Image Sensor Networks Research Articles

Adaptive Compressive Sensing Based on Sparsity Order Estimation for Wireless Image Sensor Networks

Adaptive Compressive Sensing Based on Sparsity Order Estimation for Wireless Image Sensor Networks

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

Wavy water-to-air optical camera communication system using rolling shutter image sensor and long short term memory neural network.

We propose and experimentally demonstrate for the first time up to the authors' knowledge a wide field-of-view (FOV) water-to-air optical transmission using rolling-shutter (RS) based optical camera communication (OCC). Here, we evaluate the proposed OCC system without water ripple and with different percentage increases of water ripple. Long short term memory neural network (LSTM-NN) is utilized to mitigate the wavy water turbulence induced link outage and to decode 4-level pulse-amplitude-modulation (PAM4) RS pattern by meeting the pre-forward error correction bit-error-rate (pre-FEC BER = 3.8 × 10-3). We also evaluate the FOVs of the proposed water-to-air RS-based OCC system. This can be implemented by using different angular rotations of the camera. Experimental results show that the proposed OCC system can support ±70°, ± 30°, and ±30° rotations around the z-, y- and x-directions, respectively when operated at 6 kbit/s and decoded using LSTM-NN.

Enhancing Crop Protection and Yield through Precision Agriculture and Integrated Pest Management: A Comprehensive Review

The increasing global population has intensified the necessity for sustainable and efficient agricultural practices. One promising avenue for meeting this demand is the synergy between Precision Agriculture (PA) and Integrated Pest Management (IPM). This review paper aims to scrutinize the multifaceted relationship between PA and IPM in augmenting crop protection and yield. Utilizing a comprehensive analysis of existing literature, the study elucidates how cutting-edge technologies in PA, such as drone imaging and soil sensor networks, can be harmoniously integrated with IPM strategies. These encompass biological, chemical, and cultural tactics to manage pest populations and mitigate damage, thereby fostering an environment conducive to optimal crop growth. The review identifies that the confluence of PA and IPM not only enhances the efficiency of resource use but also mitigates the environmental footprint of agricultural activities. Moreover, we delve into case studies that demonstrate significant yield improvements and cost reductions, underscoring the economic viability of integrating PA and IPM. The findings highlight the transformative potential of marrying these two domains, suggesting that such integration could be a cornerstone in the future of sustainable agriculture. The paper concludes by outlining research gaps and proposing avenues for future studies, emphasizing the need for multi-disciplinary approaches to fully unlock the potential of this integration.

IOT COLOR BASED PRODUCT SORTING MACHINE

The IoT Color-Based Product Sorting Machine is a cutting-edge automated system designed to revolutionize the manufacturing and packaging industries. This innovative solution employs Internet of Things (IoT) technology to enable efficient and precise sorting of products based on their color attributes. The system integrates advanced image recognition algorithms, sensor networks, and cloud computing to streamline the sorting process. Color Based Object Sorting has a wide usage in fruit sorting as well as candy sorting industries. This system puts forward a mechanism to detect color and sort items through image processing. Once identified a mechanism is used to sort the candies into particular bins baskets. We here demonstrate this mechanism using a camera with electronic circuitry along with sorting mechanism using 3 bins. The system uses raspberry pi connected to a controller circuit to achieve this task. The controller circuit consists of a camera attached to it that detects color of a small object in front of it. A motor is used to feed an object to the camera chamber. As soon is the color is detected a signal is sent to the sorter mechanism which uses a motor to position the sorting tube towards respective section. A feeder is then used to push the object towards the tubs so that it gets sorted and next object is pulled in by the feeder. The action details are sent to the IOT server using iotgecko platform to keep track of the number of objects sorted in each section. Thus we achieve a completely automated IOT based sorting system.Color based product sorting has a wide usagein fruits sorting as well as candy sorting industries. Now a day’s industrial area requires demand for automation. Due to automation human efforts are goes on decreasing day-byday. Since last decade .This system puts forward mechanism to detect color and sort item through image processing. This mechanism is used to sort the candies into particular bins or baskets Key Words: IoT, colour sensor, servo motor, node MCU,,

