Characterization of Link Quality Fluctuation in Mobile Wireless Sensor Networks

Abstract

Wireless sensor networks accommodating the mobility of nodes will play important roles in the future. In residential, rehabilitation, and clinical settings, sensor nodes can be attached to the body of a patient for long-term and uninterrupted monitoring of vital biomedical signals. Likewise, in industrial settings, workers as well as mobile robots can carry sensor nodes to augment their perception and to seamlessly interact with their environments. Nevertheless, such applications require reliable communications as well as high throughput. Considering the primary design goals of the sensing platforms (low-power, affordable cost, large-scale deployment, longevity, operating in the ISM band), maintaining reliable links is a formidable challenge. This challenge can partially be alleviated if the nature of link quality fluctuation can be known or estimated on time. Indeed, higher-level protocols such as handover and routing protocols rely on knowledge of link quality fluctuation to seamlessly transfer communication to alternative routes when the quality of existing routes deteriorates. In this article, we present the result of extensive experimental study to characterise link quality fluctuation in mobile environments. The study focuses on slow movements (<5 km h -1 ) signifying the movement of people and robots and transceivers complying to the IEEE 802.15.4 specification. Hence, we deployed mobile robots that interact with strategically placed stationary relay nodes. Our study considered different types of link quality characterisation metrics that provide complementary and useful insights. To demonstrate the usefulness of our experiments and observations, we implemented a link quality estimation technique using a Kalman Filter. To set up the model, we employed two link quality metrics along with the statistics we established during our experiments. The article will compare the performance of four proposed approaches with ours.

Revisiting Link Quality Metrics and Models for Multichannel Low-Power Lossy Networks

“…Due to its resilience to wireless interference and multi-path fading, multichannel communication can effectively improve the reliability of LLN under cross-technology interference, which has received extensive attention from academia and industry. However, existing LQE metrics and models are generally established and verified under the single-channel scenario [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], and most of them are under the interference-free channel [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 18 , 19 ]. In the multichannel scenario, as the channels used will change from time to time, whether these metrics and models are still applicable is hard to judge directly because of the lack of corresponding studies.…”

“…Moreover, node mobility or cross-technology interference (CTI) will also affect the accuracy of LQE. Wen et al [ 19 ] showed that low-power links are highly sensitive to node mobility and predicting the existence and range of transitional regions is more difficult. Barać et al [ 20 ] showed that LQI and RSSI obtained from interfered WSN links are deceptive and cannot be used to assess link quality accurately.…”

“…Liu et al also established a model of LQI and PRR using logical regression and pointed out that it is more accurate than those of SNR, RSSI, and PRR [ 12 ]. Almost all the works above are established and verified under a single-channel scenario [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], and most of them are under the interference-free channel [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 18 , 19 ]. The impacts of radio interference and channel change are not fully considered.…”

“…There have been many works in the literature which analyze the characteristics of low-power wireless links through experiments and propose many effective LQE metrics (e.g., Packet Reception Ratio (PRR), Signal to Noise Ratio (SNR), Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI)) and models [ 9 ]. However, almost all these metrics and models are established and verified under the single-channel scenario [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], and most of them are under the interference-free channel [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 18 , 19 ]. The impacts of radio interference and channel change are not fully considered.…”

ConnecSenS, a Versatile IoT Platform for Environment Monitoring: Bring Water to Cloud

“…In order to analyze the performance of the networks, we studied the data workflow, the battery consumption, the rate of data loss during data transmission and the received signal strength over a given period. These metrics are those classically used in the literature to qualify wireless sensor networks [ 45 , 46 ].…”