Smart Internet Of Things Research Articles

Artificial intelligence - enabled soft sensor and internet of things for sustainable agriculture using ensemble deep learning architecture

IoT (Internet of things) and Artificial Intelligence (AI), as well as other advanced computing technologies, have long been used in agriculture.AI-enabled sensors function as smart sensors and IoT has made various types of sensor-based equipment in the field of agriculture. This research proposes novel techniques in AI technique based soft sensor integrated with remote sensing model using deep learning architectures. The input has been pre-processed to recognize the missing value, data cleaning and noise removal from the image which is collected from the agricultural land. The feature representation has been carried out usingweight-optimized neural network with maximum likelihood (WONN_ML). after representing the features, classification process has been carried out using ensemble architecture of stacked auto-encoder and kernel-based convolution network (SAE_KCN). The experimental results have been done for various crops in terms of computational time of 56%, accuracy 98%, precision of 85.5%, recall of 89.9% and F-1 score of 86% by proposed technique.

Designing green IoT communication by adaptive spotted hyena tunicate swarm optimization‐based cluster head selection

AbstractThe Internet of Things (IoT) has completely transformed the digital as well as virtual worlds of interconnected objects. In today's environment, the IoT describes a critical enabler for a broader variety of applications. Energy is at the center of the smart IoT application that allows the sensors to function. The sensors' ability to perform efficiently is hampered by rapid energy loss. To avoid the rapid loss of energy from sensors in the IoT, and energy‐effective method is necessary. Because heuristic approaches are not ideal for these issues and may turn into NP‐hard problems, meta‐heuristic techniques are mainly successful in solving various problems with near‐optimal solutions. This paper intends to develop a novel clustering protocol in Green IoT using a well‐performing novel hybrid adaptive meta‐heuristic algorithm. Here, an energy‐efficient clustering is performed by the hybrid spotted hyena optimization (SHO) and tunicate swarm algorithm (TSA) referred to as adaptive spotted hyena tunicate swarm optimization (ASHTSO) by deriving a multi‐objective function with “distance, energy, delay, security, and QoS”. The simulation findings show that the suggested technique outperforms existing algorithms significantly.

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Post-Quantum Secure Identity-Based Encryption Scheme using Random Integer Lattices for IoT-enabled AI Applications

Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, where the IoT smart devices send securely the sensing data to their nearby gateway nodes(s) with the help of IBE and the gateway node(s) secure aggregate the data from the smart devices by decrypting the messages using the proposed IBE decryption. Later, the gateway nodes will securely send the aggregated data to the cloud server(s) and the Big data analytics is performed on the authenticated data using the Artificial Intelligence (AI)/Machine Learning (ML) algorithms for accurate and better predictions.

Hybrid Malware Detection Based on Bi-LSTM and SPP-Net for Smart IoT

In this article, we propose the hybrid malware detection scheme, HyMalD, with bidirectional long short-term memory (Bi-LSTM) and the spatial pyramid pooling network (SPP-Net). Its purpose is to protect Internet of Things (IoT) devices and minimize the damage caused by infection through obfuscated malware. HyMalD performs the static and dynamic analyses logically simultaneously to detect obfuscated malware, which is impossible to do using static analysis alone. First, it extracts static features of the opcode sequence using a reconstructed dataset according to the obfuscation and extracts the application programming interface (API) call sequence dynamically. The extracted features are trained through the Bi-LSTM and SPP-Net models, which HyMalD uses to detect and classify IoT malware. The performance of HyMalD was evaluated, and its detection accuracy was 92.5%. The false-negative rate (FNR) of HyMalD was 7.67%. Thus, HyMalD detects IoT malware more accurately and with a lower FNR than in the static analysis, which had 92.09% detection accuracy and 9.97% FNR.

Intelligent Solutions for Manipulating Purchasing Decisions of Customers Using Internet of Things during Covid-19 Pandemic

It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks.

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.

FEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning

Federated Learning (FL) is a promising distributed training model that aims to minimize the data sharing to enhance privacy and performance. FL requires sufficient and diverse training data to build efficient models. Lack of data balance as seen in rare classes affects the model accuracy. Generative Adversarial Networks (GAN) are remarkable in data augmentation to balance the available training data. In this article, we propose a novel Federated Deep Learning (DL) Intrusion Detection System (IDS) using GAN, named FEDGAN-IDS, to detect cyber threats in smart Internet of Things (IoT) systems; smarthomes, smart e-healthcare systems and smart cities. We distribute the GAN network over IoT devices to act as a classifier and train using augmented local data. We compare the convergence and accuracy of our model with other federated intrusion detection models. Extensive experiments with multiple datasets demonstrates the effectiveness of the proposed FEDGAN-IDS. The model performs better and converges earlier than the state-of-the-art standalone IDS.

An Accurate Detection Approach for IoT Botnet Attacks Using Interpolation Reasoning Method

Nowadays, the rapid growth of technology delivers many new concepts and notations that aim to increase the efficiency and comfort of human life. One of these techniques is the Internet of Things (IoT). The IoT has been used to achieve efficient operation management, cost-effective operations, better business opportunities, etc. However, there are many challenges facing implementing an IoT smart environment. The most critical challenge is protecting the IoT smart environment from different attacks. The IoT Botnet attacks are considered a serious challenge. The danger of this attack lies in that it could be used for several threatening commands. Therefore, the Botnet attacks could be implemented to perform the DDoS attacks, phishing attacks, spamming, and other attack scenarios. This paper has introduced a detection approach against the IoT Botnet attacks using the interpolation reasoning method. The suggested detection approach was implemented using the interpolation reasoning method instead of the classical reasoning methods to handle the knowledge base issues and reduce the size of the detection fuzzy rules. The suggested detection approach was designed, tested, and evaluated using an open-source benchmark IoT Botnet attacks dataset. The implemented experiments show that the suggested detection approach was able to detect the IoT Botnet attacks effectively with a 96.4% detection rate. Furthermore, the obtained results were compared with other literature results; the accomplished comparison showed that the suggested method is a rivalry with other methods, and it effectively reduced the false positive rate and interpolated the IoT Botnet attacks alerts even in case of a sparse rule base.

IoTracker: A probabilistic event tracking approach for data-intensive IoT Smart Applications

Smart Applications for cities, industry, farming and healthcare use Internet of Things (IoT) approaches to improve the general quality. A dependency on smart applications implies that any misbehavior may impact our society with varying criticality levels, from simple inconveniences to life-threatening dangers. One critical challenge in this area is to overcome the side effects caused by data loss due to failures in software, hardware, and communication systems, which may also affect data logging systems. Event traceability and auditing may be impaired when an application makes automated decisions and the operating log is incomplete. In an environment where many events happen automatically, an audit system must understand, validate, and find the root causes of eventual failures. This paper presents a probabilistic approach to track sequences of events even in the face of logging data loss using Bayesian networks. The results of the performance analysis with three smart application scenarios show that this approach is valid to track events in the face of incomplete data. Also, scenarios modeled with Bayesian subnets highlight a decreasing complexity due to this divide and conquer strategy that reduces the number of elements involved. Consequently, the results improve and also reveal the potential for further advancement.

Making Cities Smarter-Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints.

One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

OQR-SC: An Optimal QoS Aware Routing Technique for Smart Cities Using IoT Enabled Wireless Sensor Networks

Internet of things (IoT) makes a machines optimization in everyday which processing the data by very intelligently and make communication more effectively and efficiently. In smart city environments, the data collection and communication play an important role in defining quality. Since the research period, it has been recommending a new data acquisition and data communication software framework for IoT smart applications. For further improvements, we recommend an optimal QoS aware routing technique for smart cities using IoT enabled wireless sensor networks (OQR-SC). In data gathering phase, the proposed system introduce chaotic bird swarm optimization (CBSO) algorithm for IoT sensor cluster formation; the improved differential search (IDS) algorithm used to estimate the faith degree of each sensor node, the highest trust node act as cluster head (CH). In data transferring phase, first illustrates lightweight signcryption technique for data encryption between two IoT sensors. Then, we use optimal decision making (ODM) algorithm to compute the optimal path between source–destination in IoT platform. Finally, the proposed OQR-SC technique is implemented using network simulation (NS2) tool and analyzes the performance of proposed technique with existing state-of-art techniques. The result summarizes that average energy consumption of proposed OQR-SC technique is 12.39% lower than the existing techniques; the average network lifetime of proposed OQR-SC technique is 15.96% higher than the existing techniques; the average delay of proposed OQR-SC technique is 21.08% lower than the existing techniques; and the average throughput of proposed OQR-SC technique is 17.89% higher than the existing techniques.

