Interworking between Modbus and internet of things platform for industrial services

In the era of rapid development of Internet of Things(IoT),numerous machine-to-machine technologies have been applied to the industrial domain.Due to the divergence of IoT solutions,the industry is faced with a need to apply various technologies for automation and control.This fact leads to a demand for an establishing interworking mechanism which would allow smooth interoperability between heterogeneous devices.One of the major protocols widely used today in industrial electronic devices is Modbus.However,data generated by Modbus devices cannot be understood by IoT applications using different protocols,so it should be applied in a couple with an IoT service layer platform.oneM2M,a global IoT standard,can play the role of interconnecting various protocols,as it provides flexible tools suitable for building an interworking framework for industrial services.Therefore,in this paper,we propose an interworking architecture between devices working on the Modbus protocol and an IoT platform implemented based on oneM2M standards.In the proposed architecture,we introduce the way to model Modbus data as oneM2M resources,rules to map them to each other,procedures required to establish interoperable communication,and optimization methods for this architecture.We analyze our solution and provide an evaluation by implementing it based on a solar power management use case.The results demonstrate that our model is feasible and can be applied to real case scenarios.

Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

Potential Benefits and Obstacles of the Use of Internet of Things in Saudi Universities: Empirical Study

Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

Anomaly Detection for Industrial Internet of Things Cyberattacks

The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.

Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling representations.The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system.Compared to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are minimized.Furthermore,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central servers.Therefore,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed method.Finally,four scenarios are chosen to investigate the projected design’s effectiveness.In all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Joint optimization of serving node selection and wireless resources allocation for transactions data in mobile blockchain enhanced Internet of Things

With the increased emphasis on data security in the Internet of Things(IoT), blockchain has received more and more attention.Due to the computing consuming characteristics of blockchain, mobile edge computing(MEC) is integrated into IoT.However, how to efficiently use edge computing resources to process the computing tasks of blockchain from IoT devices has not been fully studied.In this paper, the MEC and blockchain-enhanced IoT is considered.The transactions recording the data or other application information are generated by the IoT devices, and they are offloaded to the MEC servers to join the blockchain.The practical Byzantine fault tolerance(PBFT) consensus mechanism is used among all the MEC servers which are also the blockchain nodes, and the latency of the consensus process is modeled with the consideration of characteristics of the wireless network.The joint optimization problem of serving base station(BS) selection and wireless transmission resources allocation is modeled as a Markov decision process(MDP), and the long-term system utility is defined based on task reward, credit value, the latency of infrastructure layer and blockchain layer, and computing cost.A double deep Q learning(DQN) based transactions offloading algorithm(DDQN-TOA) is proposed, and simulation results show the advantages of the proposed algorithm in comparison to other methods.

The Impact of Internet of Things in Manufacturing Management

The study investigated the impact of the Internet of Things in manufacturing management. Specifically, the study examined how IoT implementation and management affect organizational efficiency in Camanov Ltd.;and to what extent IoT implementation contributes to the saving of cost and time of the organization. The research design is a survey. The population of this study consisted of all 141 staff of Camanov Ltd. Port Harcourt. Since the population is not large, the researcher conducted a census of all, and 126 staff completed a structured questionnaire. The two research questions were analyzed using simple percentages and all two hypotheses were tested using sample proportion statistics (Z test) at a 0.05 level of significance. The result showed that the Internet of Things has a significant impact on organizational efficiency in Camanov Ltd. (Z = 4.73);and that the Internet of Things significantly contributes toward saving cost and time of the organization Camanov Ltd (Z = 4.95). It was recommended that organizations should encourage training of personnel in the improved limitless possibility of information gathered from the Internet of Things framework which supports planning, budgeting and monitoring approaches, providing more reliable information to support actions, in particular in the decision-making process, to enhance productivity.

Implementation of Machine-to-Machine Solutions Using MQTT Protocol in Internet of Things (IoT) Environment to Improve Automation Process for Electrical Distribution Substations in Colombia

In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.

