Internet Of Things Paradigm Research Articles

On Blockchain-Based Cross-Service Communication and Resource Orchestration on Edge Clouds

With the advent of 5G verticals and the Internet of Things paradigm, Edge Computing has emerged as the most dominant service delivery architecture, placing augmented computing resources in the proximity of end users. The resource orchestration of edge clouds relies on the concept of network slicing, which provides logically isolated computing and network resources. However, though there is significant progress on the automation of the resource orchestration within a single cloud or edge cloud datacenter, the orchestration of multi-domain infrastructure or multi-administrative domain is still an open challenge. Towards exploiting the network service marketplace at its full capacity, while being aligned with ETSI Network Function Virtualization architecture, this article proposes a novel Blockchain-based service orchestrator that leverages the automation capabilities of smart contracts to establish cross-service communication between network slices of different tenants. In particular, we introduce a multi-tier architecture of a Blockchain-based network marketplace, and design the lifecycle of the cross-service orchestration. For the evaluation of the proposed approach, we set up cross-service communication in an edge cloud and we demonstrate that the orchestration overhead is less than other cross-service solutions.

Security and Privacy in IoT Smart Healthcare

Patient care is a key element of healthcare practices. The use of Healthcare IoT could enhance the lives of patients and the quality of services provided by medical practitioners, nurses, clinicians, pharmaceutical companies, and government in general. Wireless healthcare monitoring systems tremendously used in hospitals and other healthcare practices and witnessed as a revolution in the medical field. Nevertheless, security and privacy for interconnected things are mostly overlooked on the Internet of Things paradigm. In the context of healthcare and remote health monitoring, it is crucial a systematic approach for security and privacy measures must be used in device manufacturing, interconnecting things, communication, data handling and storage, and destruction of such devices and data. This article examines the current state of security and privacy of the Internet of Things in the healthcare system, the challenges encountered in implementing security frameworks and advocates security and privacy solutions.

Discovery of Resident Behavior Patterns Using Machine Learning Techniques and IoT Paradigm

In recent years, technological paradigms such as Internet of Things (IoT) and machine learning have become very important due to the benefit that their application represents in various areas of knowledge. It is interesting to note that implementing these two technologies promotes more and better automatic control systems that adjust to each user’s particular preferences in the home automation area. This work presents Smart Home Control, an intelligent platform that offers fully customized automatic control schemes for a home’s domotic devices by obtaining residents’ behavior patterns and applying machine learning to the records of state changes of each device connected to the platform. The platform uses machine learning algorithm C4.5 and the Weka API to identify the behavior patterns necessary to build home devices’ configuration rules. Besides, an experimental case study that validates the platform’s effectiveness is presented, where behavior patterns of smart homes residents were identified according to the IoT devices usage history. The discovery of behavior patterns is essential to improve the automatic configuration schemes of personalization according to the residents’ history of device use.

The utilization of algorithms for cloud internet of things application domains: a review

Cloud internet of things (IoT) is an emerging technology that is already impelling the daily activities of our lives. However, the enormous resources (data and physical features of things) generated from Cloud-enabled IoT sensing devices are lacking suitable managerial approaches. Existing research surveys on Cloud IoT mainly focused on its fundamentals, definitions and layered architecture as well as security challenges. Going by the current literature, none of the existing researches is yet to provide a detailed analysis on the approaches deployed to manage the heterogeneous and dynamic resource data generated by sensor devices in the cloud-enabled IoT paradigm. Hence, to bridge this gap, the existing algorithms designed to manage resource data on various CloudIoT application domains are investigated and analyzed. The emergence of CloudIoT, followed by previous related survey articles in this field, which motivated the current study is presented. Furthermore, the utilization of simulation environment, highlighting the programming languages and a brief description of the simulation packages adopted to design and evaluate the performance of the algorithms are examined. The utilization of diverse network communication protocols and gateways to aid resource dissemination in the cloud-enabled IoT network infrastructure are also discussed. The future work as discussed in previous researches, which pave the way for future research directions in this field is also presented, and ends with concluding remarks.

