Edge Computing-Based ERBS Time Synchronization Algorithm in WSNs

Abstract

In the practical application of large-scale photovoltaic module monitoring, adopting wireless sensor network (WSN) technology is a method worth researching. With increasing nodes in the wireless sensor network, widely existing clock skew, increased geometrically, is bringing about greater energy consumption. Due to the random distribution of nodes, in order to improve the transmission efficiency and reduce the computational load of the coordinator, the node processor needs to the use edge computing for preliminary analysis. This paper puts forward an improved energy-efficient reference broadcast synchronization algorithm (ERBS). This algorithm firstly calculates the average phase offset of nonadjacent nodes in the network after receiving a message. It then uses the least square method to solve the clock skew to achieve high-precision synchronization of the whole network. Simulation results show that compared with RBS, the time synchronization precision of ERBS is greatly improved and synchronization times are greatly reduced, decreasing energy consumption significantly.

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things

“…Domestic theories of emergency management still lack systematic and comprehensive research, or they only emphasize the responsibility of the government or relevant organizations [ 19 ], which should attract the attention of relevant people, or they only discuss how to formulate corresponding laws, policies, and regulations or just some problems related to system design. Emergency organizations are generally divided into national and local levels [ 20 , 21 ]. The highest administrative body for state emergency management is the State Council.…”

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

“…However, these works also suffer from the inherent resource wastage issue associated with edge computing due to the extra computational overhead and energy consumption introduced by edge computing for sensor devices. To address these issues, we introduced a heterogeneous multiclass communication system that consists of ordinary water quality sensor devices and two edge computing-enabled HAP devices that can perform edge computing tasks [23,24]. Additionally, we introduced the concept of a sequential multi-class WPCN to optimally and sequentially schedule the operation of network devices for data transmission with a low-complexity dynamic resource allocation method.…”

“…Each class of the network transfers its data to two HAPs to achieve a good channel gain among the sensor devices and the HAPs. We assume that the proposed multi-class communication system is a heterogeneous system such that the sensor devices are ordinary water quality sensor devices and HAPs s 1 and s 2 are edge computing-enabled and can perform edge computing tasks [23,24]. This circumvents the potential extra computational overhead and energy consumption that edge computing may introduce for sensor devices owing to complex tasks relating to data collection, processing, analysis, and decision making.…”

GPS-Based Network Synchronization of Wireless Sensors for Extracting Propagation of Disturbance on Structural Systems

“…As a result of this last situation, different synchronization techniques have been proposed to implement when obtaining sound measurements. Examples of such techniques are the Reference broadcast Synchronization (RBS) [ 31 ], Time Sync Protocol for sensor Network (TPSN) [ 32 ], Flooding Time Synchronization Protocol (FTSP) [ 33 ], Pulse Per Second Sync (PPS) [ 34 , 35 ], Flooding with Clock Speed agreement (FCSA) [ 36 ], gradient time synchronization protocol (GTSP) [ 37 ], etc. Based on the study documented in [ 38 ], the existence of an error between pulses of approximately ±400 ns was found; however, it turns out to be the best synchronization method, since the wireless system can be easily implemented on it.…”