Internet Of Things Data Transmission Research Articles

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

Secure Data Transmission in IoT based on Optimization Based Routing Protocol for Wireless Body Area Networks

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across body area networks. The main purpose is to enhance the security level of data transmission between patients and health service providers by considering the availability of energy at sensor nodes through the Wireless Body Area Network. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important systems is selecting the relay node, which can be modeled as an optimization problem. This research proposes the patient health collected data are needed for transmission in IoT-Patient Care Monitoring (PCM) based on the Optimized Routing Protocol along WBANs. The proposed model focuses on a routing mechanism that makes use of an Optimization Routing-based Algorithm along with the relay node selection based on distances and residual energies. DO NOT USE SPECIAL CHARACTERS, SYMBOLS, OR MATH IN YOUR TITLE OR ABSTRACT.

INTERNET OF THINGS DATA TRANSFER METHOD USING NEURAL NETWORK AUTOENCODER

Background. The number of devices in the Internet of Things is constantly increasing. At the same time, the number of solutions on the market for such technologies is growing. Statistics confirm that these factors lead to an increase in data transfer volumes. This raises the number of resources spent on data transmission. The growing trend in the number of users of the Internet of Things technology leads to the emergence of the problem of a rapid increase in the data transmitted by the network.
 Objective. The purpose of the paper is to improve the process of data transmission in the Internet of Things by modifying the neural network autoencoder to reduce network resources use.
 Methods. Analysis of publications dedicated to Internet of things data transmission. Integration of existing data coding solutions based on a neural network autoencoder in the process of transmitting data from the Internet of things.
 Results. The neural network autoencoder has been improved by using an algorithm that additionally includes an arithmetic encoder and further training a new model on the output of a full-fledged autoencoder.
 Conclusions. The process of data transmission in the Internet of Things network has been modified by improving the neural network autoencoder by using the training of a smaller neural network on the initial data of the main autoencoder, which has reduced the amount of data transmitted and, accordingly, reduced the use of network resources.

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Internet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion.

Novel Secure Data Transmission Methods for IoT Based on STP-CS With Multilevel Critical Information Concealment Function

The Internet of Things (IoT) is a large-scale network of various sensing devices connected via the Internet to achieve intelligent functions. Efficient secure data transmission is a significant guarantee for specific functions of IoT. In recent years, compressive sensing (CS) has been applied to the field of the IoT and a substantial number of CS-based IoT data transmission schemes have been proposed. While existing IoT data transmission schemes based on CS could mostly accomplish signal sampling, data compression, and encrypted transmission, the goal of privacy protection has rarely been achieved. Based on chaos theory and semi-tensor product CS (STP-CS), this article proposes two novel secure data transmission methods for different scenarios of IoT: 1) multiple concealing method (MC method) and 2) precise concealing method (PC method). Both methods can provide different levels of reconstruction results for different receivers with various authorization levels. The feasibility, storage requirements, robustness, and security of the proposed methods are theoretically analyzed and experimentally simulated. The results show that the proposed methods ensure both the stability and security of data transmission, save storage space of sensors, and flexibly protect the privacy of the content of data transmitted.

Decentralized Blockchain Network for Resisting Side-Channel Attacks in Mobility-Based IoT

The inclusion of mobility-based Internet-of-Things (IoT) devices accelerates the data transmission process, thereby catering to IoT users’ demands; however, securing the data transmission in mobility-based IoT is one complex and challenging concern. The adoption of unified security architecture has been identified to prevent side-channel attacks in the IoT, which has been discussed extensively in developing security solutions. Despite blockchain’s apparent superiority in withstanding a wide range of security threats, a careful examination of the relevant literature reveals that some common pitfalls are associated with these methods. Therefore, the proposed scheme introduces a novel computational security framework wherein a branched and decentralized blockchain network is formulated to facilitate coverage from different variants of side-channel IoT attacks that are yet to be adequately reported. A unique blockchain-based authentication approach is designed to secure communication among mobile IoT devices using multiple stages of security implementation with Smart Agreement and physically unclonable functions. Analytical modeling with lightweight finite field encryption is used to create this framework in Python. The study’s benchmark results show that the proposed scheme offers 4% less processing time, 5% less computational overhead, 1% more throughput, 12% less latency, and 30% less energy consumption compared to existing blockchain methods.

