Smart Home IoT System by Using RF Energy Harvesting

Abstract

IoT system becomes a hot topic nowadays for smart home. IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circularly polarized array rectenna system to harvest enough power from any direction to increase lifetime of the batteries used in the IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node, and a low-power microcontroller are used to operate in lower power modes. The proposed system has an 84.6-day lifetime which is approximately 10 times the lifetime for a similar system. On the other hand, the proposed power management circuit is operated at 0.3 V DC to boost the voltage to ~3.7 V from radio frequency energy harvesting and manage battery level to increase the battery lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a nonstatic plan, approve energy consumption, and avoid failure of sensor nodes in a smart home.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Smart home services become the key feature of an environment of this era where data is collected from different sensors and control them. Smart home support services such as security and access control across multiple devices are related to the home [1]. These applications may be service timing oriented; thus, they need to save and manage the energy consumption [2]. Internet of Things (IoT) should be able to integrate several and diverse terminal systems to develop oversize of digital services from smart homes. IoT is a good solution to connect everything by wireless rather than physically connected. Many different features are required to complete and integrate the system [3]. The choice of the antenna system is more important since it is a crucial component in these smart devices that end in the node. To create an effective antenna performance in IoT system, many factors should be examined as size of the antenna, radiation pattern, and gain [4]. Electromagnetic wave is used nowadays in empowering implantable biomedical devices, wireless charges, WSN nodes, etc. Radio frequency harvesting is necessary for biomedical devices especially when it is planted inside the body, as increase lifetime of the replacement of the batteries in their structure. Radio frequency becomes a popular type of energy harvesting; this is due to the fact that the environment around us is full of RF waves extended from 300 MHz to 3000 MHz in different wireless applications such as broadcasting, mobile, satellite, and Wi-Fi communications [5]. This abundant energy can be reused as harvested and stored in batteries for the IoT applications.

The spectrum of smart home applications is vast, and many have contributed in different areas of this spectrum. Many researchers have developed smart home applications based on wireless sensor networks and Internet of Things [6–9]. For example, in [6], a smart home control system based on Zigbee wireless network is proposed. The proposed system uses an embedded network gateway to bridge the local Zigbee network with the Internet. Instead of just controlling the environment, the proposed smart home monitoring system in [7] concentrates on finding patterns in the recorded data and analyzing resident’s behavior based on it. In addition to the work developed in [8], many have taken the concept of smart home step further to develop smart building systems [9–12]. Others have developed systems for indoor ambient monitoring in general, as in [13, 14].

Generally, the rectenna is the key element in RF energy harvesting. Rectenna is an antenna integrated with rectifier. Antenna arrays are capable of capturing more RF power than single elements due to the larger aperture area [4, 5, 15–17]. In [4, 5], antenna arrays are used as receiving units in different rectenna structures to harvest RF energy. There are different techniques to increase the harvested RF energy. Smart antenna [18–20] is one of these techniques that uses beam steering to suitable directions of radiation. However, this technique is very complicated structure as well as expensive. Second used technique is the efficient CP dielectric resonator antenna array fed by cavity-backed substrate integrated waveguide (SIW) which has been proposed in [5]. In [4], a wideband left-handed metamaterial (WBLHM) substrate has been used to improve the array gain and provide wideband CP operation. Wider half power beamwidth (HPBW) and better coverage can be achieved by using $1\times 4$ beamwidth-enhanced antenna array [4]. Nonuniform amplitude and phase distributions are applied, and two auxiliary antennas are added besides the $1\times 4$ antenna array in order to maximize the power harvested efficiency [5]. In [5], the antenna wireless sensor consists of two layers in wireless sensors node but it is difficult to be integrated with the rectifier. Furthermore in [18], the antenna is designed on one layer; the maximum gain is small (3 dBi) as well as it is not applied to IoT system. In addition in [19], the antenna consists of a multilayer system which makes it expensive, complex, and not easy to be integrated with the rectifier. Concerning the lifetime of the system using energy harvesting in IoT system, it is found to be 84.6 days which is approximately 10 times the lifetime for similar system. However, Table 1 in [21] shows that the lifetime of the system using a dual linearly polarized antenna array is to be 50.08 days.

