Node Data Transmission Research Articles

A Comprehensive IoT Cloud-based Wind Station Ready for Real-time Measurements and Artificial Intelligence Integration

Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.

Energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization

Aiming at the problems of large number of data transmission node deaths and large transmission energy consumption output in energy-saving clustering routing communication of wireless sensor networks, an energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization is proposed. Firstly, a network communication energy consumption model is constructed, and the network node distribution is optimized in time with the goal of reducing energy consumption combined with differential bee colony algorithm; then, based on the network node distribution optimization results, an energy-saving clustering method for heterogeneous wireless sensor networks is formulated, and the cluster head is determined by using the energy iteration cluster selection method, and the cluster head radius is obtained to complete the energy-saving clustering of communication nodes in heterogeneous wireless sensor networks; finally, the distance between the communication cluster head node and the base station is set, the routing level of the node communication is determined, and the energy-saving routing communication of the heterogeneous wireless sensor network is realized by combining the multi-hop routing communication mode. The experimental results show that when the method is used for network energy-saving clustering routing communication, the number of data transmission nodes that die is at most 22, and the maximum energy consumption of node transmission is 21 nJ/bit , indicating that the node communication energy-saving effect of this method is good.

Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

SummaryWireless body area network (WBAN), a popular radio communication technique, tracks patients' health conditions remotely using small, low‐power, portable sensors positioned on the body to sense vital signs and send data to a control node. To sustain long‐term health monitoring, WBANs require significant energy from the nodes. This research proposes an energy efficient and reliable data transmission using multivariate gradient divergence African buffalo optimization (EEMGDABO) to minimize the delay of critical node data transmission by identifying the optimal path to the control node. The protocol comprises two key processes: winsorized correlative segmented symbolic regression with reinforcement learning (WCS‐SRRL) and the walrus optimization algorithm (WaOA) for accurate priority‐based classification of health data (normal, abnormal, and critical). The second process involves transferring these priority‐based data with reduced energy consumption and delay while increasing throughput by determining the best path from the critical node to the destination (hospital/doctor) using EEMGDABO, which calculates node distance and energy fitness value. Implemented in MATLAB with a health monitoring WBAN dataset, the proposed protocol demonstrates 23% less energy consumption, 12.33% less delay, and 23.44% higher network lifetime compared to existing optimization approaches.

Blockchain-machine learning fusion for enhanced malicious node detection in wireless sensor networks

In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.

A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

Optimizing Opportunistic Routing between Communities Using Decision Trees and Apriori Algorithm

This study proposes a novel data transmission framework aimed at optimizing node data transmission between different communities. We have designed an intelligent routing strategy based on node characteristics for situations where the source node and destination node have different communities. Through the decision tree algorithm, we trained a model to identify "messenger nodes" that are specifically responsible for cross community data transmission. In addition, the association rule model constructed using the apriori algorithm can calculate the probability of establishing a connection between the "messenger node" and the destination community node, thereby selecting the optimal path for data transmission. This method effectively reduces transmission delay and improves the efficiency and reliability of data transmission.

Multi-hop clustering routing protocol design based on simultaneous wireless information and power transfer technology and imperfect spectrum sensing for EH-CRSNs

Existing clustering routing protocols for multi-hop energy harvesting-cognitive radio sensor networks (EH-CRSNs) generally assume perfect spectrum sensing, which is not aligned with the practical spectrum sensing capabilities of nodes in real networks. Additionally, the severe imbalance in residual energy among cluster heads (CHs) negatively affects the successful data delivery. To resolve these problems, this paper introduces a simultaneous wireless information and power transfer (SWIPT)- and imperfect spectrum sensing-based multi-hop clustering routing protocol (ES-ISSMCRP). ES-ISSMCRP makes full use of downlink EH and intra-cluster SWIPT technologies to replenish and equalize the remaining energy among nodes, further extending network lifespan while maintaining network surveillance capabilities. Specifically, to reduce the adverse impact of imperfect spectrum sensing on network performance and improve energy utilization, this paper proposes an EH-based energy level function and associated selection criteria for CHs and relays, facilitating distributed cluster formation and multi-hop routing selection between clusters. To equalize the residual energy among nodes within a cluster, ES-ISSMCRP protocol enables cluster members (CMs) to decide whether employ SWIPT technology with a power splitting (PS) receiver architecture to transmit energy to their CH while sending data. The actual energy value transmitted by CMs using SWIPT technology is deduced by calculating the PS ratio and the expected energy expenditure of nodes for data transmission. Simulation results show that ES-ISSMCRP protocol offers significant improvements over other comparative protocols in terms of extending network lifespan and enhancing network surveillance capabilities.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

WHOOPH: whale optimization-based optimal placement of hub node within a WBAN

Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

Efficient Compressed Sensing Reconstruction Algorithm for Nonnegative Vectors in Wireless Data Transmission

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm.