DATA SECURITY IN HEALTHCARE USING IOT

While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data don't seem to be focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this project, the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks related to unsecured data transmission, especially between IoT sensor devices and network routers, then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified employing a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end to-end security solution of healthcare data transmission in IoT. This project also provides further research avenues on IoT sensor driven security.

Secure and Scalable Healthcare Data Transmission in IoT Based on Optimized Routing Protocols for Mobile Computing Applications

The Internet of Things (IoT) has impacted various aspects of life, but its profound effects on the health sector are particularly striking because of its cutting-edge nature. Mobile computing characteristics enable IoT to play a more important role when used with mobile computing. A significant part of the benefits of IoT in healthcare can be attributed to mobile health, which is greatly enhanced by mobile computing. Wearables transmit large amounts of data to IoT devices through sensors, actuators, and transceivers. Threats, attacks, and vulnerabilities abound for data on the Internet of Things. Therefore, addressing IoT-related security, privacy, and vulnerability issues call for a robust security solution. This paper proposes a secure and scalable healthcare data transmission framework in IoT based on an optimized routing protocol. Initially, the health data is collected from various IoT devices like wearable devices and sensors. The raw data is preprocessed via data cleaning and data reduction techniques. K-nearest neighbor (KNN) imputation is performed and principal component analysis (PCA) is employed for dimension reduction of the data. Utilizing modified local binary patterns (MLBP), the features are extracted from the preprocessed data. By combining the fuzzy dynamic trust-based RPL algorithm with the butter ant optimization (BAO) algorithm for low-power and lossy networks, the proposed fuzzy dynamic trust-based RPL (FDT-RPL) protocol improves the overall security of data transmission. The algorithm has been implemented for a smart healthcare system, and the performance is analyzed by comparing it with traditional approaches. The proposed routing protocol provided a secure and scalable healthcare data transmission.

Parallel Meta-Heuristics for Solving Dynamic Offloading in Fog Computing

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud datacenters are located far away from IoT devices, and consequently, the transmission of IoT data may be delayed. In this paper, we investigate fog computing, which is a new paradigm that overcomes many issues of cloud computing. More importantly, dynamic task offloading in fog computing is a challenging problem that requires an optimal decision for processing the tasks that are generated in each time slot. Thus, exact optimization methods based on Lyapunov function have been widely used for solving dynamic offloading which represents an NP hard problem. To overcome the scalability issue of exact optimization techniques, we have explored famous population based meta-heuristics for optimizing the offloading process of a set of dynamic tasks using Orthogonal Frequency Division Multiplexing (OFDM) communication. Hence, a parallel multi-threading framework is proposed for generating the optimal offloading solution while selecting the best sub-carrier for each offloaded task. More importantly, our contribution associates a thread for each IoT device and generates a population of random solutions. Next, each population is updated and evaluated according to the proposed fitness function that considers a tradeoff between the delay and energy consumption. Upon the arrival of new tasks at each time slot, an evaluation is performed for maintaining some individuals of the previous population while generating new individuals based on some criteria. Our results have been compared to the results achieved using Lyapunov optimization. They demonstrate the convergence of the fitness function, the scalability of the parallel Particle Swarm Optimization (PSO) approach, and the performance in terms of the offline error and the execution cost.

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

The Internet of Things (IoT) plays a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although this IoT paradigm has suffered from intrusion threats and attacks that cause security and privacy issues, existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, the IoT intrusion threat has been analyzed using the sparse convolute network to contest the threats and attacks. The web is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly, Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. Effective utilization of training patterns in the network successfully classifies the standard and threat patterns. Then, the effectiveness of the system is evaluated using experimental results and discussion. Network intrusion detection systems are superior to other types of traditional network defense in providing network security. The research applied an IGA-BP network to combat the growing challenge of Internet security in the big data era, using an autoencoder network model and an improved genetic algorithm to detect intrusions. MATLAB built it, which ensures a 98.98% detection rate and 99.29% accuracy with minimal processing complexity, and the performance ratio is 90.26%. A meta-heuristic optimizer was used in the future to increase the system’s ability to forecast attacks.