Table 1

Comparison between the proposed harvesting system and related technologies.

Work	$F$ (GHz)	Antenna size (mm3)	Max gain (dBi)	Rectenna eff. (%)	Structure	Technology	Polarization type
[5]	5.05-7.45	$$120\times 80\times 27$$	12	61 @ 5.82 GHz	Two layers	HSMS 2860	Circular
[18]	2.3-2.9	$$41\times 35.5\times 0.8$$	3	59.5 @ 2.45 GHz	One layer	SMS7630	Circular
[19]	0.908-2 and 2.35-2.5	$$120\times 120\times 8.6$$	5.41 and 7.94	43 @ 0.9 GHz and 39 @ 2.45 GHz	Multilayers	SMS7630	Circular
Proposed rectenna	1.7-3	$$178\times 148\times 0.813$$	9.902	60	One layer	SMS7630	Circular

Different designs have been presented for CP arrays [5, 15, 16]. A wideband left-handed metamaterial (WBLHM) substrate has been used in [5] to improve the array gain and provide wideband CP operation. However, two layers are needed with a separation of about 27 mm. In [15], a simple and highly efficient rectenna was presented which has been constructed on a loonly about 100 MHz for dual-band CP radiation which is realized in w-cost commercial FR4 substrate. The achieved axial ratio bandwidth is only about 100 MHz for dual-band CP radiation which is realized in [16] by stacking the slotted-circular-patch (SCP) on the tapered-slit-octagon patch (TSOP) and the microstrip feedline with metallic via to SCP. A complicated structure with multilayers was proposed which increased the rectenna size. In this scenario, a simple and wideband CP rectenna is proposed. As stated before, novelty of the design lies on the simplicity to implement a high-efficient energy harvesting rectenna using a single layer structure with high gain and wideband CP performance. There are many research works considering the problem of time series prediction, but few of them tackling the problem of optimization deep learning models using bioinspired algorithms [22–25]. In [22], three different traditional machine learning techniques were used for forecasting the next 24-hour indoor environment quality parameters. In [23], feature selection methods and genetic algorithm (GA) were utilized to improve the performance of long short-term memory (LSTM) deep learning model for prediction of electric load. Also, in [24], a hybrid approach for forecasting stock market is proposed; this approach integrates GA and LSTM network to determine the optimal time window size and topology for the LSTM network. Another optimization technique was used to fine-tune the parameters of the echo state network for time series prediction in [25], where particle swarm optimization (PSO) algorithm is utilized to pretrain some fixed weights values of the network.

In this paper, an integrated cloud-based IoT system is presented for smart homes. The proposed system is powered using the RF energy harvested from surrounding ambience. The overall system that integrates all of its IoT components in home is shown in Figure 1. The experimental results that demonstrate the performance of the different components of the proposed smart home IoT powering harvesting radio frequency at Bluetooth and 4th mobile generation are done. The smart home control action plan combines both instantaneous sensor readings and foreseen environmental conditions to overcome faults in the sensors’ readings. The prototypes not only extended lifetime of the system but also its ability to effectively control smart home.

[figure omitted; refer to PDF]

The organization of the paper is as follows. We discuss the related literature in Section 1. Section 2 explains the proposed smart home system architecture. In Section 3, we detailed the implementation of components and the system prototype, while Section 4 evaluates the system performance. Finally, Section 5 concludes the paper work.

2. Proposed Smart Home System Architecture

The design of complete RF energy harvesting system is introduced. The energy harvesting system consists of a high gain $2\times 2$ linearly polarized antenna array (LPAA) as a dedicated transmitted source and $2\times 2$ circularly polarized antenna array (CPAA) as a receiving component in the rectenna structure. The receiving array is integrated with high-efficiency rectifier circuit in order to complete the rectenna array structure. The main advantage of the proposed $2\times 2$ CPAA is to obtain a constant output DC voltage regardless of the polarization of the harvested waves. Usually, the harvested electromagnetic field is linearly polarized at all available mobile frequency bands and at the Wi-Fi 2.45 GHz band. However, in the environment of the indoor and outdoor, the multipath effects are taking place which make a depolarization. This leads to decrease the efficiency of the energy harvesting system because the received EM waves have unknown angle of incidence. This problem can be solved by using CP antennas. The novelty of the design lies in the simplicity to manufacture of a high-efficiency energy harvesting rectenna using a single layer structure with high gain and wideband CP performance.

The use of layered approach to design and implement an energy efficient IoT system with reduced overall energy consumption such that it can be powered using the harvested RF energy is shown in Figure 2. As the sensor node is the only element in the system that is RF energy powered, we optimize its energy consumption by the following:

(i) Utilizing a low-power microcontroller and configuring it to operate in lower power modes as much as possible, thereby minimizing the sensor node’s wake up duration

(ii) Optimizing the sensor node’s software to exploit the tasks and semaphores of the used Texas Instruments real-time operating system (TI-RTOS). This improves the energy efficiency of the node

(iii) Adopting the Zigbee protocol as the internode communication protocol since Zigbee has low power consumption compared to other widely used wireless protocols (e.g., Wi-Fi). We assemble a prototype of the developed system. Excessive experiments show that the smart home system as shown in Figure 1 effectively controls lighting and cooling devices, thereby reducing the home’s power consumption. Furthermore, the system has an 84.6-day lifetime which is approximately 10 times the lifetime reported in existing literature

[figure omitted; refer to PDF]

The design and implementation of the power management and voltage regulation circuit accepting as low as 0.3 V DC from the energy harvesting rectifier circuit are to not only boost the voltage to proper voltage level needed by the batteries (~3.7 V) but also manage its level to be between the undervoltage and overvoltage ratings of the battery to ensure long lifetime of the batteries. Additional regulation stage is added to support a different needed battery voltage level (~3.2 V).

A predictive indoor environment monitoring system is developed which is based on a novel hybrid system based on gated recurrent unit (GRU), grey wolf optimization (GWO) algorithm GRU-GWO, and fuzzy logic system. The developed system uses GWO to find optimal time lags, number of predicted steps, and number of layers for GRU predictive model with optimized performance. The GWO optimization algorithm has the ability to achieve very competitive results in terms of improved local optima avoidance. Moreover, the proposed system depends on the fuzzy control optimization for energy conservation in heating, ventilation, and air conditioning (HVAC) systems.

3. Design Methodology

In this section, the system design and the implementation details of the proposed system are presented. We start with the proposed RF energy harvesting system, the designed rectennas, and power management unit. Then, the design of the various used IoT nodes is given. Finally, we discuss the proposed cloud-based artificial intelligence system developed to control the home ambience.

3.1. RF Energy Harvesting System Design

An RF energy harvester is generally composed of receiving antenna, band pass filter, matching network, rectifier, and terminal load [21]. The full system presented includes a transmitter of $2\times 2$ linearly polarized antenna array (LPAA) and a $2\times 2$ circularly polarized antenna array (CPAA) receiving rectenna of RF energy harvester.

3.1.1. Transmitting Antenna Design

A $2\times 2$ linearly polarized antenna array (LPAA) with high gain is designed and fabricated to be used as electromagnetic waves transmitting source. The proposed array consists of four identical elements of circular patch with V-shaped slot integrated over rectangular slot ground plane. The elements arranged in a planar array structure and excited with same phase. The configuration of the antenna element has many advantages as wide impedance bandwidth with high radiation intensity in both directions. The array is designed on the Roger RO4003C substrate with dielectric constant of 3.38, dielectric loss tangent of 0.0027, substrate height of 0.813 mm, and the copper thickness of 0.035 mm. The photos of the top and bottom view for the fabricated transmitting antenna array are shown in Figure 3. The proposed $2\times 2$ LPAA has total area of $118.8\times 156.8\text{m}{\text{m}}^2$. The details of the physical parameters of the proposed transmitting antenna array system are optimized and published in [26].

