I Introduction
Internetofthings (IoT) is gradually maturing due to a considerable amount of research interest received in recent years [3]. It seems evident that the myriad applications of IoT in healthcare, industrial automation, and agriculture, are going to usher an era of massive connectivity of millions of devices. According to the researchers, the increase in IT consolidation and the rapid adoption of reliable solutions are some of the main contributors to such massive connectivity of wireless IoT devices [4]. Modernization of legacy infrastructure is another focal point to provide seamlessly connected solutions that have a significant impact on the performance of such massive IoT networks. In this regard, ensuring the security of the IoT devices is at the forefront of critical issues. Due to this reason, a great number of research efforts are directed towards securing IoT devices against a number of attacks [9]. Another important aspect of such networks is energy consumption and reliability on rechargeable battery solutions. In particular, there is a growing demand to develop energy harvesting solutions to reduce the dependency and consumption of fossil fuels and move towards green communications [18]. To ensure such ubiquitous connectivity of green IoT networks, several interesting yet daunting issues need to be addressed for future IoT networks.
The first challenge stems in the form of prevention against eavesdropping attacks. Due to broadcast nature, it seems inevitable that the wireless links among IoT devices would be susceptible to the eavesdropping attacks. Moreover, since many of the IoT networks may operate privately in adhoc mode, the security techniques involving a trusted third party may not be realizable. In this regard, one of the promising solutions has been proposed in the form of physical layer security (PLS). The basic principle of PLS is to ensure link security by harnessing the randomness of the wireless channel [7]. This randomness of the wireless channel can be used to confuse the eavesdropper and reliably transfer the message to the intended receiver. Moreover, it can ensure good communication secrecy without requiring exhaustive cryptographic operations [11]. Besides this, message authentication can also be performed for ensuring the confidentiality of messages in the presence of malicious IoT devices.
The second issue is related to the limited lifecycle of wireless devices which can be addressed by employing efficient energy harvesting techniques [14]. The term energy harvesting which is sometimes called power harvesting is a technique that allows devices to harvest energy from already available sources. For RF energy harvesting, these sources include radio, cellular and television broadcasting signals that are mostly available and free to use for harvesting. This idea also gave birth to the transfer of power and data through electromagnetic (EM) waves [10]. As shown in Figure 1, the transfer of energy can take place using dedicated or ambient resources in the environment. The dedicated energy harvesting can be performed using dedicated towers to transfer energy to remote devices, whereas, ambient energy harvesting can be performed using radio frequency (RF) waves generated from wireless devices and fixed nodes. This dual nature of EM waves (i.e., ability to transfer information and energy) has given birth to a promising field of research namely simultaneous wireless information and power transfer (SWIPT) [14].
(a) 
(b) 
As mentioned earlier, the inherent nature of wireless IoT devices makes them vulnerable to eavesdropping attacks [17]. In this regard, it is worth noting increasing the power for ensuring the reliability of messages can have a negative impact on the secrecy performance of the network. In case, when a power receiver is a malicious IoT device, then improving the power transfer efficiency can reduce secrecy. It is because by increasing the power of the information signal, one may reduce the secrecy rate of the wireless links [13]. In this context, few studies have recently explored the concept of PLS and energy harvesting to efficiently use the transmit power. For instance, the authors of [15] proposed a band allocation scheme, whereby, the band reserved for energy harvesting cannot be used for information decoding. In another study, the concept of secrecy design for lowpowered devices was proposed and evaluated [8]. They introduced power splitting (PS) and time switching (TS) techniques for information processing and energy harvesting. Security in fullduplex (FD) relays with SWIPT systems was studied in [19]. An optimization problem was formulated for minimizing power consumption and ensuring link security for multipleinputmultipleoutput (MIMO) FD relays. In another work [1], Bi et al. proposed a strategy based on energy accumulation and then jamming the eavesdropper reception. The authors of [20] provided a secrecy maximization scheme for energy harvesting relays. They first formulated a utility maximization problem and then solved it with the help of the Lagrange dual method.
The main contribution of this paper lies in the performance evaluation of energy harvesting and secure IoT. Specifically, we consider two fading channel models i.e., Rician and Nakagamim fading. We derive closedform expressions of ergodic secrecy capacity for both separated and integrated SWIPTenabled IoT device architecture. In contrast to the conventional approach, we consider imperfect channel estimation for both legitimate and eavesdropping links. To validate our findings, we perform MonteCarlo simulations in MATLAB.
