Long-range Network Research Articles

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.

Path Loss Analysis of ZigBee for Smart Meter Network Deployment in NAN

A fundamental and vital aspect of Smart Metering infrastructure is the communication technologies and techniques associated with it, especially between the Smart Meters and the Data Concentrator Unit. Among many available communication technologies, ZigBee provides a low-cost, low-power, and easy-to-deploy network solution for a Smart Meter network. There exists limited literature that discusses ZigBee as a potential communication technology for long-range networks. Hence thorough analysis is demanded on the suitability of ZigBee for smart meter deployment under different types of environmental conditions, coverage ranges, and obstacles. This work evaluates the performance of an extended ZigBee module in outdoor as well as indoor conditions in the presence of different types of obstacles. Parameters are obtained for path loss exponent and the standard deviation of the Gaussian Random variable to validate the Log Normal Shadowing model for modeling long-range ZigBee communication. The impact of obstacles on path loss is also considered. The results show that the Log Normal Shadowing model is a good approximation for the behavior of ZigBee path loss. Accordingly, the suitability of ZigBee for a Smart Meter network spanned as a Neighborhood Area Network is also assessed based on the approximated model.

LRAF-Net: Long-Range Attention Fusion Network for Visible-Infrared Object Detection.

Visible-infrared object detection aims to improve the detector performance by fusing the complementarity of visible and infrared images. However, most existing methods only use local intramodality information to enhance the feature representation while ignoring the efficient latent interaction of long-range dependence between different modalities, which leads to unsatisfactory detection performance under complex scenes. To solve these problems, we propose a feature-enhanced long-range attention fusion network (LRAF-Net), which improves detection performance by fusing the long-range dependence of the enhanced visible and infrared features. First, a two-stream CSPDarknet53 network is used to extract the deep features from visible and infrared images, in which a novel data augmentation (DA) method is designed to reduce the bias toward a single modality through asymmetric complementary masks. Then, we propose a cross-feature enhancement (CFE) module to improve the intramodality feature representation by exploiting the discrepancy between visible and infrared images. Next, we propose a long-range dependence fusion (LDF) module to fuse the enhanced features by associating the positional encoding of multimodality features. Finally, the fused features are fed into a detection head to obtain the final detection results. Experiments on several public datasets, i.e., VEDAI, FLIR, and LLVIP, show that the proposed method obtains state-of-the-art performance compared with other methods.

Rice husk-based activated carbon/carbon nanotubes composites for synergistically enhancing the performance of lead-carbon batteries

Lead-carbon batteries (LCBs), an advanced form of lead-acid battery (LAB) technology, incorporate super-capacitive carbon materials into the negative electrode. Rice husk-based activated carbon (RHAC) is a promising additive for LCBs due to its favorable properties. However, RHAC's amorphous structure impedes electronic conduction, and its zigzag microporous channels hinder ion transport, leading to degraded high-rate performance. This study addresses these issues by growing carbon nanotubes (CNTs) in situ on RHAC through a one-step heat treatment, resulting in carbon nanotubes-loaded RHAC (CNTs/RHAC), and the electronic conductivity and ionic conductivity are simultaneously enhanced. When CNTs constitute 30 wt.% of CNTs/RHAC, the negative electrode achieves a cycle life of 4721 cycles at a 2C rate under 50% state of charge, which is 10.04 times that of the blank anode. The enhanced performance is attributed to the synergistic effects of CNTs and RHAC, where CNTs form long-range conductive networks among the negative electrode active material (NAM) and facilitate the diffusion and electro-deposition of Pb2+, while the high specific surface area (SSA) and hierarchical porous structure of RHAC enhance its capacitive function, leading to a stable lead-carbon composite structure.

