Internet Of Things Objects Research Articles

A Genetic Algorithm-Based Approach for Fluctuating QoS Aware Selection of IoT Services

In recent years, the Internet of Things (IoT) has evolved at an exceptional speed, which enables to interconnect a very large number of heterogeneous, distributed, and mobile devices. This number will exceed 70 billion by 2025 according to Statista. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^a</xref> with this huge amount of connected objects, the fulfillment of complex IoT applications, which usually requires a combination of several IoT objects, remains a real challenge. Besides, several requirements of Quality of Service (QoS) must be fulfilled, which makes the problem of selecting the appropriate IoT services NP-hard. In the literature, two main techniques for QoS-driven service selection are proposed: global selection characterized by a poor performance in dynamic and distributed huge environments and local selection which considers pre-defined local QoS constraints. Mainly, the existing works consider static QoS. However, in real life scenarios, QoS of IoT services can be fluctuating. To enhance the reliability of IoT applications, it is of paramount importance to consider the fluctuation dimension. In this context, we propose a QoS fluctuation-aware selection approach of IoT services. To do so, we propose a near-to optimal distributed approach that relies on decomposing the global QoS into distinct local constraints that serve as upper/lower bounds for selection while enhancing the reliability of the resulting composition by considering the QoS fluctuation of the candidate IoT services. The approach, we propose is based on a multi-objective evolutionary algorithm (MOEA) to solve the global QoS decomposition problem. Then a local selection using the obtained local constraints is performed in a parallel and distributed way. The performance of the proposed approach is evaluated and validated via experiment series. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>^a</label> <uri>https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/</uri> </fn>

A bibliometric method for analyzing IoT hacking solutions in network services

With the increasing number of connected objects in the world, security is essential for various Internet of Things (IoT) objects to protect sensitive data and infrastructure. As these objects have very limited resources, traditional security mechanisms are not suitable for IoT objects. The issue of security has become crucial for IoT to mitigate the security risks for organizations as well as for end users. This paper presents a bibliometric study to get structural overview of the work done using the keywords solution AND hacking AND IoT AND network AND services. We use Dimensions database as a data source, VOSviewer and bibliometric methods to visually analyze 10751 papers published in the research area of IoT hacking in network services from 2012 through 2022. We provide co-author maps, keyword co-citations, citations, bibliographic links, or co-citations based on the bibliographic data and keyword analysis.

Engineering Approaches for Programming Agent-Based IoT Objects Using the Resource Management Architecture.

The Internet of Things (IoT) allows the sharing of information among devices in a network. Hardware evolutions have enabled the employment of cognitive agents on top of such devices, which could help to adopt pro-active and autonomous IoT systems. Agents are autonomous entities from Artificial Intelligence capable of sensing (perceiving) the environment where they are situated. Then, with these captured perceptions, they can reason and act pro-actively. However, some agent approaches are created for a specific domain or application when dealing with embedded systems and hardware interfacing. In addition, the agent architecture can compromise the system’s performance because of the number of perceptions that agents can access. This paper presents three engineering approaches for creating IoT Objects using Embedded Multi-agent systems (MAS)—as cognitive systems at the edge of an IoT network—connecting, acting, and sharing information with a re-engineered IoT architecture based on the Sensor as a Service model. These engineering approaches use Belief-Desire-Intention (BDI) agents and the JaCaMo framework. In addition, it is expected to diversify the designers’ choice in applying embedded MAS in IoT systems. We also present a case study to validate the whole re-engineered architecture and the approaches. Moreover, some performance tests and comparisons are also presented. The study case shows that each approach is more or less suitable depending on the domain tackled. The performance tests show that the re-engineered IoT architecture is scalable and that there are some trade-offs in adopting one or another approach. The contributions of this paper are an architecture for sharing resources in an IoT network, the use of embedded MAS on top IoT Objects, and three engineering approaches considering agent and artifacts dimensions.

