Head Nodes Research Articles

LEACH-D: A low-energy, low-delay data transmission method for industrial internet of things wireless sensors

In recent years, the Internet of Things (IoT) has experienced extensive adoption in industrial environments, healthcare, smart cities, and more, playing a vital role in these domains. Within IoT-based systems, wireless sensor networks (WSNs) have emerged as a crucial method for collecting peripheral environmental data within industries, owing to their self-organizational attributes. Nevertheless, the enormous volume of heterogeneous data from various sensing devices presents many challenges for IoT-enabled WSNs, encompassing high transmission delay times (TD) and excessive battery energy consumption (EC). To address these challenges, it is imperative to prioritize efficiency and optimize energy utilization. Moreover, enhancing energy efficiency within the Industrial Internet of Things (IIoT) realm hinges significantly on factors such as data transmission modes and the allocation of cluster head nodes. Numerous researchers have proposed algorithms to minimize transmission time and energy consumption, specifically focusing on industrial environments. This paper introduces an inventive clustering-based data transmission algorithm for IIoT, LEACH-D, to enhance efficiency. The LEACH-D algorithm improves the transmission task duration while maintaining consistent battery energy consumption. It also seeks to elevate performance in metrics such as average transmission time during the first node death (FND). Numerous experimental results provide strong evidence that the algorithm introduced in this paper has effectively reduced the average transmission time by remarkable percentages: 51.32%, 12.12%, 12.96%, and 5.42%, while simultaneously increasing the number of FND rounds by significant margins: 222.43%, 36.63%, 33.72%, and 7.81%, respectively. These improvements stand in stark contrast to the performance of existing algorithms, including FREE_MODE, LEACH, EE-LEACH, and ETH-LEACH.

Self-Configuration Management towards Fix-Distributed Byzantine Sensors for Clustering Schemes in Wireless Sensor Networks

To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.

Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty

In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided.

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare.

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.

Research on Deployment Scheme and Routing Optimization Algorithm of Distribution Cable Condition Monitoring Devices

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data.

Man vs. Machine (Learning): Comparing Speed and Cost-Effectiveness of Manual vs. Deep Learning Segmentation-Aided Volume Delineation for Head and Neck Lymph Node Targets.

The purposes of the following study were to: 1) compare the time required to delineate head and neck lymph nodal volumes using a manual versus deep learning segmentation (DLS) - aided approach and 2) investigate the potential cost-savings per scan. 10 de-identified head and neck CT simulation datasets were randomly selected for segmentation and analysis. Using published consensus definitions of 27 total lymph node regions1, 1 radiation oncologist manually segmented all 10 CT datasets. For each patient, the time required to contour and review the dataset was recorded. Next, for each dataset, the time required to generate and modify DLS-generated volumes via a proprietary software was recorded, including image export from treatment planning system (TPS), uploading to DLS software, volume generation, downloading volumes, importing volumes into the TPS, and manual edits required. Mean and standard deviations for each treatment modality were calculated. Using two-sided t-test, the statistical difference between the measured outcomes were compared. Finally, assuming a salary of $500,000 per year, average cost savings per scan were calculated. Time required to segment each dataset manually (mean = 59.32 min, SD = 9.16) versus DLS-aided (mean = 14.55 min, SD = 3.16) was statistically significant (p-value = 1.91E-07). Estimated cost required to segment each dataset manually (mean = $237.67, SD = $36.69) versus DLS-aided (mean = $58.27, SD = $12.64) was also statistically significant (p-value = 1.91E-07). The DLS-aided approach resulted in a $179.40 savings per CT dataset segmented, as well as an absolute time savings of 44.78 minutes per scan. This preliminary study shows that using a DLS-aided approach to head and neck lymph node stations can be feasibly implemented within a standard radiation oncology workflow and has the potential for significant time and cost savings.

