Measurement and Analysis of Link Quality in Wireless Networks: An Application Perspective

Abstract

Abstract-Estimating the quality of wireless link is vital to optimize several protocols and applications in wireless networks. In realistic wireless networks, link quality is generally predicted by measuring received signal strength and error rates. Understanding the temporal properties of these parameters is essential for the measured values to be representative, and for accurate prediction of performance of the system. In this paper, we analyze the received signal strength and error rates in an IEEE 802.11 indoor wireless mesh network, with special focus to understand its utility to measurement based protocols. We show that statistical distribution and memory properties vary across different links, but are predictable. Our experimental measurements also show that, due to the effect of fading, the packet error rates do not always monotonically decrease as the transmission rate is reduced. This has serious implications on many measurementbased protocols such as rate-adaptation algorithms. Finally, we describe real-time measurement framework that enables several applications on wireless testbed, and discuss the results from example applications that utilize measurement of signal strength and error rates.

QoS in IoT Networks based on Link Quality Prediction

“…The authors in [12] presented an experimental study of wireless link quality variation over a period of several days in a sensor network placed in two different indoor office environments. Kola et al [13] analyzed the received signal strength and error rates in an IEEE 802.11 indoor wireless mesh network.They demonstrated that statistical distribution and memory properties vary across different links, but are predictable.…”

Geographic HWMP (Geo‐HWMP) routing method for AMI network with lossless packet forwarding

“…For every successful reception of packets with size ≥ 2 5 , a weight of 2 is assigned and for others, a weight of 1 is assigned for RSS and inversely for PER. Then, a weighted average is recorded for both parameters as given in (17) and (18) (19) where P is the transmitting power, and β( ≥ 2) is the path loss coefficient PER = Number of bits modified/lost at reciever Number of bits transmitted by sender/forwarder (20) From the collected PER [7] and RSS values, three divisions are created, and the links are categorised as strong, medium and weak links according to the dynamic values. The next hop is selected based on weighted airtime metric.…”

“…There are a number of literatures for link quality-based next hop selection [7,8], energy and region-based neighbour selection [9][10][11] and location-based routing [4,[12][13][14]. Vinay Kolar et al [7,8] described the method of determining link quality in WMNs. Links are categorised based on a pair of parameters called packet error ratio (PER) and received signal strength (RSS) of each packet at the receiver.…”

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

“…Wireless mesh networks (WMNs) constitute a suitable alternative to 5G/6G cellular communication technologies, especially in cases they are either not available or there is a demand for low-cost deployment and operation [1]. However, the dynamic and multi-hop nature of WMNs [2] is being reflected on users as inadequate service Quality of Experience (QoE) due to long delays and communication disruptions [3]. Consequently, there is a need for intelligent network control that adapts to changes to link quality by employing appropriate protocol strategies, taking into account the particular characteristics and requirements of each environment.…”