Zero Energy IoT Devices in Smart Cities Using RF Energy Harvesting

Abstract

The invention of batteries made it possible to store electricity for many purposes. One of the purposes is to keep the operations of WSN running without any interruptions. The main drawback of sensor nodes is their limited energy sources. The researcher introduces the energy harvesting (EH) concept for IoT-based WSNs to overcome energy limitations and charge the IoT devices. Many energy harvesting techniques have been introduced, such as solar, thermal, and flow-based ones, but radio frequency (RF)-based EH techniques received great attention from researchers due to their easy availability (from TV, radio, and wireless frequencies). In this paper, we have conducted a real-world experiment on Powercast energy harvesting devices and examined the behavior of sensed data in different scenarios, such as indoor, distance (feet/meters), and directional antennas. In our experiments, we have observed that when a device is removed from the charger, the energy scavenging process degrades and reaches a dead state. To stop a device from entering a dead state, we introduce a mobile charger technique to charge the device and find the optimal place for chargers and sensor devices. During mobile charging, we have also observed that when the directional antennas change their angle, the energy scavenging process degrades. To tackle these problems, we introduced two algorithms for directional and omni-directional antennas that efficiently solve the problem. Furthermore, we have obtained results for these scenarios and show that this technique has a promising output.

1. Introduction

The IoT-based wireless sensor network is a set of physical devices deployed in an area of interest. Research says that there were more than 75 billion IoT devices (sensors, actuators, wireless devices, smart meters, lighting systems, etc.) all over the world in 2025, used in different domains [1]. IoT-based WSN can be used in several domains, such as smart health, smart grids, smart cities, and smart environments [2,3]. In a smart environment, WSN is used to collect data about physical conditions such as temperature, humidity, light, acceleration, etc. The collected data is then sent to the data station through the WSN cooperatively for further inspection. The development of semiconductor technology has attracted researchers and technology companies to produce convenient, low-power, and low-cost devices for IoT-based WSN.

One of the main drawbacks of IoT-based WSN devices is the power supply, which degrades the performance of the network. It is difficult to replace the battery in harsh areas frequently, which can increase operational expenses [4,5]. The researchers tried to find the solution to this problem and introduce the concept of energy harvesting techniques from renewable energy sources such as solar, wind, thermal, radio frequency (RF), etc. The RF energy harvesting technique has obtained the attention of emerging technology platforms, such as Google, WISP, PoWiFi, Powercast, and EnOcean STM 300 [4], due to its clean, green, and freely available nature in continuous form and its introduction of wireless charging devices (WCD). WCD is a suitable choice to prolong the lifetime of IoT-based WSNs to near-perpetual. Figure 1 shows the overview of energy harvesting in smart cities.

In previous works, data collection and energy transfer were done through wireless power communication networking (WPCN) and simultaneous wireless information and power transfer (SWIPT). In WPCN, the uplink is used for wireless information transfer (WIT), and the downlink is used for wireless energy transfer (WET) [6]. In SWIPT, data collection and energy transfer were assumed to run simultaneously. However, the commercial wireless charging technology platforms, such as Powercast [7] and WISP [8], do not use the WPCN or SWIPT concepts. These two platforms used capacitors for energy buffering due to being environment-friendly, having longer lifetimes, higher recharge cycles, a broader range of voltage and current, low cast [9], and higher performance in low temperatures over batteries [3]. The Powercast platform used the recharge-then-transmit procedure and, at any time, deprived the sensor node of working and harvesting energy (recharging). At this stage, the sensor node halts its normal work and waits for recharging. These phenomena put the sensor node in the idle position. After getting the required time, the sensor node becomes active and starts its normal work. The idle and active positions of the sensor node make the network topology dynamic, which leads to several problems such as dynamic topology, charging latency, communication latency, and farthest-nearest node problems.

The farthest-nearest node problem is one of the most important problems in charging IoT devices. This means that a node that is near the charger can harvest more energy as compared with the farthest node. As the charger moves from nearest to farthest, it consequently changes the result of the transmission of sensed data from fast to slow.

These phenomena put the sensor node in the idle position. After getting the required time, the sensor node becomes active and starts its normal work. The idle and active positions of the sensor node make the network topology dynamic, which leads to several problems such as dynamic topology, charging latency, communication latency, and farthest-nearest node problems. These problems lead us to find an optimal charging tour that guaranty optimal positions for the charger.

As it is clear from our previous practical experiment, the nodes that are near the charger harvest energy and the sensor sends sensed data quickly, while the nodes that are farthest from the charger take a long time to send the sensed data. So, distance plays an important role in charging devices.

The main purpose of this report is to develop a charger and sensor placement strategy to provide continuous energy to the sensor board and prevent it from being in an idle position.

The previous methods can also prolong the lifetime of the zero-powered energy devices; however, our concentration will be on one charger and multiple sensor boards because we only have one Powercast RF energy transmitter and two sensor boards. The following problems will be taken into consideration and overcome by our strategy:

• The Powercast energy transmitter has a central frequency of 815 MHz with 3W EIRP emitting power signals. The power transmitter antenna has 60° horizontal and 60° vertical beam patterns. This means that we have constrained the placement of sensors in this direction; otherwise, it will not charge the sensors to transmit the data, and consequently, it will lead the sensor to the idle position.
• The evaluation board receives this power through patch and dipole antennas and converts it into DC with capacitor-regulated voltage output up to 5.25 V, and output current up to 50 mA to charge the capacitor of size 50 mF. The patch and dipole antennas have 6.1 dBi and 1 dBi of gain power, respectively. The beam pattern of the patch antenna is 122 horizontal and 68 vertical, while the beam pattern of the dipole antenna is 360. So, we have a constraint on the patch antenna that it will only place in 122 horizontal and 68 vertical positions; otherwise, it will not receive the transmitter power, and consequently, it will lead the sensor to the idle position.
• The distance plays an important role between charger and sensor, and the received signal strength is inversely proportional to the square of the transmission distance, so we also have a constraint on the distance that the distance between charger and sensor should be less.

