**8. Energy saving and network lifetime improvement for IoT-HSNs**

A key question related to IoT-HSNs entails the energy-fidelity trade-off. When sensor data is transmitted after processing and transformation, it is expected that the fidelity level of the received data must be acceptable and appropriate to be useful. Any data transformation and transfer need to be done in an energy efficient manner. This requirement advocates for selective processing of collected physiological data samples.

#### **8.1 Sample reduction with prediction for energy saving**

Another major operation and design issue with IoT-HSNs involves improving the lifetime of sensing for sensor nodes and thus that of the networks. The issue is caused due to the constraints on batteries that need to be small in size and cannot pack a lot of power due to this constraint [66, 67]. The sensor nodes collect data samples and relay them to the CSS at an acceptable rate as dictated by the QoS of the physiological parameter. However, the total number of samples collected and transmitted by the sensor does not take the nature and frequency of variations in the physiological parameter into account by default. In this work, an attempt has been made to address the energy-fidelity trade-off [68] by reducing this data content through signal processing techniques. The approach involved selective exclusion of some sample data from transmission. Prediction techniques were used to recreate the missing samples that were not transmitted. The approach used in this work was different from the dual prediction technique proposed by Mishra et al. [69]. Prediction techniques involve approximations that come with errors but if the error is negligible, the recreated signals can be used for an early diagnosis if not for full diagnosis, while the patient is on the way to hospital.

The fidelity of data and the accuracy of information contained would undoubtedly be better if all the data samples sensed by the physiological sensor were transmitted. Although this sampling approach would satisfy the Nyquist criterion, it

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

would result in transmission of several samples which could be predicted with reasonable accuracy using numerical techniques within some range of error. While such data might not truthfully reflect what continuous monitoring would reveal, the medical personnel would still be helped by early diagnosis, planning or determination on the course of action.

The author first reduced the transmitted samples for each of the parameters in the 24-channel IoT-HSNs to half by skipping transmitting alternate samples and tried predicting the skipped samples at the receiving end by using a simple proportional-integral-derivative (PID) scheme and a more computationally involved prediction using non-linear regression involving an artificial neural network (ANN-NLR). The results are shown for a couple of cycles of prediction for the ART parameter in **Figure 3**. The approximation used in the two prediction strategies generates some error, which is still not too big to alter the characteristics of the ART signal appreciably. This error is shown in **Figure 4**.

The amount of data was reduced by periodically skipping those samples from the original set and predicting the missed samples at the receiving end. The bulk of data marked for transmission could be further used by delta encoding to pack more amount of data in every transmission [59].

Sample data sets for the 24-channel IoT-HSN involving critical physiological parameters such as ECG, central venous pressure, pulmonary artery pressure and arterial pressure signals obtained from Physionet [60], were used as the source to progressively cut down samples and create four different subsets of the original sets like the approach used in [70]. For the sample analysis and graphical evaluation, the programs were written in in MATLAB r2020 [71].

Alternate samples of each of the original sample sets were used to create the first subset, every third sample was picked up to create the second, every fourth sample for the third set and every fifth sample for the fourth set. Thus, the sample sizes of these sets were half, one-third, one-fourth, and one-fifth of the original, respectively. The four sets were transmitted and recreation of original by a variety of numerical interpolation algorithms was attempted at the receiving end. The reconstructed sets were compared with the original set of samples with all samples intact, and the error was calculated. **Figure 5** shows the results of the recreation for

**Figure 3.** *Plots of comparison of prediction performance by PID and ANN-NLR algorithms for arterial pressure.*

**Figure 4.** *Error plots of comparison of prediction performance by PID and ANN-NLR algorithms for arterial pressure.*

**Figure 5.** *Prediction of skipped samples for four sample elimination rates through pChip.*

the representative ART signal using ANN-NLR prediction after four different rates of sample reductions, with only a few cycles covered for the sake of conciseness. **Figure 6** shows the error in prediction for the four sample reductions. A similar analysis was also done on the other signals of the 24-channel IoT-HSN with comparable results. **Table 1** shows the particulars of the ART signal used as a representative of the results.