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Internet of Things (IoT) infrastructure connects consumer electronics, household appliances, and other smart gadgets. For designing IoT systems and applications, different architectures and protocols are being used. The design quality of an IoT system and its services is assessed with quality of service (QoS) factors such as complexity, functional appropriateness, performance, efficiency, compatibility, maintainability, portability, and usability. The existing methods in the literature focus on measuring the quality of an IoT system during execution time; however, there is a need to define some standards that may be used to determine the quality of an IoT system from its design. This work models the IoT EcoSystem as a schema graph and presents a suite of metrics to determine the design quality of an IoT system and its services under the defined QoS factors. The presented metrics are mapped to the ISO-9126 QoS factors to ensure the standards and quality of IoT services during the design phase. The proposed metrics are validated with benchmarking on a case study smart healthcare IoT system. The results proved that the presented metrics are practical to quantify the quality of IoT design models under QoS factors, and the suite of metrics is able to compare differently designed IoT services.

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”.

Information Harvesting for Far-Field Wireless Power Transfer

In today's smart world, the Internet of Things (IoT) offers a very large scale of technologies and use cases. An IoT system comprises a huge amount of low-power IoT network elements, sensors, and smart devices connected to each other. For these devices, it is important to have a sufficient level of energy resource for communication and to receive software/firmware updates periodically. In this aspect, wireless power transfer is a popular paradigm to tackle energy limitations. Therein, the battery of IoT devices can be remotely replenished by means of radio-frequency-radiated signaling, referred to as far-field wireless power transfer. Furthermore, harvesting energy from ambient communication signals available in the environment introduces simultaneous wireless information and power transfer technology. These systems face the trade-off between information capacity and energy harvesting efficiency. Accordingly, various signaling design frameworks have been proposed to comply with different system preferences between power and information. In this work, aside from existing trends, we propose a novel concept, information harvesting (IH), which introduces a novel protocol that lies in transmitting information on top of the existing wireless power transfer mechanism. Considering the diversity of IoT networks and the availability of wireless power transfer infrastructure, the proposed IH principle may turn out to be a pivotal methodology that combines physical layer security with wireless power transfer, particularly for cases where a large number of IoT devices require software/firmware updates along with periodical battery recharging needs.

Monitoring and Control System for Energy Management Using Internet of Things

Abstract: The use of technology has become an essential part of improving lifestyle, work efficiency, and a catalyst for economic growth. The benefit of the Internet of Things (IoT) and connected nodes has been on a steep incline in recent years. This paper aims to research, build, test and implement a low-cost energy monitoring and control system using IoT devices. Electrical appliances (e.g., air conditioning units and overhead lighting) can be controlled and monitored using IoT technology from any place in the world. In order to accomplish this goal, a complete front-end to back-end system that includes a smart device application (iOS platform), a cloud-based database, an Application Programming Interface (API), and a hardware development is proposed. A small programmable specialized computing device (e.g., Raspberry Pi) for preliminary testing. This smart node was chosen due to familiarity, and its capabilities, such as general purpose pins and built-in Wi-Fi chip. The end goal is to observe energy efficiency by monitoring and controlling air conditioning appliances and standard overhead lighting units. These smart IoT devices allow for the usage energy data from each unit to be collected and stored in a Cloud-based database that can be analysed and reported for energy conservation and analysis. Keywords: Internet of Things (IoT), Energy Monitoring, Control System, Security.

Secure cloud‐based data storage scheme using postquantum integer lattices‐based signcryption for IoT applications

AbstractThe primary objective of postquantum cryptography (also known as quantum‐resistant cryptography) is to develop the cryptographic systems that need to be robust against both quantum and classical computers, and can also interoperate with the existing communications protocols and networks. In an Internet of Things (IoT) environment, the communicated messages contain sensitive information that are transmitted over an open channel, where message integrity and data privacy become challenging tasks. Although several traditional cryptographic security protocols can be applied for IoT security and data privacy, such as authentication, access control, key agreement, and digital signature, but they are not resilient against quantum attacks. To overcome these issues, in this article, we first present an advanced and efficient construction of postquantum lattice‐based signcryption scheme, and then apply the constructed lattice‐based signcryption in IoT applications, where data sensed by the deployed IoT smart devices is securely stored at the cloud, via the gateway nodes (aggregators). The data stored at the cloud servers cannot be even modified by them due to the involved signatures generated by the aggregators. The formal security analysis shows the robustness of our designed lattice‐based signcryption scheme. Other detailed information security analysis and a performance analysis with the traditional number‐theoretical based public key cryptosystems show the efficacy, and significantly better security and functionality features of the proposed scheme under the lattice‐based postquantum context.

Comprehensive survey of IoT based arduino applications in healthcare monitoring

The Internet of Things (IoT) enables a range of applications in the area of information technology, one of which is linked and smart health care. A successful Monitoring patients' health state in real time through an IoT healthcare system is the goal, to avert critical patient scenarios, and to enhance patient quality of life via smart IoT settings. Additionally, healthcare expenses are reduced, and patient outcomes are improved. Edge devices (glucose monitors, ventilators, pacemakers) are common in IoT systems. etc.). We reviewed the essential methodologies and cutting-edge technology for remote patient monitoring in this paper, taking into consideration data privacy and security concerns. The primary emphasis of this study is on the many solutions suggested in healthcare for IoT in real-time remote patient monitoring, blockchain technology in healthcare, and data security.

A Research on the Realization Algorithm of Internet of Things Function for Smart Education.

The traditional teaching mode is to use a point-to-point mode or a computer-aided system for teaching, but this limits students' enthusiasm and interest in learning. The Internet of Things (IoT) technology is a technology that integrates sensors, the Internet, and terminals to transmit information in real time. The smart education based on the Internet of Things can realize remote teaching and actual scene teaching, and students can freely choose the learning location and time, which can greatly improve students learning interest and learning efficiency, which is a development trend of a new teaching method. Smart IoT teaching is a teaching method that combines IoT technology and artificial intelligence technology. This paper mainly studies the research and analysis of the smart education model based on the IoT in remote teaching. In this paper, sensor technologies such as cameras will be used to collect students' expressions, speech, and other actions in class from different regions. These data features will be processed by the terminal's intelligent algorithm, and the desired knowledge will be obtained according to the students' behavior information. The information processed by the intelligent algorithm will be transmitted to the terminal system where the teacher is located, such as computer and mobile phone. This paper focuses on analyzing the reliability and accuracy of the intelligent algorithm of the IoT smart education terminal. The results show that the prediction error of the student behavior information is within 3% and the correlation coefficient reaches 0.99.

Comparison of Blackhole and Wormhole Attacks in Cloud MANET Enabled IoT for Agricultural Field Monitoring

In Mobile Ad hoc Network (MANET) enabled Internet of Things (IoT) agricultural field monitoring, sensor devices are automatically connected and form an independent network that serves as a cloud for many services such as monitoring, securing, and properly maintaining. Cloud-based services in MANET models can prove to be an extremely effective way of smart agricultural functionalities for device-to-device information exchange. Security is a serious issue with Cloud-MANET-based IoT since nodes are scattered, mobile, and lacking centralized administrator, which makes it possible for data tampering and illegal actions on cloud servers. Therefore, these types of networks are more vulnerable to Denial of Service (DoS) attacks such as Blackhole and Wormhole. The MANET Enabled IoT-Agricultural Field Monitoring environment is deployed through a case study. The effect of Blackhole and Wormhole attacks is analyzed using the Ad hoc On-demand Distance Vector (AODV) routing protocol with the help of Network Simulator 3 (NS-3) in order to determine which has the most impact on network performance. We computed performance constraints such as throughput, packet delivery ratio (PDR), end-to-end delay (EED), and Jitter-Sum of preprocessed data gathered with the flow-monitor module of NS-3. The effect of attacks on MANET Enabled IoT-Agricultural Field Monitoring is compared on the varying number of nodes participating in the Cloud-MANET-based IoT network. The throughput and goodput capability of every node is computed through the trace metric package. This method is also highly useful for future Cloud-MANET-Based IoT smart agricultural field security research.