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Electronics
  • Article
  • Open Access

Published: 7 December 2022

Dynamic Voltage and Frequency Scaling and Duty-Cycling for Ultra Low-Power Wireless Sensor Nodes

,
and
1
Measurement and Sensor Technology, Chemnitz University of Technology, 09126 Chemnitz, Germany
2
Computer & Embedded Systems Laboratory, National School of Engineers of Sfax, Sfax 3038, Tunisia
*
Author to whom correspondence should be addressed.
This article belongs to the Special Issue Wireless Sensors Networks in the IoT Era: Advanced Technologies, Recent Challenges, Smart Applications & Future Prospects

Abstract

Energy efficiency presents a significant challenge to the reliability of Internet of Things (IoT) services. Wireless Sensor Networks (WSNs) present as an elementary technology of IoT, which has limited resources. Appropriate energy management techniques can perform increasing energy efficiency under variable workload conditions. Therefore, this paper aims to experimentally implement a hybrid energy management solution, combining Dynamic Voltage and Frequency Scaling (DVFS) and Duty-Cycling. The DVFS technique is implemented as an effective power management scheme to optimize the operating conditions during data processing. Moreover, the duty-cycling method is applied to reduce the energy consumption of the transceiver. Hardware optimization is performed by selecting the low-power microcontroller, MSP430, using experimental estimation and characterization. Another contribution is evaluating the energy-saving design by defining the normalized power as a metric to measure the consumed power of the proposed model per throughput. Extensive simulations and real-world implementations indicate that normalized power can be significantly reduced while sustaining performance levels in high-data IoT use cases.

1. Introduction

With the continued advancement of communication technologies, the Internet of things (IoT) is integrated with Wireless Sensor Networks (WSNs) in various applications supporting universal data access with near real-time decision-making [,,]. Such applications depend strongly on data processing, where batteries and/or energy harvesters supply sensor nodes. However, power sources have restricted energy densities and low charge rates. The energy efficiency in WSNs remains an open research problem. One challenge for WSNs is to achieve low-power features for intelligent sensing systems in a data-driven environment. Exploiting the variable workload characteristics of WSNs is an efficient method to improve energy efficiency. For example, in intelligent irrigation, monitoring air temperature can be performed in hours instead of minutes or seconds. It involves extended periods of inactivity, where there is a potential for energy savings. In other applications, such as landslide monitoring or fire detection, it is essential to make measurements quickly to trigger urgent signals enabling immediate decisions. Thereby, increasing energy efficiency under variable workload conditions can be accomplished through appropriate energy management for data processing [,].

1.1. Motivation

In this context, Dynamic Voltage Frequency Scaling (DVFS) presents one of the most energy-efficient management techniques by solving both challenges of on-demand performance and energy reliability. Generally, it is introduced as a framework that enables the change of the frequency and operating voltage of the processor based on system performance requirements at a certain point in time [].
In this paper, the DVFS is experimentally implemented to optimize the system’s power consumption during the operating time. Implementing DVFS in microcontrollers (MCUs) represents a major technological breakthrough in ultra-low power applications, such as for the deployment of wireless sensor nodes or for low-power radio networks, where the power saving should cover the time during the inactive or idle phases. Furthermore, reducing the time spent on the active node through sleep periods helps to minimize the node’s energy consumption and thus increases the lifetime of the wireless sensor network []. A batch experiment is proposed to make an intelligent duty-cycle selection that will meet the quality of service and energy-saving needs. DVFS and duty-cycling have given rise to our hybrid energy management approach.
In this regard, the total energy consumption of a wireless sensor node is evaluated when it performs specific tasks under very low-power conditions. The aim is to empirically determine the most efficient operating clocking frequency for different specific MCUs operations by measuring and analyzing the normalized power consumed. This evaluation parameter is defined as a measure of power per throughput (mW/MI) in one second. The throughput presents the ratio between the Central Processing Unit (CPU) frequency and the Cycles Per Instruction (CPI). The reason behind measuring the normalized power in active mode is not to focus on the power consumption but on the energy needed to accomplish a unit operation. A high-precision four-wire measuring system is used for all measurements, which involves the guarding and shielding of all interfaces and the measuring cave.

1.2. Contributions

The paper focuses on optimizing the MCU power consumption by considering hardware and software aspects. The main contributions of the work are:
1.
Investigation of energy optimization techniques in WSNs;
2.
Reducing the overall power consumption of a wireless sensor node through the selection of a low-power MCU and the implementation of a power management-based DVFS technique;
3.
Implementing the duty-cycling technique to reduce the energy consumed by the transceiver;
4.
Measuring the consumed power during Bluetooth communication;
5.
Defining the normalized power as an evaluation metric to measure the energy saving of the implemented solution with high accuracy.

1.3. Paper Organization

The rest of the paper is organized as follows: Section 2 surveys energy reduction techniques in WSNs. In Section 3, the application of DVFS technique is described in detail. Section 4 presents real measurement results obtained from the MSP430f5529LP launchpad. Section 4.2 describes the potential values of the proposed method and possible future works. Section 5 concludes the paper with the main results achieved.