The signals recreated at the receiver using five different interpolation techniques over reduced samples for the arterial pressure parameter were compared with the original full sample sets for error in prediction by interpolation. The results of the

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

#### **Figure 6.**

*Error plots for prediction of skipped samples for four sample elimination rates through pChip.*


#### **Table 1.**

*Signal specifications for the arterial pressure vital sign IoT-HSN parameter.*

prediction for the ART signal and the associated error analysis for just the cubic interpolation technique are presented in **Table 2**.

Five numerical interpolation techniques – linear, near, Spline, Pchip and cubic were employed for rebuilding the missing IoT-HSN sample data for the parameters of the 24-channel IoT-HSNs at the receiving end. **Table 3** shows the comparison between the five techniques for the ART parameter.

From **Table 3**, it is evident that the nearest neighbor interpolation algorithm performs the poorest of all but the other four yield lesser error, almost in the same range, with the linear spline interpolation performing better across the sample sets.

Twenty random sets (with 3600 samples transmitted in 10 seconds) of the signals for the 24 channel IoT-HSNs from healthy individuals and patients were employed to assess the performance of the prediction algorithms. The physiological parameters are all different in range, wave-shape, and type of variations. **Figure 3** shows the results of error evaluation after reconstructing the signal using the five interpolation algorithms for one such set of values for the ART signal.

The first column in **Figure 5** illustrates the signal sets with successive reduction in samples. The second column indicates the reconstruction of lost


**Table 2.**

*Peak error with sample reduction for arterial pressure using cubic interpolation.*


**Table 3.**

*Maximum percentage error values for ART from the five numerical interpolation techniques.*

samples for the corresponding row after data reception done using the Pchip interpolation prediction.

### **9. Considerations for battery usage in IoT-HSNs**

A key requirement of IoT-HSNs is low power wireless which in turn makes signal detection difficult. Low power wireless is required, which makes signal detection more challenging. Common and proven technologies such as Bluetooth, ZigBee, General Packet Radio Service (GPRS) and Wireless Local Area Network (WLAN) might not offer good and optimal solutions to the low power requirement problem.

The growing miniaturization and cost drop on IoT-HSN sensors, circuits and wireless communication electronics is establishing new opportunities for wireless sensor networks in wearable applications. Nevertheless, for sensors to be untethered, the design needs to use wireless communication between nodes along with wireless powering of sensors. This requirement is fulfilled by batteries in most of the portable electronic devices, making them an obvious answer for IoT-HSN wireless applications. However, the batteries have a finite life and require to be replaced or recharged. This limitation presents a cost and convenience penalty which is undesirable in wireless applications including IoT-HSN while the market for such applications and demands grows. One possible solution to this problem involves harvesting energy from the environment for recharging of power sources. Energy scavenging from motion (vibration) and thermal (body heat) sources offer some options for recharging mechanisms that are being investigated. While the power demands of many electronic functions including wireless communication are being actively reduced, energy efficiency of power sources remains a problem because IoT-HSN nodes are intended to operate for a long period of time, especially if they are implanted.

#### **9.1 Batteries and fuel cells for IoT-HSN sensor nodes**

Wireless devices can be powered by primary, or rechargeable batteries. Of these, primary batteries are better in energy densities, shorter in leakage rates and lower in cost. The energy density of Lithium-ion batteries that are most used in electronics, is around 700–1400 J/cc for rechargeables [72], and the figure for primary cells is higher. Batteries used for IoT-HSN applications are preferred to last at least a year. A lifetime of 1 year corresponds to 32 J/micro-watt of average power for an average power requirement of some tens of micro-watts.

Hence, a finite battery-life of some tens of microwatt-years is attainable for a battery under 1 cc. Search for better alternatives is on because such batteries require replacement and have issues related to toxicity, safety, and operating temperature range. While ultracapacitors are drawing rising interest for powering electronics as their energy densities are much higher than those of conventional capacitors, the density still are way lower than those of batteries [73].