[figures omitted; refer to PDF]

3.1.2. Receiving Rectenna Design

The proposed rectenna consists of a $2\times 2$ CP antenna array (CPAA), a matching circuit, and a rectifier circuit. The basic antenna element, in the array structure, is a coplanar waveguide (CPW) slot antenna that consists of a main slot radiator, a grounded-L strip, a stepped impedance matching stub, two chamfered corners for CP behavior, and asymmetric U-shaped strip acting as a perturbed element; more details of the designed antenna are in [21]. Furthermore, the single element was optimized and published in [27]. Optimization was carried on the separation between elements for obtaining wide axial bandwidth and higher gain. The $2\times 2$ array as shown in Figure 4 has an equal phase feeding network in order to obtain high gain besides the CP operation as well as high radiation efficiency. The designed array is built on RO4003C substrate with a dielectric constant of 3.38, thickness of 0.813 mm, and loss tangent of 0.0027. The feeding network of the $2\times 2$ array is corporate microstrip lines that perform impedance matching and delivering equal phases to the elements. The four elements are in the same orientation and are fed by a microstrip line from the right side of the array. The spacing between the elements is 0.768 $\lambda$0 at a central resonance frequency of 2.45 GHz. In order to equate the phases between the elements, the lengths of transmission lines have been turned around in a meander shape to deliver equal phase to all elements. Three-equal division microstrip Wilkinson power dividers are used to provide the equal amplitude distribution for the 4 elements. A ground plane is placed below substrate for the Wilkinson divider. In addition, the output phases for the feeding network ports are almost equal with a maximum phase difference of 2.8°. In order to connect the feeding system to the elements, a microstrip-line-to-CPW transition using via holes [28] is applied. An extra substrate section for normal ground plane layer is added at the top of the array for providing ground symmetry and more uniform radiation pattern. The total size of the $2\times 2$ array is $178\times 148\text{m}{\text{m}}^2$.

[figures omitted; refer to PDF]

A rectifier circuit is designed and implemented for providing the conversion process. The topology of the conversion circuit used in this paper is a half wave rectifier circuit that has been investigated in [29, 30]. The circuit is designed using the Schottky diode SMS-7630 [31] which has a very high sensitivity to the low values of the received power. The rectifier circuit is matched with the $2\times 2$ CPAA at 2.45 GHz, in order to achieve maximum power transfer between the $2\times 2$ CPAA and the half wave rectifier circuit. The process of designing the rectifier circuit has been done using the Advanced Design System (ADS) simulator. Figure 4 depicts the fabricated $2\times 2$ rectenna array.

The schematic diagram of the proposed rectifier circuit is studied in [30]. The circuit is designed on the same Roger RO4003C substrate. Figure 5 shows the fabricated rectifier circuit. A short-ended stub, 1 pF capacitor, and 10 nH inductor are used to match antenna input impedance of 50 Ω with rectifier circuit input impedance. The rectifier circuit consists of a single Schottky SMS7630 diode, smoothing capacitor, and load resistance of 10 kΩ. The output voltage and rectenna efficiency variations versus the frequency are shown in Figure 6. The rectenna system has maximum efficiency of 60% at 2.45 GHz.

[figures omitted; refer to PDF]

[figure omitted; refer to PDF]

The BQ25504 also covers suitable range of output voltage that starts from 2.2 V to 5.5 V; it also provides safe operation for both the battery and the load by controlling the design of the undervoltage and overvoltage of the output voltage from the BQ25504 through the proper design of the resistances $R$_uv1, $R$_uv2, $R$_ov1, and $R$_ov2, respectively, shown in the schematic of the power management circuit in Figure 7, where the undervoltage and overvoltage thresholds are determined from the following equations. $$\begin{array}{}\text{(1)}& V_{\text{UV}}=V_{\text{BIAS}}\times 1+\frac{R_{\text{uv}2}}{R_{\text{uv}}},\text{(2)}& V_{\text{OV}}=\frac{2}{3}\times V_{\text{BIAS}}\times 1+\frac{R_{\text{ov}2}}{R_{\text{ov}1}},\end{array}$$where $V_{\text{BIAS}}$ is of typical value 1.25 V.

Figure 8 shows the voltage regulation part, while the final compacted manufactured part for the power management and voltage regulation unit (PMVRU) is shown in Figure 9.

