E min and E max

In this section, we seek to determine the network performance as a function of pa-rameter E_min and E_max. In Fig. 5.5, we fix the value of packet size L and change the value of m(macMAXCSMABackoffs). With the larger number of m, the number of times that the device performs binary backoff for a packet can be increased. In Fig. 5.5(a) , for λ = 0.035, because the channel usage is low, the device can easily access the channel when NB ( number of backoffs) is small, which means that increasing the value of m has no effect on the charging time when the energy harvesting rate is small. For λ = 0.14, when m is increased, the device may spend more time on the binary backoff, so the average charging time is decreased. For λ = 2.5, since the average charging time with m = 2 is small enough, the decrement of energy charging time is not very obvious when increasing m.

In Fig. 5.5(b), when N = 20, since only the network with λ = 2.5 has optimized the channel usage, the throughput can be increased when we increase the value of m. In Fig. 5.5(c), larger m leads to the smaller probability that a packet is discarded due to the access failure, and more packets can be transmitted by increasing N B, but it will cause the larger average delay.

In Fig. 5.6, we seek to determine the network performance as a function of parameter Emax. We compare the network charging time, throughput and delay with different energy harvesting rate and E_max = 30, 40, 50, and 60. We find that the performance difference is insignificant with increasing E_max. Although larger E_max can let a device work for longer time before entering the energy replenishing process, but the time staying in energy harvesting states is longer too. Hence, we conclude that E_max is not an important factor on the network performance.

Based on the results presented in Figs. 5.2–5.6, we demonstrated that our Markov chain model successfully predicts the behavior of the slotted CSMA/CA protocol of IEEE

30 35 40 45 50 55 60

802.15.4 standard with energy harvesting process. Additionally, we reaffirm previous findings [13] that the network performance is indeed different if the energy constraint of each device in considered.

Chapter 6 Conclusions

In this paper, we have analyzed the performance of the slotted CSMA/CA mechanism of the IEEE 802.15.4 standard taking into consideration the energy harvesting process in each IoT device. Insights to networked IoT performance with energy harvesting is expected to contribute to improving the prevailing energy constraints plaguing WSNs.

A Markov chain model is presented to analyze the performance of the slotted CSMA/

CA mechanism with the energy harvesting process, and the performance is compared in terms of charging time, throughput and delay. The validity of the proposed model is proven by simulation. Analytical result shows that the performance of IoT devices with energy harvesting sources is different from typical CSMA/CA curves. We find that IoT devices with higher energy harvesting rate may have lower throughput if the network has large number of active nodes.

As the first attempt to incorporate energy harvesting process into the CSMA/CA mech-anism for IEEE 802.15.4 standard, we make the assumption that the energy harvesting process follows the Poisson distribution and devices consume energy in discrete units. In practice, these assumptions may introduce errors into the predicted performance. Relax-ing the above mentioned assumptions are immediate directions to improve the model and is left as the future work.

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