Moving average method based air pollution monitoring system using IoT platform

Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network.

The Internet of Things (IoT) platform consists of numerous IoT devices and a small number ofservers. In order for the platform to operate efficiently and reliably, servers need to have completecontrol over many IoT devices. The advantage of using network boot in this environment is thatthe server has control over the device’s boot process. When applying network boot on an IoT platform,the server needs to be able to handle the network boot that many IoT devices request. If theserver does not have enough processing power for the network boot request, IoT device boot wouldbe delayed. In this paper, we propose a resource-efficient network boot to solve this problem. Theproposed framework has three mechanism. First, the server reserves the right to control which systemimage to transfer during the network boot process on the platform. Second, the server appliesdeduplication to the system image to minimize the amount of data required for transmission. Third,the server monitors network boot requests and performs file transfers appropriate to the situation,minimizing time spent on transfers. The proposed system allows the server to efficiently control numerousIoT devices when using network boot on the IoT platform.

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The paper deals with the definition, implementation and performance assessment of an Internet of things (IoT) platform for measuring and monitoring power consumption in smart home or smart industry applications. Thanks to the adoption of suitable cost-effective hardware components and a proper software architecture implemented on Raspberry PI, the IoT platform can assure real-time power monitoring on electrical loads connected to domestic and industrial power plugs. Particular attention has been paid to the selection of the application layer protocol mandated to exchange data between the power measuring nodes and the Raspberry PI in order to assure reliable communication with a reduced computational and connection overhead. The availability of the measurement results on a web page that does not require any specific software but a web browser highlights the efficacy of the adopted platform to assure aware in power consumption for domestic and industrial customers.

In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) and an embedded system are first used to acquire vibration data from a powder metallurgy molding machine. These data are collected on an Internet of Things (IoT) platform using the Message Queueing Telemetry Transport (MQTT) protocol. For data analysis, the vibration signal on the Z axis is segmented to label the contact section of the upper and middle molds, and the corresponding vibration data of the stamping friction on the X, Y, and Z axes are extracted. Using only historical vibration data from normal stamping, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with an attention mechanism is trained to predict normal stamping vibrations several minutes in advance. By comparing the predicted stamping vibrations with the observed data at the current time, the mean square errors (MSEs) are calculated to evaluate the health status of the mold. Several ablation experiments were conducted to assess the performance of the trained model. The average MSE values for normal samples and abnormal samples were smaller than 0.5 and larger than 1.0, respectively. The experimental results confirm that the trained prediction model and evaluation indicators can effectively notify operators in advance. An early warning system using vibration data for mold damage was successfully implemented, enhancing predictive maintenance.

The Internet of Things (IoT) is a hot topic in the engineering and technology community, and IoT-enabled devices are becoming increasingly common due to advancements in technology. IoT platforms provide valuable and specific services for IoT applications, and the number of platforms used by IoT applications is growing rapidly commensurate with the increasing number of IoT devices. However, fierce competition between IoT platforms is making it difficult for companies to select a suitable type of IoT platform for their business needs. This paper attempts to identify the important factors of IoT platforms that can be consider before selecting an IoT platform. In this paper, Twenty-one important IoT platform factors are identified from the literature and these factors were then verified by the Delphi method and categorized. This paper aims to help companies choose an appropriate IoT platform from the huge number and variety of IoT platforms available in the market.

Due to the rise in coronavirus disease (CoVID-19) cases, it has been a significant concern for governments all over the world to control the spread of the virus. We aim to aid in controlling the spread of the CoVID-19 virus. The face mask detection and crowd counting model with the Internet of things (IoT) platform (Thingspeak) can be deployed in situations where there is a lack of workforce, and additional assistance is required. Using a camera, an area can be remotely monitored, restricting contact between people and the frontline workers, promoting their safety. The research conducted to date about this topic is solely on detecting facemask or doing crowd counting, respectively. We included both face mask detection and crowd counting, connecting both to the IoT platform, enabling us to access the results online. In previous research work, traditional/wired cameras were used. But in our research work, we have used the concept of a Wi-Fi camera, i.e., wireless camera, by which the camera can be positioned in places where the placement of wires is difficult. For face mask detection, we have created two data sets which include images of faces with masks and without a mask, and this data set is used to train the model. For training and testing the model, we have included TensorFlow, Keras and SKLearn libraries. We have included different deep learning layers in training the model like Dense, Dropout and AveragePooling2D. In the test model, python code is connected to a wireless camera, which can capture live video feed and detect whether the person is wearing a mask or not. We have used OpenCV Histogram of oriented gradients (HOG) descriptor in crowd counting, often used to extract features from image data. Using an OpenCV HOG Descriptor, we can count the number of people present in a crowd. Face mask and crowd count is then sent to the IoT platform. Whenever the person is found wearing a mask, a green box appears around the face, and whenever the person is not wearing a mask, a red box appears around the face. When lots of people are detected by a crowd counting model, a green color box enclosing the entire body appears. This study helps in getting face mask detection and crowd size estimation on a single IoT platform, which helps monitor the data with great ease.