Blockchain and artificial intelligence enabled privacy‐preserving medical data transmission in Internet of Things

AbstractAdvancements in information technology have benefited the healthcare industry by providing it with distinct methods of managing medical data which improve the quality of medical services. The Internet of Things (IoT) and artificial intelligence are the foundations for innovative sustainable computing technologies in e‐healthcare applications. In the IoT‐enabled sustainable healthcare system, the IoT devices normally record the patient data and transfer it to the cloud for further processing. Security is considered an important issue in the design of IoT networks in the healthcare environment. To resolve this issue, this article presents a novel blockchain and artificial intelligence‐enabled secure medical data transmission (BAISMDT) for IoT networks. The goal of the BAISMDT model is to achieve security and privacy in reliable data transmission of the IoT networks. The proposed model involves a signcryption technique for secure and reliable IoT data transmission. The blockchain‐enabled secure medical data transmission process takes place among the IoT gadgets and service providers. The blockchain technique is applied to generate a viable environment to securely and reliably transmit data among different data providers. Next to the decryption process, the modified discrete particle swarm optimization algorithm with wavelet kernel extreme learning machine model is applied to determine the presence of disease. An extensive set of simulations were carried out on a benchmark medical dataset. The experimental results analysis pointed out the superior performance of the proposed BAISMDT model with the accuracy of 97.54% and 98.13% on the applied Heart Statlog and WBC dataset, respectively.

Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment

With the growth of Internet of Things (IoT) services and applications, the efficient transmission of IoT data has been crucially required. The IETF has recently developed the QUIC protocol for UDP-based multiplexed and secure transport. The Multipath QUIC (MP-QUIC) is also being discussed as an extension of QUIC in the multipath network environment. In this paper, we propose a proxy-based adaptive MP-QUIC transmission for throughput enhancement in the IoT environment. In the proposed scheme, a proxy device is employed between IoT clients and IoT server to aggregate the traffics of many clients in the access network. The proxy will transport a large among of traffics to the server, adaptively to the network conditions, by using multiple paths in the backbone network. For this purpose, the proxy device employs a path manager to monitor the current network conditions and a connection manager to manage the MP-QUIC connections with the IoT server over the backbone network with multiple paths. For effective MP-QUIC transmission, the proxy will transmit the prioritized packets to the server using the best path with the lowest round-trip time (RTT), whereas the non-prioritized packets are delivered over the other paths for traffic load balancing in the network. From the testbed experimentations with the MP-QUIC implementation and ns-3 simulation modules, we see that the proposed scheme can outperform the normal QUIC (using a single path) and the existing MP-QUIC scheme (using the round-robin policy) in terms of response delay and total transmission delay. Such performance gaps tend to increase as the link delays and packet loss rates get larger in the network.

Latent Discriminative Low-Rank Projection for Visual Dimension Reduction in Green Internet of Things

Internet of Things (IoT) terminals have been widely deployed for data sensing and analysis, and efficient data storage and transmission plays an important role in green IoT due to the explosive data growth. To simultaneously reduce the data dimension and preserves the discriminative intrinsic knowledge of data, this paper develops a novel latent discriminative low-rank projection (LDLRP) method for visual dimension reduction. Specifically, a data self-expressiveness model is established by considering the low-rank and discriminative similarity relations of data. Then, the developed model is efficiently optimized and solved via an augmented Lagrange multiplier (ALM) based-iterative algorithm, and a block-diagonal solution can be found for intraclass and interclass graph construction. Afterwards, a discriminative dimension reduced-subspace is derived by concurrently minimizing the intraclass scatter and maximizing the interclass scatter. The experimental results on benchmark datasets show that the proposed method can learn discriminative lower-dimensional expressions of high-dimensional data, and yield promising classification accuracy compared with several state-of-the-art methods. Hence, the effectiveness and efficiency of proposed method in data dimension reduction and knowledge preservation are verified, which will facilitate efficient data storage, transmission and application in green IoT.

