Quality of Service in Wireless Sensor Networks using Machine Learning: Recent and Future Trends

Introduction

WSNs, or wireless sensor networks, are extremely creative networks used for extensive deployments in challenging environments. Sensing and gathering environmental data, sensor nodes send this information to the sink node for further processing. The development of varied WSN applications is a difficult and demanding task. When designing a WSN, the designer must take into account a number of different factors, including localization, routing, Quality of Service (QoS), security, fault detection, anomaly detection, energy harvesting, event scheduling, data dependability, node clustering, and data aggregation (Pundir & Sandhu, 2021).

The most important problem in WSN is QoS, which has generated a lot of interest in it. The performance, privacy, and security of the network in a real-world setting all depend heavily on quality assurance. According to the classification shown in Fig. 1, this performance is dependent on the QoS parameter’s priority. According to network- or application-oriented criteria, the priority can be determined. A significant amount of energy is consumed by the network when trying to improve all QoS factors at once, such as reducing latency (Rawat & Chauhan, 2021).

Figure 1: Quality of Service (QoS) Parameters

Wireless Sensor Network

Wireless Sensor Networks (WSNs) are self-organizing systems that allow for multi-hop communication throughout the network. It is described as “a collection of scattered mobile sensor nodes utilized for monitoring and recording the external elements present in the environment and centrally arranging the obtained data.” Small hardware components called “motes” or “wireless sensor nodes” are used in these networks’ development. The sensor node perceives the dynamic environment in which it is placed and collects data for a variety of uses, including industrial monitoring, tracking fires started by wildlife, monitoring agricultural practices, and defense systems (Shafique et al., 2020).

This data, which is in raw format, was sensed by a sensor node located in a particular cluster. The cluster head (local aggregator) receives this information, which is then sent to the base station in order to conserve network energy. The gathered data is processed by the base station, which then derives accurate and valuable information. Finally, utilising an internet gateway, the base station transmits this data to the remote locations.

Recent trends in Quality of Service

Known as a group of services required by a network for the transfer of data in the form of packets from source to destination, QoS is a key parameter of WSN. It can be assessed using metrics including packet loss, throughput, latency, jitter, delay, scalability, availability, maintainability, priority, packet error ratio, reliability, bandwidth, deadline, energy usage, and periodicity (Mekonnen et al., 2020). Two tiers can be used to classify a network’s quality of service:

• Performance level: The deployment phase, layered architecture, measurability, network, and application specific QoS metrics are divided into four categories that are taken into account at the performance level.
• Privacy and security level. This level’s parameters address network safety, security, confidentiality, and integrity concerns. To meet the QoS requirements for various application areas, there is a crucial problem. ML offers promise and is applied at the base station in order to address the dynamic nature of WSN.

Conclusion

A group of dispersed, autonomous tiny devices known as a wireless sensor network (WSN) can sense and monitor the physical conditions of their surroundings. As per the statistical analysis, among the many uses for WSN are natural catastrophe prediction, habitat monitoring, medical monitoring, environmental monitoring, and border surveillance. WSN performance can be measured in a number of ways, including localization, Quality of Service (QoS), data aggregation, energy use, event detection, and anomaly detection (Alsheikh et al., 2014). The most well-known and important network parameter today that improves the performance of the network is QoS. Depending on how demand is applied, machine learning (ML) improves the QoS goal parameter.

There has been very little study done to improve the deadline parameter of QoS, with the majority of researchers concentrating on the energy efficiency parameter. The reinforcement learning method is most frequently used in publications to improve energy efficiency. Finally, the unexplored potential for each QoS parameter has mostly been studied from a machine learning standpoint since the performance is better in ML when compared with other methods..

Future Scope

In the future, an ensemble ML-based integrated approach based on artificial intelligence can be used to enhance a variety of QoS parameters, including bandwidth, energy consumption, throughput, delay, jitter, residual energy, packet loss ratio, packet error ratio, and packet delivery ratio, availability, reliability, priority, and deadline. To improve the overall performance of the WSN, these parameters can be calculated utilising cross-layered design. In order to improve a specific parameter at a given layer, multiple mechanisms can be offered at different layers of the WSN. Additionally, the heterogeneous traffic must be examined for a number of network metrics, including dependability, jitter, energy usage, bandwidth, packet loss, and energy consumption. These have a significant impact on the MAC layer metrics including channel access delay, congestion factor, and queuing delay.

• The network layer can integrate fault tolerance and a trust-based multichannel routing system.
• To increase dependability and lessen network congestion, a distortion-based rate adaptation technique can be implemented at the MAC layer.
• By finishing the task by the deadline, the application layer’s responsiveness parameter can be increased. At the application layer, new priority-based algorithms can be added to distinguish between sensitive and non-sensitive data, maintaining the integrity and reliability of the data.

References

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014). Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications. IEEE Communications Surveys & Tutorials, 16(4), 1996–2018. https://doi.org/10.1109/COMST.2014.2320099

Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2020). Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture. Journal of The Electrochemical Society, 167(3), 037522. https://doi.org/10.1149/2.0222003JES

Pundir, M., & Sandhu, J. K. (2021). A Systematic Review of Quality of Service in Wireless Sensor Networks using Machine Learning: Recent Trend and Future Vision. Journal of Network and Computer Applications, 188, 103084. https://doi.org/10.1016/j.jnca.2021.103084

Rawat, P., & Chauhan, S. (2021). A survey on clustering protocols in wireless sensor network: taxonomy, comparison, and future scope. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03381-9

Shafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020). Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios. IEEE Access, 8, 23022–23040. https://doi.org/10.1109/ACCESS.2020.2970118