Smart Internet Of Things Applications Research Articles

Design Smart Door Locks With Internet Of Things Based On Pin Security Features

Smart door lock systems have become an interesting topic of research in recent years due to the need for higher security when accessing rooms or buildings. This research aims to design and implement a smart door lock system that uses an Internet of Things (IoT)-based personal identification number (PIN) that can be controlled through the Telegram messaging platform.
 The system is designed so that users can give commands via Telegram messages to open or lock doors securely. Users can access the door without needing to be physically near it. In addition, the level of security is enhanced by allowing only authorized users to control the door via Telegram messages, as well as by implementing strong encryption and authentication mechanisms.
 In conclusion, this study succeeded in designing and implementing an IoT-based smart door lock system that can be controlled through the Telegram messaging platform using a personal identification number (PIN) as a security method. This system provides an efficient, secure, and practical solution for managing smart door access remotely. It is expected that this research can make a positive contribution to the development of smart door security technology and IoT applications at large

RFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW.

Self-Organized and Distributed Green Resource Allocation for Space–Air–Ground IoT Networks

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBS) on unmanned aerial vehicles (UAV) to generate heterogeneous space-air-ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space-air-ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed Energy efficiency (EE) optimization. Then we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space-air-ground IoT networks is implemented under the SON framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space-air-ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space-air-ground networks IoT applications.

RETRACTED ARTICLE: VR system application in the inheritance of intangible cultural heritage gongs and drums based on video simulation

In recent years, with the rapid popularization of Internet technology and the development of a large number of smart terminal devices for the Internet of Things, Internet of Things systems and devices have become more and more popular in people’s daily lives, and the types and numbers of smart Internet of Things applications are constantly increasing. In VR, more and more input devices and more and more data and information directly affect the depth of embedding. In the era of big data, data storage is overvalued, and a reasonable solution to data storage strategies is needed. When sending video simulation applications, the emergence of VR technology has played a huge role in the inheritance of intangible cultural heritage. Through immersive virtual reality or interactive virtual reality, it has injected new blood of the times into excellent traditional culture and has become an intangible help. The list of intangible cultural heritage protection includes many traditional folk music with unique local characteristics. The gong and drum music in a certain place is one of them. This thesis analyzes the problems in depth from the development status of gongs and drums in a certain place and the problems faced in the later period, and explores executable inheritance and protection strategies.

Internet of Things: Device Capabilities, Architectures, Protocols, and Smart Applications in Healthcare Domain

Nowadays, the Internet has spread to practically every country around the world and is having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more popular and has a high level of interest in both practitioners and academicians in the age of wireless communication due to its diverse applications. The IoT is a technology that enables everyday things to become savvier, everyday computation towards becoming intellectual, and everyday communication to become a little more insightful. In this paper, the most common and popular IoT device capabilities, architectures, and protocols are demonstrated in brief to provide a clear overview of the IoT technology to the researchers in this area. The common IoT device capabilities including hardware (Raspberry Pi, Arduino, and ESP8266) and software (operating systems, and built-in tools) platforms are described in detail. The widely used architectures that have been recently evolved and used are the three-layer architecture, SOA-based architecture, and middleware-based architecture. The popular protocols for IoT are demonstrated which include CoAP, MQTT, XMPP, AMQP, DDS, LoWPAN, BLE, and Zigbee that are frequently utilized to develop smart IoT applications. Additionally, this research provides an in-depth overview of the potential healthcare applications based on IoT technologies in the context of addressing various healthcare concerns. Finally, this paper summarizes state-of-the-art knowledge, highlights open issues and shortcomings, and provides recommendations for further studies which would be quite beneficial to anyone with a desire to work in this field and make breakthroughs to get expertise in this area.

Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review.

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

An Efficient Plant Disease Recognition System Using Hybrid Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for Smart IoT Applications in Agriculture

In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no exception that convolutional neural networks (CNNs) have achieved the latest accomplishment in many aspects of human life and the farming sector. Semantic image segmentation is considered the main problem in computer vision. Despite tremendous progress in applications, approximately all semantic image segmentation algorithms fail to achieve sufficient hash results because of the absence of details sensitivity, problems in assessing the global similarity of image pixels, or both. Methods of post-processing improvement, as a wonderfully critical means of improving the underlying flaws mentioned above from algorithms, depend almost on Conditional Random Fields (CRFs). Therefore, plant disease prediction plays important role in the premature notification of the disease to alleviate its effects on disease forecast investigation purposes in the smart farming arena. Hence, this work proposes an efficient IoT-based plant disease recognition system using semantic segmentation methods such as FCN-8 s, CED-Net, SegNet, DeepLabv3, and U-Net with the CRF method to allocate disease parts in leaf crops. Evaluation of this network and comparison with other networks of the state art. The experimental results and their comparisons proclaim over F1-score, sensitivity, and intersection over union (IoU). The proposed system with SegNet and CRFs gives high results compared with other methods. The superiority and effectiveness of the mentioned improvement method, as well as its range of implementation, are confirmed through experiments.

