Internet Of Things Deployments Research Articles

IoT, Enabling Technologies, and Sensor Node Deployment Pattern in WSN

In Internet of Things (IoT), various computing devices and mechanical and digital machines are interconnected. These devices have unique identifiers (UIDs) and transmit the information on the network with no human interaction. Sensors are the integrated part in IoT as these are used to collect the data. The IoT technologies have a lot of issues like addressing problems, scalability problems, security, and standardization issues that required to be solved. In this paper, the authors facilitate the reader to have necessary understanding of IoT, importance on protocols, technologies, application related issues, various types of sensors used in IoT, new generation of alternative sensors for IoT, and the issues which needs to resolved for the future. A depth overview of 5G IoT systems is also given. The five layers in 5G IoT systems and empowering technologies associated with it are discussed. A comparative analysis of QC-PC-MCSC for strip based deployment pattern and for random deployment is given. This paper provides a support to academician about the working of diverse protocols, relation between IoT and other emergent technologies together with big data and cloud, energy efficiency based on sensor node deployment pattern, etc.

Utilization of Internet of Things and Wireless Sensor Networks for Sustainable Smallholder Agriculture.

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science.

A Systematic Review of Internet of Things Adoption in Organizations: Taxonomy, Benefits, Challenges and Critical Factors

Despite the evident growth of the Internet of Things (IoT) applications, IoT deployments in organizations remain in their early stages. This paper aims to systematically review and analyze the existing literature on IoT adoption in organizations. The extant literature was identified using five electronic databases from 2015 to July 2021. Seventy-seven articles have met the eligibility criteria and were analyzed to answer the research questions. This study produced a coherent taxonomy that can serve as a framework for future research on IoT adoption in organizations. This paper presents an overview of the essential features of this emerging technology in terms of IoT adoption benefits and challenges in organizations. Existing theoretical models have been analyzed to identify the factors that influence IoT adoption and to understand the future requirements for widespread IoT adoption in organizations. Six critical factors affecting and playing a key role in IoT adoption in organizations were identified based on the critical review findings: technological, organizational, environmental, human, benefit, and value. Decision-makers and developers can prioritize these critical factors and progressively improve their development to enhance IoT adoption efficiency. This review also includes an in-depth analysis to bridge gaps and provide a comprehensive overview to further understand this research field.

Security of IoT in 5G Cellular Networks: A Review of Current Status, Challenges and Future Directions

The Internet of Things (IoT) refers to a global network that integrates real life physical objects with the virtual world through the Internet for making intelligent decisions. In a pervasive computing environment, thousands of smart devices, that are constrained in storage, battery backup and computational capability, are connected with each other. In such an environment, cellular networks that are evolving from 4G to 5G, are set to play a crucial role. Distinctive features like high bandwidth, wider coverage, easy connectivity, in-built billing mechanism, interface for M2M communication, etc., makes 5G cellular network a perfect candidate to be adopted as a backbone network for the future IoT. However, due to resource constrained nature of the IoT devices, researchers have anticipated several security and privacy issues in IoT deployments over 5G cellular network. Off late, several schemes and protocols have been proposed to handle these issues. This paper performs a comprehensive review of such schemes and protocols proposed in recent times. Different open security issues, challenges and future research direction are also summarized in this review paper.

NoD: A content retrieval scheme for intermittently-connected IoT networks

In recent years, Internet of Things (IoT) devices have been deployed even to the most isolated and hard-to-reach areas. Content retrieval from such communication-disruptive remote areas therefore becomes a challenging task, mainly due to the increased infrastructure cost. In this paper, we present a gateway-oriented scheme which facilitates content retrieval from remote IoT deployments, using mobile devices. We call our scheme NDN-over-DTN (NoD), as it combines the information-centric design of the Named Data Networking (NDN) architecture with the store-carry-and-forward approach of Delay/Disruption-Tolerant Networking (DTN). In this work, we evaluate the performance of NoD in a real IoT-content-retrieval scenario, in terms of Average Delay, Cache Hit, Interest Satisfaction and Overhead Ratio. We model the behavior of NoD analytically and introduce an improved NoD version which utilizes cross-layer interaction between NDN and DTN, called NoD-Discovery. We show, both analytically and experimentally, that NoD can exploit jointly the benefits of NDN and DTN and achieve significant performance gains in highly disruptive environments. Notably, our experiments demonstrated that NoD-Default can achieve up to a 48.77% decrease in Average Delay compared to host-centric DTN, while NoD-Discovery can improve performance further by decreasing the Overhead Ratio up to 70.34%, compared to the original NoD-Default implementation.