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

Hydrocarbon fuels are known to have very high specific energy in the range of 16 kJ/cc for pure methanol [74] or 31 kJ/cc for iso-octane [75]. For miniature electronics, exhaustible sources of energy that use hydrocarbon fuel of some type are also under review, although primarily for greater power levels. Small, micromachined and only few inches big external combustion heat engines have been built to provide power for portable electronics that can generate up to 200 micro-watts [76]. Such engines have a disadvantage of moving parts and very high temperatures, and hence fuel cells are also being widely investigated for applications involving low power. In the pure methanol-based device used in [74], the electrochemical reaction of methanol with water after passing through a polymer membrane results in oxidation of methanol producing free electrons and protons and generating high power levels of 195 mW/sq.cm.

Miniature fuel cells for implantable sensors as small as 0.5 mm thick can also utilize energy harvesting to provide inexhaustible power up to 4.4 micro-watts/sq. cm if they use body fluids such as oxygen dissolved in blood and glucose as the fuel source [77]. A crucial challenge for such power sources that needs to be addressed is their operational lifetime.

#### **9.2 Challenges related to IoT-HSN power sources**

Due to difficulties in changing or recharging batteries in IoT-HSN sensors (some implanted), the management of energy consumption for network longevity and resourceful network operation is an important design consideration. The network design methods utilize a sleep-awake cycle for conserving energy and increasing the network operation time because the power requirement for the communications unit in a sensor node is several orders higher in comparison to the transducer and A/D converter unit in the sensor electronics. The author attempted to assess the lifetime of the proposed IoT-HSN framework created using commercial sensors and power supplies focusing on the period that the sensors would remain powered on.

The sample rate for the ART signals used in the representative evaluation was 360 samples per second with the samples encoded in 8-bits and the more popular 12-bits. The total energy necessary for the operation of a IoT-HSN sensor node varies based on factors such as sleep-awake cycle, inter-sensor distances, the time for which the node stays in a specific mode, as well as a system constant.

Based on Heinzelman's sensor node transceiver model [78], the transmission energy required to transmit a k-bit message to a distance of *d* can be computed as:

$$E\_{\rm Tx}(k,d) = E\_{\rm Tx-elec}(k) + E\_{\rm Tx-Aml}(k,d) = E\_{\rm Ele} \ast k + \epsilon k d^2 \tag{1}$$

where,

*ETx*�*elec* is the energy expenditure in the transmission electronics, *ETx*�*Ampl* is the energy expenditure in the transmission pre-amplifier, ϵ is an amplification factor.

*d* is the communication distance between sensors. Their model has the below assumptions:

$$\begin{aligned} \epsilon &= \mathbf{100}pf/bit/m^2\\ E\_{\text{Rx-elec}} &= E\_{\text{Tx-elec}} = E\_{\text{Elec}} \end{aligned} \tag{2}$$

To receive a k bit message, the energy expended in the receiver is

$$E\_{R\mathbf{x}}(k) = E\_{R\mathbf{x}-elec}(k) = E\_{E\text{lec}} \ast k \tag{3}$$

The energy expended in the transceiver electronics for most sensor nodes is identical for transmission and reception circuitry and in a few tens of nJ/bit.

The energy required for transmitting all the samples (and not skipping any of the 360 samples) while continuously operating for a minute was 8.65 mJ when the samples are encoded in 8-bits and 12.97 mJ when the samples are encoded in 12-bits.

The author attempted to assess the life cycles of wireless networks comprising of two commercially available low-power ultra-compact sensor nodes. The minutelong sensor duty cycle comprises of 10 seconds each of transmission and reception succeeded by 40 seconds of sleep. The author used three sensor modes for this evaluation – the Eco [79], Texas Instruments' TI CC3100 [80] in Direct Sequence Spread Spectrum mode (1DSSS) and the TI CC3100 in Orthogonal Frequency Division Multiplexing mode (54OFDM).

The Eco sensor required a current of 16 mA during transmission, 22 mA during reception and mere 2 μA while sleeping.