3. Dynamic Voltage and Frequency Scaling, and Duty-Cycling

Figure 2 describes the proposed hybrid energy management system combining the three aspects, including energy-efficient communication protocol, hardware optimization, and energy management. It meets the needs of quality of service (QoS) requirements in terms of delay and data throughput. The focus is to test the performance of the proposed solution in terms of energy consumption and execution time under different frequencies and voltages. By satisfying the QoS constraints at the application, network, and data link layers, the expected Quality of Experience (QoE) results are performed.
Figure 2. Proposed hybrid energy management system for ultra-low power wireless sensor node towards QoS and QoE. (Blue arrow: Connection between sensor node components, Blue filled arrow: Energy-efficient communication between sensor nodes, Black arrow: Energy-efficient techniques applied at the sensor node level, Dotted line: QoE parameter from the QoS requirements to the user satisfaction).
The architecture of a microcontroller has a significant impact on its energy consumption. Based on the deep study provided in [], the MSP430 microcontroller outperforms other microcontrollers in terms of power consumption by having a well-balanced CPU, ALU, and peripheral architecture. For this reason, it is selected to be used in this work.

3.1. DVFS Algorithm Overview

Modern processor architectures deliver high computing performance with a high-power consumption, averaging around 100 Watts. The energy-saving policies applied to the CPU are based on the state change, either in a standby state or a given speed state. In this work, the developed methodology proposes to change the voltage and frequency couple following the Equation (1) principle. Generally, the power consumption of a microprocessor can be divided into three parts []:
P c m o s = P s t a t i c + P d y n a m i c + P s h o r t c i r c u i t = V D D I l e a k a g e + P d y n a m i c + T s c V D D I p e a k
where V D D defines the input voltage, I l e a k a g e presents total leakage current, T s c is rising time of the input signal and I p e a k denotes the peak current According to the structure of the Complementary Metal–Oxide–Semiconductor (CMOS) circuit, P s t a t i c is generated due to reverse-biased diodes. It refers to the dissipated power when the CMOS circuit is inactive or dormant. The leakage current presents the primary factor of P s t a t i c , which occurs mainly due to the short-channel effects, such as the sub-threshold leakage current and the gate leakage current []. The sub-threshold leakage current is the small current across the source and drain terminals during the off-state of the transistor due to the weak inversion layer at the oxide–substrate interface. This takes place when the gate voltage is lower than the threshold voltage. The sub-threshold leakage current increases exponentially as the size of the features continues to decrease. The reduction in transistor geometry is accompanied by a reduction in the thickness of the gate oxide, resulting in improved performance. However, this thinning of the oxide layer results in a leakage current between the substrate and the gate through the oxide. It is important to optimize the leakage power in WSNs.
P d y n a m i c is determined by charging and discharging capacitors. P s h o r t c i r c u i t is caused by switching from the supply voltage to the ground. The energy consumed during the data processing can be divided into switching and leakage energies. When running a program, the switching energy is determined by the software level’s supply voltage and the total switched capacitance. The highest power consumption is P d y n a m i c , which can be determined in Equation (2):
P d y n a m i c = α C V 2 f
where α stands for the switching factor, a constant value presenting the probability of switching on any particular clock period. V is the supply voltage, and C is a constant, presenting the capacitance currently switched. f is the clock frequency. According to Equation (2), reducing the frequency decreases the power. This is called Dynamic Frequency Scaling (DFS). This technique adjusts the frequency to reduce the power consumption at the cost of the time necessary to complete instruction within the task deadline. The dynamic power and static power are proportional to V 2 and V, respectively. Thus, reducing the supply voltage of some blocks of a System on a Chip (SoC) to a level near their minimal operating voltage directly affects the total dissipated power.
The DFS technique can be extended by adjusting both frequency and voltage. This method is known as DVFS, which reduces power consumption by driving the device with the minimum core voltage and operating frequency subject to the task deadline. The higher the microcontroller operating frequency is, the higher the core voltage needed to drive the microcontroller. However, a higher core voltage than that needed to execute operating instructions can lead to significant power losses. The power management module (PMM) can adjust the core voltage so that the microcontroller operation runs at the given frequency with minimal power losses. Once the microcontroller is powered on, the PMM gains control of the system initialization. The DVFS technique is used to reduce both leakage and dynamic power consumption.
The DVFS technique is widely adopted in energy efficiency approaches while meeting the QoS requirements of micro-architectural components, such as GPUs [], data servers [], MCUs [], multi-core [], memory [], Network on Chip (NOCs) [], in the cloud computing [], in the network packet processing [], in solving task execution scheduling problems [], and in solving the problems of the IT industry. The approaches mentioned in Table 2 consider battery-powered devices combined with scarce external power sources while allowing performance penalties to increase the lifetime of the sensor nodes. In addition, they provide a glimpse into the premise of DVFS use in energy harvesting devices. Some works even violate voltage limits risking performance degradation as well. However, in our work, we try to respect the performance and reliability metric by increasing the amount of work achieved when the sensor node is active. We provide a real-world implementation of such a technique and a comprehensive evaluation that pinpoints the benefits of using DVFS.
The implementation of DVFS in a networked sensor system leads to reducing the overall power consumption in soft real-time WSN applications. To meet the deadline with our approach, a system’s clock commonly sustains its accuracy by using a synchronization mechanism, such as the Network Time Protocol (NTP), Timing-sync Protocol for Sensor Networks (TPSN), and the Flooding Time Synchronization Protocol (FTSP) [].

