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Network Protocols, Schemes, and Mechanisms for Internet of Things (IoT): Features, Open Challenges, and Trends

Internet of Things (IoT) constitutes the next step in the field of technology, bringing enormous changes in industry, medicine, environmental care, and urban development. Various challenges are to be met in forming this vision, such as technology interoperability issues, security and data confidentiality requirements, and, last but not least, the development of energy efficient management systems. In this paper, we explore existing networking communication technologies for the IoT, with emphasis on encapsulation and routing protocols. The relation between the IoT network protocols and the emerging IoT applications is also examined. A thorough layer‐based protocol taxonomy is provided, while how the network protocols fit and operate for addressing the recent IoT requirements and applications is also illustrated. What is the most special feature of this paper, compared to other survey and tutorial works, is the thorough presentation of the inner schemes and mechanisms of the network protocols subject to IPv6. Compatibility, interoperability, and configuration issues of the existing and the emerging protocols and schemes are discussed based on the recent advanced of IPv6. Moreover, open networking challenges such as security, scalability, mobility, and energy management are presented in relation to their corresponding features. Lastly, the trends of the networking mechanisms in the IoT domain are discussed in detail, highlighting future challenges.

An Analysis of IoT Congestion Control Policies

The Internet of Things (IoT) is common platform provide connectivity between various type of devices are having communication capability. Main objective of IoT is to deploy effective and high-quality smart services which offer different type services. Therefore, IoT need an advanced protocol stack which provides inter-communications between different types of devices have differet service requirements. IoT uses TCP and UDP as transport layer protocols to achieve the quality of service (QoS) required by different IoT devices. The XMPP (Extensible Messaging and Presence Protocol), MQTT (MQ Telemetry Transport) and RESTful HTTP are IoT application protocols which use TCP as a transport layer protocol. The transport layer protocol offers service point addressing, flow control, congestion control and segmentation, reassembling. As a number of different types of device increases over the Internet, network congestion also increases. In current internet scenario, TCP is responsible for congestion control over the Internet. However, current available TCP versions are not enough capable to handle different type of devices to fulfil the need of IoT. Therefore, this paper provides a survey of various congestion control algorithms used at transport layer. This paper includes available congestion control algorithms, their advantages, disadvantages and existing problems with TCP in IoT domains.

A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems

The Internet-of-Things (IoT) is rapidly becoming ubiquitous. However the heterogeneous nature of devices and protocols in use, the sensitivity of the data contained within, as well as the legal and privacy issues, make security for the IoT a growing research priority and industry concern. With many security practices being unsuitable due to their resource intensive nature, it is deemed important to include second line defences into IoT networks. These systems will also need to be assessed for their efficacy in a variety of different network types and protocols. To shed light on these issues, this paper is concerned with advancements in intrusion detection practices in IoT. It provides a comprehensive review of current intrusion detection systems (IDSs) for IoT technologies, focusing on architecture types. A proposal for future directions in IoT based IDS are then presented and evaluated. We show how traditional practices are unsuitable due to their inherent features providing poor coverage of the IoT domain. In order to develop a secure, robust and optimized solution for these networks, the current research for intrusion detection in IoT will need to move in a different direction. An example of which is proposed in order to illustrate how malicious nodes might be passively detected.

Clustering Internet of Things: A Review

Internet of Things is a paradigm shift in networking that seeks to connect virtually all things on the planet. Given the constrained nature of smart devices, energy efficient routing would play a key role in successful deployment of such networks. Clustering algorithms organize nodes of a network into groups or clusters and a specific designated node, cluster head is responsible for its cluster. Clustering algorithms have been particularly suggested in the context of Wireless Sensor Networks (WSN) but their application may also address similar challenges in Internet of Things (IoT). Clustering would facilitate energy efficient routing and topology management by delegating large chunk of communication overhead to cluster head. This paper presents a review of various clustering algorithms, analyses routing characteristics of various IoT domains and suggests appropriate clustering algorithms for each domain.

Ensemble based spam detection in social IoT using probabilistic data structures

A social approach can be used for the Internet of Things (IoT) to connect large number of objects in social networks like Twitter, Facebook, Instagram, etc. Social networks within the IoT domain have simplified the task of dynamic discovery of services and information. Detecting spam in social media, especially when massive data flows continuously and large number of attributes are associated with it, is a daunting task which requires lot of technical insight. This paper proposes a semi-supervised technique for spam detection in Twitter by employing ensemble based framework comprising of four classifiers. The framework is based on usage of Probabilistic Data Structures (PDS) like Quotient Filter (QF) to query the URL database, spam users, spam words databases and Locality Sensitive Hashing (LSH) for similarity search, as classifiers in various stages which provide fast results with less computational effort. Performance of the framework has been evaluated by comparative analysis of PDS with the similar data structures and through the standard evaluation parameters which include precision, recall and F-score.

IoT Middleware Architecture based on Ontologies to Model Logistic Process

In recent years, the Internet of Things (IoT) become a promising topic of technical social and economic significance, especially with the high number of developed sensors and technologies. Logistic applications are a perfect domain of IoT as it adds new functionalities in identification, traceability, storage and real-time tracking of good in the supply chain. Handling the huge quantities of heterogeneous IoT components and logistic items is an important challenge. We present in this paper a new Internet of Things middleware architecture for logistic transport applications. This architecture focuses mainly on a semantic model that uses ontologies for sensor data representation by describing the main entities involved in the logistic scenario.

Provenance aware run‐time verification of things for self‐healing Internet of Things applications

SummaryWe propose a run‐time verification mechanism of things for self‐healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processing technique by applying rule‐based pattern detection on the events generated real time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, and bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed mechanism, we developed prototype applications using different event processing platforms. We test the prototype implementations for performance and scalability under increasing message rates. The results are promising because the processing overhead of the proposed verification mechanism is negligible.

Internet of Things

This article describes how the Internet of Things (IoT) enables interaction and integration of real world things or objects—such as cars, electronic devices, homes, etc.—using various inter-operable communication protocols. This leads to a digital society of billions of devices that can sense, process, share, track, shop, chat and work in cooperative manner. There exist many challenges in the realization of IoT based distributed systems. This article intends to introduce various aspects of IoT such as views/visions, Architecture and protocols of IoT, communication technology, general requirements and various application domains. This article intends to disseminate the state-of-the-art multi-fold information on IoT to researchers, academicians, and student community.

Role of Internet of Things in Smart Passenger Cars

The rapid development of the Internet of Things (IoT) and Smart Things provides an opportunity for the informationization for the automotive industry. Smart Things is another paradigm shift in IT world. Smart Things are the things that are having embedding smartness or intelligence, identification, automation, monitoring and controlling calibre. Smart Things are assisting human life a lot, nowadays without their applications life is becoming cumbersome. The introduction of the concept of the Internet of things in the automotive industry will carry on the unified tracking, monitoring and managing and processing the entire automobile car operation, and has become a new trend. This paper presents the role of Internet of things and the new technologies in making the passenger car smarter. This paper exhibits systematically on Internet, Things, and then explores on Internet of Things and finally Smart Things from researchers’, and corporate perspective. Moreover, this article focuses on the state of Smart Things and its applications. This in turn would help the new researchers, who want to do research in this IoT domain