[figure omitted; refer to PDF]

[figures omitted; refer to PDF]

Figure 16 illustrates the achieved gain as a function of frequency of $2\times 2$ CPAA with equal phases. The gain, directivity, and radiation efficiency of the array are 9.902 dBi, 10.31 dBi, and 91.13%, respectively, at 2.45 GHz. This array is suitable for dedicated RF energy harvesting applications where the positions of the source and receiving antennas are known, and consequently, this array can collect power from specific direction when it is used as the receiving antenna in a rectenna structure. In addition, circularly polarized rectennas are used due to their ability to obtain constant DC power at random polarization angles.

[figure omitted; refer to PDF]

The proposed rectenna is tested at different measurement distances from the $2\times 2$ transmitting antenna. Table 5 lists the values of the obtained DC voltages at different frequencies. Table 5 shows that our proposed RF energy harvesting system has a larger number of covered frequency bands, higher gain, higher rectenna efficiency, simple one-layer structure, and superior operation, as compared to different types of polarization techniques. Table 1 gives a comparison between the RF energy harvesting system proposed in this paper and the literature [5, 18, 19].

Table 5

The output $v$_out using CP rectenna array.

Distance (cm)	$V$out (volt) @$F=1.8\text{GHz}$	$V$out (volt) @ $F=2.1\text{GHz}$	$V$out (volt) @ $F=2.45\text{GHz}$
15	0.85	0.6	1.54
25	0.66	0.53	1.2
35	0.5	0.45	1.08
50	0.4	0.35	0.97
65	0.35	0.23	0.64
75	0.25	0.2	0.41

4.2. Power Management Unit Performance

Simulation is conducted at first using TINA-TI for the power management part based on the power management voltage regulation unit (PMVRU) unit on the TI BQ25504, and then the Proteus 8.0 is used to verify the voltage regulation output level against different input voltage levels for the voltage regulation part of the PMVRU unit; output voltage at battery reaches 3.7 V after some transients at first few milliseconds which is shown in Figure 17. Simulation results follow a fixed nature after saturation with different input voltage levels as shown in Figure 18, while the practical results show slight variations but converge to the same value of 3.7 V; the practical results are tested on a AA-3.7 V-lithium ion 4200 mAh battery, and using a DC source of different input voltage levels and maximum current limit of 1 mA is shown in Figure 18.

[figure omitted; refer to PDF]

The next experiment illustrates the system’s ability to control relative humidity in the home. The value of relative humidity is kept in a predefined range set by the user using system’s dashboard. Figure 20 shows the relative humidity value with signal sent to the dehumidifier and how the system keeps relative humidity inside the predefined range. At time 5:29 PM, the value of relative humidity passed down the lower threshold then the dehumidifier is turned off. Then when the relative humidity value reached the upper threshold at 5:33 PM, the system turned on the dehumidifier again to lower the relative humidity.

[figure omitted; refer to PDF]

4.4. Time Series Prediction Performance

The proposed hybrid GRU-GWO time series prediction approach depends on the collected data of our sensor nodes (temperature, humidity, and CO₂ readings at hourly rate) over a period of 120 days. Root mean square error is calculated to validate the performance of the proposed hybrid GRU-GWO approach for time series prediction using the following equation. $$\begin{array}{}\text{(3)}& \text{RMSE}=\sqrt{\frac{1}{N}\sum _{i=1}^N{a_i-p_i}^2},\end{array}$$where $a$ is the actual value, $p$ is the predicted value using hybrid GRU-GWO approach, and $N$ is the total number of values.

Table 6 shows configuration parameters for both GWO and PSO optimization algorithms. Table 7 represents the average RMSE of the predicted indoor temperature, humidity, and CO₂ on testing dataset using both GRU-PSO and GRU-GWO for optimizing time window size and GRU parameters, while Table 8 shows the RMSE of each parameter individually. Although the results of GRU-PSO and GRU-GWO are close, the shortcoming of the PSO algorithm is that it has the problem of ease of falling into the local optimum. But unlike PSO, in GWO algorithm, the position of the best solution is assessed by three solutions not a single solution. So, GWO can significantly reduce the probability of falling into the local optimum. From our experiment, it is noticed that the GRU-PSO convergence curve has no improvement starting from iteration number 5, unlike the convergence curve of GRU-GWO which keeps improving.

