**7.1 Performance check on intrinsically safe routing models**

While assessing the accomplishment of an IoT-HSN, it becomes vital to evaluate the intrinsic safety aspect of the wireless system and the possible risks of damage to healthy human cells and tissues. As pointed out earlier, the heat generated because of dissipation of wireless energy can cause discomfort to the subject and has the capacity to damage healthy human cells and tissues if endured for long times. For instance, the incessant monitoring of peripheral capillary oxygen saturation (SPO2)

**Figure 1.** *Four 24-channel IoT-HSNs in action.*

*A Thermal and Energy Aware Framework with Physiological Safety Considerations… DOI: http://dx.doi.org/10.5772/intechopen.99655*

levels using a pulse oximeter for over 8 hours would cause a rise of temperature of 43 degrees Centigrade and is hence deemed risky as it could cause burns [61]. The detrimental effects of such sensor radiation caused heating can be evaluated by applying Penne's bio-heat Equation [62] that offers the heat transfer relationship between the temperature of blood vessels and the tissue surrounding the vessels. IoT-HSNs can follow temperature-aware routing algorithms [63–65] that consider parameters like antenna radiation and the ensuing power dissipation as temperature rise in the surrounding tissue and make routing decisions to minimize the generation of heat. Combined with an efficient MAC protocol, the thermal-aware routing algorithms can be used for generating transmission and sleep duty cycles that allow a reduced rise in temperature than individual schemes [56]. Although the outcomes in [56] are improved over the other attempts at temperature-aware routing, the approach does not take into consideration the base network energy requirement and additional energy consumption required for retransmissions of lost sensor data. The author attempted to estimate the implementation of the three models in [56] with regards to energy in a network involving actuator control applications with sensors for Internet of Things Healthcare Sensor Networks. The model in [56] uses up to 25 sensors in its IoT-HSN, which is very close to the author's model involving 24 sensors [59].

All the models considered in the present evaluation study the effect of four probability distributions for network parameters in addition to temperature rise, namely Poisson, Laplace, Binomial and LogNormal. Of the three, the SCMC model is a sensor-centric model that permits a random generation of packets based on a probability distribution while presuming fixed rise and fall in temperatures. A stable solver comprising of a fixed CSS is employed in the TBFC model for a stepped packet generation to offer improved heat performance than the SCMC. The tradeoff for the TBFC model is a higher packet loss which is improved in the third model (TBFC-1HC). This modified TBFC model employs 'one-hop caching' in sensors to cache data packets for transmission delays up to their one hop neighbor that is nearest to the CSS. Data packets wait for a clear-to-send signal after which they are transmitted to the CSS.

#### **7.2 Performance evaluation on traffic parameters of the model**

The thermal aware routing algorithms for reducing the amount of heat generated have a trade-off in the form of loss of packets. The lost packets need to be retransmitted. The author tried to assess the data overhead due to retransmission resulting from packet loss for the four distributions across the three models. The results of the comparison can be seen in **Figure 2** below. It is evident from the results that of the three models, TBFC fared the worst on the retransmission of packets that were dropped, while SCMC was found to be the best. Comparing the retransmission overhead for the distributions, the Poisson distribution had the lowest values while Log-Normal had the highest retransmission overhead among the four distributions. The work has the potential to be extended by including other distributions involving a more realistic human model.

Even if lost transmissions cause additional data traffic due to retransmissions, a data transmission scheme that involves reducing the frequency on transmissions of the sensor data and sending alternate samples as suggested by the author in the next section would effectively cut down the heating effects in the same proportion. Merely skipping alternate samples would reduce the amount of heat generated to half, thereby allowing longer node operation. If the final recreation does not alter the doctor's initial diagnosis, the sample cut rate can be increased, thereby improving the heat performance to three or even four folds of the default.

**Figure 2.** *Number of retransmitted packets in unit time for the three models.*
