Computational Intelligent Sensor-Rank Consolidation Approach for Industrial Internet of Things (IIoT)

Abstract

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep learning inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles

“…where W di s refer content rank based on the sensor and we request to readers to refer (Mekala, Rizwan, & Khan, 2021), di refer format of distribution.…”

“…Offloading and processing costs are consider for vehicle side optimization and for server side, continuous relaxation method and lagrangian decomposition methods are considered in (Du et al, 2018). In (Mekala, Rizwan, & Khan, 2021; Zhang et al, 2017) MEC‐offloading scheme has been designed by the consideration of vehicle heterogeneous computation requirements.…”

“…Let refer service request called content and the content size remain denoted as , and it should be in the bounded range such as . The requested content may possible to share in the elapsed time and its probability of the service request is follows where refer content rank based on the sensor and we request to readers to refer (Mekala, Rizwan, & Khan, 2021), refer format of distribution.…”

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

“…In the second level, linear programming is used for MOT as per the first-level outcome for effective tracking. Data association procedures such as Hausdorff distance metric [121,122], joint association probability filter, and nearest neighbor filter [123] are used to estimate the object path based on normalized Mahalanobis distance [124]. The 'nuScenes' tracking strategy is used to estimates the current state and predicted state of the objects for MOT based on Bayes estimation methods like an unscented-Kalman filter or extended-Kalman filter [125,126].…”

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“…For example, there is an instance in the field of robotics [6] where smart sensors enable robots to have human-like functions of the five senses and the brain to sense various phenomena and perform various actions. In industrial production [7][8][9], certain product quality indicators (e.g., viscosity, hardness, surface finish, composition, color and flavor) cannot be quickly and directly measured and controlled online using conventional sensors. However, some physical quantities in the production process (such as temperature, pressure, flow, etc.)…”