Wireless 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

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.

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.

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.

High-Efficiency Progressive Transmission and Automatic Recognition of Wildlife Monitoring Images With WISNs

Wireless image sensor networks (WISNs) are widely applied in wildlife monitoring, as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions and narrow transmission bandwidth. For the contradiction between the above limitations of WISNs and the wildlife monitoring images with high resolution and complex background, we propose a novel wildlife intelligent monitoring system. On the foundation of saliency object detection, the convolutional encoder-decoder network is utilized to realize the progressive compression transmission and restoration for wildlife monitoring images, which guarantees the transmission efficiency and quality of wildlife part. Moreover, to deal with the problems of high labor intensity, low efficiency and low recognition accuracy in classical manual sorting method, an improved Faster RCNN algorithm is proposed on the automatic recognition of wildlife images. The experimental results on our own wildlife dataset, show that the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) are improved by 7.93%, 18.15% and 7.01%, 12.67% respectively on reconstruction image, when compared with the set partitioned in hierarchical tree (SPIHT) and embedded zerotree (EZW) algorithms. Compared with the traditional Faster RCNN algorithm, the recognition accuracy of six species wildlife is respectively improved by 1%, 18%, 5%, 17%, 2% and 19%, and the final mAP value reaches to 92.2% in test set increased by 10.9%, which demonstrates the proposed algorithm can ideally achieve the wildlife intelligent monitoring with WISNs.

Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks

Reducing the algorithmic complexity of image compression techniques is a major challenge in wireless image sensor networks (WISNs). Many image compression standards, such as JPEG and JPEG2000, are unsuitable for implementation in WISNs because of their high energy consumption. In this paper, a solution to this problem is proposed. It consists of a region-of-interest (ROI) based image compression using the discrete Tchebichef transform (DTT).The main idea is about compressing only the ROI instead of the whole image. The DTT is used as an alternative to the discrete cosine transform (DCT) due to its low complexity and good energy compaction. Simulation results have shown that the proposed method reduces the number of arithmetic operations, the processing/transmission energy consumption and the amount of transmitted data. The savings obtained generally exceed 50%. Furthermore, it has a competitive compression efficiency compared with the state-of-the-art image compression techniques.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

Low-complexity low-memory energy-efficient image coding for wireless image sensor networks

ABSTRACTWireless image sensor networks are capable of sensing, processing and transmitting the image in hard-to-access regions without expensive network infrastructure and will have great contribution in Internet of Things. These networks are resource constraint systems with limited memory, energy, processing speed and bandwidth. Low computational energy and communication energy will improve the lifetime of these resource-limited networks. In this paper, an energy-efficient low-memory and low-bitrate image coding is designed exclusively for low-power camera-equipped sensor node. The performance of the proposed image coder is analysed in terms of bitrate, image quality, memory size and energy consumption. Experiments are carried out with Atmel ATmega128 processor. The experimental results show that this system consumes only 0.23% of energy consumed by true Discrete Cosine Transform-based Joint Photographic Experts Group (JPEG) standard and offers reasonable image quality suitable for visual perception at low bitrate. This system requires only 19% of memory required by standard JPEG.

LOW BITRATE HYBRID SECURED IMAGE COMPRESSION FOR WIRELESS IMAGE SENSOR NETWORK

Wireless image sensor networks are capable of sensing, processing and transmitting the visual data along with the scalar data and have attainedwide attention in sensitive applications such as visual surveillance, habitat monitoring, and ubiquitous computing. The sensor nodes in the network are resource constrained in nature. Since the image data are huge always high computational cost and energy budget are levied on the sensor nodes. The compression standards JPEG and JPEG 2000 are not feasible as they involve complex computations. To stretch out the life span of these nodes,it is required to have low complex and low bitrate image compression techniques exclusively designed for this platform. The complicated scenarioof wireless sensor network in processing and transmitting image data has been addressed by a low complex hybrid secured image compression technique using discrete wavelet transform and Bin discrete cosine transformation.