Neighbor Coverage and Bandwidth Aware Multiple Disjoint Path Discovery in Wireless Mesh Networks

With the ease of appending new nodes without re-installing the whole network, the Internet of Things (IoT) builds several smart applications on Wireless Mesh Network (WMN). One of the important aspects of integrating WMN and smart IoT applications is to provide an energy-efficient and reliable routing protocol. Seeking the communication route that delivers the high-quality stream quickly over WMN is an important issue, but the maximum utilization of a single high-quality path leads to poor throughput and large communication delay, including route discovery and data forwarding delay. The broadcasting mechanism creates redundant transmissions of control packets into the network and reinitializes the blind route discovery process due to link disconnections leading to network resource constraints and high delay during the route discovery process. Moreover, the congestion in the communication route incurs data transmission latency. This paper proposes the Multiple Disjoint Path Determination (MDPD) mechanism based on-demand routing in WMN to formulate the path discovery and data transmission latency. Reducing the neighbor list into the uncommon neighbor set reduces the unnecessary latency in route discovery, and deriving high capacity multiple disjoint communication routes reduce the communication delay in the proposed work. The proposed work employs the queue dynamics in queuing delay, which mainly provides adaptability to the dynamics in network capacity and efficient diversity paths to the gateway node to infer the available bandwidth and optimize the network traffic. To fully utilize the advantage of heterogeneous routers, it disables the flooding of control packets across the stable mesh routers, excluding the initial route discovery process, because it enables the available route storage system in each mesh router. Hence, the proposed work efficiently supports wireless broadband internet access with reduced delay and control overhead. The simulation results demonstrate the fast detection of the multiple disjoint routes and data traffic optimization over the discovered disjoint routes in the proposed MDPD mechanism over WMN.

Understanding IoHT and Edge/Fog Computing Solutions for Smart In-Home Remote Healthcare

Healthcare information systems have been dominated by cloud technology and the Internet of Things (IoT) for decades today. In some urgent scenarios, we reveal a prevailing architectural framework that is based on fog/edge optimal computing approaches smart in-home remote healthcare solutions and architectures and recognize the challenges and requirements of IoHT devices for diverse utilization instances. Even with these upsides, conventional centralized access constraint confronts privacy problems and patient health data security. This study likewise constructs a “blockchain-enabled edge that computes” mechanism, through which smart contracts with the consensus protocol produced by Edge Intelligent Server are deployed to secure privacy topics and balance scalability in trustless surroundings. We expect this paper to be a significant guideline for the subsequent elaboration of fog/edge-based systems that compute solutions for smart in-home remote healthcare IoT applications. There will be a change of paradigm from “hospital-based” to “distributed patient in-home healthcare”.

Smart textile lighting/display system with multifunctional fibre devices for large scale smart home and IoT applications

Smart textiles consist of discrete devices fabricated from—or incorporated onto—fibres. Despite the tremendous progress in smart textiles for lighting/display applications, a large scale approach for a smart display system with integrated multifunctional devices in traditional textile platforms has yet to be demonstrated. Here we report the realisation of a fully operational 46-inch smart textile lighting/display system consisting of RGB fibrous LEDs coupled with multifunctional fibre devices that are capable of wireless power transmission, touch sensing, photodetection, environmental/biosignal monitoring, and energy storage. The smart textile display system exhibits full freedom of form factors, including flexibility, bendability, and rollability as a vivid RGB lighting/grey-level-controlled full colour display apparatus with embedded fibre devices that are configured to provide external stimuli detection. Our systematic design and integration strategies are transformational and provide the foundation for realising highly functional smart lighting/display textiles over large area for revolutionary applications on smart homes and internet of things (IoT).

A Full Lifecycle Authentication Scheme for Large-scale Smart IoT Applications

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not applicable to recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for trending IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire life cycle of the IoT device for real-world scenarios of smart IoT systems, which contains factory manufacturing, daily usage, and system resetting. For each stage in the life cycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.

Fuzzy-Based Microservice Resource Management Platform for Edge Computing in the Internet of Things.

