RBM: Region‐Based Mobile Routing Protocol for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are employed for different applications for the reason of small-sized and low-cost sensor nodes. However, several challenges that include a low powered battery of the sensor nodes restrict their functionality. Therefore, saving energy in the routing process to extend network life is a serious concern while deploying applications on WSN. To this end, the key technology is clustering, which helps maximize scalability and network lifecycle. Base station (BS) collects data, aggregates it, and extracts the required information. To obtain the maximum outcome, the lifetime of the network is maximized by the use of different techniques and protocols. Data transmissions consume most of the network energy, and the transmissions over normal ranges require less energy as compared to transmissions over long ranges. Moreover, the nodes closer to the BS deplete their energy faster as compared to distant nodes because of traffic overload. The proposed protocol is aimed at reducing energy consumption and increasing the network lifetime. For this purpose, the network is divided into two regions: region 1 closer to the BS communicating directly, whereas region 2 farther away from the BS having routing nodes to communicate with the BS. Routing nodes do not take part in sensing function but will only move in region 2 collecting data and forwarding it to BS. MATLAB is used as the simulation tool for evaluation, and the results are compared with the existing optimized region-based efficient routing (AORED) and low-energy adaptive clustering hierarchical protocol (LEACH) techniques. The comparison showed that energy conservation and lifetime increased by 15%, and throughput is increased by more than 5% approximately.

Research on a Vehicle-Mounted Intelligent TCM Syndrome Differentiation System Based on Deep Belief Network

“…Pre-training and fine-tuning are the two stages of DBN training. [23] • Pre-training stage: At this stage, DBN uses the unsupervised greedy layer-by-layer learning algorithm of its restricted Boltzmann machine mechanism to generate its model, and then uses the result of layer-by-layer accumulation of RBM to reconstruct the original features, and then always performs the initial feature vector Fitting, the parameters of DBN are continuously adjusted through this method. At this stage, it is assumed that a vector is used to represent the state of the visible layer, and a vector is used to represent the state of the hidden layer.…”

“…RBM is a typical deep network model that learns the probability distribution of the input data set, its basic network structure consists of visible layer v and a hidden layer h. The explicit nodes are used to accept input data and implicit nodes are used to extract features [23]. Each layer of RBM nodes can be divided into an active state represented by 1 and an inactive state represented by 0 [24] on RBM mechanism to generate its model.…”

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network

“…Researchers have put forward a variety of traditional [29][30][31][32][33][34][35][36] and tailor-made [27,[37][38][39][40][41][42][43][44][45][46]] routing approaches to tackle these problems. Among the propounded strategies, Cluster-Based Routing (CBR) schemes have demonstrated marked network performance improvements over flat-based, chain-based, and tree-based routing topologies for large-scale time-sensitive WSN applications [2,[47][48][49].…”

Low-Delay and Energy-Efficient Opportunistic Routing for Maritime Search and Rescue Wireless Sensor Networks

“…Numerous research has been investigated in terms of routing protocol in WSNs with mobile nodes [6][7][8][9][10][11][12][13][14][15]. To name a few.…”