For the prevention of a device being in the idle position, the researchers used several techniques for providing continuous energy to the device, such as single/multiple mobile charger placement, single/multiple fixed charger placement strategies, set covers, and probabilistic charger placement. However, it has its problems, such as cast, charging time, etc. In this work, our focus will be on a single mobile charger with fixed sensors. Our great attention will be on:

• The effect of the moving charger on receiving data from the sensors.
• The speed of the mobile charger.
• The direction (theta) of the mobile charger.

2. Literature

IoT-based WSN is a collection of sensor nodes and other devices to monitor a given area for some specific purpose. Each sensor node has the capability of sensing the environment and sending the data to the BS or sink. Each sensor node has a power source (a battery), wireless transceiver, processing unit, and memory. Battery life is one of the main research topics for researchers because battery replacement is a challenging task. To cope with the challenges, researchers have introduced energy harvesting from different sources, such as RF, solar, sound, etc., to recharge the battery of a sensor node [3]. Following this line, the researchers have proposed energy harvesting-aware protocols. According to [3], the main goal of IoT-based EH-WSN protocols is not to focus on energy but to use the variable energy to support QoS requirements. In this paper, we present some EH-WSN protocols that use RF-EH for IoT-based WSNs. EH-WSN is not a limited area for research. From the extensive literature review, we divided the RF-EH for WSNs into the following categories:

2.1. Wireless Power Communication Networking (WPCN)

WPCN is RF-EH-based communication networking that uses the uplink and downlink concept for wireless energy transmission (WET) and wireless information transmission (WIT). The basic idea of WPCN is as follows: each node harvests energy from RF and transmits this energy wirelessly to other nodes by using the downlink. Other nodes use this harvested energy to transmit the data to the destination over the uplink. The authors of [6] presented an efficient data collection algorithm for WPCN-based WSNs. In the algorithm, WPCN nodes harvest their energy from RF and store it in capacitors or rechargeable batteries. The downlink is used for WET, and the uplink is used for WIT at the same time. The authors assume a one-hop star topology in which the sink node is surrounded by sensor nodes, and the sensor nodes harvest their energy from the sink node. The sink node collects the sensed data from the nodes by using the uplink and sends wireless energy to one hope node by using the downlink. The authors investigate the throughput of the node during the collection of sense data to the sink per unit time. MAC is a layer 2 protocol for communication between nodes to access a transmission line. A lot of research has been conducted to develop MAC protocols for EH-WSNs. The authors of [10,11,12] presented the Slotted ALOHA (SALO-HA)-based protocol and claimed that this is the first work to apply the SALOHA in EH-WSNs for WPCN. SALOHA has time slots; each node waits for a time slot and sends the data during the time slot. Otherwise, it waits for the beginning of the next time slot. We can describe EH-SALOHA as follows: each node harvests the energy from the RF energy harvester, and when a slot is accessed, it transmits the data. In [10,11], the authors proposed the harvest-until-access technique, where nodes harvest energy continuously until a slot is accessed and then send the packet. In [12], the same authors proposed another scheme for the harvest-or-access technique.

2.2. Simultaneous Wireless Information and Power Transfer (SWIPT)

The first paper in this area was [13], as claimed by [14]. SWIPT uses a single transmission line for information as well as for energy harvesting. In SWIPT, information decoding (ID) and EH are performed on each node. We review and discuss some papers related to SWIPT. Ref. [14] presented an energy-aware routing algorithm for RF-based EH-WSN, which is based on the SWIPT technique. SWIPT uses a relay node to transmit data and power to nodes at the same time. The relay node decodes and forwards the data between nodes (see Figure 2). Some researchers adopt the amplify-and-forward technique instead of the decode-and-forward. The authors introduced the energy and information allocation problem, then they presented an energy-aware SWIPT routing algorithm called ESWIPTR. The protocol is improved to a distributed synchronous proactive version and an asynchronous proactive table-driven version, respectively. The basic routing algorithm is based on an energy equation called Ecost. ESWIPTR finds the minimum energy cost path for routing through a routing function. The author also presents a distributed version of ESWIPTR using the distributed Bellman Ford protocol.

The author considered a WSN with sensor nodes, each equipped with a single antenna. The data flow between the source and destination nodes. Two transmission modes are used between any two (i, j) nodes, information transmission (IT) and simultaneous wireless information and power transfer (SWIPT). The IT is used when the battery of the sensor is full, and SWIPT is used when the battery energy is less than the minimum energy requirements. The author addressed three problems in this work, routing, information, and energy allocation. In decode-and-forward (DF) protocols, the receiver node first decodes the information and then forwards it to the next node. Therefore, successful decoding is essential for DF protocols. The author presented a decode as follows:

$${{\rm Y}}_{ij}^{SWIPT}=\frac{p_{ij}|h_{ij}{|}^2{P}_{ij}}{{\sigma }_{ij}^2-n_{ij}^2} \ge R_{min}$$

(1)

where P_ij is the sender node’s sending power, p_ij is the power splitting value, h_ij is the channel gain, σ_ij is the power of the signal, R_min is the required SNR requirement, and n_ij is the antenna noise.

The forward equation is as follows:

$$E_{ij}^{eh}=ϵ\left(1-p_{ij}\right)\left(|h_{ij}{|}^2{P}_{ij}+{\sigma }_{ij}{}^2\right)\ge P_{cj}$$

(2)

where P_cj is the energy harvesting power requirement for forwarding the information to the next hop node.

$$P_{cj}=P_{ji}, \mathrm{if} r_{ji}=1$$

(3)

P_ji is the power cast for forwarding to the neighbor/next node, and r_ji is the link state (active = 1, not active = 0). In routing, the main objective of the author was to find the minimum cost link.