Application Domains, Evaluation Data Sets, and Research Challenges of IoT: A Systematic Review

We are at the brink of Internet of Things (IoT) era where smart devices and other wireless devices are redesigning our environment to make it more correlative, flexible, and communicative. IoT is now evolving to Internet of Everything, as it incorporates and builds a system that includes wireless networks, sensors, cloud servers, analytics, smart devices, and advanced technologies. The IoT systems are equipped with embedded devices having network connectivity and sensors to have a machine-to-machine interaction. The field of IoT is advancing to provide smart solutions to various applications, such as transportation, healthcare, farming, to name a few. Considering the diverse perspective of IoT systems/network, this article aims at discussing different applications of IoT across various research domains and to highlight the use of IoT in a variety of applications. We have systematically reviewed applications of IoT with the data sets to understand the characteristics of the data collected by the IoT devices. In this article, a comprehensive survey on the role of IoT in various application domains is presented for the years 2015-2019. This article lists different commercialized solutions developed in various IoT application domains. The aim to discuss commercialized IoT paradigms is to highlight research efforts performed in the field of IoT that have been transformed into real-life solutions. This article also discusses research challenges in the field of IoT to strengthen the research for developing future IoT applications.

Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area

The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and monitoring in real time. Various IoT schemes have successfully been applied to some areas such as smart health and smart agriculture. Since the agriculture areas are getting narrow, the development of IoT in agriculture should be prioritized to enhance crop production. This paper proposes the IoT scheme for long range communication based on Long Range (LoRa) modules applied to smart agriculture. The scheme utilizes the low power modules and long-distance communication for monitoring temperature, humidity, soil moisture, and pH soil. Our IoT design has successfully been applied to agriculture areas which have unstable network connections. The design is analyzed to obtain the maximum coverage using different spreading factors and bandwidths. We show that as the spreading factor increases to 12, the maximum coverage can be transmitted to 1000 m. However, the large coverage also comes with the disadvantages of the increased delays.

Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure. However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats. Adversaries carry out privacy-oriented attacks to gain access to the sensitive and confidential data of critical infrastructure for various self-centered, political and commercial gains. In the past, researchers have employed several privacy preservation approaches including cryptographic encryption and k-anonymity to secure IoT-enabled critical infrastructure. However, for various reasons, those proposed solutions are not well suited for modern IoT-enabled critical infrastructure. Therefore, Dwork’s differential privacy has emerged as the most viable privacy preservation strategy for IoT-enabled critical infrastructure. This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids (SGs), Intelligent Transport Systems (ITSs), healthcare and medical systems, and Industrial Internet of Things (IIoT). Finally, we discuss some promising future research directions in differential privacy for IoT-enabled critical infrastructure.

Metaheuristics-based routing optimisation, balanced workload distribution and security strategy in IoT environment

The advancement of wireless sensor networks (WSNs) made the society ever so slightly gain new degrees of freedom, intrinsically with respect to connectivity. The emergence of internet of things (IoT) together with the onset and development of evolutionary computing, address the various needs of the growing urbanisation with a significant allusion altogether. Applications built on cloud infrastructure are deployed for job segregation, rapid processing and utilisation but with limitation of proper computation in high density IoT environment. This paper strives on resolving some of the issues so accosted by the IoT paradigm, with the help of a new metaheuristics-based data routing hybrid algorithm based on directed artificial bat algorithm (DABA) and particle swarm optimisation (PSO) to optimise connection issues such as real time delay and network congestion. This approach also introduces the clustering concept together with fog computing to distribute the network stress and to optimise the usage of bandwidth using the dynamic graph partitioning algorithm. The paper further proposes advance metaheuristics constricted PSO (C-PSO) in the hybrid algorithm and effective proposal for security strategy to enhance the efficiency of the IoT environment.

IEEE Access Special Section Editorial: Innovation and Application of Internet of Things and Emerging Technologies in Smart Sensing

The Internet of Things (IoT) technology is not just about the connection among billions of devices but also about enabling a wide range of new capabilities, architectures, or service paradigms. The scale of the IoT is set to have a major economic, social, and environmental impact. However, the implementation of IoT faces various challenges, including sensing ability, energy consumption, power supply, data security, privacy, and data accountability. In order to mitigate these challenges, more and more IoT systems are embracing the new IoT paradigm, such as millimeter-wave (mmWave), sensor-less sensing, backscattering Wi-Fi/RFID, wireless/vision hybrid, blockchain, fat client, and so on. These modes shed light on the emerging technologies that could revolutionize the conventional IoT systems.

Metaheuristics-based routing optimisation, balanced workload distribution and security strategy in IoT environment

The advancement of wireless sensor networks (WSNs) made the society ever so slightly gain new degrees of freedom, intrinsically with respect to connectivity. The emergence of internet of things (IoT) together with the onset and development of evolutionary computing, address the various needs of the growing urbanisation with a significant allusion altogether. Applications built on cloud infrastructure are deployed for job segregation, rapid processing and utilisation but with limitation of proper computation in high density IoT environment. This paper strives on resolving some of the issues so accosted by the IoT paradigm, with the help of a new metaheuristics-based data routing hybrid algorithm based on directed artificial bat algorithm (DABA) and particle swarm optimisation (PSO) to optimise connection issues such as real time delay and network congestion. This approach also introduces the clustering concept together with fog computing to distribute the network stress and to optimise the usage of bandwidth using the dynamic graph partitioning algorithm. The paper further proposes advance metaheuristics constricted PSO (C-PSO) in the hybrid algorithm and effective proposal for security strategy to enhance the efficiency of the IoT environment.