Balance energy based on duty cycle method for extending wireless sensor network lifetime

Due to the limited resources acquired by the sensor, energy consumption is one of the main issues that have drawn the attention of researchers in the field of wireless sensor networks (WSN). In this paper, we propose a new protocol based on the duty cycle method and the energy threshold. The suggested protocol aims to balance the traffic among all nodes. As a result, the energy consumption in the entire network will be balanced to prevent service interruptions on a portion of the network. Furthermore, a good result has been obtained compared to other related approaches. As the energy retention was 28%, 33% higher than the compared approach in member nodes, while nearby 33% and 61% in cluster heads. Moreover, the Suggested method outperforms expectations in terms of time, since it reduces the required transfer time by 60% less than the compared approaches. Also, the first dead member node occurs in many later rounds in the suggested method while there is no cluster head node die in 2,000 rounds. Finally, our research objective was met because the energy depletion process in the nodes was nearly convergent.

A QoS-Aware Energy-Efficient Chimp Optimization Routing Protocol with Efficient Sensor Node Deployment Strategy in Underwater Acoustic Sensor Network

As an extension of wireless sensor networks in the underwater environment, Underwater Acoustic Sensor Networks (UASNs) have led to a broad consideration of academicians. One of the problems that lower UWSN effectiveness in terms of network lifespan is premature energy depletion. It might be caused by the network nodes using different amounts of energy. In UASNs, the effectiveness and dependability of data transfer remain so adverse because of the intricate underwater environment in diverse ocean applications like surveilling atypical submarine oil pipelines. Inspired by the significance of UASNs’ quality of service in several implementations, this study proffers a metaheuristic optimization algorithm (AG) called Chimp Optimization-based Routing Protocol (CH-ORP) for UASNs obtaining intricate features of underwater (UW) medium into concern like 3D changing topology, high propagation delay, node mobility, and density, and, also, cluster head nodes’ rotation mechanism. Initially, the entire network (NW) paradigm has been considered as a three-dimensional cube out of a grid point of view, and the three-dimensional cube has been split into several little cubes by employing Delaunay Triangulation. The optimization has been carried out for lessening node failure rate and NW energy consumption rate by ideally placing the sensor nodes in UW acoustic communication. The NW topology’s steadiness has been assured by the AG, and this optimizes the node redeployment scheme by computing the fitness function for all nodes. The proffered AG’s simulation substantiations have been performed for exhibiting the CH-ORP’s efficiency that executes finer when compared to the advanced methodologies concerning energy efficiency, reliability, and end-to-end delay. It has been found that the proposed Ch-ORP achieves 698 packets received with 28% of energy consumption, 156-sec Network delay, 257 packet loss, 97.23% of PDR, and 1256 Mbps of Network throughput.

Clinical Acceptability of Automatically Generated Elective Lymph Node Volumes for Head and Neck Cancer Patients

Manual contouring of head and neck lymph node levels is a time-intensive process prone to provider-specific variation. The purpose of this work is to generate a clinical segmentation tool while minimizing the amount of manual effort required by physicians to develop training datasets and review contours. Here we investigate an approach to curate, develop, and clinically validate an auto-contouring model for standard cervical lymph node volumes in the head and neck using a publicly available deep learning architecture. This model updates our previously validated tool to reflect modern practices in lymph node segmentation. With the assistance of a resident physician, five radiation oncologists manually contoured individual lymph node levels on CT scans for three separate patients treated definitively with radiation or chemoradiation for oropharynx cancer, resulting in 15 unique ground truth cases. These cases were then used to train an nnUnet deep-learning model to generate automated contours for 32 additional cases. These 32 cases were reviewed, manually edited, and used to create the final model. Finally, the model was used to generate contours on the original 15 CT scans (testing cohort), and providers compared these automated contours with the ground-truth (manual) contours. Two blinded studies were performed. In a double-blinded fashion, providers were first asked to select which set of contours they would prefer to use in clinical practice as a starting point for actual cases. Second, they scored each contour on a Likert scale (1-5) to indicate clinical acceptability, ranging from completely unusable to usable without modification. Across all lymph node levels (IA, IB, II, III, IV, V, RP), average Dice Similarity Coefficient ranged from 0.77 to 0.89 for AI vs manual contours in the testing cohort. These AI and manual lymph node contours were reviewed by 5 physicians each, resulting in 525 preference scores. Across all lymph nodes, the AI contour was superior to or equally preferred to the manual contours at rates ranging from 75% to 91% in the first blinded study. In the second blinded study, physician preference for the manual vs AI contour was statistically different for only the RP contours (p < 0.01). Thus, there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all physician-generated contours, 82% were rated as usable with stylistic to no edits, and across all AI-generated contours, 92% were rated as usable with stylistic to no edits. An approach to generate clinically acceptable automated contours for cervical lymph node levels in the head and neck was demonstrated. Furthermore, for nodal levels I-V, there was no significant difference in clinical acceptability in manual vs AI contours. Because we were able to generate and validate a model for each lymph node level individually, the output is applicable to a complete range of disease in which cervical lymph nodes are treated.