The author presented two types of routing algorithms, i.e., centralized and distributed. The routing algorithm is based on the concept that when a node has low energy, it selects the next node for routing. The presented routing algorithms, ESWIPTR, are based on Equations (1)–(3). A Dijkstra-based centralized routing algorithm was used in this work. The Dijkstra algorithm first checks the shortest path between the neighbor nodes and selects the node with the shortest or closest path to the destination. Just like the Dijkstra algorithm, the authors first examine the path with the minimum energy from the source to the destination node and then allocate resources. The minimum energy can be calculated using the Ecost equation. The authors also presented the distributed version of the ESWIPTR algorithm by using the distributed Bellman–Ford protocol. The algorithms are evaluated by convergence rate, the impact of the node density, minimum energy requirements for packet forwarding, and the impact of the barrier. The authors presented another paper [15] for interference-aware routing. Interference occurs when a one-directional link affects other directional links or when an uplink affects a downlink in cellular networks. Ref. [16] presented another SWIPT-based RF-EH WSN with amplify-and-forward relay nodes. Ref. [17] presented a selection cooperation protocol with feedback from the destination to the source node. Ref. [18] presented a cluster-based SWIPT protocol.

2.3. Polling Based

Ref. [19] presents a polling-based MAC protocol for energy harvesting. The sink fires a packet to other nodes that contain a contention probability instead of an ID. Nodes in the network decide through this packet whether to transmit their packet or not. The contention probability is based on the number of nodes, the current energy harvesting rate, and packet collision. The contention probability increases when other sensor nodes respond and decreases when a collision occurs. The polling-based MAC protocol for energy harvesting uses the charge-and-spend harvesting strategy, in which it first accumulates enough energy and then goes into the receiving state to listen for and receive the polling packet. The author used three parameters for the performance of the given protocol, throughput, fairness, and interarrival time. Throughput was used to successfully receive the packet at the sink node. The fairness is used for a balanced degree of the network. Fairness and inter-arrival time equations are as follows:

$$F=\frac{{\left({\sum }_{i=1}^{n}Ri\right)}^2}{n\left({\sum }_{i=1}^n{R}_i{}^2\right)}$$

(4)

$$\Gamma =\frac{\left({\sum }_{i=1}^n\frac{1}{Ri}\right)}{n}$$

(5)

Both SWIPT and WPCN techniques use the RF-EH technique, but the question is: which one is good?

2.4. Other RF-Based EH-WSN Protocols

Ref. [5] presented a routing protocol for EH-WSN. In this work, he used the improved energy-efficient ant-based routing algorithm (IEEABR) for RF energy harvesting and management of the harvested and available energy for wireless sensor networks. He first discusses the RF power density, storage of the harvested power, calculation of the received power, and management of the power in the EH-WSN protocol. IEEABR technique used for routing. The author of this paper used the Friis equation [20], which is used in a situation where the distance between two nodes or antennas is known, and this equation is discussed in [20] in detail. Ref. [21] presented an energy harvesting-aware routing protocol.

$$\frac{P_t}{G_t}=\frac{G_t{G}_r{\lambda }^2}{{\left(4\pi \right)}^2{d}^2}$$

(6)

where P_t is the transmitted power, P_r is the received power, G_t is the transmitter gain power, G_r is the receiver antenna’s received power, d is the T-R-Separation distance in meters, and λ is the wavelength in meters. Gain power is based on the aperture of the antenna.

Power density equation is as follows:

$$P_D=\frac{P_t}{\left(4\pi \right)R^2}$$

(7)

where P_D is the power density and R is the distance between the transmitter and the receiving antenna, P_t is the peak or average power. The energy storage equation is as follows:

$$\mathrm{T} =\frac{C}{l_N}$$

(8)

where C is the theoretical capacity of the battery expressed in ampere-hours, I is the current drawn in Ampere (A), T is the time of discharge in seconds, and n is the Peukert number. The power management algorithm’s main idea is as follows: First of all, initialize the routing table and find the neighbor nodes; if a node is visited two times, then halt otherwise selects the next node according to the probabilistic equation. This process will continue until the destination node is reached. The author used NS-2 for simulation results. Ref. [22] used the AODV traditional routing algorithm for presenting the EH-based routing protocol EH-AODV. EHAODV uses the advantages of AODV with only minor changes in the packet header by replacing hop count with transmission cost (TC). TC is used in route request (RREQ) and route reply (RREP) operations. TC is a predictable technique that can be predicted through the below equations. Ref. [23] presented a MAC protocol. The basic idea is that, first of all, a sensor node will harvest energy through RF, and then the sensor node will charge its battery. After charging, the node will sense and gather the respected information. A backup time concept is used for packet collision avoidance and then the sensor waits for a certain amount of time. At last, the packet can be transmitted when there is no collision. Ref. [14] presented an RF-EH battery-free energy cooperation wireless network for reducing communication latency to improve network performance. The authors of [24,25] presented a cluster-based protocol that harvests energy from solar.

The work of [26] is related to our study but does not consider mobile, so nearest-farthest problems exist. The research in [27] used distributed secure state estimation (SSE) under sparse sensor attacks to recover the states of the considered CPS from corrupted measurements.

3. Materials and Methods

3.1. Experimental Setup Devices

In this report, we have used Powercast Technology Company (Pittsburgh-PA-USA)-provided devices for our research and practical testbeds. There are four components of the Powercast energy harvesting model: an energy transmitter, a sensor board, an evaluation board, and antennas. The Powercast technology company introduces RF-based energy harvesting WCD to replenish the sensor devices’ energy. The Powercast Technology Company has provided energy harvesting devices that power IoT devices since 2003. It provides a temperature scanning system, a wireless charging grip for the Nintendo joy-con, power spot, a UHF RFID retail price tag, and development kits. The development kits are used for research and powering IoT devices, which consist of evaluation boards (P1110, P2110), antennas, RF field-detecting light sticks, and sensors. We are using the P2110-EVAL-01 development kit for our research work, which is designed for extremely low-power IoT devices. Our focus is to study the data received from dipole and patch antennas. We aim to find the relationship between distance, RSSI, recharge time of the capacitor, angle impact on the packet transmission time and energy harvesting, and the routing of a packet to the access point. The following devices are used in our testbeds:

• RF Transmitter

The RF Powercast transmitter omits both data and power in the form of RF signals with a unique ID and 915 MHz frequency. The output power (Pt) is 3 w EIRP with a beam pattern of 60° in vertical polarization, and the frequency range is 915 MHz. The distance for permanent installations of the TX91501B transmitter is eight feet or more above floor level. The Powercast Company provides the transmitter, which is covered in a black box with fixed output power and settings. The user cannot make changes to the transmitter.