WoTemu: An emulation framework for edge computing architectures based on the Web of Things

The edge computing model is an approach to Internet of Things (IoT) architectures based on the redistribution of services and infrastructure from centralized clouds to locations closer to IoT devices. The Web of Things (WoT) is another important IoT trend, currently led by the W3C, which aims at solving the IoT interoperability problem by adopting proven technologies and patterns from the Web. The design and validation of IoT deployments based on these paradigms is a complex task that involves multiple services, heterogeneous hardware and diverse communication technologies. Testing such projects in real world conditions usually requires a significant investment of resources. There are simulation tools that can assist in this process with much lower barriers of entry, however, they involve the designer making modelling assumptions that are not always representative of the real systems. This work presents an emulation tool for IoT projects based on the edge computing model that is able to seamlessly scale horizontally by leveraging container orchestration (Docker swarm mode). Furthermore, the W3C WoT model is included as a first-class citizen, enabling the designer to model all actors in the system as Things. The tool can run the real production code with minimal modifications and provides meaningful insights into the behaviour of the proposed architecture. This knowledge serves to rapidly iterate the optimization process, simplifying design issues and the detection of bottlenecks before committing to a real deployment in the field. A real-world scenario is also emulated in order to demonstrate its capabilities and validate its contribution.

CIRCE: Architectural Patterns for Circular and Trustworthy By-Design IoT Orchestrations

The adoption of Internet of Things (IoT) devices, applications and services gradually transform our everyday lives. In parallel, the transition from linear to circular economic (CE) models provide an even more fertile ground for novel types of services, and the update and enrichment of legacy ones. To fully realize the potential of the interplay between IoT and CE, the design-time definition of IoT orchestrations with proven circularity properties, and the run-time management of these orchestrations based on said properties, is of paramount importance. Nevertheless, the circularity requirements and associated properties are not only difficult to achieve at the IoT orchestration design and deployment initialization phases, but also hard to prove and maintain at run-time. Motivated by this, this paper presents the CIRCE framework for circular and trustworthy by-design IoT orchestrations. The CIRCE approach leverages concepts from pattern-driven engineering, whereby patterns are used to encode proven dependencies between the Location, Condition, and Availability (LCA) properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them. These are augmented by patterns encoding trustworthiness-related properties, namely Connectivity, Security, Privacy, Dependability, and Interoperability (CSPDI). Thereby, these patterns are used to generate IoT orchestrations with proven LCA and CSPDI properties, as needed, at design time. At runtime, these properties are monitored in real-time, leveraging reasoning engines deployed across system layers, triggering adaptations to return the deployed orchestration to the desired LCA and CSPDI states, when required. Details are provided on the above novel combination of IoT, CE and pattern-based engineering, along with a proposed architecture and implementation approach. Furthermore, an assessment of a proof-of-concept implementation is provided, validating the feasibility of the proposed approach.

Scope and Impact of Internet of Things (IoT) and Artificial Intelligence (AI) in the Global Construction Industry

Construction Industry projects for buildings are typically unique designs that are rarely imitated in terms of their arrangement, pattern, and structure. Smart manufacturing systems benefit from network performance optimization and the ability to reconfigure projects. The Global Construction Industry is changing in 195 countries because of advancement in digital technology. The Global Construction Industry are currently revolutionizing with synchronized communications, ecommerce and production with AI and IoT. Construction Industry is also strategically vital on a provincial, a national, and an international scale. The construction sector faces numerous challenges and significantly restrict its ability to grow, such as budget overruns, concerns about health and safety, productivity issues, availability, optimum utilization of skills and abilities of labours. Along with resolving the complicated issues systems built on IoT and AI capabilities will open up new business prospects, cost reduction and revenue increase possibilities and as well as innovative business models. AI and IoT is impacting in positive way used and use for real-time tracking and communications of labours, equipment and things on the site. It can also enhancing the quality of design and planning, project management and scheduling and alert managers of potential safety concerns, construction faults, and output concerns. Artificial Intelligence (AI) and Internet of Things (IoT), two highly developed digital technologies having Vast Scope and Impact for making life easy, confortable, hygienic and safe. Lowering the risk of accidents, avoid of expensive mistakes, improved operations, intelligent tasks such as speech recognition, language understanding, language translation, self-learning, reasoning, problem solving, perception, user interface, jobsite image handling, knowledge representation, predictive ability to be carried out by AI and IoT assisted home automation with or without human intervention. Digital sensors, Smart machinery, Mobile platforms, Cloud-based apps and new software system applications empowers the IoT and AI Construction Industry and home automation capabilities in several folds. India has hug scope of growth in Global Construction Industry using AI and IoT. This paper provides a concise literature analysis of AI and IoT use in the Global Construction Industry. The deployment of IoT and AI emerging technologies has had a significant positive long term impact with new scope on the construction industry's profitability.

