Internet Of Things Transmission Research Articles

A controllable privacy data transmission mechanism for Internet of things system based on blockchain

With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.

IEPSBP: A Cost-Efficient Image Encryption Algorithm Based on Parallel Chaotic System for Green IoT

With the fast development of the Internet of Things (IoT) technologies, more IoT devices are currently connected with the Internet, resulting in more exchange of information. However, data privacy and security threats have become emerging challenges of the IoT. In this paper, we are concerned about the security of image transmission in green IoT. Image encryption algorithms for green IoT are faced with two challenges: 1) To save the cost, devices in green Internet of Things (IoT) always have very low computing power, so they cannot support high-precision computing and 2) The algorithm deployed on the device should be efficient to guarantee the device to run for a long time. To solve the problem of precision limited, we propose a parallel chaotic system named PSBP. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> The PSBP is composed of Piecewise Linear Chaotic Map (PWLCM), Skew Tent Map (STM) and Bernoulli map in parallel model with the limited precision of 16-bit, which can generate good-performance key matrix for encryption. Then, we introduce a cost-efficient image encryption algorithm based on PSBP (named IEPSBP) which addresses the second challenge. Instead of bits or bytes, we use one row or one column as the basic unit to realize permutation and diffusion. The security analysis and performance experiments indicate that IEPSBP is secure and cost-efficient for green IoT.

Visualization of Railway Transportation Engineering Management Using BIM Technology under the Application of Internet of Things Edge Computing

In the past, railway line planning usually required engineers to design based on their own experience after a series of field visits, leading to heavy workload and low efficiency. Moreover, operation and maintenance management is more complicated due to an abundance of railway station equipment. Based on the above problems, this paper first puts forward the railway transportation line planning and design method based on Building Information Modeling (BIM) technology. Besides, LocaSpace Viewer realizes the three-dimensional (3D) visual scene modeling of the railway environment to improve the efficiency of railway line planning and design. Secondly, the railway station’s visual operation and maintenance management system is constructed via BIM Technology. Besides, the Internet of Things (IoT) is combined with edge computing and deep learning technology to build a 3D model of station equipment, collect data in real time, and analyze data efficiently. Finally, the design effect of the model, the performance of the visual management system, and the test results of network transmission delay are displayed and analyzed. The results show that BIM can construct the 3D visualization model with high fidelity for the railway environment. This model can get a reasonable line planning scheme and analyze its feasibility, provide a reliable basis for engineers to plan railway transportation lines, and improve design efficiency. In addition, the GPU occupation rate, CPU occupation rate, and memory occupation rate of the operation and maintenance management system in different operating environments are within the standard range; when multiple clients access the system, the system data access delay is 100% less than 8 ms, which has good performance. Furthermore, the performance of the IoT transmission data real-time scheduling model and the edge computing optimization algorithm applied to this system is better than other popular methods, which can significantly improve the operation efficiency of the system. This study aims to enhance the efficiency of railway transportation line planning and station operation and maintenance management with the help of digital technologies.

Joint flight scheduling and task allocation for secure data collection in UAV-aided IoTs

With the flexible mobility and deployment, Unmanned Aerial Vehicles (UAVs) have created a new dimension to provide data collection services for a large number of distributed wireless sensory devices. Unfortunately, the broadcast nature of wireless channels make it hard to guarantee the security of data transmission in Internet of Things (IoTs). In this paper, focusing on the scenario where potential eavesdroppers intend to intercept the data uploading of legitimate nodes, we take the advantages of the UAV technology and the physical-layer security approach to study the Secure Data Collection Problem (SDCP). We first explore the flight scheduling and task allocation, that is, determining which areas to be visited and who to be served by UAVs, to establish favorable communication channels. Then we set up the utility function with secrecy rates and non-negative budget constraints. Due to the coupling between the UAV scheduling and task allocation optimization variables in the SDCP, we construct a surrogate function to achieve decoupling and reformulate SDCP into the SDCP-m problem, which is analyzed and proved to be a nonnegative monotone submodular function subject to a general constraint. Finally, an efficient Cost–Benefit Greedy (CBG) algorithm, which is theoretically analyzed to seek the quantitative value of approximation ratio, is proposed. The simulation results show that the CBG algorithm not only outperforms benchmark algorithms in terms of the average secrecy rate and the iteration number, but also generates a nearly optimal trajectory.

Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

A novel geo-opportunistic routing algorithm for adaptive transmission in underwater internet of things

A novel geo-opportunistic routing algorithm for adaptive transmission in underwater internet of things

Relay-assisted secure short-packet transmission in cognitive IoT with spectrum sensing

Cognitive Internet of Things (IoT) has attracted much attention due to its high spectrum utilization. However, potential security of the short-packet communications in cognitive IoT becomes an important issue. This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming (MRC/ZFB) scheme to guarantee the secrecy performance of dual-hop short-packet communications in cognitive IoT. This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ratio (SNR) regime and the infinite blocklength. In addition, the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput. The numerical results verify the impact of the system parameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a secrecy constraint. It is shown that the proposed scheme achieves better secrecy performance than other benchmark schemes.

Energy-efficient data collection for UAV-assisted IoT: Joint trajectory and resource optimization

Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. As energy-efficient operations are critical to prolong the lifetime of the energy-constrained IoT devices, the Unmanned Aerial Vehicle (UAV) can be dispatched to geographically approach the IoT clusters towards energy-efficient IoT transmissions. This paper intends to maximize the system energy efficiency by considering both the IoT transmission energy and UAV propulsion energy, where the UAV trajectory and IoT communication resources are jointly optimized. By applying large-system analysis and Dinkelbach method, the original fractional optimization is approximated and reformulated in the form of subtraction, and further a block coordinate descent framework is employed to update the UAV trajectory and IoT communication resources iteratively. Extensive simulation results are provided to corroborate the effectiveness of the proposed method.

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.

Elman-Based Secure Data Transmission Quality Prediction for Complex IoT Networks

The information age has brought earth-shaking changes. For interconnection of all things, the data transmission has widely employed the Internet of Things (IoT). The IoT transmission faces complex environments. The secure data transmission is very important for mobile IoT networks. The secure data transmission quality prediction is investigated for mobile IoT networks. The probability of strictly positive secrecy capacity (SPSC) is used to evaluate the secure data transmission quality, and the expressions are first derived. Then, employing Elman network, a secure data transmission quality intelligent prediction approach is proposed. The extensive simulations are run to evaluate the proposed approach. The simulation results show that the Elman-based approach can achieve a higher quality precision than other methods. The Elman-based approach also can achieve a lower time complexity.

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.

WITHDRAWN: Enhanced security in IOT environment using Blockchain: A survey

Blockchain (BC) methods is one of the best technologies in the Internet of Things (IoT) environments, it is a distributed sharing mechanism and communicate the IoT devices with secure manner. A blockchain is a connection of blocks and each block is linked to its earlier blocks. All block has the security hash code, previous block hash, and its data. The transaction between the BlockChain are the data is transfer between one IoT block nodes to another IoT Block node. The IoT device nodes are various types of physical devices like smart devices with embedded sensors, actuators, and capable of be in touch with other IoT device nodes. The responsibility of BlockChain in IoT is to grant a method to provide protected data transmission through IoT device nodes. In this paper discussed about the blockchain technology is how to secure the IoT transmission data and what are the challenges comes into these technologies. BlockChain is a safe method that can be used widely. IoT needs this type of skill to permit protected communication among IoT device nodes in mixed atmosphere.