Edge computing exhibits the advantages of real-time operation, low latency, and low network cost. It has become a key technology for realizing smart Internet of Things applications. Microservices are being used by an increasing number of edge computing networks because of their sufficiently small code, reduced program complexity, and flexible deployment. However, edge computing has more limited resources than cloud computing, and thus edge computing networks have higher requirements for the overall resource scheduling of running microservices. Accordingly, the resource management of microservice applications in edge computing networks is a crucial issue. In this study, we developed and implemented a microservice resource management platform for edge computing networks. We designed a fuzzy-based microservice computing resource scaling (FMCRS) algorithm that can dynamically control the resource expansion scale of microservices. We proposed and implemented two microservice resource expansion methods based on the resource usage of edge network computing nodes. We conducted the experimental analysis in six scenarios and the experimental results proved that the designed microservice resource management platform can reduce the response time for microservice resource adjustments and dynamically expand microservices horizontally and vertically. Compared with other state-of-the-art microservice resource management methods, FMCRS can reduce sudden surges in overall network resource allocation, and thus, it is more suitable for the edge computing microservice management environment.

Artificial intelligence and machine learning for internet of things

For decades, humans have been intrigued by the concept of an intelligent and independent self-learning machine. The idea behind Machine Learning (ML) is to simplify the development of analytical models such that, with the help of available data, algorithms can learn continuously. Internet of Things (IoT) enabled devices are the major sources of data generation with a number of multiple modalities and differing data consistency, defined by velocity in terms of time and position dependence. Intelligent processing and analysis of this generated data (Big Data) is the key to developing smart IoT applications. ML may be used in cases where the desired effect is defined (supervised learning) or where data itself is not defined beforehand (unsupervised learning) or where learning is the outcome of the interaction among the learning model and the environment (reinforcing learning). In this chapter, we present and discuss a taxonomy of machine learning algorithms that can be used with IoT. Furthermore, how different machine learning techniques are used to derive higher-level information from the data is illustrated. Lastly, we investigate, what are the real-world IoT data characteristics that involve an interpretation of the data?

Cloud-Based Remote RFID Authentication for Security of Smart Internet of Things Applications

Radio frequency is the technology which enables smart labels to things. Thus, even physical things can participate in computing process. It is becoming popular due to its technological innovation and ability to overcome line of sight problem. With Internet of Things (IoT) technology, RFID usage became ubiquitous in smart applications or IoT use the cases like smart education, smart homes, smart healthcare and smart cities to mention a few. Integration of digital and physical worlds is made possible with IoT, RFID and host of other technologies and standards. When connected devices and things are uniquely identified using RFID technology, it is essential to know its utility in authentication process and security challenges thrown as well. RFID tag and RFID reader are involved in wireless communication and identification. RFID tags may carry sensitive information and its vulnerabilities if any are exploited by adversaries. Moreover, heavy computation is involved in RFID-based authentication. To overcome issues like privacy, security and overhead improvements have been proposed as found in the literature. Researchers used cryptographic tools, hash functions and symmetric key encryption for secure RFID communications. However, the level of security is still inadequate. In this paper, we proposed a cloud-based remote RFID authentication scheme with smart home as the case study. The proposed scheme has features like forward secrecy, anonymity and untraceability besides being light weight. It can withstand various security attacks. Our simulation study revealed that the proposed system model and the cloud-based remote RFID authentication scheme are effective in providing privacy and security as part of access control system in smart home IoT use case.

Artificial Intelligence-Based Sensors for Next Generation IoT Applications: A Review

Sensors play a vital role in our daily lives and are an essential component for Internet of Things (IoT) based systems as they enable the IoT to collect data to take smart and intelligent decisions. Recent advances in IoT systems, applications, and technologies, including industrial Cyber-Physical Systems (CPSs), are being supported by a wide range of different types of sensors based on artificial intelligence (AI). These smart AI-based sensors are typically characterized by onboard intelligence and have the ability to communicate collaboratively or through the Internet. To achieve the high level of automation required in today’s smart IoT applications, sensors incorporated into nodes must be efficient, intelligent, context-aware, reliable, accurate, and connected. Such sensors must also be robust, safety- and privacy-aware for users interacting with them. Sensors leveraging advanced AI technologies, new capabilities have recently emerged which have the potential to detect, identify, and avoid performance degradation and discover new patterns. Along with knowledge from complex sensor datasets, they can promote product innovation, improve operation level, and open up novel business models. We review sensors, smart data processing, communication protocol, and artificial intelligence which will enable the deployment of AI-based sensors for next-generation IoT applications.

