Design and Analysis of Sensing Scheduling Algorithms under Partial Coverage for Object Detection in Sensor Networks

Many sensor networks are deployed for the purpose of covering and monitoring a particular region, and detecting the object of interest in the region. In this paper, based on the probabilistic sensing model, we conduct comprehensive analytical and simulation studies on collaborative information coverage and object detection in wireless sensor networks. More specifically, we first define point information coverage and based on that we define p-coverage as a measure of the coverage performance for a randomly-deployed wireless sensor network. Then, we investigate the coverage and object detection performances of several simple decision fusion-based collaborative mechanisms and find that simple collaborations among active sensors indeed degrade the coverage performance due to the requirement of maintaining the target false detection probability. This motivates us to develop an on-demand collaborative framework for object detection, whose effectiveness is supported by detailed theoretical analysis and simulation-based validation. Finally, we investigate the energy efficiency performance of our proposed framework and identify the trade-offs among various system parameters in network power consumption.

Wireless sensor networks (WSNs) are employed in a variety of applications. One of the key applications of WSNs, which gained much attention, is the target tracking. Directional sensor networks (DSNs) are a subset of WSNs with some unique characteristics. Since optimizing the tracking system under the energy and coverage constraints in DSNs is of paramount importance, in this paper, we introduce a reliable algorithm for tracking mobile targets using directional WSNs. First, by selecting a minimum set of boundary and borderline sensor nodes, we achieve the desired coverage for an incoming detection. Second, for both deterministic ordered and random node deployments, we propose an efficient mechanism for determining the minimal interior sensor nodes that should be activated. Doing so, the network lifetime can be maximized by the employment of much fewer sensor nodes. Third, we use a geometric method for collecting data using two active sensors at a time. Accordingly, target position is estimated using the extended Kalman filter (EKF). Finally, we compare the proposed algorithm with a genetic algorithm and present the comparative simulation results of the EKF and the random walk. The results demonstrate the effectiveness of our proposed scheme in terms of the energy efficiency, coverage, and tracking accuracy.

Internet of Things (IoT) is constructed from billions of sensor devices connected over Internet. Wireless sensor networks (WSNs) are very important communication technologies of IoT for providing large scale data from the environment. Since the data collected grows exponentially, the design of cloud based big data analysis techniques is crucial. Localization in sensor networks is finding the location of a node based on the reference nodes’s coordinates. In many applications such as target tracking and military surveillance, providing localization is necessary. Manually entering the locations of nodes during the deployment phase is not applicable especially for large scale sensor networks. Besides, integrating a GPS receiver to each sensor node is a very costly solution and may not be affordable for large scale networks. Since sensor nodes are mostly battery-powered, design of an energy-efficient localization method is highly desirable to prolong the lifetime of the network. Existing localization techniques may require many message transmission which causes high energy consumption. To tackle with this problem, we propose an energy-efficient localization framework in this paper. A mobile robot is traversed along the sensing area and communicate with sensor nodes to localize these nodes. Different than the previous approaches, our proposed approach requires only 3 messages per node. Besides, most of the execution needed for the localization is not carried by ordinary nodes. We simulate our proposed approach in ns2 simulator. We measure the localization quality and energy consumptions of our proposed approach with its counterparts. Also, we measure the localization quality and energy consumption against varying node counts and degrees. From extensive simulation results, we obtain that the localization qualities of our proposed approach is significantly better than its competitors. Besides, the energy consumption of our proposed algorithm is just 0.06 J per node and far more better than the distributed algorithm. Conclusively, our proposed framework is a significant candidate for IoT and big data applications requiring energy-efficient localization.

To monitor objects of interest, such as wildlife and people, image-capturing devices are used to collect a large number of images with and without objects of interest. As we are recording valuable information about the behavior and activity of objects, the quality of images containing objects of interest should be better than that of images without objects of interest, even if the former exhibits more severe distortion than the latter. However, according to current methods, quality assessments produce the opposite results. In this study, we propose an end-to-end model, named DETR-IQA (detection transformer image quality assessment), which extends the capability to perform object detection and blind image quality assessment (IQA) simultaneously by adding IQA heads comprising simple multi-layer perceptrons at the top of the DETRs (detection transformers) decoder. Using IQA heads, DETR-IQA carried out blind IQAs based on the weighted fusion of the distortion degree of the region of objects of interest and the other regions of the image; the predicted quality score of images containing objects of interest was generally greater than that of images without objects of interest. Currently, the subjective quality score of all public datasets is in accordance with the distortion of images and does not consider objects of interest. We manually extracted the images in which the five predefined classes of objects were the main contents of the largest authentic distortion dataset, KonIQ-10k, which was used as the experimental dataset. The experimental results show that with slight degradation in object detection performance and simple IQA heads, the values of PLCC and SRCC were 0.785 and 0.727, respectively, and exceeded those of some deep learning-based IQA models that are specially designed for only performing IQA. With the negligible increase in the computation and complexity of object detection and without a decrease in inference speeds, DETR-IQA can perform object detection and IQA via multi-tasking and substantially reduce the workload.

Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered & transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power & limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation & fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

Edge-guided oceanic scene element detection

On the lifetime of large scale sensor networks

3-D object detection from mobile phones in Device-to-Device (D2D) system provides a new smart payment tool for the next generation of fintech, which is more flexible and efficient than the traditional barcode. In this article, we propose a monocular 3-D object detection method based on depth-guided local convolution. The method combines the information of RGB image mode and depth mode by using a convolution kernel through depth image and works on a single RGB image locally. According to the multiscale input information, the convolution kernel is adaptively adjusted to capture the target objects of different scales, so as to improve the performance of 3-D object detection. In addition, we use the soft-non-maximum suppression algorithm instead of traditional non-maximum suppression to select the best prediction box. In order to further improve the accuracy of 3-D object detection, the depth estimation network and 3-D object detection network are jointly trained in this method to make the two networks constrain each other and achieve the best performance.

In recent times, Underwater Wireless Sensor Networks (UWSNs) have involved various applications in oceanographic data collection, pollution revealing, environmental monitoring, offshore exploration, disaster prevention, military surveillance, etc. nevertheless the unique characterization of UWSNs are designed for the grid division on geographical system with polar tracing function to attain the shortest- path and minimizing the energy consumption for end - to - end transmission. In this paper, we proposed a Grid Division Polar Tracing (GDPT) routing protocol function using greedy algorithm for reducing the energy consumption by incorporating into cubic grids and polar tracing functions. The NS2 simulation results proved that the optimal distance of the found path, packet delivery ratio, network lifetime, energy consumption and propagation delay are the performance metrics in the routing protocols. In proposed case used for application of underwater sensor based health monitoring system for wireless in under water environment.

Energy hole is inherently problematic for sensor networks because of their many-to-one architectures, and a feasible solution has not yet been discovered. This paper aims to provide a possible solution for future underwater sensor networks with the ability of depth control only depending on the buoyancy of water currents. We propose a Depth Rotation-based traffIc oFfloading sTrategy (DRIFT) to balance the traffic load over the entire network. DRIFT joint considers the network deployment and energy allocation to obtain a maximized network lifetime. We provide an approach to calculate the precise rotating scheduling cycle and energy allocation based the proposed network model. Extensive simulation shows that when a network lifetime ends, less than 0.5% of the initial energy is wasted, and the network lifetime is prolonged on average by more than a 5-fold factor.

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.

One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime.

Background: The use of point clouds to perform quality control (QC) of prefabricated structural systems is growing markedly. The sustained application of point clouds on construction projects at scale, however, requires reliable and automated frameworks to foster practicality. Literature Review: Existing solutions seldom incorporate the impact of instrument artifacts, construction errors, and complex element geometries, rendering the frameworks error-prone and sensitive to thresholds, outliers, and missing data. More specifically, current methods: 1) are not generalizable to any freeform element; 2) rely on local primitives, which increases runtime and threshold subjectivity; 3) do not formulate the point cloud to model fitting based on geometric distance; and 4) do not incorporate a dedicated outlier detection to explicitly handle large outliers. Methodology: This study presents, scan-to-QC, a generic solution for automatic QC of prefabricated elements with non-analytic cross-sections using point clouds. The framework addresses the current state-of-the-art limitations (above) by providing a generic, direct artificial-intelligence (AI)-based geometric distance fitting robust to outliers. The proposed approach introduces four new algorithms to: 1) represent the cross-sectional boundaries of elements with trigonometric polynomials, 2) perform orthogonal fitting of points to these polynomials, 3) remove outlying observations through robust concentration steps (CS), and 4) perform assembly verification of prefabricated elements in construction projects. As the convergence of CS depends on the initial fitting parameters, the performances of seven deterministic, heuristic, and AI-based (i.e., metaheuristic and global computational intelligence-based) optimization strategies to recover these parameters were evaluated on point clouds of five common prefabricated elements with complex geometries. The comparative analysis considered factors such as the fit quality, object detection accuracy, and computation runtime. Results and Conclusions: Among these methods, the Particle Swarm (PS) achieved the best results, significantly outperforming Simulated Annealing, Bayesian, and Genetic Algorithm. Furthermore, by fine-tuning the swarm size, the PS attained 99% and 96% accuracies in object detection and fitting quality, respectively. Finally, the calibrated PS algorithm was applied to the point cloud of a real-world prefabricated spaceframe structure, yielding 98.5% reliability in nodal coordinate estimation compared to the ground truth.

Autonomous driving systems usually depend on RGB images but face difficulties in low-visibility conditions. Thermal infrared (TIR) imaging offers an alternative that performs well in such environments. However, TIR is hindered by low contrast, indistinct boundaries, and the absence of color, making it less compatible with standard algorithms. Recent advances in thermal infrared colorization are promising, but they face challenges such as semantic distortions, frame instability, and issues with small objects. We introduce the frame-based Weber-law-driven Generative Adversarial Network (FWGAN), a new model that converts thermal images into visible-spectrum images. FWGAN improves object recognition by maintaining textures, increasing temporal consistency, and enabling reliable detection in different scenarios. Tests on benchmark datasets like FLIR and KAIST show that FWGAN's enhancements in semantic accuracy, object detection, and visual quality significantly push forward autonomous driving technology.