TCaS‐VN: A novel V2V communication scheme to improve traffic control and safety in vehicular cloud networks

SummaryRoad accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning.

Cluster head selection strategy of WSN based on binary multi-objective adaptive fish migration optimization algorithm

In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficiently solve this problem, but there are many factors to be considered, such as energy consumption, the coverage of cluster head nodes, and the number of cluster head nodes. Each factor has a profound impact on the performance of wireless sensor networks, and there are conflicts among them. In order to solve the conflict of multiple factors and obtain the optimal selection strategy of the cluster head node, this paper proposes a Binary Multi-Objective Adaptive Fish Migration Optimization (BMAFMO) algorithm. The algorithm introduces the Pareto optimal solution storage strategy to improve the global search ability of the optimization algorithm and transform the continuous solution into a binary solution according to the sigmoid transformation function to solve the problem of cluster head node selection. The new algorithm was comprehensively tested using eight test problems and four test metrics. At the same time, the reliability of the algorithm is tested by rank sum test. The test results show that the BMAFMO algorithm obtained the best results in 78.13% test problems compared with other algorithms. Finally, the BAMFMO algorithm is applied to solve the cluster head selection problem of WSN and the simulation results show the novel algorithm has better optimization ability than other heuristic algorithms.

A Method for Routing Traffic in a Three-Dimensional High-Density IoT Network Using Gray Relational Analysis

Statement of the problem: the development of the infocommunication system is accompanied by the development of communication technologies and services. The general trends of this process are expressed in three main directions: the growth of throughput, reducing the delay in data delivery and mass communications. The latter is characterized by the development of the Internet of Things (IoT). IoT networks are built using various technologies. A large number of connected devices requires the use of new approaches to modeling and methods for building and managing such networks. To model networks with a high density of devices, it is often not enough to use flat models, but you have to resort to building models in three-dimensional space. For the functioning of networks with a large number of nodes, effective methods for choosing their structure are needed, such as choosing head nodes, clustering, and choosing traffic delivery routes. The task of routing is a classic task of building a logical structure of a communication network, however, in conditions of high density networks, it is necessary to use additional opportunities to increase the efficiency of its solution. Classical routing methods and algorithms operate, as a rule, with one selection criterion, which may not be an effective solution in such conditions. In high-density IoT wireless networks, more parameters must be taken into account, since the quality of the route in them depends on many factors. Thus, to improve the efficiency of IoT networks, it is important to develop a routing method according to a number of criteria. This problem is the subject of the present work. The aim of the work is to develop a method for multi-criteria traffic routing in a high-density IoT network. To achieve the goal, the paper proposes an approach to the use of Gray Relational Analysis, which makes it possible to effectively solve the problem of multi-criteria route optimization, including with a small amount of initial data. The object of the study is the Internet of Things network. The subject of the study is the multi-criteria routing method implemented using Gray Relational Analysis. The results of simulation modeling showed the effectiveness of the proposed method in comparison with the methods of single-criteria route selection. The method used is the method of Gray Relational Analysis, which allows solving problems of multicriteria optimization. The novelty of the work lies in the proposed method of applying Gray Relational Analysis to solve a multicriteria routing problem in a high-density IoT network. The result of the work is a method of applying Gray Relational Analysis in the problem of multi-criteria traffic routing in a high-density IoT network. Theoretical/Practical significance. The theoretical significance of the obtained results lies in the description of a new method of applying Gray Relational Analysis in the routing problem and confirmation of its effectiveness by simulation results. The practical significance lies in the fact that this method can be used in traffic routing protocols in high-density IoT networks.