Low Complexity DCT Approximation for Image Compression in Wireless Image Sensor Networks

Energy consumption is a critical problem affecting the lifetime of wireless image sensor networks (WISNs). In such systems, images are usually compressed using JPEG standard to save energy during transmission. And since DCT transformation is the most computationally intensive part in the JPEG technique, several approximation techniques have been proposed to further decrease the energy consumption. In this paper, we propose a low-complexity DCT approximation method which is based on the combination of the rounded DCT with a pruned approach. Experimental comparison with recently proposed schemes, using Atmel Atmega128L platform, shows that our method requires less arithmetic operations, and hence less processing time and/or the energy consumption while providing better performance in terms of PSNR metric.

Adaptive compressed sensing for wireless image sensor networks

Compressed sensing (CS) based image compression can achieve a very low sampling rate, which is ideal for wireless sensor networks with respect to their energy consumption and data transmission. In this paper, an adaptive compressed sensing rate assignment algorithm that is based on the standard deviations of image blocks is proposed. Specifically, each image block is first assigned a fixed sampling rate. In addition to the fixed sampling rate, an adaptive sampling rate is then given to each block based on the standard deviation of the block. With this adaptive sampling strategy, higher sampling rates are assigned to blocks that are less compressible (e.g., blocks with complex textures are less compressible than blocks with a smooth background). The sensing matrix is constructed based on the assigned sampling rate. The fixed measurements and the adaptive measurements are concatenated to form the final measurements. Finally, the measurements are used to reconstruct the image on the decoding side. The experimental results demonstrate that the proposed algorithm can achieve image progressive transmission and improve the reconstruction quality of the images.

Security in big data

Security in big data

Energy‐efficient low bit rate image compression in wavelet domain for wireless image sensor networks

The problem related to energy conservation in a resource-constrained wireless visual sensor network applied for habitat monitoring, border patrol, battlefield surveillance and so on, where images have to be transmitted over a wireless medium with limited bandwidth, is addressed. The proposed approximation band transform algorithm is unique and novel since it extracts and encodes only approximations of the image using fixed-point arithmetic without applying highpass analysis filters. This low bit rate image compression algorithm is specifically designed for resource-constrained low-power sensors. Its performance is analysed in terms of bit rate (bits per pixel), image quality, processing time and its energy consumption in an Atmel Atmega128 processor. It is shown that this algorithm consumes only 12% of energy needed by joint photographic experts group (JPEG) [independent JPEG group (IJG)] version and gives better results than JPEG at very low bit rates and thus the proposed scheme can significantly enhance the lifetime of low-power sensors.

TECHNOLOGIES FOR ENHANCING PECAN PRODUCTION AND PROCESSING

This paper summarizes research at Oklahoma State University (OSU) to develop innovative technologies that provide solutions to issues and problems affecting pecan production and processing. Some of the projects areas include; Pecan yield estimation technique using backscattered terrestrial microwave sensing. Dielectric spectroscopy for estimating quality of in-shell pecans. Wireless image sensor networks in estimating the population of pecan weevils. Low-cost small-scale sanitizer for in-shell pecans. Optical sensors and algorithms that adequately predict plant N status for nitrogen management. X-ray machine vision inspection systems for pecan defect identification. Accurate estimates of pecans in the field prior to harvest are critically important for production management decisions and marketing. Pecan producers, processors and marketers identified improved accuracy of crop estimates as a research priority for the industry. The pecan weevil (Curculio caryae) is considered a key pest. Without timely insecticide treatments, crop losses can exceed 75%. Research has focused on the design of a wireless sensor network for real-time monitoring and population estimation of pecan weevils. Nitrogen (N) has become a major cost in producing pecans. Traditionally, N is applied once or twice per season. Application rates usually exceed the minimum N requirement for optimum production. Research has focused on reducing N inputs while maintaining production levels. Sorting of defective nuts is difficult because nutmeat defects are not fully recognizable by physical properties, color and appearance of whole unshelled nuts. Commercial sorters are available to sort nutmeat after shelling the nuts, but this results in unnecessary shelling of defective nuts. Development of automated inspection systems to identify good pecan nuts from defective ones before shelling would reduce processing costs. Due to space limitations, only the highlights of each research area will be presented. Interested readers desiring more comprehensive discussion of the projects are encouraged to access the references.