2.

Wireless Sensor Board

The board can measure and transmit light, temperature, humidity data, and external inputs. The sensor board is connected with the evaluation board through a 10-pin connector to obtain the energy from the evaluation board for the transmission of data. We can set the ID of the sensor nodes from 0 to 7 by using ID SELECT switches. The sensor board has a PICkit connector, through which the PICkit programmer can be connected.

3.

P2110 Evaluation Board

The evaluation board has the responsibility of energy harvesting. The board contains the functionality of energy storage JP1 (C3, C4, and C5 jumpers), a 10-pin connector (J2) for wireless sensor board connection, a rectifier to convert the RF energy into DC, an SMA connector for an antenna or RF input (J1), and a visual LED indicator. The sensor board obtains the harvested energy from the evaluation board.

4.

Powercast Antennas

The Powercast development kit comes with two types of antennas: dipole and patch. These antennas are connected to the evaluation board through an SMA connector for the antenna (J1). The dipole antenna has the RF connector at the bottom, and the patch antenna has the RF connector in the middle. The dipole antenna is flat, omnidirectional, and vertically polarized, and the gain power is 1.0 dBi with a 360-degree reception beam pattern. The patch antenna is two-layered, directional, and vertically polarized, and the gain power is 6.1 dBi with a 120-degree reception beam pattern.

5.

Vehicle for Transmitter

The energy transmitter is equipped with a vehicle to make it mobile, and then it moves in a circle to charge the devices.

3.2. Experimental Models

In our testbed model, we are using the Powercast P2110-EVAL-01 development kit, which is used for energy harvesting. The TX 91501-3W-ID power transmitter (transmitter) transmits the power; the P2110 Evaluation Board P2110-EVB (receiver) receives this power; the Wireless Sensor Board WSN-EVAL-01 plugin with the P2110 Evaluation Board P2110-EVB sends the sensed data to the access point; and the hyper terminal is used to show the sensed data on a computer/laptop screen. The TX 91501-3W-ID transmitter is responsible for transmitting the energy signal to the P2110 Evaluation Board P2110-EVB. The evaluation board obtains the energy signal, converts it to DC, and recharges the super capacitor. Then, the harvested energy is used for sending data by the wireless sensor board. In our previous experiment (fixed charger and fixed boards), if the charger went farthest from the evaluation board, then it degraded the sensing and communication processes. So, we need a technique that can cover the farthest and nearest problems. For the solution to this problem, we are going to equip the energy transmitter with a moving toy to make it a mobile charger. The movement will occur in such a way that it can keep in mind our constraints, not going away from the maximum transmission range of the energy transmitter and not crossing a 60-degree area. The sixty-degree area has the beam pattern of the transmitter, which has 60° widths and 60° heights. For simplicity, we used 60° areas.

To fulfill these constraints, we are going to present our solution. Our solution is based on two sub-problems: an optimal charging tour to find optimal positions for a charger and discovering the charging area or points that are under the eye of the charger from any point when the charger moves.

• Optimal charging tour to find optimal positions for charger.

The charger moves in circular form with a constant velocity in an anti-clockwise manner and completes its one tour in Tk time.

2.

Discovering the charging area or points.

As we know, the charger transmits the energy in a fixed directional, i.e., 60°. We need a fixed area that has points that come under the direction of the energy transmission whenever the charger moves; otherwise, the charging device will never receive energy when the charger moves, and it will go to a dead position. For this purpose, we are going to find the fixed area or points for the charging device to obtain energy from every position of the charger during movement. Since we have two antennas, a patch and a dipole, with different receiving patterns, the dipole is omnidirectional, vertically polarized, and its energy pattern is 360°, while the patch is directional, vertically polarized, and its energy pattern is 122°. For simplicity, we are using 360 coverage areas for the dipole and 122 coverage areas for the patch antenna. The Powercast technology company used the Friis equation for energy calculation. It has an online calculator (in the form of .xls) [28] through which we can obtain the received energy. For example, if the distance is 1 m, then the received energy is 8.11; at 2 m, it is 2.030; and so on.

$$P_r=G_t{G}_r\left({\frac{\lambda }{4\pi \left(d+\beta \right)}}^2\right)P_t$$

(9)

where P_t is the transmitted power, P_r is the received power, G_t is the transmitter antenna gain power, G_r is the receiver antenna received power, d is the T-R separation distance, and λ is the wavelength. Gain power is based on the aperture of the antenna. Keeping in mind the polarization loss in power transfer, signal power should be rectified and converted to electrical energy before it can be used.

$$P_r=\frac{G_t{G}_r\eta }{Lp}\left({\frac{\lambda }{4\pi \left(d+\beta \right)}}^2\right)P_t$$

(10)

where L_p represents polarization loss, η is rectifier efficiency, and β is a parameter to adjust the Friis free space equation for short distance transmission.

Now we can describe our model as follows: Let M_k be the set of chargers, and v_k be the set of nodes; then, we give the charging model based on Equation (10) as follows:

$$P_r=\frac{\alpha P_t\left(M_k\right)}{{(||v_k-M_k||+\beta )}^2}$$

(11)

Where ||v_k – M_k|| represent the distance between the device v_k and charger M_k, P_t represents the transmission power of the charger M_k, $\alpha =\frac{G_t{G}_r\eta }{Lp}{\left(\frac{\lambda }{4\pi }\right)}^2$.