Flexible, solution-processed, indium oxide (In2O3) thin film transistors (TFT) and circuits for internet-of-things (IoT)

Over the last decade, novel approaches to explore low voltage flexible devices and low power flexible circuits are being widely researched by the scientific community. To realize the true potential of energy thrifty Internet-of-Things (IoT) objects, low power circuits and hence their low-voltage operating devices are a paramount prerequisite, especially when their power is constrained by autonomous energy scavenging. At present, through advanced manufacturing processes, silicon-based semiconductor devices are powering the modern electronics industry. However, processing temperatures are inhibiting them from flexible and printed electronics, as well as being too costly for scalability to the trillions of IoT objects anticipated. Therefore, development of solution-processed metal oxide semiconductors creates huge opportunities for IoT and wearables. Here, flexible solution-processed indium oxide (In2O3) thin film transistors (TFT) and inverter circuits with low operating voltage are reported. The operating voltage of the TFTs is ≤ 3 V with threshold voltage (Vth) 0.82 V, on/off ratio 105 and extracted mobility (μ) in saturation regime is 14.5 cm2/V·s. The gain of the inverter at VDD 1, 2 and 3 V was determined to be 10, 22 and 32 respectively. Furthermore, measured transconductance (gm) and subthreshold swing (S) are found to be 140 μS and 0.22 V/dec, respectively.

The role of low‐power wide‐area network technologies in Internet of Things: A systematic and comprehensive review

SummaryWith the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB‐IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery‐powered IoT objects that enable long‐distance communication in low‐power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.

IOT-5G-Exploring next-generation media consumption in the area of Journalism

Abstract: The most common buzzwords in the world is “The Internet of things” (IoT) and IOT describes the network of physical objects, so known as, "things" those are rooted with sensors in the devices, application software, technologies that is used for the resolution of connecting one end to another end and exchanging information with other devices and systems over the Internet. The IoT 5G technologies can also be used in journalism and the primary focus is to increase M2M interaction of mass communication devices. One way it is “ubiquitous computing” can occur using any device, in any location, and in any format. The Internet of Things (IoT) is all about small cost sensors grabbing data to communicate with one device to another device using cloud solutions. Coming to the 5th generation mobile network. We have already started using 4G networks and as we know that, start with 1G, 2G, 3G, and 4G networks. The 5th generation network is going to enable an upcoming new network that will associate virtually every person globally connected and everything organized including IoT devices, objects and machines. Central communication and Journalism is the activity of gathering right information, evaluating, generating, and presenting broadcast information. It is high time to start using IOT Technology using 5th generation high-speed network connectivity devices to communicate or data transfer in the area of journalism. Keywords: IOT – Internet Of Things, 5G- Fifth Generation in data network, JMC – Journalism and Mass Communications, M2M – Machine to Machine, Cloud , Artificial intelligence and Machine Learning.

A Service-Oriented Approach for Sensing in the Internet of Things: Intelligent Transportation Systems and Privacy Use Cases

This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT.

Internet of things platform for energy management in multi‐microgrid system to enhance power quality: ARBFNOCS technique

AbstractThis manuscript proposes an Internet of Things (IoT) platform for energy management (EM) in multi‐microgrid (MMG) system to enhance the power quality with hybrid method. The proposed method is the consolidation of opposition based crow search optimizer (OCSO) and radial basis functional neural network (RBFNN), hence it called RBFNOCS technique. The main aim of this manuscript is to optimally managing the power and resources of distribution system (DS) by constantly track the data from IoT‐based communication framework. In the proposed work, every devices of home is interfaced with data acquisition module (DAM) that is IoT object along unique IP address resultant in large mesh wireless network. Here, the IoT‐based communication framework is used for facilitating the development of a demand response (DR) energy management system (EMS) for distribution system. The transmitted data is processed by RBFNOCS technique. By utilizing the RBFNOCS method, the active with reactive power processing for optimal capacity unbalance compensation smart VSIs share the obtainable neutral current (NC). Likewise, the DS IoT framework enhances these networks flexibility and gives feasible use of obtainable resources. Moreover, the RBFNOCS method is responsible for satisfying the total supply with energy demand. The proposed model is activated in MATLAB/Simulink site and the performance is compared with existing models, namely improved artificial bee colony, squirrel search algorithm and gravitational search algorithm based artificial neural network (SOGSNN), GOAPSNN, fruit fly optimization, and FORDF technique. When compared with the existing methods, the efficiency of the RBFNOCS method is 93.4501%.