The TI CC3100 sensor fared fine in the 1DSSS mode while performing amply better in the 54OFDM mode. The author evaluated the performance of these sensors based on three commercially available batteries that supply 3.0–3.6 volts, 0.5A – the CR2032, CR123A, iXTRA and ER34615. **Table 4** summarizes the battery characteristics and the findings for transmission power requirements of the two sensor nodes without any power management applied.

**Table 4** indicates that the innovations in low-power sensor design and battery technology enhance the lifetime of the IoT-HSN network.

If the sample reduction algorithm suggested by the author is used, the sensor, and hence the network lifetime would improve in accordance with the sample chop rate. **Figure 7** shows the network lifetime improvement for the sample chop rates.

#### **9.3 Battery life for models using thermal-aware routing**

The author also attempted to evaluate the performance of the three thermal aware routing models for network lifetime with the three batteries that were shortlisted and considered by [56]. Of the battery models evaluated, the model based on ECO sensor nodes running on the 19000 mAH ER34615 battery had the best performance for network lifetime without any power management.

The TBFC thermal-aware routing model was found to offer the poorest economy on the battery power in these evaluations as compared to the other two models for the four probabilistic packet distributions. **Figure 8** shows the battery and network lifetime for the model despite retransmissions using the mentioned battery-sensor combination in the number of hours of operation, in conjunction with the details in the tables.

The three models pave a way for a study towards efficient and intrinsically safe, thermal-aware IoT-HSNs for wearable computing. **Figure 9** shows the improvement for the sample chop rate of 3, if the reduction in samples is used with


#### **Table 4.**

*Life in days for the different battery models as per their capacities and node power requirements if continuous power drawn.*

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

**Figure 7.** *Comparison plot of battery lives for 5 batteries and four sample rates.*

**Figure 8.** *A comparison of lifetime hours for the three models across four duty cycle distributions.*

**Figure 9.** *A comparison of improvement in lifetime hours for the three models for best sensor and battery combination.*

prediction for recreating the original signal. This was done for the ECO sensor when used in conjunction with the ER34615 battery, evaluated over the four probability distributions for the three thermal aware routing algorithms. The author's findings indicated that the results were the best for the SCMC routing algorithm with Poisson sample distribution where the sensor and battery combination lasted for almost 66500 hours (7.6 years) with the results for other distributions not very different for the combination. The sensor and network lifetime are seen to be improved in accordance with the sample chop rate.

#### **10. Conclusions**

In this article, the author has presented a comprehensive survey of the different types of routing models used for IoT-HSN data. The author has also proposed a thermal and energy aware model that enhances the lifetime of IoT-HSN for intranetwork as well as inter-network traffic and evaluated the performance of the model. The author has also demonstrated energy savings by reduction in transmission using a linear elimination algorithm and recreating the missed data at the receiver using a variety of techniques involving a variety of interpolation techniques and prediction using PID and NLR-ANN with very low error values. The savings shown from the model and the enhancement of network lifetime have been demonstrated in quantified as well as graphical forms.

While the basic factors of the network look good for employing energy optimization in IoT-HSN applications, the dynamic execution of the proposed model

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

needs to be studied in better detail for real life HSN applications. An extension of this work could focus on a clinical implementation covering several vital parameters with varying rates of change in them. More possibilities could emerge if the model is tested on well-founded and strong applications such as pacemakers, insulin monitors or movement sensors and prosthetic control.

This article opens the arena for further probing of thermal, QoS and energy aware design of micro-hardware for wearables and implantable bionics. The thermal and energy aware model offers an encouraging prospect to be selected as a design standard for IoT-HSN applications whereas none exists at this time.

### **Acknowledgements**

The author expresses his heartfelt thanks towards Physionet [60] and the doctors from the Department of Pediatric Cardiology at Cincinnati Children's Hospital for their judgment on the tolerable drop in volume of physiological data for competent supervision of vital body parameters for human subjects.

This work was not supported by any funding whatsoever from any sources.

#### **Conflict of interest**

The author declares no conflict of interest.

#### **Abbreviations**


### *IoT Applications Computing*