Probabilistic performance evaluation of the class-A device in LoRaWAN protocol on the MAC layer

LoRaWAN is a network technology that provides a long-range wireless network while maintaining low energy consumption. It adopts the pure Aloha MAC protocol and the duty-cycle limitation at both uplink and downlink on the MAC layer to conserve energy. Additionally, LoRaWAN employs orthogonal parameters to mitigate collisions. However, synchronization in star-of-star topology networks and the complicated collision mechanism make it challenging to conduct a quantitative performance evaluation in LoRaWAN. Our previous work proposes a Probabilistic Timed Automata (PTA) model to represent the uplink transmission in LoRaWAN. It is a mathematical model that presents the nondeterministic and probabilistic choice with time passing. However, this model remains a work in progress. This study extends the PTA model to depict Class-A devices in the LoRaWAN protocol. The complete characteristics of LoRaWAN’s MAC layer, such as duty-cycle limits, bidirectional communication, and confirmed message transmission, are accurately modeled. Furthermore, a comprehensive collision model is integrated into the PTA. Various properties are verified using the probabilistic model checker PRISM, and quantitative properties are calculated under diverse scenarios. This quantitative analysis provides valuable insights into the performance and behavior of LoRaWAN networks under varying conditions.

A comprehensive evaluation of lightning location accuracy using a weighted gridding method

For the newly built very low-frequency long-range lightning location network (VLF-LLN) in China based on the equivalent propagation velocity method, we have evaluated the probability of deviation distance between the locations results and the reference points by using a weighted gridding method, and have drawn some conclusions. (1) By analyzing the effects of different numbers and configurations of stations on the location deviations, it is found that when the number of participating stations increase, the location deviation relatively decreases, and the numbers and configurations of participating stations has much more effect on the probability of location deviation of the lightning flashes outside the network than those in the network; (2) We statistically analyzed the location deviation for each grid of the network by using a weighted gridding method. The deviation probability using 11-station synchronization as a reference is similar to that for 13-station synchronization per grid. Assuming that the deviation distance is less than 3–5 km, the average location results are almost unchanged when the station number exceeds 7 or 8; (3) Comparing with the Advanced Direction and Time-of-Arrival Detecting network (ADTD), our VLF-LLN has an average deviation value of 3.47 km and a median value of 1.81 km for 63767 lightning flashes, and 90% samples have the location deviation less than 8 km and 54% have the location deviation less than 2 km.

Chromoionophoric molecular probe infused bimodal porous polymer rostrum as solid-state ocular sensor for the selective and expeditious optical sensing of ultra-trace toxic mercury ions

This study presents a distinctive solid-state naked-eye colorimetric sensing approach by encapsulating a chromoionophoric probe onto a hybrid macro-/meso-pore polymer scaffold for fast and selective sensing of ultra-trace Hg(II). The customized structural/surface properties of the poly(VPy-co-TM) monolith are attained by specific proportions of 2-vinylpyridine (VPy), trimethylolpropane trimethacrylate (TM), and pore-tuning solvents. The interconnected porous network of poly(VPy-co-TM), inherent superior surface area and porosity, is captivating for the homogeneous/voluminous incorporation of probe molecules, i.e., 7-((4-methoxyphenyl)diazenyl)quinoline-8-ol (MPDQ), for the target-specific colorimetric detection. The structural morphology, surface topography, and phase characteristics of the bare poly(VPy-co-TM) monolith and MPDQ@poly(VPy-co-TM) sensor are examined using HR-TEM-SAED (High-Resolution Transmission Electron Microscopy - Selected Area Electron Diffraction), FE-SEM-EDAX (Field Emission Scanning Electron Microscopy - Energy Dispersive X-ray Spectroscopy), XPS (X-ray Photoelectron Spectroscopy), p-XRD (Powder X-Ray Diffraction), FT-IR (Fourier Transform Infrared Spectroscopy), UV-Vis-DRS (Ultraviolet-Visible Diffuse Reflectance Spectroscopy), and BET/BJH (Brunauer-Emmett-Teller / Barrett-Joyner-Halenda) analysis. The distinctive properties of the sensor reveal a constrained geometrical orientation of the MPDQ probe onto the long-range continuous monolithic network of meso-/-macropore template, enabling selective interaction with Hg(II) with peculiar color transfiguration from pale yellow to deep brown. The sensor demonstrates a linear spectral-color alliance in the 0–200 ppb concentration range for Hg(II), with quantification and detection limits of 0.63 and 0.19 ppb. The sensor efficacy is verified using certified contaminated water and tobacco samples, with excellent reusability, reliability, and reproducibility of ≥ 99.23 % (RSD ≤1.89 %) and ≥ 99.19 % (RSD ≤1.94 %) of Hg(II), respectively.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