Table 6

Parameters for GWO and PSO.

Parameter	Value
No. of search agents	10
No. of iterations	20
Problem dimension	15
Search domain	$$0,1$$
Fitness function	Minimize RMSE

Table 7

Average RMSE of the proposed approach.

Approach	RMSE	Window size	GRU parameters
GRU-PSO	34.9455	58	298
GRU-GWO	35.2515	51	435

Table 8

RMSE of the proposed approach for each parameter.

Approach	Temperature	Humidity	CO2
GRU-PSO	0.77	2.42	101.71
GRU-GWO	0.87	2.62	102.3

5. Conclusion

In this paper, a complete IoT system application is designed and fabricated using EM energy harvesting. The proposed system is consisting of complete RF energy harvesting system starting from dedicated transmitted high gain $2\times 2$ linearly polarized array antenna and $2\times 2$ circularly polarized rectenna array as a receiving component in a rectenna structure. The receiving antenna array is integrated with efficient rectifier circuit in order to improve the rectification efficiency. An energy efficient IoT system is designed to reduce the overall energy consumption. As the sensor node is optimized, its energy consumption is reduced by using a low-power microcontroller and operating in lower power modes. In addition, optimizing the sensor node is achieved by the sensor node’s software and using operating system (TI-RTOS) with adopting the Zigbee protocol. The system has an 84.6-day lifetime which is approximately 10 times the lifetime reported in existing literature. On the other hand, a power management unit was designed with voltage regulation circuit operated at 0.3 V DC from the energy harvesting rectifier circuit to boost the voltage to ~3.7 V and manage battery level to ensure long lifetime of the batteries. Then, a predictive indoor environment monitoring system was developed based on a novel hybrid system with gated recurrent unit, grey wolf optimization algorithm GRU-GWO, and fuzzy logic system to provide a nonstatic plan for IoT sensor system to reduce energy consumption and avoid the problem of sensor nodes’ failure.

Acknowledgments

This work is funded by the National Telecommunications Regulatory Authority (NTRA) and the Ministry of Communications and Information Technology (MCIT), Egypt, through a contract with the Electronics Research Institute (ERI).

References

[1] A. Okba, A. Takacs, H. Aubert, "Compact flat dipole rectenna for IoT applications," Progress In Electromagnetics Research C, vol. 87, pp. 39-49, DOI: 10.2528/PIERC18071604, 2018.

[2] K. Shafique, B. A. Khawaja, M. D. Khurram, S. M. Sibtain, Y. Siddiqui, M. Mustaqim, H. T. Chattha, X. Yang, "Energy harvesting using a low-cost rectenna for internet of things (IoT) applications," IEEE Access, vol. 6, pp. 30932-30941, DOI: 10.1109/ACCESS.2018.2834392, 2018.

[3] M. Zeng, Z. Li, A. S. Andrenko, Y. Zeng, H. Z. Tan, "A compact dual-band rectenna for GSM900 and GSM1800 energy harvesting," International Journal of Antennas and Propagation, vol. 2018,DOI: 10.1155/2018/4781465, 2018.

[4] L. Lu, Y. C. Jiao, Z. B. Weng, L. Zhang, C. Y. Cui, R. Q. Wang, "High-efficiency circularly polarized dielectric resonator antenna array fed by the cavity-backed SIW," IEEE Antennas and Wireless Propagation Letters, vol. 17 no. 7, pp. 1145-1148, DOI: 10.1109/LAWP.2018.2832212, 2018.

[5] N. H. Nguyen, T. D. Bui, A. D. Le, A. D. Pham, T. T. Nguyen, Q. C. Nguyen, M. T. Le, "A novel wideband circularly polarized antenna for RF energy harvesting in wireless sensor nodes," International Journal of Antennas and Propagation, vol. 2018,DOI: 10.1155/2018/1692018, 2018.

