International Journal of Sensors Wireless Communications and Control - Volume 12, Issue 6, 2022

The Internet of Things has witnessed an upsurge in the number of sensors communicating with each other over the Internet, and this number, currently in billions, is increasing at an expeditious pace. However, this augmentation in the number of interlaced devices can lead to profusion and jamming of the network, thereby degrading energy, latency, and throughput. Load balancing of the network is one of the techniques which could alleviate this issue. This paper reviews the methods that have been employed for load balancing of the Internet of Things, thereby serving the research community two-fold. Firstly, it gives a comprehensive introduction to the classification of load balancing algorithms. Secondly, it offers researchers the prospect of developing intelligent novel algorithms catering to the load balancing predicament.

Background: In recent years, deep learning techniques have dramatically enhanced mobile robot sensing, navigation, and reasoning. Due to the advancements in machine vision technology and algorithms, visual sensors have become increasingly crucial in mobile robot applications in recent years. However, due to the low computing efficiency of current neural network topologies and their limited adaptability to the requirements of robotic experimentation, there will still be gaps in implementing these techniques on real robots. It is worth noting that AI technologies are used to solve several difficulties in mobile robotics using visuals as the sole source of information or with additional sensors like lasers or GPS. Over the last few years, many works have already been proposed, resulting in a wide range of methods. They built a reliable environment model, calculated the position of the model, and managed the robot's mobility from one location to another. Objective: The proposed method aims to detect an object in the smart home and office using optimized, faster R-CNN and improve accuracy for different datasets. Methods: The proposed methodology uses a novel clustering technique based on faster R-CNN networks, a new and effective method for detecting groups of measurements with a continuous similarity. The resulting communities are coupled with the metric information given by the robot's distance estimation through an agglomerative hierarchical clustering algorithm. The proposed method optimizes ROI layers for generating the optimized features. Results: The proposed approach is tested on indoor and outdoor datasets, producing topological maps that aid semantic location. We show that the system successfully categorizes places when the robot returns to the same area, despite potential lighting variations. The developed method provides better accuracy than VGG-19 and RCNN methods. Conclusion: The findings were positive, indicating that accurate categorization can be accomplished even under varying illumination circumstances by adequately designing an area's semantic map. The Faster R-CNN model shows the lowest error rate among the three evaluated models.

Background: A Wireless Sensor Network (WSN) consists of a large number of sensor nodes deployed randomly over an area that can sense the surrounding environment to gather some data and interconnect over a wireless channel to share the information with neighboring nodes or with some users over the internet. WSN has a wide range of applications in the field of military, healthcare, industry, agriculture, livestock farming, and smart cities. The pertinence of WSN in healthcare, defense, agriculture, and industry is discussed in detail in the background section of this paper. Objective: The objective of this paper is to examine and simulate Dijkstra’s Algorithm, Bellman Ford’s Algorithm, and Floyd-Warshall’s Algorithm applied for routing in WSN. Methods: Simulation is completed on CupCarbon U-one 4.2 simulator for these algorithms to compute the shortest distance between a randomly deployed source node and a destination node in different sized networks. Simulation of the three algorithms is carried out considering the vital simulation parameters including sensor radius, radio range, and traffic. Also, Simulation is carried out to show the implementation of Floyd Warshall’s algorithm in the field of smart mobility. Results: The obtained results show that the simulation time for the calculation of the shortest route from source to destinations for the three algorithms is the same which is also suitable for various applications of smart mobility, smart accident management, and smart traffic management. Conclusion: The simulation results are also examined to measure the performance of each algorithm and its suitability in the context of WSN. The epilogue of this paper is provided in the conclusion section.

Background: LEACH protocol, even after 20 years, is of interest due to the consideration of the examination network, using wireless sensor networks (WSNs) that demonstrate the significance of the LEACH protocol. Professionals have thought of different types of the LEACH convention. Beneficiaries of the LEACH convention are presently accessible from single hop to multi-hop positions. Methods: The present paper reviews different variations of LEACH steering conventions introduced and examines their upgradation and working. We have examined 65 unique variations of LEACH and inspected how they improve imperativeness usage in WSNs and augmentation framework's lifetime. This study classifies conventions in two areas, to be specific, single-hop correspondence and multihop correspondence, dependent on information communication from the cluster head to the base station. Results: A complete investigation utilizing eight unique features, such as energy efficiency, scalability, delay, overhead, etc., has been given in an ordered manner. Conclusion: The article additionally talks about the solid and feeble purposes of each variation of LEACH. At last, the paper concludes with proposals for future research spaces in the zone of WSN.

Aims & Background: Energy saving or accurate information transmission within resource limits are the major challenges for IoT Underwater Sensing Networks (IoT-UWSNs) on the Internet. Conventional transfer methods increase the cost of communications, leading to bottlenecks or compromising the reliability of information supply. Several routing techniques were suggested using UWSN to ensure uniform transmission of information or reduce communication latency while preserving a data battery (to avoid an empty hole in the network). Objectives and Methodology: In this article, adaptable power networking methods based on the Fastest Route Fist (FRF) method and a smaller amount of the business unit method are presented to solve the problems mentioned above. Both Back Laminated Inter Energy Management One (FLMPC-One) networking method, that employs 2-hop neighborhood knowledge, with the Laminated Inter Energy Management Two (FLMPC-Two) networking procedure, which employs 3-hop neighborhood data, were combined to create such innovative technologies (to shortest path selection). Variable Session Portion (SP) and Information Speed (IS) were also considered to ensure that the suggested method is flexible. Results and Conclusion: These findings show that the suggested methods Shortest Path First without 3-hop Relatives Data (SPF-Three) or Broadness Initial Searching for Shortest Route and Breadth First Search to 3-hop Relatives Data (BFS-Three) successfully developed (BFS-SPF-Three). These suggested methods are successful in respect of minimal Electric Cost (EC) and Reduced Transmission Drop Rates (RTDR) given a small number of operational sites at a reasonable latency, according to the simulated findings.