Energy-Saving Strategy Based on Image Super-Resolution for Wireless Image Sensor Networks Assisted by Cloud

Wireless image sensor networks (WISNs) collect surveillance images, resulting in copious quantities of data requiring processing and transmission within the network. To reduce and balance energy expenditure during in-network image data processing and transmission, this study introduces an energy-saving strategy based on image super-resolution for WISNs assisted by cloud. The strategy constructs an image data processing and transmission system for WISNs, leveraging cloud infrastructure. By utilizing image quality feedback, the adaptive adjustment of image node sensing resolution is facilitated, minimizing image data transmission volume while ensuring high-quality super-resolution reconstructed image. The establishment of low-overhead, multipath transmission routes enables image nodes to transmit data in blocks, contingent upon neighboring transmission node statuses, thereby equalizing energy consumption for image data transmission. Cloud servers are employed to reconstruct high-resolution images and provide users with surveillance images that satisfy quality requisites. Empirical findings demonstrate that the proposed approach extends the lifetime of WISNs, furnishes users with superior-quality surveillance images, and markedly enhances network monitoring efficacy.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm.

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Integrated Image Sensor and Deep Learning Network for Fabric Pilling Classification

Integrated Image Sensor and Deep Learning Network for Fabric Pilling Classification

Sieć miniaturowych czujników termowizyjnych do wykrywania i śledzenia obiektów

This paper presents a concept and implementation of an infrared imaging sensor network for object localization and tracking. The sensor network uses multiple low-resolution (80× 80 pixels) microbolometric thermal cameras to detect, track and locate an object within the area of observation. The network uses information simultaneously acquired from multiple sensors to detect and extract additional information about object’s location. The use of thermal-imaging systems responsive to objects’ natural infrared radiation, makes the system resistant to external illumination and environmental conditions. At the same time, the use of infrared sensor requires application of specially designed, dedicated image processing techniques appropriate for this kind of sensor. The paper describes: image processing techniques, means of object localization, accuracy measurements, comparison to other known solutions and final conclusions.

WHISPER: Wireless Home Identification and Sensing Platform for Energy Reduction

Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95%; ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.

Satellite-observed lightning hotspots in India and lightning variability over tropical South India

Lightning characteristics in India are examined with satellite-based Lightning Imaging Sensor (LIS) and ground-based Indian Lightning Detection Network (ILDN). LIS observations indicated that synoptic weather systems are major contributors of lightning in Indian hotspots. Western disturbances (mid-tropospheric systems with extratropical origin) were the greatest contributor of lightning in the Himalayas (93%), tropical cyclonic storms and low-pressure systems (oceanic in origin) were key lightning contributors in parts of eastern India (43%), and lower tropospheric troughs were major contributors in other hotspots. For the first time, this study reported the occurrence of significantly high lightning activity before active monsoon spells in the Central India region (65–87° E, 18–27° N). Consequently, satellite-based lightning observations could be used to predict active monsoon spells. Harmonic analysis was used to study diurnal lightning-flash density. The maximum observed standardized diurnal amplitude of lightning activity was 0.35, and maximum explained diurnal variation was 15%. Also, for the first time, we compared the ILDN data and LIS observations, and found good agreement regarding lightning variability. LIS data showed an increase in annual lightning activity in tropical South-western India (SWI), and the results also suggested that during El Niño and negative Indian Ocean Dipole periods, SWI experiences above-average lightning activity.

Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.