A WiSN node SoC with real-time image compressor and IEEE 802.15.4 MAC accelerator

This article presents a wireless image sensor node SoC (system-on-a-chip) for low-power wireless image sensor network (WiSN), in which camera chip interface, high-quality image compression and IEEE 802.15.4 compliant acceleration modules are integrated on chip. The proposed SoC contains a hardware-implemented real-time lossless JPEG (JPEG-LS) compression engine for Bayer Color Filter Arrays (Bayer CFA), reaching a 3.5 bits/pixel with peak signal to noise ratio (PSNR) greater than 46.3 dB and achieving a maximum 5 frames/s @16 MHz for VGA (640 × 480) colour images. The proposed hardware accelerator for IEEE 802.15.4 media access control (MAC) layer covers crucial protocol defined functions and algorithms, and reduces 45% software code in the host processor. This SoC has been fabricated in UMC 0.18 µm 1P6M CMOS process. The average power of the prototype chip is 18.2 mW at 3.0 V power supply and 16 MHz clock rate.

Secure and efficient data collection in wireless image sensor network based on ellipse batch dispersive routing

AbstractWireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node‐disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. In this paper, we propose an ellipse batch dispersive routing (EBDR) algorithm to address the secure and efficient data collection issue in wireless image sensor network. Images are broken into many shares using (K, N) threshold secret sharing. A multi‐hop path is built for each share. All hop nodes are constrained in an ellipse. The routing chooses a relay node for each share of an image in each hop selection step. Relay nodes are dispersed in the whole ellipse area. We analyze the interception probability of shares and delivery delay of EBDR compared with multicast tree‐assisted random propagation. Simulation experiments show that EBDR can obtain better security and efficiency for the routing of image data than multicast tree‐assisted random propagation routing protocol. Copyright © 2013 John Wiley & Sons, Ltd.

A semi-oblivious energy-aware adaptive watermarking for wireless image sensor networks

In this paper, we propose a semi-oblivious energy-aware adaptive watermarking scheme for wireless image sensor networks, which considered key characteristics such as the embedding capacity, security, imperceptibility, computation, and communication energy requirements. We evaluated the distortion in cover image due to watermark redundancies, the number of embedding locations with respect to two channel adaptive parameters, and the impact of compression of cover image on the correctness of extracted watermark. In addition, we investigated the robustness of the scheme against statistical analysis attacks. The results have shown that the proposed scheme has sufficient capacity to embed redundant watermarks in the cover image in an imperceptible manner with reasonably low distortion. The scheme is also considered relatively robust against collusion and middleman attacks.

Energy‐efficient distributed approach for clustering‐based fault detection and diagnosis in image sensor networks

This study proposes an algorithm for the design of a distributed protocol for detecting and diagnosing permanent faults in wireless image sensor networks (WISNs). This work explicitly takes into account the possibility of faults in different sections of the image sensor node and communication channel. The fault diagnosis is achieved by disseminating local decision made at each node. Theoretical analysis and simulation results show that high level (>0.95) of diagnosis accuracy can be achieved for a wide range of fault rates. Correctness and complexity proofs are provided, and they show that the proposed algorithm performs better than the existing protocols from both communication and energy complexity perspective. Analytical study shows that time complexity is O ( n c ) and message complexity O ( n ) for an n -node WISN where n c is the number of cluster heads.

Equal energy dissipation in wireless image sensor network: A solution to energy-hole problem

In wireless image sensor networks (WISNs), the nodes closer to the sink are likely to get over-burdened and run out of their battery energy at the early stage of network deployment. This has been reported in the literature as the energy-hole problem. In this paper, we propose to establish equal energy dissipation condition over the network to mitigate energy-hole problem. We consider a data gathering linear array of wireless camera sensor nodes. Each camera sensor node generates one raw image per data gathering cycle. The raw image captured by each camera sensor node is processed locally and sent to the sink node using intermediate nodes. Exact placement of nodes is derived to ensure equal energy dissipation over the network. The raw image is locally processed using discrete wavelet transform (DWT). Network lifetime and peak signal-to-noise-ratio (PSNR) of the reconstructed image are calculated at the sink node for several test images.