A Novel Hypergraph Clustered Gray Relational Analysis HGPSO Algorithm for Data Aggregation in WSN

Abstract Wireless Sensor Networks (WSN) aggregate data from multiple sensors and transfer it to a central node. Sensor nodes should use as little energy as possible to aggregate data. This work has focused on optimal clustering and cluster head node selection to save energy. HyperGraphs (HGC) and cluster head selection based on distance and energy consumption are unique approaches to spectral clustering. GRA computes a relational matrix to select the cluster head. The network’s Moving Agent (MA) may use Hypergraphed Particle Swarm Optimization (HGPSO) to collect data from cluster heads. Compared to the clustering algorithm without agent movement, the HGC-GRA-HGPSO approach has increased residual energy by 5.59% and packets by 2.44%. It also has improved residual energy by 2.45% compared to Grey Wolf Optimizer-based Clustering (GWO-C).

REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks

An essential component of the Internet of Things (IoT) is wireless sensor networks (WSNs). Since individual sensor nodes are strongly power-constrained, several techniques are adopted to save power. By grouping nodes into clusters—thus reducing the transmission distance between sensor nodes and the base station (BS)—a clustering protocol can ensure energy preservation and increase the lifetime of the network. However, current clustering techniques have problems with the clustering structure that negatively impact their performance. Whenever routing protocols were implemented for a longer period of time, it was observed that they had a higher rate of energy consumption, a shorter period of stability, and fewer data transfers to the BS. In this paper, an improved region-based routing protocol (REERP) is developed for wireless sensor networks in the IoT is developed. It is based on (i) the addition of new nodes to the already formed clusters, (ii) the selection of the new head node based on the amount of residual energy, (iii) the setup of the multi-hop communication in all the regions of network, and (iv) the utilization of the energy hole reduction method. All of these tactics increase the useful life of the network. Performance has been evaluated against (1) a stable election protocol, (2) a gateway energy-aware routing protocol, and (3) a heterogeneous gateway energy-aware routing protocol, and using the metrics lifetime, energy consumption, number of dead nodes, and number of packets sent to the base station vs. number of packets acquired by the base station. The results of the proposed routing protocol have been found to outperform the state-of-the-art approaches considered.

Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks.

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.

Augmentation of spectral efficiency in optical wireless communication networks using grid‐based elephant swarm optimization protocol

SummaryThe emergence of 5G and 6G technologies has resulted in a faster, more automated, and more intelligent data flow in the digital world. A wide spectrum of optical wireless communication is made available to support 5G and 6G technologies, and it can meet the demands of high‐speed optical wireless communication. Trade‐offs involving latency, power consumption, deployment costs, and hardware/computational overhead have always been a part of 5G/6G technologies. The decline in the trade‐offs indicated above has major implications for networking capacity. The proposed work modulated a new grid‐based communication protocol (GCP) with elephant swarm optimization (ESO) to surpass those issues with optical wireless communication devices. This protocol employs an integrated strategy to split the network and choose the cluster head effectively. Different GCP‐ESO‐related algorithms have been explored. Various components of the OSNs, including network energy usage, cluster, and non‐cluster header nodes, are also calculated using mathematical analysis. The results have been simulated using MATLAB software. The simulation precisely demonstrates that the proposed GCP‐ESO outperforms the emerging MCH‐EOR and RDSAOA‐EECP methods concerning energy consumption (0.46 J), packet loss ratio (PLR) (0.60%), end‐to‐end latency (11.0 s), packet delivery rate (PDR) (99.41%), routing (0.112), the lifetime of the network (3718 s), and throughput (0.989 Mbps).