3.2. DVFS Implementation

Using the internal clock source, the methodology followed to perform the DVFS algorithm at the microcontroller is depicted in Figure 3.
Figure 3. Clocking MSP430F5529LP using the internal clock source.
The board and inputs/outputs initialization step includes two operations. The first one is stopping the Watchdog Timer (WDT). It is crucial since the tested operations run continuously while the measured data acquisition occurs. The watchdog timer’s default behavior is resetting the microcontroller if the firmware appears stuck in a loop. This default feature has to be disabled to prevent an infinite boot loop. The second operation is setting up a general purpose Input/Output (GPIO) array. This step is necessary to set up directions for available pins on the board. The second step, called a Digitally Controlled Oscillator (DCO) setup, targets performing the PMM core level setting, clock initialization, and frequency-locked loop setting.
The power management module sets the core power level according to the PMM frequency and core voltage relation. The selected PMM core level must be sufficient to drive the microcontroller application. Accounting for the desired microcontroller frequency, the PMM sets the voltage thresholds of the Supply Voltage Supervisor (SVS) and Supply Voltage Monitor (SVM). The PMM circuit used for the threshold setting is described in Figure 4.
Table 2. WSN-DVFS based techniques’ comparison.
Table 2. WSN-DVFS based techniques’ comparison.
TechniquesReferencesYearPaper Contributions
DVFS+workload[]2017
-
Inter-cluster exploitation: thread-to-core mapping
-
Adaptation process to select proactively an appropriate V-F pair for a predicted workload
-
Memory Reads Per Instruction (MRPI) workload classification
EA-DVFS[]2008
-
Delays task execution if sufficient energy is not available
-
Runs at high speed if not
GPU+DVFS[]2013
-
GPU Watch to simulate DVFS of the GPU core at the cycle level, based on the Fermi GTX48 GPU according to the 45 nm prediction technology model
-
DVFS simulation slow off-chip and fast on-chip
DVFS HESS[]2020
-
Energy-harvesting based on solar energy
-
DVFS based low-power microprocessor
-
Low-power lazy task scheduling policy
DVFS+DPM[]2013
-
Two frequencies of voltage/frequency
-
Turning off a part circuit or running it in low-power modes when no workload
Workload management[]2013
-
Transition overhead-aware with modern DC–DC converters for high-end, embedded and ultra low-power applications
DPM+GEDF[]2022
-
Time-out DPM in idle mode
-
Intertask DVFS in active mode
-
Global Earliest Deadline First, (GEDF) with modified scheduling assumption
Multi-cores+run time
management
[]2019
-
Performance prediction model when making run-time decisions
-
Application state and workload monitoring for previous DVFS parameters and mappings between threads and kernels
Multicore+task
mapping
[]2018
-
Mapping of imprecise computation tasks on multicore DVFS platforms based on QoS
-
A low complexity optimal DVFS task mapping based on Benders decomposition
Run time+energy
harvesting
[]2020
-
Capturing the capacitor voltage by a voltage detectors and undervoltaging it with a voltage regulator.
-
A software modification of the frequency
Our solution 2022
-
Ultra low power Microcontrollers
-
Duty-cycle aware in the transceiver level
-
DVFS
-
Workload management
Figure 4. Power management module block diagram following [].
When the application requires a higher operating frequency to increase the performance, the user can programmatically increase the threshold of High-SVM and High-SVS values, which detects external power voltage to ensure that the power supply is sufficient to drive the core voltage. The Low-SVM is then updated to a higher level, and an SVS/SVM delay mask is activated to enter in a Power-On-Reset (POR) state until the SVS and SVM circuits have settled.
After that, the PMM increases the core voltage to a higher level and waits for the voltage to reach this increased level. Finally, the system sets Low-SVS to a higher level, and the core starts to run. Whenever the core voltage level is modified, the PMM resets the core. The flowchart of the PMM core level mechanism is described in Figure 5.
Figure 5. Setting PMM core level (Purple color: Action, yellow color: Condition).
The microcontroller clock is initialized by setting a user-defined clock to a specified clock source oscillator. For the presented tests, the REFOCLK is used as the source for configuring the internal Frequency Locked Loop (FLL) with no clock divider.
In this stage, the FLL is calibrated to use the MCLK as its oscillator source. The DCO is also configured using the MCLK and FLL reference clock sources. Once the DCO has been configured to operate the microcontroller at a defined frequency, the desired operation under test is specified within an infinite loop. The microcontroller operations used for our evaluations are nop and add.