Overview of Present-Day IoT Data Processing Technologies

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution.

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition, when there is an inconsistent connection delay between the fog and the terminal device, the deployment of such applications becomes more complicated. In this article, we propose an algorithm that can effectively place application modules on network nodes while considering connection delay, processing power, and sensing data volume. Compared with traditional cloud computing deployment, we conducted simulations in iFogSim to confirm the effectiveness of the algorithm. The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption. Therein, latency and execution time is insensitive to the number of sensors.

Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia

In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant impact on business operations and supply chain management (SCM). However, research into the influence of IoT deployment on supply chains and organizational performance is relatively sparse. Therefore, this study is to determine the relationship between benefits and challenges of IoT adoption and organizational performance. Furthermore, this study looks into the mediating role of supply chain performance in the relationship between IoT adoption benefits and challenges and organizational performance. The population of this study is comprised of 3019 manufacturing companies in Malaysia, while the minimum sample size needed is 43 manufacturing companies.1160 complete set of survey questionnaire were distributed through email and 63 responses received representing five per cent of response rate. Partial Least Square Structural Equation Modelling (PLS-SEM) is used to assess all of the study's hypotheses. The results of this paper support six out of the seven hypotheses tested. In conclusion, the manufacturing industry in Malaysia needs to be exposed more to the benefits of IoT rather than keep discussing its challenges. This study can be a guideline to the manufacturing companies in decision making for IoT adoption. The limitations and recommendation for future study is highlighted.

A Novel Predictor for Exploring PM2.5 Spatiotemporal Propagation by Using Convolutional Recursive Neural Networks

<p>The spread of PM2.5 pollutants that endanger health is difficult to predict because it involves many atmospheric variables. These micro particles could spread rapidly from their source to residential areas, increasing the risk of respiratory disease if exposed for long periods. However, the existing prediction systems do not take into account the geographical correlation among neighboring nodes spatially and temporally resulting in loss of important information, lack of PM2.5 propagation resolution, and lower forecasting accuracy. In this paper, a novel scheme is proposed to generate propagation heat maps of PM2.5 prediction by using spatiotemporal datasets. In this scheme, the deep learning model is implemented to extract spatiotemporal features on these datasets. This research was conducted by using the dataset of air quality monitoring systems in Taiwan. Moreover, the robust model based on the convolutional recursive neural network is presented to generate the propagation maps of PM2.5 concentration. This study develops an intelligence-based predictor by using Convolutional Recursive Neural Network (CRNN) model for predicting the PM2.5 propagation with uncertain spread and density. It is also one of key technologies of software and hardware co-design for massive Internet of Things (IoT) applications. Finally, the proposed model the proposed model provides accurate predictive results over time by taking into account the spatiotemporal relationship among sensory nodes in order to give a prediction solution for the massive IoT deployment based on green communication.</p> <p> </p>

Lessons learned from an IoT deployment for condition monitoring at the Port of Felixstowe

The ports sector is critical to global trade. While digitalisation of infrastructure asset management in other sectors such as manufacturing, healthcare, water supply, railway and road is rapidly growing with the possibilities of the Internet of Things (IoT) solutions, the maritime industry lags significantly behind. IoT solutions and the near real-time data they produce provide new impetus to improve fault diagnosis of assets and prevent disruptions caused due to asset breakdown. Such solutions also require reliable communication systems to support low latency and high bandwidth. To this end, we are building an IoT-based asset management solution at the Port of Felixstowe, the UK's largest container port, using 5G technology. This paper presents the steps taken, challenges faced and the lessons learned with sourcing, installation, calibration and communication of sensors in this deployment.

Relay Placement Algorithms for IoT Connectivity and Coverage in an Outdoor Heterogeneous Propagation Environment

A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations that maximise coverage. In this paper, we propose an automated network deployment framework using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.