A case study of intelligent manufacturing for key components of vehicles

Currently, most research on intelligent manufacturing stays at the theoretical stage, which can enhance the understanding of smart factory concepts, but it is still difficult to establish a clear strategy for the industry. In this research, cyber-physical system (CPS) is applied to connect the manufacturing equipment of the production system, so as to develop the internet of things (IoT) object layer. Furthermore, a smart factory framework consisting of IoT object layer, CPS network layer, and CPS service layer is developed, connecting the smart shop-floor and the cloud service platform through the industrial network so as to implement the integration of physical manufacturing and virtual manufacturing. This framework is applied in the production of vehicle components, and the results show that the application of this framework in an actual automobile chassis smart factory can greatly improve production efficiency and energy efficiency, reduce enterprise costs, and shorten product development cycles, accelerating the process of enterprise transformation to informatization, digitalization, and intelligence. Since this framework for intelligent manufacturing has been verified under actual conditions, it can be taken as a reference for further development of the framework for intelligent manufacturing.

A range‐free localization algorithm for IoT networks

Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost-efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range-free approaches, distance vector-Hop (DV-Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far-off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV-Hop. In the most recent approach, meta-heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV-Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p-value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty-eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.

Secure and Efficient Key Exchange Mechanism for Heterogeneous Connected Objects

The fast evolution in microelectronics and the emergence of wireless communication technologies, have allowed the appearance of the promising field of Internet of Things (IoT). The latter is more and more present in the human life, that is why it becomes essential to secure the communications done with the connected objects. Almost all communicating systems attach great importance to security, consequently, on the cryptographic key management. The existing key management schemes for conventional networks are relatively resource-intensive, that is why they are not adequate for resource-constrained networks like IoT, especially since the nodes’ capabilities are heterogeneous. In this paper, we focus on exchanging and updating of cryptographic keys among the IoT objects often limited in resources, where we propose a new form of key exchange based on the mechanism of concealing encryption keys, while exploiting the misused spaces in the header fields of the exchanged packets by the communication standards, such as ZigBee, BLE, WiFi.

IoT based energy management in smart energy system: A hybrid SO2SA technique

AbstractIn this manuscript, an energy management system (EMS) is proposed to the distribution system (DS) using Internet of Things (IoT) framework with a hybrid system. The proposed hybrid method is the combination of the Seagull Optimization Algorithm (SOA) and Owl Search Algorithm (OSA), hence it is called SO2SA technique. The principle objective of the SO2SA technique is to optimize managing distribution system power and resources through continuous monitoring of the data from a communication framework based on IoT. In SO2SA technique, every home device is connected to the module of data acquisition, which indicates an IoT object along with a unique IP address as a result of huge mesh wireless network devices. The sending data are processed through SO2SA technique. Similarly, the IoT architecture of the distribution system enhances the flexibility of these networks and gives optimal utilization of obtainable resources. In addition, the SO2SA technique is responsible for meeting the overall power and supply requirements. The proposed method is implemented in MATLAB/Simulink site and the efficiency is likened to the other different methods. In 50 trail numbers, the RMSE, MAPE, and MBE range of SO2SA technique represents 5.63, 0.90, and 1.035. Thus, the proposed technique is highly competent over all the existing approaches.