4. Experimental Performance Evaluation

To evaluate the power consumption of the MSP430F5529LP development board, clocking the used board via an internal clock source is performed. Thus, the consumed power of the board for different frequencies and voltages is measured, and the obtained results are analyzed.
Measurements are carried out by interfacing the device under test (DUT) with the measurement mainframe E5270A 8-channel Precision IV Analyzer (Keysight Technologies GmbH, Böblingen, Germany) [] as shown in Figure 6. This device provides power, controls the output voltage, and measures the drained current. It consists of the four wires’ measurement techniques. The Source Measure Unit (SMU) applies a voltage to the force connection and measures it over the DUT with a sensing interface. The controller adjusts the supply voltage automatically to meet the voltage drop due to the parasitic resistances of the force wires. The current profile has been measured using the built-in ammeter in series with the force connector. The resulting voltage drop is removed by the feedback-controlled four-wire configuration.
Figure 6. Four-wire measurement configuration involving a Keysight Technologies E5270 8-channel precision mainframe.
An appropriate PMM power level is settled for each measured frequency according to Figure 7. The numbers within the fields denote the supported PMMCOREVx (core voltage level) settings. ‘0’, ‘1’, ‘2’ and ‘3’ correspond to ‘00’, ‘01’, ‘10’ and ‘11’. The minimum voltage, in this case, is 1.8 V.
Figure 7. PMM core levels and related frequencies and threshold voltages.
The reduction of the frequency via DVFS increases the execution time. To realize a trade-off between the execution time and the power consumption, we introduce normalized power as an evaluation metric. This metric considers the number of instructions executed in one second, which allows energy consumption profiling. Otherwise, reducing the power only would lead to a significant drop in performance. The benefit of using normalized power is that it accounts for the influence of CPU throughput:
N o r m a l i z e d P o w e r = A v e r a g e P o w e r T h r o u g h p u t × 1 μ s
T h r o u g h p u t = C P U F r e q u e n c y C y c l e s p e r I n s t r u c t i o n
Firstly, the frequency is set to 6 MHz and the supply voltage, Vs, varies from 1.8 V to 3.6 V. As seen in Figure 8, the most significant voltage, 3.6 V, matched initially to the greatest normalized power, even though this value drops with time. As depicted in Figure 8a,b, for a voltage equal to 3.6 V, the normalized power increases by 32% once switching from 6 MHz to 20 MHz. When it comes to whole-system performance, the higher the value of the normalized power measured, the faster the processor. Therefore, the processor can provide higher throughput with the same algorithm and the same voltages.
Figure 8. Normalized power during the time for different supply voltages at (a) 6 MHz; (b) 20 MHz.

4.1. Impacts of DVFS Implementation on Active Operation

The power consumed during active mode is often identified and characterized as a multiplication of the supply voltage and average current of the microcontroller’s active mode. This argument is correct when the microcontroller operates in active mode indefinitely. This can be inaccurate, however, when a microcontroller operates in a duty cycle since the total active power is affected by the microcontroller’s performance. As seen in Figure 9, every load can be presented by one or multiple resistor–capacitor (RC) circuits. When the CPU requires current, the voltage waveform across the capacitor would produce RC waveforms with small exponential effects. The reason for measuring normalized power in active mode is that the focus will be not on power consumption but on the power required to accomplish a unit operation. Therefore, the normalized power is defined as a measure of power per throughput in 1 s.
Figure 9. Drained current of the microcontroller during active mode (The dotted line defines the end of the active mode. The orange color presents the consumed current for each mode).
To this end, a set of experiments are realized. Both “nop” and “add” operating instructions are flashed unto the microcontroller alongside clocking instructions.
As depicted in Figure 10a,b, the consumed normalized power is proportional to the supply voltage for a given frequency and given operation. This can be explained by the relationship between the dynamic power consumption and the core voltage of the MCU. This power dissipation is due to the toggling activity of the logic gates. As seen in Equation (2), the consumed power increases as a factor of the square of the core voltage. As shown in Figure 10a, for the frequency equal to 24 MHz, we notice that, when the voltage is gradually increased from 1.8 V to 3.6 V, the normalized power goes from 8 MIPS/W to 34 MIPS/W, resulting in an increase of 76%.
Figure 10. Normalized power vs. supply voltage at various frequencies for (a) “nop” operation; (b) “add” operation.
Since one of the most consuming parts of a wireless sensor node is communication, an evaluation of the DVFS implementation is carried out using Bluetooth as a communication module. The Bluetooth pairing is settled to test the power consumption of MSP430F5529LP. The transmitter is configured to continuously send the character “a” during the test duration. As seen in Figure 11, the MSP430F5529LP-based transmitter is connected to the E5270B analyzer. The power supply is tested at 3.6 V supply voltage, as the minimum supply voltage necessary to power the HC-06 Bluetooth module.
Figure 11. Experimental setup for Bluetooth communication.
In Figure 12, the peak power consumption period occurs during the time slot, t 0.42 s, which corresponds to the packet transmission time. Firstly, the MCU is active, and the radio is in sleep mode, consuming about 35 mW. Then, the radio was activated to listen to the channel, leading to increased consumed power. After that, by receiving a packet, the radio consumes about 135 mW. The radio switches to the transmission mode and consumes about 195 mW, corresponding to about 54 mA as drained current.
Figure 12. Bluetooth power consumption.