For a directional charging model, the charger and devices are equipped with directional antennas, so the angle of the charger and devices will be kept in mind. Let $\overrightarrow{{\varphi }_{mk}}$ be the directional vector of the charger (i.e., charger angle θ_mk) and $\overrightarrow{{\mathsf{\varphi }}_{vk}}$ be the directional vector of the device (device angle θ_vk) then equation 11 can be written for the directional model as follows:

$$P_r\left(M_k,v_{kr}\right)=\frac{\alpha P_t\left(M_k\right)}{{(||v_k-M_k||+\beta )}^2} \left\{\begin{array}{c}\left|\right|v_k-M_k\left|\right|\le D\\ \frac{\overrightarrow{\left|\right|v_k-M_k\left|\right|} .\overrightarrow{{\mathsf{\varphi }}_{vk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{\mathsf{\theta }vk}{2}\right)\\ \frac{\overrightarrow{\left|\right|M_k-v_k\left|\right|} \overrightarrow{.{\varphi }_{mk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{\mathsf{\theta }mk}{2}\right)\end{array} \right.$$

(12)

Let M_kr be the maximum transmitter range of the energy transmitter and v_kr be the maximum transmission range of the device. Since the energy transmitter has a 0 to 60° angle, the dipole has a 0 to 360° angle, and the patch has a 0 to 122 degree range then Equation (12) can be written for a dipole antenna as follows:

$$P_r\left(M_k,v_k\right)=\frac{\alpha P_t\left(M_k\right)}{{(||v_k-M_k||+\beta )}^2} \left\{\begin{array}{c}\left|\right|v_k-M_k\left|\right|\le M_k^r\\ \frac{\overrightarrow{\left|\right|v_k-M_k\left|\right|} .\overrightarrow{{\mathsf{\varphi }}_{vk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{\mathsf{\theta }}_{vk}}{2}\right) \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} 0 \le {\mathsf{\theta }}_{vk}\le 2\pi \\ \frac{\overrightarrow{\left|\right|M_k-v_k\left|\right|} \overrightarrow{.{\varphi }_{mk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{\mathsf{\theta }}_{mk}}{2}\right) \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} 0 \le {\mathsf{\theta }}_{mk}\le 60\end{array} \right.$$

(13)

Equation (12) can be written for a patch antenna as follows:

$$P_r\left(M_k,v_k\right)=\frac{\alpha P_t\left(M_k\right)}{{(||v_k-M_k||+\beta )}^2} \left\{\begin{array}{c}\left|\right|v_k-M_k\left|\right|\le M_k^r\\ \frac{\overrightarrow{\left|\right|v_k-M_k\left|\right|} .\overrightarrow{{\mathsf{\varphi }}_{vk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{\mathsf{\theta }}_{vk}}{2}\right) \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} 0 \le {\mathsf{\theta }}_{vk}\le 122\\ \frac{\overrightarrow{\left|\right|M_k-v_k\left|\right|} \overrightarrow{.{\varphi }_{mk}}}{\left|\right|v_k-M_k\left|\right|} \ge \mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{\mathsf{\theta }}_{mk}}{2}\right) \mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} 0 \le {\mathsf{\theta }}_{mk}\le 60\end{array} \right.$$

(14)

The maximum transmission power received at nod v_k from charger M_k can be calculated as follows: from Equation (11), we now calculate the nearest-farthest problem as:

$$P_r=\frac{\alpha P_t\left(M_k\right)}{{(||v_k-M_k||+\beta )}^2} if 0 \le (||v_k-M_k||\le Mkr$$

(15)

In our model, we changed the position of the charger according to different distances from the energy transmitter, but the position of the energy transmitter remains constant. In our experiment, we have checked the received power and the incoming data time for one to three-meter distances in outdoor as well as indoor environments.

Currently, we have two sensors and one charger, so we can create fixed points for sensors when the charger is moving.

The Powercast transmitter directionally transmits the energy up to a 60-degree area. The evaluation board receives this signal and converts it to DC. If the evaluation board is not lying in the direction of the transmitter, it cannot harvest energy. Consequently, it halts all processes and leads to a dead situation. Next, the Powercast Company provides a transmitter range of up to 80 feet [29], which means that the farthest node can be placed 80 feet away from the transmitter. So, the maximum range is from 0 to 80 feet. Here, we can define two problems for the energy transmitter: distance and range. If the distance is 80 feet above then the receiver antenna (device) cannot receive energy, and if the evaluation board took place outside of a 60-degree area, then the receiver antenna cannot recieve the energy. We need a solution that meets the above problems efficiently and makes the network nearly perpetual.

3.3. Testbeds Studies

In this section, we are going to study experimental testbed setups. Our experimental setup is based indoors.

3.3.1. Testbeds Model

In our testbed model, we are using the P2110-EVAL-01 energy harvesting development kit, which contains components. We used a small vehicle toy for the transmitter to make it mobile, and it starts working by blinking the blue light. Moreover, we used this toy to make the sensor mobile. After starting the TX 91501 transmitter, we connected the antenna to the evaluation board. After the evaluation board, we plug in the wireless sensor board into the evaluation board. Next, we installed the hyper-terminal on the laptop and connected the access point to the laptop through a USB cable. After the installation of the hyper-terminal emulator, we open it and start the step-by-step installation process. In the installation process, we gave the name Powercast and clicked the ”ok” button. Another dialogue appeared that required region, area code, and connection port. We entered Pakistan, 46000, and COM16 and clicked the ”ok” button. Another screen opened some basic information such as bit rate, parity, and flow control. We only set the bit rate to 19200 and clicked the “ok” button. A blank screen appeared, but after clicking switch PB1 on the access point board, the emulator started working and showed the built-in message of the Powercast Company. We have to make points on the ground in circular form with the help of scotch tape. The circular points are separated from one another by a distance of one foot and are identified by numbers. So, we draw two circular points one foot away from each other. The circular shape is used for moving the charger, and the circular points are used for getting data from these specific points. The charger is equipped with a vehicle toy and uses a small wire to make it movable. The charger moves in an anti-clockwise direction, and the charger is equipped on the toy vehicle so that it cannot lose the 60-degree direction. Our observation is based on both antennas, patches, and dipoles.