Image Reconstruction Based on Fused Features and Perceptual Loss Encoder-Decoder Residual Network for Space Optical Remote Sensing Images Compressive Sensing

Compressive sensing (CS) technology is introduced into space optical remote sensing image acquisition stage, which could make wireless image sensor network node quickly and accurately obtain images in the case of two constraints of limited battery power and expensive sensor costs. On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction. FFPL-EDRNet consists of a convolution layer and a reconstruction network. We train FFPL-EDRNet end-to-end, thus greatly simplifying the pre-processing and post-processing process and eliminating the block effect of reconstructed images. The reconstruction network is based on residual network, which introduces multi-scale feature extraction, multi-scale feature combination and multi-level feature combination. Feature fusion integrates low-level information with high-level information to reduce reconstruction error. The perceptual loss function based on pretrained InceptionV3 uses the weighted mean square error to define the loss value between the reconstructed image feature and the label image feature, which makes the reconstructed image more semantically similar to label image. In the measurement procedure, we use convolution to achieve block compression measurement, so as to obtain full image measurements. For image reconstruction, we firstly use a deconvolution layer to initially reconstruct the image and then use the residual network to refine the initial reconstructed image. The experimental results show that: in the case of measurement rates (MRs) of 0.25, 0.10, 0.04 and 0.01, the peak signal-to-noise ratio (PSNR) = 27.502, 26.804, 24.593, 21.359 and structural similarity (SSIM) = 0.842, 0.816, 0.720, 0.568 of the reconstructed images obtained by FFPL-EDRNet. Therefore, Our FFPL-EDRNet could enhance the quality of image reconstruction.

LoRa System for Monitoring and Facial Recognition

With the increasing tendency of incorporating technology into environments and activities in everyday life, new methods are being proposed to better integrate devices and mankind. Networks designed for monitoring areas through video and image systems are being implemented in several applications.Recent studies have shown interest from both the academy and industry to integrate wireless surveillance networks with low-cost and long range transmission technologies, such as LoRa (Long Range). With the intention to explore this topic, this work presents the development of a system prototype for intelligentmonitoring as a basis to future implementation of low-cost LoRa-based Wireless Image Sensor Networks (WISN). The system developed consists of a processing unit, a data routing interface and multiple sensor nodes. Its operation is autonomous and initiated through the detection of human presence, by the sensornode, capturing an image of the individuals present at each detection. The data is transmitted through LoRa devices to the central hub, where python-based methods of facial detection and recognition are employed. Several tests were performed to both adjust the system and to verify its efficiency. The results achievedwere satisfactory, indicating the viability of the proposed low-cost LoRa-based WISN.

Preliminary Observations from the China Fengyun-4A Lightning Mapping Imager and Its Optical Radiation Characteristics

The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first satellite-borne lightning imager developed in China, which can detect lightning over China and its neighboring regions based on a geostationary satellite platform. In this study, the spatial distribution and temporal variation characteristics of lightning activity over China and its neighboring regions were analyzed in detail based on 2018 LMI observations. The observation characteristics of the LMI were revealed through a comparison with the Tropical Rainfall Measuring Mission (TRMM)-Lightning Imaging Sensor (LIS) and World Wide Lightning Location Network (WWLLN) observations. Moreover, the optical radiation characteristics of lightning signals detected by the LMI were examined. Factors that may affect LMI detection were discussed by analyzing the differences in optical radiation characteristics between LMI and LIS flashes. The results are as follows. Spatially, the flash density distribution pattern detected by the LMI was similar to those detected by the LIS and WWLLN. High-flash density regions were mainly concentrated over Southeastern China and Northeastern India. Temporally, LMI flashes exhibited notable seasonal and diurnal variation characteristics. The LMI detected a concentrated lightning outbreak over Northeastern India in the premonsoon season and over Southeastern China in the monsoon season, which was consistent with LIS and WWLLN observations. LMI-observed diurnal peak flash rates occurred in the afternoon over most of the regions. There was a “stepwise” decrease in the LMI-observed optical radiance, footprint size, duration, and number of groups per flash, from the ocean to the coastal regions to the inland regions. LMI flashes exhibited higher optical radiance but lasted for shorter durations than LIS flashes. LMI observations are not only related to instrument performance but are also closely linked to onboard and ground data processing. In future, targeted improvements can be made to the data processing algorithm for the LMI to further enhance its detection capability.