4.2. Impact of the Duty Cycle on the Power Gain

It can be noticed that the selection of a high operating frequency can generate more efficient normalized power consumption due to the effect of the operating frequency on the duty cycle. For a given voltage, an instruction, such as add, consumes a certain amount of energy during the active phase. However, the power is still consumed by the device during the idle phase of the duty cycle, as seen in Figure 13. In the case of low operating frequency, the average consumed power P 2 may be lower due to the lower power-consuming idle phase of each cycle. However, a large portion of the power is consumed during the idle phase cycle. In the case of high operating frequency, the resulting duty cycle is higher. Hence, there is less power dissipated during the idle phase while no operation occurs despite higher average power consumption P 1 .
Figure 13. Operating frequency versus power efficiency (P1 and P2 are the average consumed power for high frequency and low frequency, respectively).
DVFS can adjust the system to optimize its performance. The duty cycle value corresponding to the maximum frequency of 24 MHz is equal to 83% and drops to 60% when the frequency is reduced to 4 MHz only proportionally to the normalized power. However, the frequency change is not instantaneous, and switching from one frequency to another requires a time delay. As a result, close frequency changes will significantly slow down the execution of the application, resulting in over consumption. As a result, when the variation reaches 4 MHz from 4 to 8 MHz or from 14 to 18 MHz, the normalized power gain is equal to 5 mW/MIPS. However, when this variation goes to 6 MHz (8–14 or 18–24 MHz), this gain becomes 2 mW/MIPS at the same supply voltage (1.8 V).
Figure 14 summarizes the average power gain for different frequencies with different core voltages. The x-axis presents the core voltage deviation, which means switching from one voltage to another considering the values defined in Figure 7. As an example, to have a deviation of 1.2 V in the core voltage, for 4 MHz, we can move from 2.4 V to 3.6 V or from 2.1 V to 3.3 V or from 1.8 V to 3 V. The average power gain, in this case, is equal to 72.75%. The average power gain increases with the increase of the core voltage deviation.
Figure 14. Power gain.
Frequent changes in the DVFS are not the only challenge, as the right frequency must still be used. A misallocated frequency can lead the program to consume more energy, and small frequencies may not be the most energy efficient. If an application runs for a certain time at a given frequency, lowering the execution frequency may extend the execution time of the application to such an extent that the energy consumed will ultimately be greater. Changing frequency is time-consuming, in the range of ten microseconds. Consequently, too frequent changes in frequency will have a negative impact on the system performance, obviously leading to the worst-case execution time (WCET) of the application, and consequently to inefficient energy consumption.
The proposed hybrid energy management system can be implemented for ultra-low-power wireless sensor network applications to ensure the trade-off between energy efficiency and system performance. Generally, the wireless sensor nodes have single-core processors. They are unable to satisfy the increasing demands of data-intensive applications, such as wireless multimedia sensor networks. Therefore, multi-core integrated sensor nodes with advanced communication and computational capacities. Our solution can be integrated into multi-core architectures leading to significant energy reductions compared to the basic methods, where there is no DVFS.
Currently, the developed hybrid energy management solution is implemented to reduce the power consumption of a wireless sensor node in a unique IoT application. However, recently, advanced IoT architectures have been established such as the multiple IoT (MIoT) architecture [,]. Generally, an MIoT can be considered as a set of things interconnected by relationships of various types and simultaneously. It involves the concept of instance of a thing in an IoT architecture. An instance presents the virtual view of the corresponding thing. Each IoT node represents instances of the objects participating in it. Therefore, a thing can have multiple instances, one for each IoT it takes part in. In future work, we will implement the proposed solution in a MIoT-based architecture, including several IoT connected to each other through cross nodes and cross edges.

5. Conclusions

This paper focuses on the reduction of the overall power consumption of a wireless sensor node through the selection of a low-power microcontroller, as well as the implementation of the power management-based DVFS technique. In fact, extensive benchmarking based on real-world measurements promoted the MSP430 as the most optimal microcontroller to ensure the best compromise between performance and energy. Therefore, both clocking and power consumption analysis of MSP430f5529LP launchpad is performed to profile mainly nop and add instructions using DVFS as a power reduction technique. Obtained results prove that high power efficiency is maintained at high operating frequencies for defined instructions. In fact, for a voltage equal to 3.6 V, the normalized power increases by 32% once switching from 6 MHz to 20 MHz. Moreover, it is demonstrated that, when the variation between two frequencies reaches 4 MHz, i.e., from 4 to 8 MHz or from 14 to 18 MHz, the normalized power gain is equal to 5 mW/MIPS at a supply voltage equal to 1.8 V.