Decentralized Access Control using Blockchain Technology for Application in Smart Farming

The application of the Internet of Things (IoT) plays a crucial role in the fourth industrial revolution. The sophistication of technology due to the integration of heterogenous smart devices open a new threat from various aspects. Access control is the first line of defence to ensure that IoT resources are secure by preventing illegitimate users from gaining access to these resources. However, access control mechanisms face the limitation of technology in large scale IoT deployments since they are based on a centralized architecture. Significant research concerning decentralized access control solutions for securing IoT resources using combined techniques, such as blockchain, have caught much research attention in recent years. Nevertheless, research for decentralized access control for application in smart farming domain remain as a gap. Thus, this study presented a structured literature review on 81 articles related to the field of access control in IoT and blockchain technology to understand the challenges of centralized access control in securing IoT resources. This study serves as a foundation for decentralized access control using blockchain technology and its application to ensure the IoT actuators and sensors security with the aim to be applied in smart farming. This paper was deliberated based on systematic literature review that was searched from four different database platforms between 2018 and 2021. This study mostly addresses the relevant techniques/approaches including blockchain technology, access control model, key management mechanism and the combination of all three methods. The possible impacts, gap, procedures and evaluation of the decentralized access control are highlighted along with major trends and challenges.

Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections

The Internet of Things (IoT) is a collection of inexpensive, semi-autonomous, Internet-connected devices that sense and interact within the physical world. IoT security is of paramount concern, because most IoT devices use weak or no encryption at all. This concern is exacerbated by the fact that the number of IoT deployments continues grow, IoT devices are being integrated into key infrastructures, and their weak or lack of encryption is being exploited. Specific Emitter Identification (SEI) is being investigated as an effective, cost-saving IoT security approach, because it is a passive technique that exploits inherent, distinct features that are unintentionally imparted to the waveform during its formation and transmission by the IoT device’s Radio Frequency (RF) front-end. Despite the amount of research conducted, SEI still faces roadblocks that hinder its integration within operational networks. Our work focuses on the lack of feature permanence across time and environments, which is designated herein as the “multi-day” problem. We present results and analysis for six distinct experiments focused on improving multi-day SEI performance through the use of multiple waveform representations, deeper Convolutional Neural Networks (CNNs), increasing numbers of waveforms, channel model impacts, and two channel mitigation techniques. Our work shows improved multi-day SEI performance using the waveform’s frequency-domain representation and a CNN comprised of four convolutional layers. However, the traditional channel model and both channel mitigation techniques fail to sufficiently mitigate or remove real-world channel impacts, which suggests that the channel may not be the dominant effect hindering multi-day SEI performance.

A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology

With the dramatically increasing deployment of IoT (Internet-of-Things) and communication, data has always been a major priority to achieve intelligent healthcare in a smart city. For the modern environment, valuable assets are user IoT data. The privacy policy is even the biggest necessity to secure user’s data in a deep-rooted fundamental infrastructure of network and advanced applications, including smart healthcare. Federated learning acts as a special machine learning technique for privacy-preserving and offers to contextualize data in a smart city. This article proposes Blockchain and Federated Learning-enabled Secure Architecture for Privacy-Preserving in Smart Healthcare, where Blockchain-based IoT cloud platforms are used for security and privacy. Federated Learning technology is adopted for scalable machine learning applications like healthcare. Furthermore, users can obtain a well-trained machine learning model without sending personal data to the cloud. Moreover, it also discussed the applications of federated learning for a distributed secure environment in a smart city.

Internet of Things in Space: A Review of Opportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living.

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning

One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.

Ethical framework for IoT deployment in SMEs: individual perspective

PurposeThis study aims to investigate the ethical issues related to the internet of Things (IoT) deployment in small- and medium-sized enterprises (SMEs) from an individual employee's perspective. To provide researchers and practitioners with concrete tools for examining these matters, an ethical framework dedicated to IoT is introduced.Design/methodology/approachFirst, the applicability of Mason's original privacy, accuracy, property and accessibility (PAPA) framework is studied in the IoT context. Second, issue category additions are proposed based on the identified coverage limitations of PAPA.FindingsWhile the original PAPA framework can be utilised as a generic ethical evaluation tool, it lacks coverage of several IoT-specific issue areas. To thoroughly address the ethical risks associated with IoT, two additional categories are introduced.Research limitations/implicationsThe new framework requires further validation to ensure its applicability and to identify potential modification requirements in continuously evolving IoT ecosystems.Practical implicationsConsidering the lack of ethical IoT frameworks, this study provides organisations with a practical framework for analysing the ethical issues in IoT deployment.Social implicationsEthical standards for IoT have not been sufficiently addressed in the current literature and frameworks, making the ethical considerations dependent on subjective stances. Thus, there is an acute demand for a practical framework that outlines the general ethical standards, helping its users to thoroughly address the potential ethical issues.Originality/valueWhile the use of IoT keeps growing in SMEs, there is an apparent lack of ethical guidelines. This study contributes to the gap by introducing a preliminary framework for both practical use and further theoretical development.