The rest of the paper is arranged as follows. Section 2 provides details of the considered system model. In Section 3, a detailed analysis of ergodic secrecy capacity for both Nakagamim and Rician fading has been provided. Section 4 provides analytical and simulation results along with their relevant discussion. Lastly, Section 5 presents some key conclusions.
Ii System Model
Let us consider a downlink communication network constituting an access point (AP) and SWIPTenabled IoT devices. Within any timeslot, the AP communicates with a legitimate IoT device () while the other IoT devices can be considered as eavesdroppers denoted as . All the IoT devices are equipped with single antennas having the capability to harvest energy and decode information from the received signal. In order to efficiently use the transmitted power from AP, during each time slot, the AP transmits information to while the other devices are supposed to harvest energy from the radio waves. Being part of the same network, the energy harvesting IoT devices may act as eavesdroppers. Furthermore, in order to decode information and harvest energy and are assumed to use PS scheme [5]. As per PS policy, the signal at the receiving IoT device is divided into two streams for energy harvesting and information decoding. Particularly, the multiplicating PS factor is used for information decoding and portion of the power is used for harvesting energy.
Iia Fading Channel Models
For many tropospheric, urban, suburban and indoor communication systems, the channel is an amalgamation of noise, fading, and interference. Therefore, an appropriate selection of the channel fading model is important to analyze the performance of a wireless network. In this context, we adopt two renowned channel fading models namely, Rician and Nakagami
distribution, for characterization of secrecy performance of SWIPTenabled IoT devices. Rice distribution has been used to model environments where a strong stable line of sight component exists between source and destination. The theoretical model is the sum of two vectors, one with Rayleigh distributed modulus and other with known modulus with a large value. The Nakagami
distribution is a multifaceted distribution which is used to model numerous fading environments. It has proven to be highly flexible for matching empirical data of indoor and outdoor environments. It includes Rayleigh and Onesided Gaussian as special cases. Some studies also indicate that Nakagami can be used to approximate Rician fading for its shape parameter [16].IiB Signal Propagation
When AP, with power , transmits its signal to , the received signal is written as [22]
(1) 
where is the wireless channel gain between and AP, and is the PS factor for . The signal processing noise is denoted as , is the additive white Gaussian noise (AWGN) given as and is the path loss. As explained earlier, the communication is overheard by the eavesdropping IoT devices. At the th eavesdropper, the received signal is expressed as
(2) 
where is the channel gain and is the path loss, represents the signal processing noise, and denotes the AWGN between the AP and th eavesdropper. We assume that the eavesdropper use similar hardware and . For tractability of analysis, we consider and and , . In this paper, we consider imperfect channel estimation which is more practical as it is generally difficult to perfectly estimate wireless channel. Thus, a renowned channel estimation model has been used to incorporate channel imperfections in our system [6, 21]
(3) 
where is the estimated wireless channel gain and . Also, denotes the channel accuracy factor wherein for the channel is assumed to be accurately known and for the channel estimation is completely inaccurate. Moreover, is given by . Since we are focusing on the secrecy performance evaluation of the system, we assume that the value of is given a priori. By substituting above equation in (2) we obtain
(4)  
(5) 
Using above equations, the instantaneous received SNR can be expressed as
(6) 
Similarly, the instantaneous received SNR at any eavesdropper is written as
(7) 
where and are the average transmit power of main and wiretap link in (6) and (7), respectively. It is worth mentioning that among all the eavesdroppers, an eavesdropper with the best CSI has a better chance to decode the received message. Thus, the received SNR of eavesdropping link yields
(8) 
Iii Ergodic Secrecy Capacity
An important security indicator is the average secrecy capacity . It is expressed as
(9) 
Now, we first discuss both channels separately.
Iiia Ergodic Secrecy Capacity under Rician Fading
IiiA1 Separated and
By using (9) we get
(10) 
Now by using integration by parts, we get
(11) 
Similarly, the cumulative distribution function (CDF) of the received SNR at
is given as(14) 
where denotes the mean value, is the modified Bessel function of the first kind and order zero [2] and is the Marcum function [2] which is given as
(15) 
Additionally, is the ratio of the main link’s power of the line of sight component to the scattered component.