Fast and Reliable Alternative to Encoder-Based Measurements of Multiple 2-DOF Rotary-Linear Transformable Objects Using a Network of Image Sensors with Application to Table Football.

Simultaneous determination of linear and angular positions of rotating objects is a challenging task for traditional sensor applications and a very limited set of solutions is available. The paper presents a novel approach of replacing a set of traditional linear and rotational sensors by a small set of image sensors. While the camera’s angle of view can be a limiting factor in the tracking of multiple objects, the presented approach allows for a network of image sensors to extend the covered area. Furthermore, rich image data allows for the application of different data processing algorithms to effectively and accurately determine the object’s position. The proposed solution thus provides a set of smart visual encoders emulated by an image sensor or a network of image sensors for more demanding spatially distributed tasks. As a proof of concept, we present the results of the experiment in the target application, where a MP image sensor was used to obtain sub-degree angular resolution at 600 rpm and thus exceeding the design parameters and requirements. The solution allows for a compact, cost-effective, and robust integration into the final product.

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System.

In recent years, 360° videos have gained the attention of researchers due to their versatility and applications in real-world problems. Also, easy access to different visual sensor kits and easily deployable image acquisition devices have played a vital role in the growth of interest in this area by the research community. Recently, several 360° panorama generation systems have demonstrated reasonable quality generated panoramas. However, these systems are equipped with expensive image sensor networks where multiple cameras are mounted in a circular rig with specific overlapping gaps. In this paper, we propose an economical 360° panorama generation system that generates both mono and stereo panoramas. For mono panorama generation, we present a drone-mounted image acquisition sensor kit that consists of six cameras placed in a circular fashion with optimal overlapping gap. The hardware of our proposed image acquisition system is configured in such way that no user input is required to stitch multiple images. For stereo panorama generation, we propose a lightweight, cost-effective visual sensor kit that uses only three cameras to cover 360° of the surroundings. We also developed stitching software that generates both mono and stereo panoramas using a single image stitching pipeline where the panorama generated by our proposed system is automatically straightened without visible seams. Furthermore, we compared our proposed system with existing mono and stereo contents generation systems in both qualitative and quantitative perspectives, and the comparative measurements obtained verified the effectiveness of our system compared to existing mono and stereo generation systems.

PAADA: Physical-World-Aware Approximate Data Acquisition for Image Sensor Networks

To observe the complicated physical world, sensor networks are widely used for data collection. Moreover, due to the limited energy, storage and computation capacity of sensor nodes, approximate data acquisition in adaptive sampling manner is a wide choice. Nevertheless, existing data acquisition methods are most designed for univariate data (e.g., temperature), and thus not applicable to image data with high dimensions and complex structures. In this paper, we propose a framework of Physical-world-Aware Adaptive Data Acquisition (PAADA) for image sensor networks, to sample data adaptively with pre-specified error bound. First, based on the convolutional autoencoders (CAEs), PAADA compresses the high-dimensional image data into a feature vector with a handful of hidden variables which compactly capture the key features of the image data. Second, PAADA designs a Physical-world-aware Adaptive Sampling (PAS) algorithm based on the Hermitee interpolation. Under the feature space, the PAS algorithm adjusts the sampling frequency automatically by considering the change trend of the feature vector. In addition, the feature vectors at non-sampling time points can be recovered with O (∈) approximation guarantee to the ground truths. Next, PAADA recovers the image data at non-sampling time points based on the recovered feature vectors. Finally, for each sensor, PAADA returns an image series composed of sampled images (at sampling time points) and approximate images (at non-sampling time points). Experiments on real-world datasets demonstrate that the proposed PAADA has high performance in both accuracy and energy consumption.

Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

Abstract This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).