Integrated Management Strategy with Feasible Smartness over Heterogeneous IoT Environments

Recently, Internet of Things (IoT) applications have been increasingly deployed in smart domains, such as homes, buildings, and so on. A wide variety of smart devices and solutions bring improved lifestyles. However, current provider-oriented and individual application deployment leads to the separation of a smart domain into respective regions by providers and applications. Such heterogeneous environments hinder unified operation and the utilization of smart IoT applications. Therefore, this Article firstly addresses analyses on conventional smart domain technologies—smart home, smart building, etc.— and deployment in the real world with heterogeneous IoT technologies; then, a novel smart domain strategy for inter-cloud and inter-service operability and mobile-user-attached interactivity is proposed. Performance is compared in terms of user experience and service availability. Finally, numeric analyses are provided to prove the proposed strategy, and the proof-of-concept is presented to show feasibility and performances.

Security Challenges and Requirements for Smart Internet of Things Applications: A Comprehensive Analysis

Smart homes, smart cities, smart grids, and smart vehicles are a few examples of the Internet of Things (IoT) applications. These applications possess distributed data delivery infrastructures for remote access, storage, distribution, and processing. As a result, the communication amongst such smart objects offers new horizons for applications to improve human well being. However, massive communication within such cyber-physical systems poses a wide variety of security challenges. These security malfunctions may disturb the entire applications/systems and lead to lethal consequences. Therefore, trust and security are major prerequisites in advanced IoT applications. To this end, there is a dire need to understand the key security challenges within smart IoT applications, such as e-health, agriculture, and energy sectors. Therefore, this paper provides and discusses major security challenges and requirements of smart IoT applications in order to enhance their security.

Theoretical analysis on applications aspects of smart materials and Internet of Things (IoT) in textile technology

Smart Materials like graphene oxide, carbon nanotubes, etc are been used to build conductive textiles so that they can enable and give a solid platform for building Internet of Things (IoT) devices which can improve the functionally and serve the purpose of communicating the information in a quick and cost effective manner. The prime objective of this work is to analyse theoretically the application of smart material and IoT enabled devices which are presently used in Textile Technology stream. The study also analyses the application of various support technologies that are linked to IoT devices directly or indirectly. However, the findings after analysis has revealed useful technical information related to the requirements and application methods that a smart material should possess and how it has to be used while building an IoT based device. The method followed in analysing the smart materials and IoT applications mainly focused on understanding about the characteristics of the smart material, their working principle and the outcome of the previous work so far carried out by eminent people in this area. Applications like IoT based health monitoring system, Artificial Intelligence, Smart manufacturing, Cloud computing and Cyber physical systems is also been discussed and the findings prove the importance of using these technologies to enhance the efficiency and overcome competition in smart materials area. In conclusion, this analysis has given us new hopes to develop IoT based devices and wearable electronics using graphene and carbon based Nano-materials.

Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

Abstract The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, healthcare, etc. Using a ubiquitous controller to interconnect devices in the IoT is still in the embryonic stage. However, it has the potential to create distributed-intelligent IoT solutions that are more efficient and secure than centric intelligence. It is essential to take a new direction to design a distributed intelligent controller for task scheduling that can firstly dynamically interact with a smart environment in efficient real-time data processing and secondly respond to flexible changes. To address these issues, we outline a two-level intelligence scheme that leverages edge computing to improve distributed IoT. The edge scheme shifts the capability of streaming processing from the cloud to edge devices to alleviate latency, support better reliable streaming analytics, and improve smart IoT applications’ performance. In this work, to enable better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller that provides low-level intelligence by using a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.

A pervasive controlled access with privacy delegation design for smart internet of things applications

Internet of Things (IoT) provides decentralized, distributed, and pervasive access to resources and services through heterogeneous device interactions. IoT platform is large and capable of handling huge volumes of data, users, resources, etc. This requires more sophisticated security measures. In this manuscript, a pervasive controlled access method with privacy delegation (PCA-PD) is introduced to improve IoT application users’ security and privacy. The proposed method permits differential access control depending on the application usage and information request. The delegation of access is ensured based on trust and a shared keying model valid within the access session. In this method, information and time-based classification are performed to differentiate the access instance and information shared between the applications and IoT services. This helps to prevent security backtracking and request regeneration and also a failure in responses. The classification is performed independently, depending on the previous behavior of the application with the service provider. The proposed method’s experimental analysis shows that it can achieve high access control level, less delegation time, improved application classification, and success rate.