A Compact FPGA‐Based Accelerator for Curve‐Based Cryptography in Wireless Sensor Networks

The main topic of this paper is low‐cost public key cryptography in wireless sensor nodes. Security in embedded systems, for example, in sensor nodes based on field programmable gate array (FPGA), demands low cost but still efficient solutions. Sensor nodes are key elements in the Internet of Things paradigm, and their security is a crucial requirement for critical applications in sectors such as military, health, and industry. To address these security requirements under the restrictions imposed by the available computing resources of sensor nodes, this paper presents a low‐area FPGA‐prototyped hardware accelerator for scalar multiplication, the most costly operation in elliptic curve cryptography (ECC). This cryptoengine is provided as an enabler of robust cryptography for security services in the IoT, such as confidentiality and authentication. The compact property in the proposed hardware design is achieved by implementing a novel digit‐by‐digit computing approach applied at the finite field and curve level algorithms, in addition to hardware reusing, the use of embedded memory blocks in modern FPGAs, and a simpler control logic. Our hardware design targets elliptic curves defined over binary fields generated by trinomials, uses fewer area resources than other FPGA approaches, and is faster than software counterparts. Our ECC hardware accelerator was validated under a hardware/software codesign of the Diffie‐Hellman key exchange protocol (ECDH) deployed in the IoT MicroZed FPGA board. For a scalar multiplication in the sect233 curve, our design requires 1170 FPGA slices and completes the computation in 128820 clock cycles (at 135.31 MHz), with an efficiency of 0.209 kbps/slice. In the codesign, the ECDH protocol is executed in 4.1 ms, 17 times faster than a MIRACL software implementation running on the embedded processor Cortex A9 in the MicroZed. The FPGA‐based accelerator for binary ECC presented in this work is the one with the least amount of hardware resources compared to other FPGA designs in the literature.

NB-IoT and Wi-Fi Technologies: An Integrated Approach to Enhance Portability of Smart Sensors

The Internet of Things paradigm has expanded the possibility of using sensors ubiquitously, particularly if connected to a cloud service for data sharing. There are several ways to connect sensors to the cloud: wearable or portable devices often lean on a smartphone that acts as a gateway, while other sensors, such as smart sensors for continuous monitoring (e.g. fall detectors) are connected through wireless networks covering a limited area (e.g. ZigBee or Wi-Fi). Their functionality can be improved using them in both outdoor and indoor environments without other devices. NB-IoT is a recently introduced wide-range protocol with a good compromise between low power, low deployment costs, payload length, and data rate. Traditionally, sensor nodes rely on only one type of radio: an innovative solution could be a sensor node exploiting a combination of different transmission technologies with the aim of achieving higher portability. In this paper, a hybrid solution based on NB-IoT/Wi-Fi is presented. The Wi-Fi connection is primarily selected due to its lower power consumption (compared to NB-IoT), while NB-IoT is activated only when a Wi-Fi network is not available. This study aims to evaluate the power consumption of the proposed solution with respect to single radio NB-IoT technology. Test boards have been implemented, and several data transmission tests have been carried out with both NB-IoT and Wi-Fi radios. Different received powers and payload lengths have been considered to analyze the impact on the energy profile of smart sensors. It has been demonstrated that using NB-IoT for both indoor and outdoor leads to an acceptable battery discharge time, with a strong dependence on the payload length. Under certain conditions, the proposed hybrid solution results in a battery duration up to two times higher than single-radio NB-IoT.

Distributed Intelligence at the Edge on IoT Networks

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.