Author Contributions

S.K. contributed with the experiment, measurement, manuscript concept, methodology, original draft writing, visualization, and editing. R.C. contributed with the manuscript concept, methodology, original draft writing, visualization, and editing, and O.K. contributed by the writing sections, reviewing, visualization and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Singh, P.K.; Sharma, A. An intelligent WSN-UAV-based IoT framework for precision agriculture application. Comput. Electr. Eng. 2022, 100, 107912. [Google Scholar] [CrossRef]
  2. Chéour, R.; Khriji, S.; El Houssaini, D.; Baklouti, M.; Abid, M.; Kanoun, O. Recent Trends of FPGA Used for Low-Power Wireless Sensor Network. IEEE Aerosp. Electron. Syst. Mag. 2019, 34, 28–38. [Google Scholar] [CrossRef]
  3. Khriji, S.; Benbelgacem, Y.; Chéour, R.; Houssaini, D.E.; Kanoun, O. Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks. J. Supercomput. 2022, 78, 3374–3401. [Google Scholar] [CrossRef]
  4. Rauber, T.; Rünger, G.; Stachowski, M. Performance and energy metrics for multi-threaded applications on DVFS processors. Sustain. Comput. Inform. Syst. 2017, 17, 55–68. [Google Scholar] [CrossRef]
  5. Senn, E.; Derouineau, N.; Tizon, N.; Boukhobza, J. Joint DVFS and Parallelism for Energy Efficient and Low Latency Software Video Decoding. IEEE Trans. Parallel Distrib. Syst. 2018, 29, 858–872. [Google Scholar]
  6. Dhall, R.; Agrawal, H. An improved energy efficient duty cycling algorithm for IoT based precision agriculture. Procedia Comput. Sci. 2018, 141, 135–142. [Google Scholar] [CrossRef]
  7. Abbas, A.; Ali, M.; Fayyaz, A.; Ghosh, A.; Kalra, A.; Khan, S.U.; Khan, M.U.S.; De Menezes, T.; Pattanayak, S.; Sanyal, A.; et al. A survey on energy-efficient methodologies and architectures of network-on-chip. Comput. Electr. Eng. 2014, 40, 333–347. [Google Scholar] [CrossRef]
  8. Khriji, S.; El Houssaini, D.; Kammoun, I.; Kanoun, O. A Fuzzy Based Energy Aware Unequal Clustering for Wireless Sensor Networks. In Proceedings of the International Conference on Ad-Hoc Networks and Wireless, Saint-Malo, France, 5–7 September 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 126–131. [Google Scholar]
  9. Salem, J.B.; Khriji, S.; Baklouti, M.; Kammoun, I.; Kanoun, O. Testbed Implementation of a Fuzzy based Energy Efficient Clustering Algorithm for Wireless Sensor Networks. In Proceedings of the 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), Istanbul, Turkey, 21–24 March 2019; pp. 351–356. [Google Scholar]
  10. El Houssaini, D.; Khriji, S.; Besbes, K.; Kanoun, O. Wireless sensor networks in agricultural applications: Technology Components and System Design. In Energy Harvesting for Wireless Sensor Networks; Walter de Gruyter GmbH & Co KG: Berlin, Germany, 2018; pp. 323–341. [Google Scholar]
  11. Brockmann, C.; Günther-Sorge, J.; Pötter, H. Realisierung und Anwendung energieautarker miniaturisierter Funksensorik: Chancen durch IoT, 5G und Narrowband. TM-Tech. Mess. 2019, 86, 630–639. [Google Scholar] [CrossRef]
  12. Radha, S.; Bala, G.J.; Kanaga, E.; Nagabushanam, P. Scheduling and adaptive listening approaches in MAC for WSN applications: A survey. J. High Speed Netw. 2020, 26, 325–338. [Google Scholar] [CrossRef]
  13. Carrano, R.C.; Passos, D.; Magalhaes, L.C.; Albuquerque, C.V. Survey and taxonomy of duty cycling mechanisms in wireless sensor networks. IEEE Commun. Surv. Tutor. 2013, 16, 181–194. [Google Scholar] [CrossRef]
  14. Stone, K.; Colagrosso, M. Efficient duty cycling through prediction and sampling in wireless sensor networks. Wirel. Commun. Mob. Comput. 2007, 7, 1087–1102. [Google Scholar] [CrossRef]
  15. George, R.; Mary, T.A.J. Review on directional antenna for wireless sensor network applications. IET Commun. 2019, 14, 715–722. [Google Scholar] [CrossRef]
  16. Sheikh, S.A.; Gupta, S.H. Implementation and Analysis of Energy Efficiency of M-ary Modulation Schemes for Wireless Sensor Network. In Data and Communication Networks; Springer: Berlin/Heidelberg, Germany, 2019; pp. 57–68. [Google Scholar]
  17. Khriji, S.; Houssaini, D.; Kammoun, I.; Kanoun, O. Energy-efficient techniques in wireless sensor networks: Technology, components and system design. In Energy Harvesting for Wireless Sensor Networks; De Gruyter: Berlin, Germany, 2018; pp. 287–301. [Google Scholar]
  18. Chéour, R.; Jmal, M.W.; Kanoun, O.; Abid, M. Evaluation of simulator tools and power-aware scheduling model for wireless sensor networks. IET Comput. Digit. Tech. 2017, 11, 173–182. [Google Scholar] [CrossRef]