3.3.2. Indoor Experimental Setup and Observations

We have conducted our experiment according to our solution discussed in Section 2. We can state our problem and solution as follows:

3.3.3. Problem Identification

• Powercast energy transmitters disseminate energy in a directional form up to 60 degrees, while the dipole and patch antennae receive this energy at 360 and 120 degrees. In our previous experiment, we used static charger position and dynamic sensing device position. We have used 1 to 5 feet distance for devices to check the energy harvesting and data sending processes. Moreover, we have conducted the 1 to 3-m distance experiment and observed that when the sensor device goes away from the charger, it degrades the energy harvesting and consequently the data sending process. This phenomenon is called the nearest-farthest charger problem, and this is the proof that received signal strength is inversely proportional to the square of transmission distance [30].
• The next problem is related to Powercast devices. The energy transmitter transmits in a directional form and not in an omnidirectional form. Next, the dipole antenna has a 360-degree receiving angle, but the patch antenna has a 122-degree receiving angle. So, we cannot shrink the distance to a point where all devices can perform their specific task efficiently. For example, if the charger was omnidirectional, then the usage of distance was nil, and we would put the charging devices near the charger.
• Powercast evaluation board/charger devices are zero-battery devices, and during our previous experimental setup, it has been observed that these zero-battery devices required continuous energy for life. If at any stage or at any time the energy transmitter direction changes, then the evaluation board goes to die.

So, we need such a technique that can overcome the nearest farthest problem.

3.3.4. Solution

The above problems are not new ones, and the researchers tried to solve these problems. A mobile charger is a good solution for charging devices. We also use the mobile charger for charging the devices, but our problems and solutions are different from those of these researchers. Let me explain that there are omnidirectional and directional phenomena. If a charger device is omnidirectional, then the charging devices are directional, and vice versa. So, the researchers solved these problems in general terms, but we are dealing with directional-directional phenomena with dipole antennas. So, we need a technique for a directional device that can solve the above three problems. For this purpose, our solution can be divided into two phases: the first is the charger movement phase to find the optimal charging tour to find optimal positions for the charger, and the second is the optimal area or points for charging/sensor/evaluation board devices.

When the charger is at point 1, the theta is 60 degrees, and the maximum distance is dependent on the charger to send the signal, as Powercast 35903 has a range of 80 feet [29], so when the charger transmits the energy in a circular form, as shown in Figure 3, the points are 1 foot from each other. This means that the lines are uniform, and one foot away, they intersect the circle. Considering two straight lines, a_1x + b_1y + c₁ and a_2x + b_2y + c₂, we will use the following equation:

$$\left(x,y\right)=\frac{b_1{c}_{2-}{b}_2{c}_1}{a_1{b}_{2-}{a}_2{b}_1},\frac{c_1{a}_{2-}{c}_2{a}_1}{a_1{b}_{2-}{a}_2{b}_1}$$

(16)

3.3.5. Optimal Charging Tour to Find the Optimal Position for a Single Charger

Figure 3 shows our charger placement strategy, in which the charger is moving in a circular way and provides energy to the evaluation board for scavenging. The charger moves by taking the transmitter angle into account so that the eye of the charger stays in the optimal place or point.

3.3.6. Optimal Points/Area for Sensor Devices

When the charger is at point 1, the theta is 60 degrees, and the maximum distance is dependent on the charger to send the signal, as Powercast 35903 has a range of 80 feet, so when the charger transmits the energy in a circular form, as shown in Figure 3, the points are 1 foot from each other. This means that the lines are uniform, and one foot away, they intersect the circle (see Figure 4).

At point 2 when the transmitter transmits the energy, it intersects the previous lines at any point, as shown in Figure 4, and we want to find the intersection points.

Let lines intersect each other at points p_s1 and p_s2 then we can find the location of intersection points by the intersection formula. In the end, we can obtain the desired intersection points. The area between these intersection points is called the optimal area, and the points are optimal points to place the charging devices.

We have presented two algorithms for our solution. Algorithm 1 is designed by keeping the dipole antenna range in mind, and Algorithm 2 is designed by keeping the patch antenna range in mind. Our main purpose is to find the optimal charger placement points and optimal points for sensor placement by keeping in mind the constraints of the transmitter at 60 degrees. IP refers to intersection points by inscribed angles (IA), while 120° + Ɛ is the angle of the patch antenna with some area of the charger because we have observed that the patch range is 120 degrees but also that it can take some benefits from the charger range to sense a large area.

Algorithm 1: Charger Placement Strategy for Dipole antenna.
Input	Set of Sensor vk ={1,2,3..}, Charger Points Mkp = {Mk1 Mk2, Mk3,…,3600}, Distance dk = {1,2, Mkr}, Tx value
Output	Optimal Points/area for charger tour, IA, θ, IP
For every point Mki ϵ Mkp
2. IP = ϕ, θ = ϕ, AT = ϕ
3. If Pki ≠ ϕ then
4. Select the Pk1 point for mobile charger.
5. AT ← An inscribed triangle start from point Pk1 with dk = 1ft
6. Θ = as Equation (13)
7. Start Moving of charger from point Pk1 to Pki
8. Add AT
9. Θ = as Equation (13)
10. IP = as Equation (16)
11. Repeat
12. Select IP
13. Until Pik ≤ 360
14. Return IP, Pki
15. end If

Algorithm 2: Charger Placement Strategy for Patch Antenna.
Input	Set of Sensor vk ={1,2,3..}, Charger Points Mkp = {Mk1 Mk2, Mk3,…,1220}, Distance dk = {1,2, Mkr}, Tx value
Output	Optimal Points/area for charger tour, IA, θ, IP
For every point Mki ϵ Mkp
2. IP = ϕ, θ = ϕ, AT = ϕ
3. If Pki ≠ ϕ then
4. Select the Pk1 point for mobile charger.
5. AT ← An inscribed triangle start from point Pk1 with dk = 1ft
6. Θ = as Equation (14)
7. Start Moving of charger from point Pk1 to Pki
8. Add AT
9. Θ = as Equation (14)
10. IP = as Equation (16)
11. Repeat
12. Select IP
13. Until Pik ≤ 1200 + ε
14. Return IP, Pki
15. end If

3.3.7. Experimental Setup with a Patch Antenna

The patch antenna has 122-degree reception. From the experiment, we have observed that the path antenna worked properly within the 122 + ε (where ε represents some benefits from the charger’s angle) direction and the charging devices would receive the energy, but when the charger moved in another direction, the charging device went into a dead position or data sending and the energy harvesting process degraded to zero. We have obtained the experimental data and shown them in

Table 1. Patch Antenna Sensed Data.