In-Plane heterogeneous Structure-Boosted interfacial polarization in graphene for wide-band and wide-temperature microwave absorption

Theoretically, the strong permittivity dispersion characteristic of materials is essential for achieving broadband electromagnetic wave (EMW) absorption, which can be realized via amplified interfacial polarization effects between a dielectric phase and a lossy phase, such as by increasing the accumulated charge density in their interface area. Herein, we propose a novel strategy to boost interfacial polarization based on heterogeneous structure design in in-plane graphene (Gr) since the in-plane conductivity of Gr is significantly higher than its out-of-plane conductivity. To this end, Gr is grown on Si3N4 nanowires skeleton using chemical vapor deposition (CVD). Subsequently, Gr is controllably etched by Ni nanoparticles, and then, open graphite nanosteps are formed, which help construct in-plane heterogeneous interfaces between Gr and a dielectric phase. Such interfaces also help improve the impedance matching performance by disrupting the long-range conductive network of CVD-grown Gr. Notably, an effective absorption bandwidth of 8.48 GHz is achieved with a thickness of 2.85 mm, which is significantly wider than the EAB (5 GHz) of the out-of-plane heterogeneous structure. In addition, the in-plane heterogeneous structure also endows the EMW absorption material with excellent high-temperature insensitivity, realizing full-frequency absorption in the X-band in the 298–873 K range. This strategy to boost interfacial polarization via novel in-plane heterogeneous structure construction in Gr provides an alternative approach to explore the potential of Gr-based hybrids with planified structures to achieve wide-band and wide-temperature EMW absorption.

Low power decentralized differentially private multi-armed bandit algorithm based performance improvement on long-range radio network

Low power decentralized differentially private multi-armed bandit algorithm based performance improvement on long-range radio network

Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain.

Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.

Stereotyped spatiotemporal dynamics of spontaneous activity in visual cortex prior to eye-opening.

Over the course of development, functional sensory representations emerge in the visual cortex. Prior to eye-opening, modular patterns of spontaneous activity form long-range networks that may serve as a precursor for mature network organization. Although the spatial structure of these networks has been well studied, their temporal features, which may contribute to their continued plasticity and development, remain largely uncharacterized. To address this, we imaged hours of spontaneous network activity in the visual cortex of developing ferrets of both sexes utilizing a fast calcium indicator (GCaMP8m) and widefield imaging at high temporal resolution (50Hz), then segmented out spatiotemporal events. The spatial structure of this activity was highly modular, exhibiting spatially segregated active domains consistent with prior work. We found that the vast majority of events showed a clear dynamic component in which modules activated sequentially across the field of view, but only a minority of events were well-fit with a linear traveling wave. We found that spatiotemporal events occur in repeated and stereotyped motifs, reoccurring across hours of imaging. Finally, we found that the most frequently occurring single-frame spatial activity patterns were predictive of future activity patterns over hundreds of milliseconds. Together, our results demonstrate that spontaneous activity in the early developing cortex exhibits a rich spatiotemporal structure, suggesting a potential role in the maturation and refinement of future functional representations.

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation.