Determination of Suitable Resource Discovery Tool and Methodology for High-Volume Internet of Things (IoT)

This paper discusses several issues supporting a knowledge-based methodology for discovery of high-volume IoT resources in the simulator NS-3 environment. We found the concept was developed in previous researches, especially based on widely accepted concepts of Q-Learning discovery model. The model is validated using samples from emulations of tested data in the NS-3 simulator. Proper simulation in NS-3 based on the different modules such as checkpoint and restore was used to model and analyse the data. The main feasibility checkpointing concept of simulations in the NS-3 processes were using Distributed Multi-Threaded Checkpointing (DMTCP) to run on a single machine and Message Passing Interface (MPI) under distributed machine to speed up the NS-3 model initialization and execution. As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. Q-learning is one of the successful techniques available for the exploration of IoT nodes, but context-based problems have already been established and simplified as issues of dedicated server management, IoT object data acquisition issues, and unique application requirements. The findings empirically support the validation of the Q-Learning model improvement for high-volume IoT resource discovery cases. The study will contribute to the new model development by providing new insights on the conceptualization and validation of knowledge-based methodology based on widely accepted techniques and approaches.

A Genetic Algorithm for Task Allocation Problem in the Internet of Things

In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonical definition for network lifetime in the IoT is to increase the period of cooperation between objects to carry out all the assigned tasks. The main contribution in this paper is to address the problem of task allocation in the IoT as an optimization problem with a lifetime-aware model. A genetic algorithm is proposed as a task allocation protocol. For the proposed algorithm, a problem-tailored individual representation and a modified uniform crossover are designed. Further, the individual initialization and perturbation operators (crossover and mutation) are designed so as to remedy the infeasibility of any solution located or reached by the proposed genetic algorithm. The results showed reasonable performance for the proposed genetic-based task allocation protocol. Further, the results prove the necessity for designing problem-specific operators instead of adopting the canonical counterparts.

Agents-Based Algorithm for a Distributed Information System in Internet of Things

Traditional information management approaches in distributed systems are most often unsuitable for modern Internet of Things (IoT) environments due to the huge amount and the extreme dynamism of the entities involved. Smart objects (enhanced devices or IoT services belonging to a smart system) interact and maintain relations which need of effective and efficient handling mechanisms better to meet users and services requirements. This article proposes an agent-based algorithm for achieving a distributed resources organization in an IoT environment. Software agents, representing each a single smart object, execute a set of local computations simultaneously, enabling an organized structure to emerge at global level. A natural language processing approach, able to capture the semantic context and represent objects with high-dimensional vectors, was exploited to map the IoT objects, so enabling advanced agents’ operations. The emerging organized virtual structure, i.e., a similarity-based overlay network of agents, allows building <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">informed</i> resource selection/discovery services, therefore more efficient, in a highly dynamic and, basically, unstructured environment. Preliminary results confirm the validity of the approach.

Index of Low-Power Wide Area Networks: A Ranking Solution toward Best Practice

Low-power wide area networks (LPWANs) are emerging Internet of Things (IoT) technologies that support smart city development. An individual application inherits distinct features that demand different characteristics of LPWANs. For instance, various applications demand different data rates or tolerate different packet drop rates. As such, quantified performance of the LPWAN will help developers to make decisions on classes and types of LPWAN to be used. Previous works only provided qualitative comparisons on LPWANs. Thus, smart application developers are still facing the challenge of inappropriately adopting LPWAN technologies. Currently, the IEEE Standard Working Group P2668 is working on developing a standard to evaluate, grade, and rank the performance of IoT objects by using indicator values referred to as IoT Index (IDex). To achieve smart selection on LPWANs, the IEEE P2668 IDex Working Group has first focused on the evaluation of LPWANs and developed a novel LPWAN-I. The proposed LPWAN-I is a sub-index of the IDex that quantitatively evaluates the applicability of LPWANs and selects the most suitable LPWAN candidate for a specific application. This work is the first article to report IDex, which helps to deliver the best practice. Finally, the challenges and future development of LPWAN-I are also presented.