Point 1 (0°)
RSSI	33.88	33.88	32.73	31.92	32.73	31.92	31.92	32.73	30.13	30.13
Temp	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5
Humidity	46	46	46	46	46	47	46	46	46	47
Light	37	25	29	48	51	40	44	40	29	48
External	1331	1352	1325	1382	1393	1343	1346	1358	1393	1340
Point 2 (50°)
RSSI	12.11	12.97	10.45	11.86	12.11	10.45	12.71	12.11	12.97	11.27
Temp	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5	70.5
Humidity	47	47	47	47	47	47	47	47	47	47
Light	48	40	48	44	48	48	51	44	29	51
External	1686	1687	1695	1687	1690	1690	1690	1686	1692	1690
Point 3 (100°)
RSSI	3.18	3.66	1.74	3.96	3.96	3.66	3.96	3.47	3.32	3.66
Temp	70	70	70	70	70	70	70	70	70	70
Humidity	48	47	48	48	47	48	47	48	48	48
Light	40	37	48	22	29	48	40	48	48	40
External	1670	1678	1676	1669	1678	1673	1665	1662	1666	1663
Point 4 (150°)
RSSI	0.6	0.52	0.49
Temp	70.5	70.5	70.5
Humidity	48	47	48
Light	51	48	29
External	1584	1594	1594
Point 5 (250°)
RSSI	0.86	0.67	0.74
Temp	70.7	70.7	70.7
Humidity	48	47	48
Light	51	48	29
External	1581	1574	1584
Point 6 (340°)
RSSI	11.86	12.71	12.42	12.11	11.53	11.86	12.11	12.97	11.53	13.3
Temp	70.7	70.7	70.7	70.7	70.7	70.7	70.7	70.7	70.7	70.7
Humidity	47	47	47	47	47	47	47	47	47	47
Light	37	55	51	48	51	44	48	33	29	51
External	1696	1693	1698	1693	1689	1685	1689	1701	1695	1698

. According to Figure 3, it is clear from the table that when the charger moves from 0° to 250°, it degrades the energy harvesting and data sending processes accordingly, but when the charger moves to 240°, the energy harvesting and data sending processes gain power. Consequently, from these experimental data, we can say that the area or points shown in Figure 5 are the optimal points/area for placing the sensors, and the points 340° to 100° are the optimal charger tour points or path to provide unstoppable energy to the sensors. For more clarification, see

Table 2. Patch Antenna Sensed Data.

0°	50°	100°	150°	250°	340°
33.88	12.11	3.18	0.6	0.86	11.86
33.88	12.97	3.66	0.52	0.67	12.71
32.73	10.45	1.74	0.49	0.74	12.42
31.92	11.86	3.96	0	0	12.11
32.73	12.11	3.96	0	0	11.53
31.92	10.45	3.66	0	0	11.86
31.92	12.71	3.96	0	0	12.11
32.73	12.11	3.47	0	0	12.97
30.13	12.97	3.32	0	0	11.53
30.13	11.27	3.66	0	0	13.3
31.12	11.27	3.96	0	0	12.97
32.73	12.42	3.66	0	0	10.45
31.12	12.42	3.37	0	0	12.71

. We have also added some figures (Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11) to better understand the patch antenna sensed data.

3.3.8. Experimental Setup with Dipole Antenna

The dipole antenna has a 360-degree reception angle to receive the energy from the charger, so there is no restriction on direction for the sensor device as the patch antenna has a 122-degree restriction. When the charger moves, the sensor receives the energy in all directions. We have collected the receiving data and shown them in

Table 3. Dipole Antenna Sensed Data.

Point 3 (50°)
RSSI	3.18	4.14	3.79	3.79	3.47	3.32	3.47	3.47	3.79	3.66
Temp	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1
Humidity	47	47	47	47	47	47	47	47	47	47
Light	33	44	48	40	33	48	40	44	44	48
External	1683	1673	1680	1677	1682	1677	1679	1677	1683	1680
Point 4 (150°)
RSSI	4.84	4.46	5.6	4.31	4.46	4.31	4.14	3.79	4.14	4.46
Temp	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1	70.1
Humidity	47	47	47	47	47	47	47	47	47	47
Light	40	40	37	40	29	48	37	29	40	29
External	1679	1674	1678	1683	1677	1678	1679	1677	1680	1678
Point 6 (250°)
RSSI	7.28	7.73	7.05	6.62	7.94	7.48	7.05	7.28	7.28	7.73
Temp	69.7	69.7	69.7	69.7	69.7	69.7	69.7	69.7	69.7	69.7
Humidity	46	47	47	46	47	46	47	47	47	47
Light	48	29	33	29	40	48	40	37	44	48
External	1687	1690	1679	1690	1683	1683	1679	1679	1687	1679
Point 7 (290°)
RSSI		5.4	6.62	5.6	5.78	5.21	5.4	4.84	5.21	4.84
Temp		69.9	69.9	69.9	69.9	69.9	69.7	69.9	69.9	69.7
Humidity		46	47	46	46	47	47	47	46	46
Light		40	48	40	44	51	44	37	48	29
External		1678	1681	1680	1681	1678	1681	1674	1674	1683

for observation. Consequently, these experimental data in Table 3 are the proof that the area or points shown in Figure 12 are the optimal points/area for placing the sensors, and the circular direction is the optimal charger tour point or path to provide unstoppable energy to the sensors.