[6] X. Mao, K. Li, Z. Zhang, J. Liang, "Design and implementation of a new smart home control system based on Internet of things," 2017 International Smart Cities Conference (ISC2),DOI: 10.1109/isc2.2017.8090790, .

[7] H. Ghayvat, S. Mukhopadhyay, X. Gui, N. Suryadevara, "WSN- and IoT-based smart homes and their extension to smart buildings," Sensors, vol. 15 no. 5, pp. 10350-10379, DOI: 10.3390/s150510350, 2015.

[8] Y. L. Hsu, P. H. Chou, H. C. Chang, S. L. Lin, S. C. Yang, H. Y. Su, C. C. Chang, Y. S. Cheng, Y. C. Kuo, "Design and implementation of a smart home system using multisensor data fusion technology," Sensors, vol. 17 no. 7,DOI: 10.3390/s17071631, 2017.

[9] Y. Gao, Z. Qin, R. Zhang, W. Zhang, Y. Duan, Z. Li, "Research on data collection design based on Zigbee wireless technology smart home system," IOP Conference Series: Materials Science and Engineering, vol. 452, article 042057,DOI: 10.1088/1757-899x/452/4/042057, 2018.

[10] N. Havard, S. McGrath, C. Flanagan, C. MacNamee, "Smart building based on internet of things technology," 2018 12th International Conference on Sensing Technology (ICST), pp. 278-281, DOI: 10.1109/icsenst.2018.8603575, .

[11] J. Shah, B. Mishra, "Customized IoT enabled wireless sensing and monitoring platform for smart buildings," Procedia Technology, vol. 23, pp. 256-263, DOI: 10.1016/j.protcy.2016.03.025, 2016.

[12] G. Alsuhli, A. Khattab, "A fog-based IoT platform for smart buildings," 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 174-179, DOI: 10.1109/itce.2019.8646480, .

[13] S. C. Folea, G. Mois, "A low-power wireless sensor for online ambient monitoring," IEEE Sensors Journal, vol. 15 no. 2, pp. 742-749, DOI: 10.1109/jsen.2014.2351420, 2015.

[14] D. Brunelli, I. Minakov, R. Passerone, M. Rossi, "POVOMON: an ad-hoc wireless sensor network for indoor environmental monitoring," 2014 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems Proceedings,DOI: 10.1109/eesms.2014.6923287, .

[15] M. M. Mansour, H. Kanaya, "Novel L-slot matching circuit integrated with circularly polarized rectenna for wireless energy harvesting," Electronics, vol. 8 no. 6,DOI: 10.3390/electronics8060651, 2019.

[16] A. M. Jie, N. Nasimuddin, M. F. Karim, K. T. Chandrasekaran, "A dual-band efficient circularly polarized rectenna for RF energy harvesting systems," International Journal of RF and Microwave Computer-Aided Engineering, vol. 29 no. 1, article e21665,DOI: 10.1002/mmce.21665, 2019.

[17] H. Sun, W. Geyi, "A new rectenna using beamwidth-enhanced antenna array for RF power harvesting applications," IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 1451-1454, DOI: 10.1109/LAWP.2016.2642124, 2017.

[18] T. Soulos, V. George, "Smart antennas for mobile communication systems: benefits and challenges," Electronics & Communication Engineering Journal, vol. 11 no. 2, pp. 84-94, DOI: 10.1049/ecej:19990204, 1999.

[19] W. Gang, Y. Qin, "MAC protocols for wireless mesh networks with multi-beam antennas: a survey," Future of Information and Communication Conference, pp. 117-142, 2019.

[20] A. S. Oluwole, M. V. Srivastava, "Features and futures of smart antennas for wireless communications: a technical review," Journal of Engineering Science and Technology Review, vol. 11 no. 4,DOI: 10.25103/jestr.114.02, 2018.

[21] N. A. Eltresy, O. M. Dardeer, A. al-Habal, E. Elhariri, A. H. Hassan, A. Khattab, D. N. Elsheakh, S. A. Taie, H. Mostafa, H. A. Elsadek, E. A. Abdallah, "RF energy harvesting IoT system for museum ambience control with deep learning," Sensors, vol. 19 no. 20,DOI: 10.3390/s19204465, 2019.

