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

Abstract

Low delay and long lifetime are a very important issue for industrial wireless sensor networks (IWSNs) in which it require long-time monitoring of industrial sites and respond quickly to events that is monitored; therefore, high delay communications can cause serious damage to property and personnel at industrial field. Due to delay, lifetime, and other performance involved to multiple layers, it is difficult to optimize from a single layer. Therefore, a cross-layer design optimal scheme for reducing delay and maximizing lifetime (RDML) scheme is proposed for IWSNs which is from several layers such as transmitted power, duty cycle, and node deployment positions to optimize the network performance of delay and lifetime etc. Firstly, due to the node which sends a packet within a cycle, different duty cycle leads to different selection of the modulation level, resulting in different power consumption efficiency of transmitting data. Through careful analysis, the optimal value of the duty cycle is given which has the lowest energy consumption per bit. In fact, the energy consumption of the node is not balanced. Therefore, an optimization method of changing the duty cycle is proposed. In this paper, larger duty cycle is chosen for nodes with residual energy to improve the reliability of data transmission, reducing the probability of data retransmission, so that the network delay can be reduced in IWSNs. Third, based on the previous analysis, a network optimization deployment algorithm is proposed, which not only maximizes the energy efficiency of a single node but also maximizes the network lifetime and the total network energy efficiency. Both our comprehensive theoretical analysis results indicate that the performance of RDML scheme is better than the previous studies. Relative to equal distance and optimal duty cycle scheme, the RDML scheme can reduce the delay by 19-30% and increase the lifetime by more than 43%.

Cross-layer optimization of wireless links under reliability and energy constraints

“…On the other hand, for each combination of M and τ max r , the joint optimum ofγ and n p can be found from (22) and (27) either by solving a system of two non-linear equations or by iteratively invoking these equations. In either case, we must ensure that the reliability conditions in (20) and (26) are satisfied.…”

A spanning tree construction algorithm for industrial wireless sensor networks based on quantum artificial bee colony

“…(2) In IWSN, once the sensor node is installed, it is generally no longer moving, unless the node failure needs to be replaced or the plant equipment is moved [15]; in contrast, the WSN node mobility is strong. (3) In addition to sensor nodes in IWSN, there are routers, handheld devices, and other nodes [16]. Different types of nodes perform different functions, forming a heterogeneous network.…”

A Trust-Based Secure Routing Scheme Using the Traceback Approach for Energy-Harvesting Wireless Sensor Networks

“…Ubiquitous sensor-based devices (e.g., sensor nodes, wearable sensing devices, and smartphones) [ 1 , 2 , 3 , 4 , 5 ] have been playing a vital role in the evolution of the Internet of Things (IoT) [ 2 , 4 , 5 , 6 , 7 , 8 , 9 ], which bridges the gap between digital and physical spaces [ 6 , 7 , 8 ]. However, the energy issue of sensor terminals poses significant challenges to the widespread use of IoT, in which the sensor devices generally have small volume and battery with limited capacity [ 10 , 11 , 12 , 13 , 14 , 15 ].…”