  19. Kanoun, O.; Khriji, S.; Naifar, S.; Bradai, S.; Bouattour, G.; Bouhamed, A.; El Houssaini, D.; Viehweger, C. Prospects of Wireless Energy-Aware Sensors for Smart Factories in the Industry 4.0 Era. Electronics 2021, 10, 2929. [Google Scholar] [CrossRef]
  20. Anane, R.; Raoof, K.; Bouallegue, R. Minimization of wireless sensor network energy consumption through optimal modulation scheme and channel coding strategy. J. Signal Process. Syst. 2016, 83, 65–81. [Google Scholar] [CrossRef]
  21. Pandey, O.J.; Hegde, R.M. Low-latency and energy-balanced data transmission over cognitive small world WSN. IEEE Trans. Veh. Technol. 2018, 67, 7719–7733. [Google Scholar] [CrossRef]
  22. Jan, B.; Farman, H.; Javed, H.; Montrucchio, B.; Khan, M.; Ali, S. Energy efficient hierarchical clustering approaches in wireless sensor networks: A survey. Wirel. Commun. Mob. Comput. 2017, 2017, 6457942. [Google Scholar] [CrossRef]
  23. Pandey, O.J.; Yuvaraj, T.; Paul, J.K.; Nguyen, H.H.; Gundepudi, K.; Shukla, M.K. Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing. IEEE Trans. Cogn. Commun. Netw. 2021, 8, 365–379. [Google Scholar] [CrossRef]
  24. Chilamkurthy, N.S.; Pandey, O.J.; Ghosh, A.; Cenkeramaddi, L.R.; Dai, H.N. Low-Power Wide-Area Networks: A Broad Overview of its Different Aspects. IEEE Access 2022, 10, 81926–81959. [Google Scholar] [CrossRef]
  25. Ouadou, M.; Zytoune, O.; El Hillali, Y.; Menhaj-Rivenq, A.; Aboutajdine, D. Energy efficient hardware and improved cluster-tree topology for lifetime prolongation in zigbee sensor networks. J. Sens. Actuator Netw. 2017, 6, 22. [Google Scholar] [CrossRef]
  26. Chéour, R.; Khriji, S.; Kanoun, O. Microcontrollers for IoT: Optimizations, Computing Paradigms, and Future Directions. In Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA, 2–16 June 2020; pp. 1–7. [Google Scholar]
  27. Al-Kofahi, M.M.; Al-Shorman, M.Y.; Al-Kofahi, O.M. Toward energy efficient microcontrollers and Internet-of-Things systems. Comput. Electr. Eng. 2019, 79, 106457. [Google Scholar] [CrossRef]
  28. Gotz, M.; Khriji, S.; Chéour, R.; Arief, W.; Kanoun, O. Benchmarking based Investigation on Energy Efficiency of Low-Power Microcontrollers. IEEE Trans. Instrum. Meas. 2020, 69, 7505–7512. [Google Scholar] [CrossRef]
  29. Trigona, C.; Andò, B.; Baglio, S.; La Rosa, R.; Zoppi, G. Vibration-based Transducer for Zero-Energy standby applications. In Proceedings of the 2016 IEEE Sensors Applications Symposium (SAS), Catania, Italy, 20–22 April 2016; pp. 1–4. [Google Scholar]
  30. Chéour, R.; Jmal, M.W.; Abid, M. New combined method for low energy consumption in Wireless Sensor Network applications. Simulation 2018, 94, 873–885. [Google Scholar] [CrossRef]
  31. Mokarippor, P.; Shirvani, M.H. A State Of The Art Survey On DVFS Techniques In Cloud Computing Environment. J. Multidiscip. Eng. Sci. Technol. JMEST 2016, 3, 50. [Google Scholar]
  32. Mei, X.; Wang, Q.; Chu, X. A survey and measurement study of GPU DVFS on energy conservation. Digit. Commun. Netw. 2017, 3, 89–100. [Google Scholar] [CrossRef]
  33. Aslam, S.; Ejaz, W.; Ibnkahla, M. Energy and spectral efficient cognitive radio sensor networks for Internet of Things. IEEE Internet Things J. 2018, 5, 3220–3233. [Google Scholar] [CrossRef]
  34. Le, T.N.; Pegatoquet, A.; Le Huy, T.; Lizzi, L.; Ferrero, F. Improving energy efficiency of mobile WSN using reconfigurable directional antennas. IEEE Commun. Lett. 2016, 20, 1243–1246. [Google Scholar] [CrossRef]
  35. Lounis, M.; Bounceur, A.; Laga, A.; Pottier, B. GPU-based parallel computing of energy consumption in wireless sensor networks. In Proceedings of the 2015 European Conference on Networks and Communications (EuCNC), Paris, France, 29 June–2 July 2015; pp. 290–295. [Google Scholar]
  36. Huang, Y.; Yu, W.; Garcia-Ortiz, A. Accurate energy-aware workload distribution for wireless sensor networks using a detailed communication energy cost model. J. Low Power Electron. 2014, 10, 183–193. [Google Scholar] [CrossRef][Green Version]
  37. Khan, M.N.; Rahman, H.U.; Almaiah, M.A.; Khan, M.Z.; Khan, A.; Raza, M.; Al-Zahrani, M.; Almomani, O.; Khan, R. Improving energy efficiency with content-based adaptive and dynamic scheduling in wireless sensor networks. IEEE Access 2020, 8, 176495–176520. [Google Scholar] [CrossRef]
  38. Hosahalli, D.; Srinivas, K.G. Enhanced reinforcement learning assisted dynamic power management model for internet-of-things centric wireless sensor network. IET Commun. 2020, 14, 3748–3760. [Google Scholar] [CrossRef]