3.3.9. Analysis of Data in Dipole Antenna

The dipole antenna can receive data from all sides because it has 360-degree reception antennas. however when the sensor devices become under the charger device, then the time differential (dT) between received packets increases, and the sensor devices gain sophisticated power for data transmission.

3.3.10. Analysis of RSSI of Patch Antenna in a Mobile Environment

We have equipped our charger with a moving vehicle to make it mobile, and the sensor with a path antenna was put on the ground for experimentation. The sensor send data successfully to the access point, and we used

Table 4. Data Sensed in a Mobile Environment.

Pkt No.	DT	RSSI	Temp	Humidity	Light	External
1	0	12.71	71.2	48	29	1670
2	1	12.97	71.2	48	33	1666
3	0	11.53	71.2	48	48	1667
4	0	14.19	71.2	48	40	1667
5	1	13.58	71.2	48	37	1667
6	0	14	71.2	48	51	1664
7	0	12.11	71.2	48	37	1656
8	1	12.11	71.2	48	29	1670
9	0	11.86	71.2	48	48	1670
10	0	8.41	71.4	48	33	1654
11	0	7.94	71.4	48	29	1648
12	0	6.82	71.4	48	37	1651
13	1	6.82	71.4	48	40	1646
14	0	3.66	71.4	48	33	1646
15	2	3.32	71.4	48	29	1624
16	1	3.79	71.4	48	40	1633
17	1	1.63	71.4	48	22	1628
18	2	1.85	17.4	47	51	1611
19	1	0.95	71.4	47	51	1611
20	9	0.31	71.2	47	33	1534

to present all the sensed data from sensors for 100 to 360 degrees.

3.3.11. RSSI of Patch Antenna during Circular Tour of the Charger

In this section, we are going to present the charger movement on a circular and obtain the RSSI of the patch antenna. We used 100 degrees as the first point and moved to 360. The RSSI is good from 100 to 280 degrees but starts degrading after 280 (see

Table 5. Data Sensed in a Mobile Environment for the Patch Antenna.

100–150°	151–200°	201–250°	251–300°	301–360°
11.02	24.1	50	47.64	8.89
13.3	31.12	50	43.05	10.45
15.17	31.92	50	41.98	8.41
16.48	39.9	50	41.98	7.73
17.45	43.05	45.39	41.98	5.4
19.59	36.9	44.16	38.9	4.31
22.49	44.16	47.64	36.9	3.91
19.59	47.64	46.45	35.89	3.96
22.49	44.16	43.05	32.73	4.31
25	47.64	44.16	9.91	4.84
25	49.66	43.05	8.89	5.01
25.82	50	44.16	8.18	2.61
26.61	50	43.05	9.68	0.24
24.1	50	46.45	9.16	0.43

).

4. Results

We have experimented according to our requirements. We have used circular points to check whether our problem identification is valid or not. The optimal points are decided through packet transmission. Suppose in Figure 3 that if the point is 200°, the sensor device transmits multiple packets in seconds (because the sensor device receives enough energy), but when the charger changes its location to 0°, the packet transmission slows down. The Powercast Company referred to this as the time differential (dT) between received packets. At optimal points, the dT will be high otherwise. In this section, we are going to present the results in a graph and discuss them in detail.

Figure 13 presents the comparison with the work of [26] based on RSSI from different angles and distances. He used fixed points for sensor devices and fixed the charger up the ground, but it creates the nearest-farthest problem. It is clear that our work is better. In the figure, existing represents the work of [26], while proposed is our work.

Figure 14 is based on the patch antenna receiving RSSI, which is placed at different points according to our requirements. The points could be from 0° to 360°. Points 1 to 4 represent RSSI within the 122-degree range, and 5 to 6 points represent outside the 122-degree range. Our optimal points for the sensor are when the optimal points of the charger go well and it receives all of the energy, but when it goes other than our points, it degrades the receiving strength. No, any sensor goes to a dead position when the charger travels to its optimal points for the charger and transmits the energy to the optimal points of sensors.

Figure 15 shows the RSSI of a dipole antenna. The dipole antenna works better at our optimal points and receives the energy of the sensor at 60 degrees from the charger all the time. No, any sensor goes to a dead position when the charger travels through optimal points.

4.1. RSSI of Patch Antenna during Circular Tour

Figure 16 is based on the RSSI, which is obtained during the circular tour of the charger in constant motion. In this experiment, the sensor devices with patch antennas are fixed (note that the patch antenna has a 122° beam pattern), and the charger is mobile (Figure 3). When the charger changes the direction angle, then the sensor device degrades charging, and when the charger angle and sensor device angle are in the line of sight then the sensor device scavenges more energy. For example, in Figure 16, data1 and data5 are not in the line of sight, while data2, data3, and data4 are in the line of sight. At data3, the sensor device scavenges 100% of the energy from the charger. For this purpose, we can say that Figure 5 is the solution for the optimal placement of the sensor device.

4.2. RSSI of Dipole Antenna during a Circular Tour

Figure 17 is based on the RSSI that is obtained during the circular tour of the charger. Since dipole antennas can receive energy from all directions, so Figure 12 shows the optimal place for a sensor device.

5. Conclusions

In this paper, we study the optimal points for the placement of chargers and the optimal area for the sensor device problem. In our experiments, we have observed that when a device moves away from the charger, the energy scavenging process degrades and reaches a dead state. To stop a device from entering a dead state, we introduced a mobile charger technique to charge the device and find the optimal place for the charger and sensor devices. During mobile charging, we have also observed that when the directional antenna changes its angle, the energy scavenging process degrades. We have proposed two algorithms for directional and omnidirectional antennae to provide an approximate solution to our problem. We considered coverage requirements and energy requirements.

In the proposed algorithms, the charger is movable and the sensor devices are fixed. Moreover, the charger movements are controlled at specific angles in such a way that each sensor device can harvest more energy. Furthermore, we have obtained results from these scenarios and shown that this technique has a promising output. This solution is for a 2D environment and cannot be applied to a 3D environment. We will extend this work to a 3D environment. Furthermore, we will use standard simulation tools to compare with other state-of-the-art solutions and also use this work in routing protocols.