A Dynamic Addressing Hybrid Routing Mechanism Based on Static Configuration in Urban Rail Transit Ad Hoc Network

With the rapid development of urban rail transit, the traditional urban rail wireless network based on fixed infrastructure is not in a position to meet the increasingly complex communication demand. At the same time, Ad Hoc network, as a special wireless mobile network, is developing rapidly. Applying this self-organized networking architecture to the urban rail vehicle–ground communication network can overcome the problems existing in the traditional urban rail communication system. The routing protocols that can achieve low delay and highly reliable data transmission are important in the urban rail transit scenario. Therefore, combined with the wireless Ad Hoc network and the characteristics of the urban rail transit scenario, this paper proposes a dynamic addressing hybrid routing mechanism based on static configuration. Using an improved AODV routing discovery algorithm and then writing the routing table into the router in advance for static configuration not only reduces network overhead but also prolongs the network’s lifetime. It also saves the delay of routing discovery. Then, the cluster head node dynamically monitors the link status, dynamically finds the path when the link needs to be replaced, and selects different update paths according to different types of communication services. Finally, each algorithm’s network performance parameters, like the routing discovery overhead, residual link lifetime, packet delivery rate, throughput, and end-to-end delay, are analyzed and compared.

An Overview of FAST Real-time Fast Radio Burst Searching System

In this paper, we report a real-time Fast Radio Burst (FRB) searching system that has been successfully implemented with the 19 beam receiver of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The relatively small field of view of FAST makes the search for new FRBs challenging, but its high sensitivity significantly improves the accuracy of FRB localization and enables the detection of high-precision neutral hydrogen absorption lines generated by FRBs. Our goal is to develop an FRB searching system capable of real-time detection of FRBs that allows high-time resolution spectro-temporal studies among the repeated bursts, as well as detailed investigations of these bursts and exploration of FRB progenitor models. The data from each beam of the 19-beam receiver are fed into a high-performance computing node server, which performs real-time searches for pulses with a wide dispersion measure (DM) range of 20–10,000 pc cm−3 with step efficiency of 25% in real time. Then, the head node server aggregates all the candidate signals from each beam within a given time, determining their authenticity based on various criteria, including arrival time, pulse width, signal-to-noise ratio and coincidence patterns among the 19 beams. Within the 1.05–1.45 GHz operating bandwidth of the FAST 19 beam receiver, the system achieves a frequency resolution of 122.07 kHz and a time resolution of 270.336 μs. Subsequently, our team detected a series of bursts with a DM of 566 on 2019 August 30 confirming them as FRB 121102. The FRB searching system enables the 19-beam receiver of FAST to detect repeated/one-off pulses/bursts in real time.

Cross-Platform UAV Swarm Key Management in Denied Environments

As resources provided by single unmanned aerial vehicles (UAVs) are limited, we propose a cross-platform UAV swarm key management scheme for task scenarios in denied environments. In denied environments where the communication link is open and the UAV nodes may go invalid, secure communication is often at stake. To solve this problem, we propose a key management scheme which, based on the Chinese remainder theorem (CRT) and the Hash function, constructs a swarm key by combining the local key and the session key to reduce the overhead of individual UAV nodes in the swarm. Meanwhile, the swarm head node constructs broadcast messages according to the key update needs, which reduces the overhead of the member nodes, improves the efficiency of key updating, and fulfills the key establishment and updating of the UAV swarm. Experiments show that our proposed scheme has forward and backward security and can defend against collusion attacks and replay attacks; our method was compared with other methods on the MIRACL cryptographic library in Visual Studio 2019, and it was found that our method has a lower computing and communication overhead, provides a solution to cross-platform key management of UAV swarms in denied environments, and ensures safe communication of UAVs in the swarm.

An Intelligent Optimization Method for Wireless Communication Network Resources Based on Reinforcement Learning

The scarcity of spectrum resources is becoming increasingly severe, and the traditional static spectrum allocation method leads to low spectrum utilization. To address these issues, this article introduces reinforcement learning (RL) into wireless communication systems and proposes an optimization method for wireless resource allocation in deep Q networks (DQN). This paper uses the deep reinforcement learning (DRL) method to model the selection process of cluster heads and relay nodes as a Markov decision process, and then uses DQN to establish a cluster head and relay node selection mechanism aimed at maximizing the life cycle of wireless sensor network (WSN). Experimental results show that compared with the existing algorithms, the proposed algorithm effectively reduces the energy consumption of the network and extends the life cycle of WSN. Ultimately, it is possible to reconfigure relevant spectrum resources to obtain available spectrum resources, and idle spectrum resources can be discovered at any time.