Comparative Study and Design Light Weight Data Security System for Secure Data Transmission in Internet of Things

Internet of things is shortened as IoT. Today IoT is a key and abrogating subject of the specialized and social importance. Results of buyers, things and vehicles, industry based and fundamental segments, sensors, and other everyday items are converged with network of internet and the solid information abilities which guarantee to change the sort in which we work and live. The proposed work demonstrates the implementation of symmetric key lightweight algorithm for secured data transmission of images and text using image encryption system as well as reversible data hiding system. In this paper, implemented symmetric key cryptography for various formats of images, as well as real time image acquisition system has been designed in the form of graphical user interface. Reversible data hiding system has also been designed for secure data transmission system.

Resource scheduling based on routing tree and detection matrix for Internet of things

Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

MNSRQ: Mobile node social relationship quantification algorithm for data transmission in Internet of things

MNSRQ: Mobile node social relationship quantification algorithm for data transmission in Internet of things

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.

Key technologies of Secure Multi-Party Computing for Perceived Data Transmission in Internet of Things

With the "wisdom of the Earth, connected to physical objects" makes it independent from the Internet gradually connected to the network and the Internet of Things. Internet of Things is another industrial upgrading of information industry after computer and mobile communication. The Internet of Things is based on the Internet. It uses technologies such as RFID, sensors and wireless sensor hardware to build a network information system covering all people and things in the world. It enables human economic, social life, production and operation and personal activities to operate in the intelligent Internet of Things. As an important support of national strategic emerging industries, the Internet of Things is the core and foundation of smart cities, national defense and intelligent manufacturing industries, and multi-domain, open and security are the theoretical requirements of the application of the Internet of Things. Aiming at the resource fragmentation problems caused by the closure of traditional the Internet of Things architecture, such as closure, tight coupling, poor scalability, and some security problems for the open sharing of the Internet of Things resources, this paper proposes an improved multi-domain Web of Things (MWoT) architecture based on the relevant standards of the Web of Things (WoT) architecture. Some related technologies, such as Internet of Things identification and edge cloud computing, are used to realize cross-domain sharing of Internet of Things resources and cross-domain collaboration of services, as well as to solve the problem of data transmission security.

Reconfigurable Intelligent Surface Enhanced Multi-User MISO Symbiotic Radio System

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

Design and Evaluation of Buffer-Aided Cooperative NOMA With Direct Transmission in IoT

The high spectrum efficiency of nonorthogonal multiple access (NOMA) is attractive to solve the massive number of connections in the Internet of Things (IoT). This article investigates a buffer-aided cooperative NOMA (C-NOMA) system in the IoT, where the intended users are equipped with buffers for cooperation. The direct transmission from the access point to the users and the buffer-aided cooperative transmission between the intended users are coordinated. In particular, a novel buffer-aided C-NOMA scheme is proposed to adaptively select a direct or cooperative transmission mode, based on the instantaneous channel state information and the buffer state. Then, the performance of the proposed scheme, in terms of the system outage probability and average delay, is theoretically derived with closed-form expressions. Furthermore, the full diversity order of three is demonstrated to be achieved for each user pair if the buffer size is not less than three, which is larger than conventional nonbuffer-aided C-NOMA schemes whose diversity order is only two in the considered C-NOMA system in the IoT.

Grant-Free Opportunistic Uplink Transmission in Wireless-Powered IoT: A Spatio-Temporal Model

Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for wireless-power Internet of Things (IoT) networks. This article jointly characterizes energy harvesting and packet transmissions in grant-free opportunistic uplink IoT networks energized via harvesting downlink energy. To do that, a joint queuing theory and stochastic geometry model is utilized to develop a spatio-temporal analytical model. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC). Such two dimensions are utilized to jointly track the scavenged/depleted energy to/from the batteries along with the arrival/departure of packets to/from devices buffers over time. Consequently, the adopted queuing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average delay, and the average buffer size. The effect of base stations (BSs) densification is discussed and several design insights are provided. The results show that the parameters for uplink power control and opportunistic channel access should be jointly optimized to maximize average network packet throughput, and hence, minimize delay.