The economic significance of Internet of Things (IoT) platforms is evident from the proliferation of smart home assistant platforms like Amazon Alexa and Google Home that center themselves around providing unique and connected experiences to users. Customers do not perceive these platforms valuable if they do not experience adequate levels of quality and security from both the platform and applications (apps) they use. Therefore, the IoT platform provider as well as app developers need to go beyond collaboration and cocreate quality and security for each app on the platform. In this setting, we study the dynamics of the financial sustainability of the platform provider and app developers and find that the collaborative nature of the IoT platform has a significant role in the behavior and profitability of all parties. Specifically, a neutral entrant to the platform not only increases the effort levels of all existing parties, but also increases the profits due to network effects that are intertwined with economies of scale. We further find that a substantial number of apps are needed in the platform for an economically viable platform and that a less efficient platform leader needs even a larger application base. However, based on the number of apps on the platform and which party is more effective in the collaboration, the platform provider might need to provide additional incentives, or sometimes, extract additional rent from the app developers to financially sustain the platform. Furthermore, we verify our results in the presence of competition or complementarity among apps and further derive additional interesting results and managerial insights. For example, we find that the entry of a competing app may be beneficial for existing apps under some conditions.

The Internet of Things (IoT) is defined as a global network that links together living and/or non-living entities, such as people, animals, software, physical objects or devices. These entities can interact with each other, gather, provide or transmit information to the IoT. Although the Internet of Things is a relatively new concept, various platforms are already available. Some of them are open platforms, enabling both the integration of people, systems, and objects from the physical and virtual world, and the visualization of data. For example, there are already some IoT platforms used, like Google Cloud Platform, Microsoft Azure IoT Hub, Amazon Web Services IoT Platform, IBM Watson IoT Platform, Nimbits, Open.Sen.se, ThingWorx, and ThingSpeak. But what if things could not only “work” and “speak”, but also “sing”? We propose a game in which the things connected to IoT can play in real time different sounds, according to the values of some monitored parameters. These things can be grouped in the IoT platform to create a virtual orchestra and make music. Besides this game allowing the creation of great songs, it can be widely used to explain the new ideas behind the fast emerging areas of the Internet of Things. In addition to many technical challenges, it is also worth considering the effect the IoT concept will have on people, society, and economy as a whole.

Technology is evolving exponentially, and researchers are seeking solutions to answer questions related to the benefits of using advanced IoT cloud computing platforms. There is a tremendous amount of research activity regarding cyberattacks on Internet of Things (IoT) devices. IoT ecosystems consist of physical objects or devices that interact with the environment, extract data, and communicate with a higher authority such as a cloud. Many IoT users are unaware of the underlying platforms that facilitate data collection. These platforms are also known as middleware, sit between the connectivity and application layers. There are more than 1000 IoT platforms on the market today, where many are open-source or commercial and provide services that include a device, data, and connection management. This paper presents a comparative analysis of some commercial and open-source IoT platforms such as Arm Pelion, Microsoft Azure cloud, ThingWorx, FIWARE, and ThingSpeak. We compare system architecture, management schemes, protocols, interoperability, and security. We used experimentation with real-world Internet of Things devices for the comparative study. We proposed a novel design architecture of a fully secure testbed that integrates critical components of an IoT platform. Hopefully, this study will motivate manufacturers to provide a better quality of service to help customers select the best IoT platform for their needs.

The subject of research is modeling the structure of the Internet of things (IoT) network for controlling audio information based on the IoT platform. The purpose of the article is to detail the process of modeling the audio information monitoring network based on the IoT platform. The authors proposed the structure of a multi-agent system (MAS) for monitoring audio information (MASAI). The structure of MASAI includes many agents of sound transformation, analysis of information received from them, and decision-making. It was decided to use the IoT network, which includes sound sensors, the IoT platform, the notification service, and the user’s application to simulate the MASAI. The structure of this network using the Amazon platform is proposed. An algorithm for modeling the Internet of things network for analyzing audio information based on the AWS platform is presented, including simulating audio sensors, transmitting this information to the platform, sensors authenticating, processing information according to certain rules, generating notifications to a user. Detailed structure of the AWS platform is provided with a description of the functions of its components such as: device gateway, rule machine, certificate block, device copy block, database, analytics block, notification service. The algorithm for connecting devices to the AWS platform is given: creating a device certificate on the platform, creating a security policy, rules for processing information received from devices, and testing the network. The features of the algorithm for modeling the readings of sound information sensors on a smartphone are shown, steps are given for organizing its communication with the platform, performing security procedures, sending data in the form of an MQTT message, and displaying the captured audio information.

This chapter covers the evolution of the Internet of Things (IoT) from different aspects. It focuses on 3GPP cellular IoT evolution for connectivity, that is, narrowband IoT (NB‐IoT). The chapter summarizes the IoT evolution from an end‐to‐end perspective, including the IoT platform, IoT protocols, connectivity, and the sensors layer. The IoT platform should support open standard protocols to communicate with different sensors, devices, and gateways from different vendors. The proposed IoT platform will support existing technologies including 2G/3G/LTE/Wi‐Fi/PLC/ Fixed and low‐power, wide‐area (LPWA) networks such as long‐range (LoRaWAN) and Sigfox as well as 3GPP standardized technologies such as LTE‐Machine‐Type Communications (MTC) and 3GPP NB‐IoT. The chapter shows link budgets for NB‐IoT with different subcarrier spacing. One machine‐to‐machine (M2M) is a global partnership developing standards for M2M communications, enabling large‐scale implementation of the IoT. The chapter addresses a high‐level architecture reference model, network elements, and reference point for cellular IoT (CIoT).

An automated intrusion detection system in IoT system using Attention based Deep Bidirectional Sparse Auto Encoder model

Many researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.