An efficient medical image encryption algorithm for telemedicine applications

Applications for healthcare execute computations that support various patient diagnostics and save private user data on cloud servers. User data is being held at ransom as a result of increasing cyberattacks on medical systems. Additionally, unreliable external parties who sell private data for profit can access mathematical calculations on data stored in the cloud. In this research, we suggest an efficient Homomorphic medical image encryption algorithm protecting medical plaintext data against unauthorised access via homomorphic encryption. To increase the security level Paillier's encryption scheme (PES) is utilized to select the optimal key. Homomorphic encryption is a method of authentication which helps one to perform observes of the encrypted information through decrypting it. Secret sharing splits up computations among a number of edge virtual nodes and hides all arithmetic operations, preventing unreliable cloud servers from figuring out what was done with the encrypted patient data. Cloud computing resources help virtual edge nodes perform computationally difficult mathematical operations and lower latency in device-edge node data transmission. During the experimental study, different factors including execution time, encryption time, and decryption time are used to evaluate the performance of the suggested approach. The proposed technique's execution time is 2% faster than the currently used HE method and 4% faster than the ECC algorithm.

Exploiting Cooperative Downlink NOMA in D2D Communications.

We propose and investigate a bidirectional device-to-device (D2D) transmission scheme that exploits cooperative downlink non-orthogonal multiple access (NOMA) (termed as BCD-NOMA). In BCD-NOMA, two source nodes communicate with their corresponding destination nodes via a relaying node while exchanging bidirectional D2D messages simultaneously. BCD-NOMA is designed for improved outage probability (OP) performance, high ergodic capacity (EC) and high energy efficiency by allowing two sources to share the same relaying node for data transmission to their corresponding destination nodes while also facilitating bidirectional D2D communications exploiting downlink NOMA. Simulation and analytical expressions of the OP, EC and ergodic sum capacity (ESC) under both perfect and imperfect successive interference cancellation (SIC) are used to demonstrate the effectiveness of BCD-NOMA compared to conventional schemes.

MDP-Based MAC Protocol for WBANs in Edge-Enabled eHealth Systems

In recent years, eHealth systems based on the Internet of Things (IoT) have attracted considerable attention. The wireless body area network (WBAN) is an essential technology of eHealth systems. A major challenge in WBAN is the design of the medium access control (MAC) protocol, which plays a significant role in avoiding collisions, enhancing the energy efficiency, maximizing the network life, and improving the quality of service (QoS) as well as the quality of experience (QoE). In this study, we apply the mobile edge computing (MEC) network architecture to an eHealth system and design a multi-channel MAC protocol for WBAN based on the Markov decision process (MDP). In this protocol, the channel condition and the reward value are considered. By continuously interacting with the environment, the optimal channel resource allocation strategy is generated. Simulation results indicate that the proposed WBAN MAC protocol can adaptively assign different channels to the sensor nodes for data transmission, thereby reducing the collision rate, decreasing the energy consumption, improving the channel utilization, and enhancing the system throughput and QoE.

Research on Efficiency Optimization of Logistics Vehicle Monitoring Model Based on Wireless Sensor Network

For enhance running effectiveness efficiency of logistics carriage supervisory depended on wireless sensor network (WSN), a novel wireless sensor network optimized by improved bat algorithm is established. Firstly, working theory and system framework of WSN depended on logistics monitoring system are analyzed. Secondly, B-MAC protocol is used in proposed wireless sensor network, and corresponding models are established, and recovery ratio of wireless sensor network is defined, and node deployment optimization of wireless sensor network is constructed. Thirdly, the optimization algorithm of node deployment of WSN is designed based on amended bat algorithm, and analysis procedure of this algorithm is established. Finally, a simulation analysis is carried out, analysis results show that performance of proposed WSN logistics carriage supervisory based on improved bat algorithm is better, which has quicker convergence speed and higher convergence precision, and reliability of proposed WSN logistics carriage supervisory is improved, the energy consumption of sensor node data transmission is reduced, and the life of WSN is improved. Proposed WSN based on logistics carriage supervisory based on improved BA has higher coverage ratio and higher efficiency. Therefore proposed wireless sensor network based on logistics carriage supervisory based on improved bat algorithm can obtain better monitoring efficiency, which has prospect application view.

