Spatiotemporal Activity Mapping for Enhanced Multi-Object Detection with Reduced Resource Utilization

The accuracy of data captured by sensors highly impacts the performance of a computer vision system. To derive highly accurate data, the computer vision system must be capable of identifying critical objects and activities in the field of sensors and reconfiguring the configuration space of the sensors in real time. The majority of modern reconfiguration systems rely on complex computations and thus consume lots of resources. This may not be a problem for systems with a continuous power supply, but it can be a major set-back for computer vision systems employing sensors with limited resources. Further, to develop an appropriate understanding of the scene, the computer vision system must correlate past and present events of the scene captured in the sensor’s field of view (FOV). To address the abovementioned problems, this article provides a simple yet efficient framework for a sensor’s reconfiguration. The framework performs a spatiotemporal evaluation of the scene to generate adaptive activity maps, based on which the sensors are reconfigured. The activity maps contain normalized values assigned to each pixel in the sensor’s FOV, called normalized pixel sensitivity, which represents the impact of activities or events on each pixel in the sensor’s FOV. The temporal relationship between the past and present events is developed by utilizing standard half-width Gaussian distribution. The framework further proposes a federated optical-flow-based filter to determine critical activities in the FOV. Based on the activity maps, the sensors are re-configured to align the center of the sensors to the most sensitive area (i.e., region of importance) of the field. The proposed framework is tested on multiple surveillance and sports datasets and outperforms the contemporary reconfiguration systems in terms of multi-object tracking accuracy (MOTA)

Distributed Network of Adaptive and Self-Reconfigurable Active Vision Systems

The performance of a computer vision system depends on the accuracy of visual information extracted by the sensors and the system’s visual-processing capabilities. To derive optimum information from the sensed data, the system must be capable of identifying objects of interest (OOIs) and activities in the scene. Active vision systems intend to capture OOIs with the highest possible resolution to extract the optimum visual information by calibrating the configuration spaces of the cameras. As the data processing and reconfiguration of cameras are interdependent, it becomes very challenging for advanced active vision systems to perform in real time. Due to limited computational resources, model-based asymmetric active vision systems only work in known conditions and fail miserably in unforeseen conditions. Symmetric/asymmetric systems employing artificial intelligence, while they manage to tackle unforeseen environments, require iterative training and thus are not reliable for real-time applications. Thus, the contemporary symmetric/asymmetric reconfiguration systems proposed to obtain optimum configuration spaces of sensors for accurate activity tracking and scene understanding may not be adequate to tackle unforeseen conditions in real time. To address this problem, this article presents an adaptive self-reconfiguration (ASR) framework for active vision systems operating co-operatively in a distributed blockchain network. The ASR framework enables active vision systems to share their derived learning about an activity or an unforeseen environment, which learning can be utilized by other active vision systems in the network, thus lowering the time needed for learning and adaptation to new conditions. Further, as the learning duration is reduced, the duration of the reconfiguration of the cameras is also reduced, yielding better performance in terms of understanding of a scene. The ASR framework enables resource and data sharing in a distributed network of active vision systems and outperforms state-of-the-art active vision systems in terms of accuracy and latency, making it ideal for real-time applications

Distributed Network of Adaptive and Self-Reconfigurable Active Vision Systems

The performance of a computer vision system depends on the accuracy of visual information extracted by the sensors and the system’s visual-processing capabilities. To derive optimum information from the sensed data, the system must be capable of identifying objects of interest (OOIs) and activities in the scene. Active vision systems intend to capture OOIs with the highest possible resolution to extract the optimum visual information by calibrating the configuration spaces of the cameras. As the data processing and reconfiguration of cameras are interdependent, it becomes very challenging for advanced active vision systems to perform in real time. Due to limited computational resources, model-based asymmetric active vision systems only work in known conditions and fail miserably in unforeseen conditions. Symmetric/asymmetric systems employing artificial intelligence, while they manage to tackle unforeseen environments, require iterative training and thus are not reliable for real-time applications. Thus, the contemporary symmetric/asymmetric reconfiguration systems proposed to obtain optimum configuration spaces of sensors for accurate activity tracking and scene understanding may not be adequate to tackle unforeseen conditions in real time. To address this problem, this article presents an adaptive self-reconfiguration (ASR) framework for active vision systems operating co-operatively in a distributed blockchain network. The ASR framework enables active vision systems to share their derived learning about an activity or an unforeseen environment, which learning can be utilized by other active vision systems in the network, thus lowering the time needed for learning and adaptation to new conditions. Further, as the learning duration is reduced, the duration of the reconfiguration of the cameras is also reduced, yielding better performance in terms of understanding of a scene. The ASR framework enables resource and data sharing in a distributed network of active vision systems and outperforms state-of-the-art active vision systems in terms of accuracy and latency, making it ideal for real-time applications

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms

Proceedings of the 6th International Conference on Modeling and Simulation in Civil Engineering

This conference proceedings contains articles on the various research ideas of the academic community and technical researchers presented at the 6th International Conference on Modeling and Simulation in Civil Engineering (ICMSC 2022). ICMSC 2022 was organized by the Department of Civil Engineering, TKM College of Engineering, Kollam, Kerala, India on December 1-3, 2022. The main aim of this conference is to bring together leading academicians, researchers, technocrats, practitioners, and students to exchange and share their experiences and research outputs on all aspects of Civil Engineering, especially related to the modeling and simulation in Civil Engineering.  Conference Title: 6th International Conference on Modeling and Simulation in Civil EngineeringConference Acronym:  ICMSC 2022Conference Date: 1-3 December 2022Conference Location: IndiaConference Organizer: Department of Civil Engineering, TKM College of Engineering, Kollam, Kerala, Indi