Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Abstract

The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5. 13-11.64% and increase the energy efficiency by 14.29-17.53% under the same lifetime with a more balanced energy utilization.

A fuzzy-rule-based packet reproduction routing for sensor networks

“…For example, to meet the low power requirements to extend the battery life, it is necessary for sensor nodes to automatically adapt to changes in the available power and adopt intelligent forwarding scheme according to the current characteristic of the entire network. [21][22][23] Since both software and hardware resources are limited for WSN nodes, it is challenging to improve the intelligence level of sensor nodes.…”

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

“…Data aggregation is a method of reducing data transmission that is closely related to the strategy of this paper [1,3,6,27] and is an important type of operation in wireless sensor networks [37][38][39][40][41]. The basic principle is that there is a correlation between the data perceived among dense sensor network nodes.…”

“…(6) end if (7) if node ∈ 1 , calculate self-energy-consumption and add it to its information. (8) end for (9) end for (10) Sink node receive data from node V ∈ 1 (11) Sink node select max energy consumption value from data received, broadcast it to all node (12) for = 1 to ⌈ / ⌉: (13) for = 1 to ℎ : (14) node V in ( , ) receives the max energy consumption value (15) node V in ( , ) calculates and adjusts transmitting power according to max energy consumption value (16) node V in ( , ) calculates the new (17) end for (18) end for Algorithm 1: Adaptive transmitting power algorithm.…”

“…There also exit some challenges due to unique characteristics of WSNs as well as their unique communication patterns [6,7]. In WSNs, sensor nodes are usually powered by battery, and it would be very challenging or sometimes infeasible to replace or recharge them after being deployed [8,9].…”

Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks

“…The wireless sensor networks have been developed for a long time [21][22][23][24][25][26]; with the development of wireless portable devices and sensor technology, the application of wireless sensor network in the industrial field becomes the focus of attention [2,4]. Industrial wireless sensor networks (IWSNs) is emerging in this background; it does not require wiring to be deployed at any time and has simple requirements for complex industrial sites; the device is small and easy to deploy and has powerful functions that can be used to detect and monitor a variety of visible and invisible physical phenomena in close range and high precision; and the strong practicality makes it to have broad application prospects in various fields of industrial production [1,2,9,10], especially with the rise of cloud computing [27] and fog computing [28][29][30], to make its development face greater opportunities.…”