[22] E. Elhariri, S. Taie, "An energy efficient system for artifacts preservation and occupant comfort," 2018 International Conference on Computer and Applications (ICCA), pp. 219-225, DOI: 10.1109/comapp.2018.8460312, .

[23] S. Bouktif, A. Fiaz, A. Ouni, M. Serhani, "Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches," Energies, vol. 11 no. 7, article 1636,DOI: 10.3390/en11071636, 2018.

[24] H. Chung, K.-S. Shin, "Genetic algorithm-optimized long short-term memory network for stock market prediction," Sustainability, vol. 10 no. 10, article 3765,DOI: 10.3390/su10103765, 2018.

[25] N. Chouikhi, B. Ammar, N. Rokbani, A. M. Alimi, "PSO-based analysis of echo state network parameters for time series forecasting," Applied Soft Computing, vol. 55, pp. 211-225, DOI: 10.1016/j.asoc.2017.01.049, 2017.

[26] N. A. Eltresy, "Ph.D. Thesis “Energy harvesting solution for wireless sensors in IoT applications”," 2020.

[27] O. M. Dardeer, H. A. Elsadek, E. A. Abdallah, H. M. Elhennawy, "A dual band circularly polarized rectenna for RF energy harvesting applications," ACES Journal, vol. 34 no. 10, 2019.

[28] O. M. Dardeer, H. A. Elsadek, E. A. Abdallah, "2x2 circularly polarized antenna array for RF energy harvesting in IoT system," 2018 IEEE Global Conference on Internet of Things (GCIoT),DOI: 10.1109/gciot.2018.8620143, .

[29] S. Fu, S. Fang, Z. Wang, X. Li, "Broadband circularly polarized slot antenna array fed by asymmetric CPW for L-band applications," IEEE Antennas and Wireless Propagation Letters, vol. 8, pp. 1014-1016, DOI: 10.1109/lawp.2009.2031662, 2009.

[30] N. A. Eltresy, D. N. Elsheakh, E. A. Abdallah, H. M. Elhennawy, "RF energy harvesting using efficiency dual band rectifier," 2018 Asia-Pacific Microwave Conference (APMC),DOI: 10.23919/apmc.2018.8617347, .

[31] "“SMS7630 datasheet,” Skyworks Solutions," . April 2019, https://datasheet.octopart.com/SMS7630-040LF-Skyworks-Solutionsdatasheet-8832283.pdf

[32] Texas Instruments, "CC2650 simple link multi-standard 2.4 GHz ultra-low power wireless MCU TI.com," . August 2019, http://www.ti.com/product/CC2650

[33] Texas Instruments, "TI-RTOS-MCU TI-RTOS: real-time operating system (RTOS) for microcontrollers (MCU) TI.com," . August 2019 http://www.ti.com/tool/TI-RTOS-MCU

[34] Texas Instruments, "TI-RTOS – Texas Instruments Wiki," . August 2019 http://processors.wiki.ti.com/index.php/TI-RTOS

[35] "Zigbee – Wikipedia," . August 2019, https://en.wikipedia.org/wiki/Zigbee

[36] E. Elhariri, N. El-Bendary, A. Ella Hassanien, "Bio-inspired optimization for feature set dimensionality reduction," 2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 184-189, .

[37] W. Ghonaim, S. A. Taie, "A new image segmentation algorithm based on particle swarm optimization and rough set," Journal of Computers, vol. 13 no. 1, pp. 130-139, DOI: 10.17706/jcp.13.1.130-138, 2018.

[38] S. H. Park, B. Kim, C. M. Kang, C. C. Chung, J. W. Choi, "Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture," 2018 IEEE Intelligent Vehicles Symposium (IV),DOI: 10.1109/ivs.2018.8500658, .

[39] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, "Learning phrase representations using RNN encoder–decoder for statistical machine translation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),DOI: 10.3115/v1/d14-1179, .

[40] E. Elhariri, N. El-Bendary, A. Ella Hassanien, A. Abraham, "Grey wolf optimization for one-against-one multi-class support vector machines," 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR),DOI: 10.1109/socpar.2015.7492781, .