On Lightweight Privacy-preserving Collaborative Learning for Internet of Things by Independent Random Projections

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This article considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this article, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light to moderate data pattern complexities.

An adaptive trust model based on recommendation filtering algorithm for the Internet of Things systems

The Internet of Things (IoT) is growing rapidly and brings great convenience to humans. But it also causes some security issues which may have negative impacts on humans. Trust management is an effective method to solve these problems by establishing trust relationships among interconnected IoT objects. In this paper, we propose an adaptive trust model based on recommendation filtering algorithm for the IoT systems. The utilization of sliding window and time decay function when calculating direct trust can greatly accelerate the convergence rate of trust evaluation.We design a recommendation filtering algorithm to effectively filter out bad recommendations and minimize the impact of malicious objects. An adaptive weight is developed to better combine direct trust and recommendation trust into synthesis trust so as to adapt to the dynamically hostile environment. In the simulation experiments, we compare our adaptive trust model with three related models: TBSM, NRB and NTM. The experimental results indicate that our trust model converges fast and the mean absolute error is always less than 0.05 when the proportion of malicious nodes is from 10% to 70%. The comparative experiments further verify the effectiveness of our trust model in terms of accuracy, convergence rate and resistance to trust related attacks.

Internet of things energy system: Smart applications, technology advancement, and open issues

The internet of things (IoT) is a distributed heterogeneous network of lightweight nodes with very minimal power and storage. The IoT energy system for smart applications such as smart grid, smart building, and smart transportations depends on the IoT architecture, determining the high or low-energy consumption levels. Most of the IoT objects are power-driven by batteries with short life spans that require replacement. The replacement phase is tedious; hence this paper comprehensively discussed the IoT energy system, energy resources, and energy storage as these three elements are crucial to enable energy efficiency for the IoT applications. In comparison to the battery-powered solutions, the scavenging of infinite quantities of energy makes the IoT systems robust. Therefore, this paper further elaborates on understanding the current situation in terms of renewable energy harvesting for these low-energy systems. The IoT energy storage highlighted in this paper includes fuel cell, lithium battery, and supercapacitor technology. This paper also provides the findings for IoT energy system challenges and open issues in management and storage in terms of bidirectional, continuity, autonomy, fluctuation, conversion, consumption, integration, multifunction, and stability. This paper will assist researchers in more quality yet practical energy usage and savings, better IoT system architecture, and smart application sustainability.

Internet of Things (IoT) for MC-CDMA-Based Cognitive Radio Network (CRN) in 5G: Performance Results

Both the internet-connected devices, i.e. IoT and Cognitive Radio Network (CRN) are considered to be the future technologies for the fifth generation of cellular wireless standards (5G). On the one hand, Internet of Things (IoT) focuses primarily on how to allow general objects to see, hear, and smell their own physical environment and make them connected to share the observations. On the other hand, a CRN is based on a complex spectrum allocation system, and licenced primary users (PUs) or unlicenced secondary users (SUs) are allowed to share the spectrum, provided they do not cause significant interference. The IoTs are meaningless if IoT objects are not equipped with cognitive radio capability. In cognitive radio, it is important to control the transmission power of SUs so that the interference should not be harmful to the quality of service of PUs. In this paper, the authors addressed the effects of imperfect power control between primary users (PUs) and the secondary users (SUs) of an IoT-based CRN. The effect of the co-channel interference (CCI) and adjacent channel interferences (ACIs) occurring in CRN using MC-CDMA system is also analysed. A new expression of the signal-to-interference-noise ratio (SINR) for CRN-based MC-CDMA system over a Nakagami-[Formula: see text] fading channel with imperfect power control condition is derived and investigated. The performance of IoT-based CRN using MC-CDMA system over the frequency selective multipath fading channel is examined with varying the number of users, the SINR per bit, number of fading path and number of sub-carriers. From the simulation results, we have seen that the SINR performance is affected by these parameters. The result of the analysis will provide relevant information to design the physical layer protocol for high-speed IoT-based CRN system for 5G.