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

Hybrid Sparrow Search Optimization technique for quality of service cooperative routing in underwater acoustic sensor networks

Underwater Acoustic networks comprise sensor nodes distributed randomly in a subsea environment. These nodes deployed at varying pressure levels forward the monitored information through multi-hop transmissions. Data transmission through acoustic links exhibits challenges like frequency-dependent attenuation, noise, multipath distortion, and low propagating speed of acoustic signal (about 1500 m/s) causing the Doppler effect. Various existing algorithms consider only the energy and pressure levels in the routing process. The consideration of channel quality in routing is necessary to enhance the overall network performance. Hence, it is envisaged that the drawbacks of meta-heuristic techniques in computing the optimal solution can be solved by constructing a hybrid optimization algorithm with the fitness function based on pressure levels, energy, and channel conditions. Hybrid-SSO integrates Sparrow Search Optimization with Grey Wolf Algorithm to detect optimal relay nodes for data transmission towards the destination node. Implementation of the proposed algorithm is done using Aquasim (NS2.30) and its performance is collated with the existing techniques. Simulation results show that the proposed Hybrid-SSO successfully reaches a maximum Packet Delivery Ratio of 99.2% for 180 nodes with a transmission range of 100 m while reducing the Average End to End Delay, Average Energy Consumption, and No. of Dead nodes.

Energy Balance Routing Protocol for Wireless Sensor Networks Based on Fuzzy Control Strategy

The existing routing protocols for wireless sensor networks were not reasonable in design, which limited their application. Most of the existing studies did not take into account the energy consumption of the network and the balanced use of the energy of sensor nodes, which led to the unsatisfactory application effect of wireless sensor networks in some fields. Therefore, from the perspective of energy balance in wireless sensor networks, this paper proposed a construction method of an energy balance routing protocol in wireless sensor networks based on a fuzzy control strategy. Firstly, based on the analysis of the basic composition of wireless sensor networks and the structure of sensor nodes, this paper expounded the basic process of wireless data transmission and summarized the classification and characteristics of routing protocols in wireless sensor networks from different angles. Secondly, according to the node data transmission characteristics of wireless sensor networks, the energy balance use model of sensor nodes was established, and the design method of the energy balance routing protocol based on fuzzy control strategy was proposed, and the data transmission link was optimized. Finally, through experimental comparative analysis, the results showed that the energy balanced routing protocol proposed in this paper can effectively realize the energy balanced use of the network data transmission process. Compared with other common routing protocols, the wireless sensor network routing protocol proposed in this paper can not only improve the data transmission efficiency and reduce the data redundancy but also save energy consumption and prolong the network running time. The design method of routing protocol proposed in this paper will be conducive to the optimization and application of routing protocol in wireless sensor networks and provide a theoretical basis for the related research of wireless sensor networks.

A Collision Avoidance Based Energy Efficient Medium Access Control Protocol for Clustered Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are typically deployed in energy constrained environments where recharging energy sources and replacing batteries are not viable. This makes energy efficiency in UWSNs a crucial directive to be followed during Medium Access Control (MAC) design. Multiplexing and scheduling based protocols are not ideal for UWSNs because of their strict synchronization requirements, longer latencies and constrained bandwidth. This paper presents the development and simulation analysis of a novel cross-layer communication based MAC protocol called Energy Efficient Collision Avoidance (EECA) MAC protocol. EECA-MAC protocol works on the principle of adaptive power control, controlling the transmission power based on the signal strength at the receiver. EECA-MAC enhances the conventional 4-way handshake to reduce carrier sensing by implementing an enhanced Request to Send (RTS) and Clear to Send (CTS) handshake and an improved back-off algorithm. Simulation analysis shows that the measures taken to achieve energy efficiency have a direct effect on the number of packet retransmissions. Compared to the Medium Access with Collision Avoidance (MACA) protocol, EECA-MAC shows a 40% reduction in the number of packets that are delivered after retransmissions. This reduction, coupled with the reduced signal interference, results in a 16% drop in the energy utilized by the nodes for data transmission.

Data Sharing Network Model and Mechanism of Power Internet of Things in Virtualized Environment

In order to solve the problems of low security and low reliability in power Internet of Things data sharing in a multi-domain environment, this paper proposes a power Internet of Things data sharing network model and mechanism in a virtualized environment. The main contributions of this paper include building a data management and control network model and a trust transfer relationship model, proposing a heuristic data transmission routing strategy and a network node credit evaluation mechanism. In terms of the data management and control network model, this paper proposes a data sharing network model for multiple domains. The model includes data usage nodes, data transmission nodes, data storage nodes, and data sharing coordination nodes. In terms of network congestion for data sharing, this paper proposes a heuristic data transmission routing strategy. This strategy analyzes the resource utilization of the bottom node and the bottom link, and realizes the load balance between the bottom node and the bottom link. In terms of data sharing authority management, this paper proposes a trust transfer relationship model. This model can solve the security problems of different security level domains when crossing authentication, and realize the transmission and evaluation of the credibility of different IoT systems. In terms of the identification and management of malicious nodes for data sharing, this paper proposes a credit evaluation mechanism for network nodes. This mechanism can solve the problem of malicious nodes causing data unavailability or data sharing routing resource allocation failure. In the performance analysis section, the algorithm in this paper is compared with the traditional algorithm, and it is verified that the algorithm in this paper has improved the security and reliability of the data in the data sharing process.

Homomorphic Encryption Based Privacy-Preservation for IoMT

Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device–edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.