A Dynamic Management Scheme for Internet of Things(IoT)Environments:Simulation and Performance Evaluation

In recent years,the Internet of Things(IoT)technology has been considered one of the most attractive fields for researchers due to its aspirations and implications for society and life as a whole.The IoT environment contains vast numbers of devices,equipment,and heterogeneous users who generate massive amounts of data.Furthermore,things’entry into and exit fromIoT systems occur dynamically,changing the topology and content of IoT networks very quickly.Therefore,managing IoT environments is among the most pressing challenges.This paper proposes an adaptive and dynamic scheme for managing IoT environments is proposed.This management scheme depends on the use of previous management methodologies,considering two main factors.The first factor is network status,which is determined in real-time.The second factor is a management method’s suitability according to its desired administration.To test the proposed management scheme,a simulation environment is created using NS3.The metrics used to measure the management scheme performance are bandwidth consumption,energy consumption,packet loss,throughput,delay,usage rate of individualmanagement techniques,and transformation.The simulation results prove that the proposed management scheme outperformed the individual 6LowPANSNMP,CoAP,and LWM2M management schemes.

Development and Application of Cell-Phone-Based Internet of Things (IoT) Systems for Soil Moisture Monitoring

Active soil moisture monitoring is an important consideration in irrigation water management. A permanent and readily accessible record of changes in soil moisture can be used to improve future water management decision-making. Similarly, accessing stored soil moisture data in near-real-time is also essential for making timely farming and management decisions, such as where, when, and how much irrigation to apply. Access to reliable communication systems and delivery of real-time data can be affected by its availability near production fields. Therefore, soil moisture monitoring systems with real-time data functionality that can meet the needs of farmers at an affordable cost are currently needed. The objective of the study was to develop and fieldtest affordable cell-phone-based Internet of things (IoT) systems for soil moisture monitoring. These IoT systems were designed using low-cost hardware components and open-source software to transmit soil moisture data from the Watermark 200SS or ECH2O EC-5 sensors. These monitoring systems utilized either Particle Electron or Particle Proton Arduino-compatible devices for data communication. The IoT soil moisture monitoring systems have been deployed and operated successfully over the last three years in South Carolina.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

A Healthcare System for Internet of Things (IoT) Application: Machine Learning Based Approach

Internet of things (IoT) has become an interesting topic in the field of technological research. It is basically interconnecting of devices with each other over the internet. Beside its general use in terms of autonomous cars and smart homes, but some of the best applications of IoT technology in fields of health care monitoring is worth mentioning. The main purpose of this research work is to provide comport services for patients. It can be used to promote basic nursing care by improving the quality of care and patient safety from patient home environment. Rural area of a country lacks behind the proper patient monitoring system. So, remote monitoring and prescribing by sharing medical information in an authenticated manner is very effective for betterment of medical facilities in rural area. We have proposed a healthcare system which can analyze ECG report using supervise machine learning techniques. Analyzing report can be stored in cloud platform which can be further used to prescribe by the experienced medical practitioner. For performance evaluation, ECG data is analyzed using six supervised machine learning algorithms. Data sets are divided into two groups: 75 percent data for training the model and rest 25 percent data for testing. To avoid any kind of anomalies or repetitions, cross validation and random train-test split was used to obtain the result as accurate as possible.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.

Delta Compression Correction Method for Covert Communication in IoT

Covert communication can conceal the existence of wireless transmission and thus has the ability to address information security transfer issue in many applications of the booming Internet of Things(IoT).However,the proliferation of sensing devices has generated massive amounts of data,which has increased the burden of covert communication.Considering the spatiotemporal correlation of data collection causing redundancy between data,eliminating duplicate data before transmission is beneficial for shortening transmission time,reducing the average received signal power of warden,and ultimately realizing covert communication.In this paper,we propose to apply delta compression technology in the gateway to reduce the amount of data generated by IoT devices,and then sent it to the cloud server.To this end,a cost model and evaluation method that is closer to the actual storage mode of computer systems is been constructed.Based on which,the delta version sequence obtained by existing delta compression algorithms is no longer compact,manifested by the still high cost.In this situation,we designed the correction scheme based on instructions merging(CSIM)correction to save costs by merging instructions.Firstly,the delta version sequence is divided into five categories and corresponding merge rules were derived.Then,for any COPY/ADD class delta compression algorithm,merge according to strict to relaxed to selection rules while generating instructions.Finally,a more cost-effective delta version sequence can be gained.The experimental results on random data show that the delta version sequences output by the CSIM corrected 1.5-pass and greedy algorithms have better performance in cost reducing.