Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques

Abstract

In most wireless sensor network (WSN) applications, the sensor nodes (SNs) are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT) applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC) based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN) using the received signal strength indicator (RSSI), while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm) by 78%.

Path-loss modelling for WSN deployment in indoor and outdoor environments for medical applications

“…The received data packets of the ref. [32] is varied between 88%-96% (indoor) and 80%-100% (outdoor), whereas for the current work have 100% for all distances value except 95% at 10 m (outdoor) and 96.67% at 2 m (indoor). However, the data packets received is 100% at high distance of 110 m for outdoor.…”

“…The current work can be extended in future by using an artificial intelligent techniques (such as neural network) or optimization algorithm (such as Particle Swarm Optimization) for accurate distance estimation between transmitter and receiver. Packet recived (outdoor) [32] Packet received (outdoor)-this work Packet received (indoor) [32] Packet received (indoor)-this work 0.000…”

“…However, the data packets received is 100% at high distance of 110 m for outdoor. This superiority for current work is attributed to the fact that the using of XBeeS2C which have high communication distance with low power consumption than XBee S2 (which used in [32]).…”

“…Where Wt is the node transmitted power in dBm. Then, the RSSI at the movable node will be [30][31][32].…”

“…In the all locations, the received data packets in indoor is better than outdoor because of aforementioned reason above in indoor environments. The Packet loss can be compared with previous work such as [32] which used the ZigBee wireless protocol to evaluated the adopted WSN for outdoor and indoor environments as shown in Figure 9. The figure shows the percentage of data packets received by the movable node for both indoor and outdoor surroundings versus distance between the movable node and anchor node (located at the center of track cycling application) [32].…”