Liquid‐Crystal Mediated Assembly of Iodinated Graphene Oxide for Ultra‐Dense Supercapacitors as Safe Power Source for Internet of Things Data Transmission

AbstractDense iodinated reduced graphene oxide (rGO) with outstanding electrical and electrochemical properties is produced through a green method comprising liquid crystal‐mediated assembly of polyiodides and GO, and subsequent UV irradiation. The dense rGO electrode (1.46 g/cm3) exhibited a very high volumetric capacitance of 226 F/cm3, a factor of three larger than commercial activated carbon, and amongst the highest reported for graphene. The scalability is demonstrated by the fabrication of supercapacitor pouch cells that realized a volumetric energy density of 0.94 Wh/l, comparable to market products with similar footprint, while advantageously using a safe and green aqueous electrolyte. Our pouch cell powered battery‐less Internet of Things (IoT) sensor nodes and demonstrated sensing and transmission of over 40 temperature and relative humidity data packets. Our work establishes the critical advantages graphene‐based materials have over activated carbons in terms of ease of fabrication, tailorability, and enhanced volumetric energy density to advance the state‐of‐art in supercapacitor device research.

Joint Optimization for Visual Data Transmission in the Resource Constraint 5G IoT

With the integration of 5G and Internet of Things (IoT), the application of large-scale IoT devices networking is increasingly extensive, such as building management, property maintenance, autonomous vehicles, healthcare, and shopping to tourism. In these scenes, the volume of data transmission is quite large, especially visual data (image, video, et al). However, due to the limited resource of IoT devices, such as battery, power, bandwidth, visual data transmission is complex to optimize, single objective optimization is difficult to insure the optimal latency, throughput and power at the same time. In this paper, we propose a method to jointly optimize the resource allocation of visual data transmission in resource constraint 5G IoT. Instead of single objective optimization, we combine the bandwidth, power consumption and latency into a hybrid model, then propose a low-complexity algorithm to solve this multiple objective optimization problem. The simulation results demonstrate that the proposed method increases the comprehensive utility of visual data transmission in the resource constraint 5G-IoT by 35%-48% compared with existing approaches.

Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice

Internet of Things (IoT), which provides the solution of connecting things and devices, has increasingly developed as vital tools to realize intelligent life. Generally, source-limited IoT sensors outsource their data to the cloud, which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved. Though encryption technology can guarantee the confidentiality of private data, it hinders the usability of data. Searchable encryption (SE) has been proposed to achieve secure data sharing and searching. However, most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to the adversary with quantum computers. Moreover, the untrusted cloud server may perform an unfaithful search execution. To address these problems, in this paper, we propose the first verifiable identity-based keyword search (VIBKS) scheme from lattice. In particular, a lattice-based delegation algorithm is adopted to help the data user to verify both the correctness and the integrity of the search results. Besides, in order to reduce the communication overhead, we refer to the identity-based mechanism. We conduct rigorous proof to demonstrate that the proposed VIBKS scheme is ciphertext indistinguishable secure against the semi-honest-but-curious adversary. In addition, we give the detailed computation and communication complexity of our VIBKS and conduct a series of experiments to validate its efficiency performance.

Blockchain-Based Continued Integrity Service for IoT Big Data Management: A Comprehensive Design