A survey on agent learning architecture that adopts internet of things and wireless sensor networks

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

CR-IoTNet: Machine learning based joint spectrum sensing and allocation for cognitive radio enabled IoT cellular networks

In recent years, the Internet of Things (IoT) paradigm has gained much popularity due to its potential ability to integrate the physical world with the digital world. However, this digital revolution is driving an insatiable demand for wireless spectrum, leading the upcoming novel personal, social and industrial IoT applications competing for limited spectrum resources. One of the main challenges that may hinder the vision of IoT is the scarcity of radio spectrum. Therefore, in order to cope with the challenge of spectrum scarcity brought about by the unprecedented number of IoT devices, this paper proposes to integrate cognitive radio (CR) technology with the IoT paradigm (CR-IoTNet). The CR is realized as a promising solution to improve the spectrum utilization and alleviate the spectrum scarcity in wireless networks. In CR, spectrum sensing is widely recognized as a key technology that enables secondary users (SUs) to detect spectrum holes and have the opportunity to access unoccupied spectrum. The CR-IoTNet, which is composed of multiple primary user (PU) base-stations and SU devices as IoT smart objects, performs the joint spectrum sensing and optimal allocation of spectrum to the requesting SU-IoT devices in the network through an intelligent fusion center (IFC). Furthermore, we employ support vector machines (SVM) in CR-IoTNet, enabling it to learn and adapt to the changing network dynamics, and identify the PU spectrum usage based on the established multi-class (J×6)-D feature set. The performance of CR-IoTNet is evaluated across several key factors, where simulation results validate the efficacy of the proposed framework in achieving high reliable identification, classification and allocation of unoccupied frequency bands in PU spectrum, especially in key areas of low signal-to-noise ratio (SNR). In addition, the trained SVM classifier obtained 95.11% accuracy, with 92.67% true positive rate and 96.34% true negative rate in CR-IoTNet, reflecting the optimal allocation of PU nodes for spectrum access.

A Survey of Cybersecurity Certification for the Internet of Things

In recent years, cybersecurity certification is gaining momentum as the baseline to build a structured approach to mitigate cybersecurity risks in the Internet of Things (IoT). This initiative is driven by industry, governmental institutions, and research communities, which have the goal to make IoT more secure for the end-users. In this survey, we analyze the current cybersecurity certification schemes, as well as the potential challenges to make them applicable for the IoT ecosystem. We also examine current efforts related to risk assessment and testing processes, which are widely recognized as the processes to build a cybersecurity certification framework. Our work provides a multidisciplinary perspective of a possible IoT cybersecurity certification framework by integrating research and technical tools and processes with policies and governance structures, which are analyzed against a set of identified challenges. This survey is intended to give a comprehensive overview of cybersecurity certification to facilitate the definition of a framework that fits in emerging scenarios, such as the IoT paradigm.

Discovering location based services: A unified approach for heterogeneous indoor localization systems

The technological solutions and communication capabilities offered by the Internet of Things paradigm, in terms of raising availability of wearable devices, the ubiquitous internet connection, and the presence on the market of service-oriented solutions, have allowed a wide proposal of Location Based Services (LBS). In a close future, we foresee that companies and service providers will have developed reliable solutions to address indoor positioning, as basis for useful location based services. These solutions will be different from each other and they will adopt different hardware and processing techniques. This paper describes the proposal of a unified approach for Indoor Localization Systems that enables the cooperation between heterogeneous solutions and their functional modules. To this end, we designed an integrated architecture that, abstracting its main components, allows a seamless interaction among them. Finally, we present a working prototype of such architecture, which is based on the popular Telegram application for Android, as an integration demonstrator. The integration of the three main phases –namely the discovery phase, the User Agent self-configuration, and the indoor map retrieval/rendering– demonstrates the feasibility of the proposed integrated architecture.

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad-hoc</i> types of IoT networks, efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topology management</i> is a crucial requirement, and in particular in massive scale deployments. Traditionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">clustering</i> has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">objectives</i> for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.

REVIEW ON IOT (INTERNET OF THINGS) PARADIGM AND ITS APPLICATIONS

REVIEW ON IOT (INTERNET OF THINGS) PARADIGM AND ITS APPLICATIONS

Predicting the energy consumption in software defined wireless sensor networks: a probabilistic Markov model approach

The smart world is connecting all universe more than ever thought possible, benefiting from the significant advances of the Internet of Things (IoT) applications using wireless sensor networks (WSN) as the core technology. A challenging issue in the IoT paradigm is the heterogeneity in different parts of the network. The network developers need to use resources belonging to different platforms for their applications, and the software defined network (SDN) approach is a mainly considered solution. In this paper, a software defined wireless sensor network (SDWSN) with an energy predictor model (SDWSN-EPM) based on the Markov probabilistic model is proposed to reduce the energy consumption and the network latency. The energy consumption rate (ECR) of the sensor nodes is modeled using the Markov model and the states of the sensor nodes. The ECR is used by the SDN controller to predict the residual energy level of the nodes and consequently, the energy consumption of the network. The cumulative distribution functions (CDF) of the delay, power consumption, and the network lifetime in both SDWSN and SDWSN-EPM schemes are compared. The results confirm that the SDWSN-EPM model significantly improves the performance of the sensor networks.