By substituting (14) in (12), we get (16) at the top of next page. Now with the help of [2, (1.211.1),(3.194.3)], ergodic secrecy capacity can be obtained as
(16) 
(17) 
where is the incomplete Beta function [2].
IiiA2 Separated and Integrated
Using the method presented above subsection, we can easily derive the expression of ergodic secrecy capacity when and employ separated and integrated receivers, respectively. It is given by
(18) 
Replacing (14) in (18) yields (19) at the top of next page. Using [2, (3.194.3)] along with Marcum approximation in (19), we get
(19) 
(20) 
IiiB Ergodic Secrecy Capacity under Nakagami Fading
The PDF of the main link is written as [12]
(21) 
Similarly, the CDF of received SNR at is expressed as
(22) 
where represents the total number of multipath clusters for main link. Using the derivation for CDF and PDF of above section, we get CDF as
(23) 
In a similar way, the PDF of is obtained as
(24) 
where represents the total number of multipath clusters for wiretap link.
IiiB1 Separated and
Using [2, (8.352.4)] we obtain
(26) 
According to the author’s best knowledge, the above integral cannot be further simplified. But it can be readily evaluated with MATLAB, Mathematica or any other standard computational software.
IiiB2 Separated and Integrated
Now with the help of [2, (3.326.2)], we get
(28) 
Iv Numerical results
This section provides some numerical examples for the derived analytical results, which are also validated by simulations. Unless stated otherwise, the simulation parameters for generation of plots in this section are provided in Table I.
S No.  Parameter  Value 
1.  Signal processing noise variance 
0 dB 
2.  Nakagami shape factor  2 
3.  Channel realizations  
4.  Antenna noise variance  0.1 dB 
5.  Rician factor  5 
6.  Main link power  30 dB 
7.  Channel estimation accuracy  0.2 
8.  No. of eavesdroppers  5 
9.  Wiretap link power  10 dB 
10.  Power splitting factor  0.8 
(a) 
(b) 
Figure 2 (a) plots the ergodic secrecy capacity against increasing . Note that the ergodic secrecy capacity generally increases with the increase in . However, the increase in secrecy capacity is hampered due to imperfect channel estimation errors. Moreover, we observe that for particular values of and , the secrecy capacity for Rician and Nakagami channel becomes similar when a large number of eavesdroppers are present in the network. Figure 2 (b) further shows the impact receiver design on secrecy performance of energy harvesting IoT devices. The secrecy capacity for Rician and Nakagami channel becomes the same despite the large difference in their shape parameters when and are equipped with separated and integrated receivers. This shows that the impact of wireless channel reduces when employs separated and employ integrated receiver architecture.
Figure 3 plots the secrecyenergy region for Nakagami and Rician fading channels. It can be observed that ideal channel estimation always achieves better secrecyenergy pair than that of imperfect channel estimation with . Moreover, when increases from 10 to 15, the secrecyenergy performance significantly decreases. This shows that no. of eavesdroppers have a significant impact on a secrecyenergy pair. Furthermore, the gap between Nakagami and Rician channel shrinks for large no. of eavesdroppers.
V Conclusion
In order to meet the future requirements of IoT networks, it is imperative that secure and environmentfriendly solutions are proposed. With this intent, we have analyzed an information and power transfer IoT network from the link security perspective. Particularly, we derived closedform expressions of ergodic secrecy capacity for two smallscale fading models. We also adopted a more practical approach by considering the availability of imperfect channel information at the transmitter. It was shown that the secrecy performance considerably deteriorates under imperfect channel estimation. In other words, an error ceiling is introduced whereby the secrecy capacity cannot be increased more even with the improvements in the SNR of the main link. This trend was observed for different receiver architectures of energyharvesting IoT devices. Moreover, an increase in the number of eavesdroppers or channel estimation errors causes a significant reduction in the secrecyenergy region for both Rician and Nakagami channels. These findings can be considered important from the design perspective of secure RF energy harvesting future networks.
Acknowledgment
This work was supported in part by the Academy of Finland under Project No. 311760 and Project No. 319003.
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