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

Optimizing lightning location accuracy: A study of propagation velocity and time of arrival in long-range lightning location algorithms

The propagation velocity (v) and time of arrival (Ta) are the primary factors affecting long-range lightning location. In this paper, we have enhanced the Very Low Frequency Long-range Lightning Location Network (VLF-LLN) in China in terms of both pulse pairing methods and location algorithms. Then, the algorithm utilized in the VLF-LLN, and four other long-range lightning location algorithms were compared using lightning strike accidents, simulated lightning strike, and other lightning data as the reference values. Building on this foundation, the discussion centers on the effect of v and Ta on location accuracy, with the results showing that an appropriate Ta yields a more significant improvement in location accuracy compared to v. VLF-LLN uses the peak time of the ground wave as Ta avoids the significant lag caused by sky wave, which is present in other algorithms, and therefore has the best location accuracy with a median location accuracy of 1.81 km.

Circular photonic crystal grating design for charge-tunable quantum light sources in the telecom C-band

Efficient generation of entangled photon pairs at telecom wavelengths is a key ingredient for long-range quantum networks. While embedding semiconductor quantum dots into hybrid circular Bragg gratings has proven effective, it conflicts with p-i-n diode heterostructures which offer superior coherence. We propose and analyze hybrid circular photonic crystal gratings, incorporating air holes to facilitate charge carrier transport without compromising optical properties. Through numerical simulations, a broad cavity mode with a Purcell factor of 23 enhancing both exciton and biexciton transitions, and exceptional collection efficiency of 92.4% into an objective with numerical aperture of 0.7 are achieved. Furthermore, our design demonstrates direct coupling efficiency over 90.5% into a single-mode fiber over the entire telecom C-band. The hybrid circular photonic crystal grating thereby emerges as a promising solution for the efficient generation of highly coherent, polarization-entangled photon pairs.

Confined grotthuss proton-conduction along polyoxometalate chains inside carbon nanotubes for high-rate charge storage

Redox-active materials with excellent rate capability and cycling performance are in highly demand for electrochemical energy storage and conversion applications. Here, we unveil the confined proton-conduction behavior of one-dimensional polyoxometalate chains inside single-walled carbon nanotubes (SWNTs). The polyoxometalate molecules including phosphomolybdic acid {HPMo} and phosphotungstic acid {HPW} are encapsulated within SWNTs via host–guest recognition, driven by the electron transfer from nanotubes to polyoxometalates. Impressively, the redox hybrids of polyoxometalate@SWNTs deliver abnormal rate performance in acidic solution with capacity retentions over 73 % at high sweep rate of 1000 mV s−1, relative to the capacity at 10 mV s−1. This value is comparable to that of the pure SWNTs, typically known as double-layer capacitive materials. Electrochemical analyses reveal the Grotthuss proton-conduction mechanism of {HPMo} in SWNTs with a low activation energy of 0.11 eV. Therefore, the {HPMo}@SWNT hybrid demonstrates quasi diffusion-free characteristic with dominant capacitive contribution over 60 % at different sweep rates. Molecular dynamic simulations and density functional theory calculations evidence the strong burst-like transport of protons in the {HPMo}@SWNT hybrid. This process is dynamically favored in the continuous and long-range hydrogen-bond network along polyoxometalate chains with bound/free water molecules inside SWNTs. The superiority of nanoconfined Grotthuss mechanism may engender an exciting avenue for developing high-performance energy storage materials.

Stability analysis of synchronization in long-range temporal networks using theory of dichotomy.

Most of the previous studies on the stability analysis of synchronization in static or time-varying networks are based on the master stability function approach, which is a semi-analytical concept. The necessary and sufficient conditions for synchronization in time-varying networks are challenging problems since the last few years. We focus on the stability analysis of synchronization in time-varying networks, particularly long-range networks. The use of dichotomy theory to derive sufficient conditions for synchronization in this context is an interesting approach. The incorporation of long-range interactions adds complexity and might lead to larger regions of synchronization, providing valuable insights into the dynamics of such networks. Analyzing the co-action of the time-varying nature in the network topology and long-range interactions is a relevant and challenging task, especially when the network is not synchronized. This work appears to explore the interplay between these factors and their impact on synchronization. Additionally, the numerical study considering long-range connections governed by a power-law within the framework of an Erdös-Rényi random network is a practical way to validate and test the analytical results. It is good to see that we are exploring the effects of varying parameters such as rewiring probability, coupling strength, and power-law exponent on the synchronization state.