  39. Reddy, B.K.; Singh, A.K.; Biswas, D.; Merrett, G.V.; Al-Hashimi, B.M. Inter-cluster Thread-to-core Mapping and DVFS on Heterogeneous Multi-cores. IEEE Trans.-Multi-Scale Comput. Syst. 2017, 4, 369–382. [Google Scholar] [CrossRef]
  40. Park, S.; Park, J.; Shin, D.; Wang, Y.; Xie, Q.; Pedram, M.; Chang, N. Accurate modeling of the delay and energy overhead of dynamic voltage and frequency scaling in modern microprocessors. IEEE Trans.-Comput.-Aided Des. Integr. Circuits Syst. 2013, 32, 695–708. [Google Scholar] [CrossRef]
  41. Ng, L.L.; Yeap, K.H.; Goh, M.W.C.; Dakulagi, V. Power Consumption in CMOS Circuits. In Field-Effect Transistor; InTech Open: Rijeka, Croatia, 2022. [Google Scholar]
  42. Leng, J.; Hetherington, T.; ElTantawy, A.; Gilani, S.; Kim, N.S.; Aamodt, T.M.; Reddi, V.J. GPUWattch: Enabling energy optimizations in GPGPUs. ACM SIGARCH Comput. Archit. News 2013, 41, 487–498. [Google Scholar] [CrossRef]
  43. Wang, S.; Qian, Z.; Yuan, J.; You, I. A DVFS based energy-efficient tasks scheduling in a data center. IEEE Access 2017, 5, 13090–13102. [Google Scholar] [CrossRef]
  44. Basireddy, K.R.; Singh, A.K.; Al-Hashimi, B.M.; Merrett, G.V. AdaMD: Adaptive mapping and DVFS for energy-efficient heterogeneous multicores. IEEE Trans.-Comput.-Aided Des. Integr. Circuits Syst. 2019, 39, 2206–2217. [Google Scholar] [CrossRef]
  45. Slimani, C.; Rubini, S.; Boukhobza, J. Hymad: A hybrid memory-aware DVFS strategy. ACM SIGBED Rev. 2019, 16, 45–50. [Google Scholar] [CrossRef]
  46. Yao, Y.; Lu, Z. DVFS for NoCs in CMPs: A thread voting approach. In Proceedings of the 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), Barcelona, Spain, 12–16 March 2016; pp. 309–320. [Google Scholar]
  47. Cotes-Ruiz, I.T.; Prado, R.P.; García-Galán, S.; Muñoz-Expósito, J.E.; Ruiz-Reyes, N. Dynamic voltage frequency scaling simulator for real workflows energy-aware management in green cloud computing. PLoS ONE 2017, 12, e0169803. [Google Scholar] [CrossRef]
  48. Kang, K.D.; Park, G.; Kim, H.; Alian, M.; Kim, N.S.; Kim, D. NMAP: Power Management Based on Network Packet Processing Mode Transition for Latency-Critical Workloads. In Proceedings of the MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, Virtual, 18–22 October 2021; pp. 143–154. [Google Scholar]
  49. Tan, Z.; Yang, X.; Pang, M.; Gao, S.; Li, M.; Chen, P. UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology Communication. Sensors 2020, 20, 5134. [Google Scholar] [CrossRef]
  50. Liu, S.; Qiu, Q.; Wu, Q. Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting. In Proceedings of the 2008 Design, Automation and Test in Europe, Munich, Germany, 10–14 March 2008; pp. 236–241. [Google Scholar]
  51. Kim, H.Y.; Lee, S.W. Lifespan Extension of an IoT System with a Fixed Lithium Battery. IEICE Trans. Inf. Syst. 2020, 103, 2559–2567. [Google Scholar] [CrossRef]
  52. Hoang, V.T.; Julien, N.; Berruet, P. Increasing the autonomy of Wireless Sensor Node by effective use of both DPM and DVFS methods. In Proceedings of the 2013 IEEE Faible Tension Faible Consommation, Paris, France, 20–21 June 2013; pp. 1–4. [Google Scholar]
  53. Chéour, R.; Jmal, M.W.; Khriji, S.; El Houssaini, D.; Trigona, C.; Abid, M.; Kanoun, O. Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks. Sensors 2022, 22, 301. [Google Scholar] [CrossRef]
  54. Mo, L.; Kritikakou, A.; Sentieys, O. Energy-quality-time optimized task mapping on DVFS-enabled multicores. IEEE Trans.-Comput.-Aided Des. Integr. Circuits Syst. 2018, 37, 2428–2439. [Google Scholar] [CrossRef]
  55. Ahmed, S.; Ul Ain, Q.; Siddiqui, J.H.; Mottola, L.; Alizai, M.H. Intermittent Computing with Dynamic Voltage and Frequency Scaling. In Proceedings of the 2020 International Conference on Embedded Wireless Systems and Networks, Lyon, France, 17–19 February 2020; pp. 97–107. [Google Scholar]
  56. Texaz Instruments. Power Management Module and Supply Voltage Supervisor, SLAU388F; Texas Intruments: Dallas, TX, USA, 2018. [Google Scholar]
  57. Lo Giudice, P.; Nocera, A.; Ursino, D.; Virgili, L. Building topic-driven virtual iots in a multiple iots scenario. Sensors 2019, 19, 2956. [Google Scholar] [CrossRef] [PubMed]
  58. Cauteruccio, F.; Cinelli, L.; Terracina, G.; Ursino, D.; Virgili, L. Investigating the Scope of a Thing in a Multiple Internet of Things Scenario. In Proceedings of the SEBD, Castiglione della Pescaia, Italy, 16–19 June 2019. [Google Scholar]
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