The state-of-the-art centralized Internet of Things (IoT) data flow pipeline has started aging since it cannot cope with the vast number of newly connected IoT devices. As a result, the community begins the transition to a decentralized pipeline to encourage data and resource sharing. However, the move is not trivial. With many instances allocating data or service arbitrarily, how can we guarantee the correctness of IoT data or processes that other parties offer. Furthermore, in case of dispute, how can the IoT data assist in determining which party is guilty of faulty behavior. Finally, the number of Service Level Agreement (SLA) increases as the number of sharing grows. The problem then becomes how we can provide a natural SLA generation and verification that we can automate instead of going through a manual and tedious legalization process through a trusted third party. In this paper, we explore blockchain solutions to answer those issues and propose continued data integrity services for IoT big data management. Specifically, we design five integrity protocols across three phases of IoT operations—during the transmission of IoT data (data in transit), when we physically store the data in the database (data at rest), and at the time of data processing (data in process). In each phase, we first lay out our motivations and survey the related blockchain solutions from the literature. We then use curated papers from our surveys as building blocks in designing the protocol. Using our proposal, we augment the overall value of IoT data and commands, generated in the IoT system, as they are now tamper-proof, verifiable, non-repudiable, and more robust.

A NB-IoT data transmission scheme based on dynamic resource sharing of MEC for effective convergence computing

The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. In the narrowband Internet of Things (NB-IoT), the huge number of IoT devices sends the information to the remote network. Simultaneously, the massive IoT devices from the heterogeneous locations access to the radio remote head (RRH) resources to communicate with the remote servers (e.g., mobile cloud computing), while there are limited resources in NB-IoT environments. Therefore, many IoT traffic may be discarded and may have too long queue times for each traffic where it can cause the battery life reduction of the IoT devices. To cope with the mentioned issues, this paper proposed a novel and efficient IoT data transmission scheme based on dynamic resource sharing of RRHs called efficient dynamic resource sharing scheme (E-DRSS). The proposed E-DRSS computes the heterogeneous IoT device with different resources into new groups, and then each group of IoT devices is selected for accessing its appropriate RRH. The evaluation of performance shows that the proposed scheme enhances quality of service (QoS) outperform on the current IoT scheme in terms of the jitters, end-to-end (E2E) latency, throughputs, and number of packets drop during transmission. Therefore, the proposed scheme is suitable to handle more IoT devices simultaneously and increase the communication reliability.

A new adaptive XOR, hashing and encryption-based authentication protocol for secure transmission of the medical data in Internet of Things (IoT).

Internet of Things (IoT) plays a prominent role in health-care of patients, which assist the physicians and patients through the assistance in effective decision-making and additionally, in the medical field, IoT plays a significant role in real-time monitoring of the patients. Even though the data provided by the IoT devices ensure the effective decision-making, the data is susceptible to the network attacks. Thus, the paper proposes an authentication protocol for enabling the secure data transmission in IoT based on three functions, such as encryption function, hashing function, and adaptive XOR function. The proposed authentication protocol is named as, Adaptive XOR, hashing and Encryption Key Exchange (AXHE) protocol, which is the combination of the functions, such as encryption function, hashing function, and adaptive XOR function. The protocol ensures the security in the communication through two successive phases, such as registration and authentication of the user, where the user name, password, public keys, private keys, and security factor are employed. The authentication is progressed as seven levels and whenever the security factor matches, the user is authenticated and the communication continues. The analysis of the proposed AXHE is performed using 50 and 100 nodes in the presence of DOS and black hole attacks, which acquires the detection rate, throughput, and detection delay of 0.3859, 0.32, and 6.535s, respectively.

IoT Sensor Initiated Healthcare Data Security

While the Internet of Things (IoT) has been instrumental in healthcare data transmission, it also presents vulnerabilities and security risks to patients’ personalized health information for remote medical treatment. Currently most published security solutions available for healthcare data are not focused on data flow all the way from IoT sensor devices placed on a patient’s body through network routers to doctor’s offices. In this paper, we studied how the IoT network facilitates healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, especially between IoT sensor devices and network routers, and then proposed an encrypted security solution initiated at IoT sensor devices. Our proposed solution provides a cryptography algorithm embedded into the sensor device such that the packets generated with patient’s health data are encrypted right at the sensor device before being transmitted. The proof of concept has been verified using a lab setup with two level encryption at the IoT sensor level and two level decryption at the receiving end at the doctor’s office. Test results are promising for an end-to-end security solution of healthcare data transmission in IoT. This paper also opens up further research avenues on IoT sensor driven security.