Tailoring Silicon Composite Anodes with Li+-Containing Organic Ionic Plastic Crystals for Solid-State Batteries

Silicon is one of the highest-capacity anode active materials and, therefore, its use in solid-state batteries (SSBs) is expected to provide both high energy density and safety. Although the creation of solid-state Si electrodes via a scalable method is important from the perspective of battery production, the effect of electrode-preparation methods on electrochemical performance of electrodes with Li+-containing organic ionic plastic crystals (OIPCs) as solid electrolytes has yet to be investigated. Here, we prepared various Si−OIPC composite electrodes using four different methods and measured their electrochemical performance to decipher the method−structure−property relationship for high-performing SSBs. Si−OIPC composite electrodes containing 50 mol% LiFSI in N-ethyl-N-methylpyrrolidinium bis(fluorosulfonyl)imide ([C2mpyr][FSI]) showed the highest initial Coulombic efficiency and cyclability. Three out of the four methods provided the Si−Li0.50[C2mpyr]0.50[FSI] electrodes with relatively large capacity retentions that were close to that of the Si electrode in a liquid electrolyte solution. Elemental analysis of electrode cross-sections showed homogeneous distribution of Li0.50[C2mpyr]0.50[FSI], except for those prepared by the drop-cast method, suggesting that well-designed methods can establish the long-range ion-conduction network in the electrode necessary to improve the electrochemical stability of Si during cycling. This study clarifies the importance of the OIPC-incorporation method in fabricating highly functional OIPC-based electrodes for SSBs.

Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network’s sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network’s localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%.

SY-Track: A tracking tool for measuring chicken flock activity level

Poultry farming plays a vital role in providing animal protein for human consumption. Implementing efficient automation in chicken farming while ensuring the welfare and health of the birds is crucial. Monitoring changes in chicken activity can reflect their welfare and overall health, and provide a basis for agricultural production systems. Thus, maintaining appropriate levels of activity is vital for successful poultry farming. This paper proposes a method for detecting the activity levels of chicken flocks based on object detection and tracking. Firstly, a dataset for chicken flock detection and tracking was established. Secondly, an improved algorithm named SY-Track was proposed, leveraging optimized YOLOv7-tiny and improved StrongSort to ensure accurate chicken flock detection, tracking, and calculation of activity indices. In the optimized YOLOv7-tiny algorithm, a new lightweight convolution named Spatial Separable and Ghost Convolution (SAGConv) was proposed, complemented by the incorporation of the Efficient Long-range Spatial Aggregation Network (ELAN-SA). SAGConv effectively reduced both model parameters and computational complexity. The proposed ELAN-SA significantly improved the performance of YOLOv7-tiny. Additionally, the adoption of SIOU improved the convergence of bounding box regression. In the improved StrongSort algorithm, the optimized Kalman filter with predicted width and height was employed to enhance tracking performance. Compared to the original model, the improved YOLOv7-tiny reduced the model size by 48.75 %, increased the FPS by 44.15 %, decreased the computational load by 44.62 %, while achieving an AP50 of 96.30 %. The improved StrongSort algorithm achieved impressive MOTA values of 84.33 %, 79.00 %, and 71.62 % on three videos with different camera angles and flock disturbances. Finally, the SY-Track algorithm was utilized for calculating the Unrest Index to reflect activity level of chicken flock. The lightweighting SY-Track algorithm demonstrates superior competitiveness compared to mainstream detection and tracking algorithms when applied to the chicken flock dataset. It effectively calculates the Unrest Index of the chicken flock, providing a foundation for intelligent agricultural production and automation in chicken farming worldwide.