A Power-Efficient Clustering Protocol for Coal Mine Face Monitoring with Wireless Sensor Networks Under Channel Fading Conditions

Wireless Body Area Sensor Networks: Survey of MAC and Routing Protocols for Patient Monitoring under IEEE 802.15.4 and IEEE 802.15.6

Wireless body area sensor networks (WBASNs) have received growing attention from industry and academia due to their exceptional potential for patient monitoring systems that are equipped with low-power wearable and implantable biomedical sensors under communications standards such as IEEE 802.15.4-2015 and IEEE 802.15.6-2012. The goal of WBASNs is to enhance the capabilities of wireless patient monitoring systems in terms of data accuracy, reliability, routing, channel access, and the data communication of sensors within, on and around the human body. The huge scope of challenges related to WBASNs has led to various research publications and industrial experiments. In this paper, a survey is conducted for the recent state-of-art in the context of medium access control (MAC) and routing protocols by considering the application requirements of patient monitoring systems. Moreover, we discuss the open issues, lessons learned, and challenges for these layers to provide a source of motivation for the upcoming design and development in the domain of WBASNs. This survey will be highly useful for the 6th generation (6G) networks; it is expected that 6G will provide efficient and ubiquitous connectivity to a huge number of IoT devices, and most of them will be sensor-based. This survey will further clarify the QoS requirement part of the 6G networks in terms of sensor-based IoT.

Power Allocation Based on Data Classification in Wireless Sensor Networks

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy.