The ubiquity of WiFi-enabled devices raises the need for advanced network monitoring and management due to security and privacy issues associated with wireless networks. One method is Network Traffic Classification (NTC). However, robust and resilient NTC when traffic is encrypted and without compromising privacy is challenging. One possibility is to use WiFi Channel State Information (CSI), as it will change depending on the characteristics of the information being transmitted. In this article, we show that it is possible to use CSI for NTC by extracting CSI amplitudes from different network traffic streams, and then using this information to create a feature set that can be used with machine learning classifiers to develop a novel NTC mechanism. We show the robustness of our CSI-based NTC by considering real-world scenarios under different wireless interference, namely overlapping frequencies, location-based interference, and interference generated by various network streams. Consequently, our proposed NTC scheme achieved 0.84 NTC F-score as a baseline, and we identify that spectrally overlapping interference reduces the overall F-score of the CSI-based NTC classifier by upto 0.6. For traffic classes with similar characteristics, the proposed framework achieved an NTC F-score above 0.95, corroborating the scalability of our non-intrusive sensing technology. 1

The rapid proliferation of devices brought about by the Internet of Things (IoT) has underpinned the inclusion of sensing capabilities in wireless systems. Therefore, integrated sensing and communication (ISAC) is emerging as a key use case for next-generation systems. The dual functionality of ISAC systems increases interference, which multiple-input multipleoutput (MIMO) arrays can mitigate through spatial diversity. However, achieving substantial diversity gains with MIMO requires large antenna arrays, which could amplify the system complexity. Fluid antennas (FAs) provide an efficient alternative, delivering comparable spatial gains without the need for massive arrays, making them a promising candidate for ISAC systems. In this paper, we propose a quality-of-service (QoS) aware powerefficient transceiver design for an FA-ISAC system. We jointly design the antenna position vector, transmit beamformers, radar signal and receive combiners to meet the sensing and communication performance constraints. Our results demonstrate a 2 dB improvement compared to conventional MIMO systems.

With the mass inclusion of machines in network architectures, integrated sensing and communication (ISAC) is emerging as a key use-case of 6 G communication systems. The dual functionality of ISAC introduces prohibitive energy and spectrum costs, which are further exacerbated by the increasing scale of modern networks. This escalating demand highlights the urgent need for green network architectures that minimize power consumption and improve energy efficiency. Backscatter technology offers a promising avenue for alleviating resource constraints, as it utilizes existing radio-frequency signals, enhancing both spectrum and energy efficiency. However, the interference between the sensing and communication signals can further increase resource consumption. Hence, green communication, with a directed focus on power minimisation and energy efficiency, is essential for next-generation systems. In this paper, we present a green, quality-of-service (QoS) -aware transceiver design for integrated sensing and backscatter communication (ISABC) systems. The sensing and communication QoS constraints ensure the functional operation of target sensing and backscatter communication. We jointly optimize the radar signal, precoding vector, and receive combiners to minimize power consumption at the dual-function base station while meeting both sensing and communication requirements. The proposed green ISABC design demonstrates a 12 dB performance improvement compared to equivalent radar sensing and uplink communication systems.

Ensuring the integrity and safety of perishable goods within cold storage facilities has become a paramount concern for industries ranging from pharmaceuticals to food production. Meanwhile, as Integrated Sensing and Communication (ISaC) capable WiFi communication is on the horizon, We examine the feasibility of wireless sensing for cold storage monitoring. To this end, we aim to leverage the ubiquitous WiFi signals from commercial Internet-of-Things (IoT) devices, combined with lightweight Artificial Intelligence (AI), to monitor cold storage temperature without using dedicated IoT temperature sensors. Specifically, we build a sensing framework implemented on WiFi-enabled IoT hardware devices. The proposed framework uses the channel state information (CSI) of WiFi signals to monitor temperature variations within cold storage facilities by adopting a classification-based machine learning approach. Our experimental results empirically confirm the correlation between CSI and ambient temperature and evaluate the framework's performance at different locations within cold storage. Furthermore, the classification models have been successfully employed to accurately categorise temperature ranges with an accuracy of 92%, demonstrating the feasibility of wireless sensing techniques in monitoring cold storage facilities.

The integration of sensing capabilities into emerging wireless standards, such as 802.11 bf, presents an increasing threat to public privacy. Recent studies have demonstrated that even minor activities, such as finger movements on a keyboard, can be detected by exploiting Wi-Fi Channel State Information (CSI). To mitigate the privacy risks associated with Wi-Fi sensing, prior research has explored methods to disrupt the CSI measurement process by injecting interference into the wireless channel. However, current techniques often inundate the channel with excessive interference, resulting in a significant degradation of the wireless communication link. This paper proposes a spectrally efficient approach by investigating the sensitivity of Wi-Fi sensing to the CSI measurement rate, and designing interference that reduces sensing accuracy while preserving the integrity of the communication link. First, we quantify the accuracy of Wi-Fi-based keystroke recognition in relation to the CSI data rate. We then present theoretical justifications for the use of interference and demonstrate its impact on CSI data rates. Then, a method of adversarial interference is applied, reducing Wi-Fi sensing accuracy by 70 % in a keystroke detection scenario. Finally, a comprehensive trade-off study is conducted to demonstrate how interference can be optimized to protect privacy with savings of up to 21 % in bandwidth, ensuring minimal degradation of service quality.

Accurate occupancy counting is essential for effective space management, safety protocols, and resource optimisation in various environments. In recent years, the rapid development of the Internet of Things (IoT) has advanced traditional WiFi sensing into a new domain of research known as Integrated Sensing and Communication (ISAC). Such technology enables simultaneous communication and sensing, allowing for efficient data transmission while gathering environmental information. In this paper, we conduct a novel experimental demonstration and robustness validation of ISAC for occupancy counting. Compared to traditional WiFi sensing technology, we first demonstrated that human presence can be detected by sensing applications using communication packets. Specifically, using our Machine Learning (ML) algorithm, we got a sensing accuracy of more than 96 %. However, we found that this accuracy decreases with increased communication rates and higher occupancy levels, as they introduce noise and interference into the WiFi channel. We also experimentally investigated edge communication, which reveals a tradeoff between occupancy monitoring and communication rate regarding the number of Transmission Control Protocol (TCP) retransmission requests for different distances. We observed that improved accuracy results in an increase in TCP retransmission requests and a reduced communication quantity. Both sensing and communication performance lie in an optimal deployment of the transmit and receive with devices. Further tests at various distances revealed novel insight that sensing performance is better at both short and long distances. Specifically, we present that sensing benefits from diverse reflections, whereas communication relies on a balance best achieved at intermediate distances.

Long-range (LoRa) can provide highly energy-efficient and cost-effective communications for low power wide area networks, playing an indispensable role in the Internet of Things (IoT). However, terrestrial LoRa networks cannot guarantee pervasive connectivity, especially in rural and remote areas. To tackle this problem, exploiting LoRa-based low Earth orbit (LEO) satellite IoT has garnered a growing interest in both academia and industry. In this paper, we provide a novel analytical framework based on spherical stochastic geometry (SG) for characterizing the uplink access probability of LoRa-based LEO satellite IoT. For practical modeling, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of near-Earth satellite communications and the unique features of LoRa network are considered to derive closed-form analytical expressions for the uplink access probability. Numerical simulations validate the accuracy of our theoretical analysis and provide insightful guidelines for the practical design and implementation of LoRa-based LEO satellite IoT.

Advanced security solutions are essential for sustainable and autonomous networking in 6G systems. Secure backscatter communications (BSC) are crucial to protecting data integrity and confidentiality against evolving threats in green communication networks. This form of wireless communication involves devices reflecting signals to the source and extends beyond existing security solutions. Physical layer security (PLS) solutions have emerged as a promising avenue for developing adaptive, robust, and lightweight security measures. We propose a unique approach involving beamforming at the multiantenna transceiver, known as the reader, to enhance data confidentiality in multi-tag monostatic backscattering systems. Our innovative framework protects against security breaches due to eavesdroppers and optimizes the sum-secrecy rate among the tags by integrating transceiver beamforming and artificial noise techniques. Given the non-convex nature of the original problem, we propose two efficient solutions: a transmitter design for a given combiner using fractional programming and a closed-form optimal receiver design for a specified precoder configuration. Comprehensive numerical analyses validate the proposed solutions and verify key analytical claims while also quantifying the significant performance gains achieved over benchmark schemes across various system parameters.

Unmanned aerial vehicles (UAVs) are now being used to efficiently collect data from passive backscattering tags and support existing terrestrial links in the Internet of Things (IoT) when these links become overloaded. However, securing UAV-aided backscatter communication (BSC) in non-terrestrial networks is challenging due to the hardware limitations of passive tags. This paper introduces a secure BSC system utilizing a UAV for radio frequency (RF) signal transmission and data collection. Batteryless tags or backscatter devices (BDs) harness these RF signals to communicate with the UAV, even under untrusted scenarios where the BDs are mutually untrusted. We enhance the uplink fair-secrecy rate of the BDs by jointly optimizing the transmit and received beamforming vectors, artificial noise (AN), and power allocation while achieving the energy harvesting requirements and UAV flight constraints. Block coordinate descent (BCD) and fractional programming (FP) algorithms are used to address the non-convexity and obtain a fast converging solution. Simulation results verify the analysis, provide valuable insights, and demonstrate the substantial performance gains of our design for UAV-aided secure BSC in improving the fair-secrecy rate. Specifically, our proposed design achieves 0.19%, 2.08%, and 69.78% higher performance compared to three benchmarks.

Orthogonal Chirp Division Multiplexing (OCDM) is a multi-carrier scheme based on the chirp spread spectrum (CSS) that has been introduced to overcome the limitations of conventional multiplexing techniques. In this paper, we derive the input-output relationship (IOR) for the OCDM scheme over frequency-selective fading channels. We consider two cases: when the guard interval (GI) between successive OCDM symbols is longer than the channel delay spread (sufficient) or not (insufficient). First, we demonstrate that the IOR of OCDM is equivalent to that of the single-carrier (SC) scheme with a sufficient GI. Next, we show that for OCDM with an insufficient GI, the intersymbol interference (ISI) at the receiver follows a Gaussian distribution. We then derive the signal-to-interference-plus-noise ratio (SINR) of the OCDM scheme at the receiver. By leveraging this SINR analysis, we present a novel single-tap linear minimum mean square error (MMSE) based frequency domain equalizer (FDE). Our simulation results, which highlight the significant performance improvement of the proposed MMSE one-tap equalizer, demonstrate that by exploiting derived SINR, its effectiveness in enhancing the performance of OCDM compared with the conventional MMSE receiver. We also evaluate the bit error rate (BER) performance and validate our analysis. Our results provide novel insights into the achievable gains by our interference-aware FDE for OCDM over the existing benchmarks for different values of key system parameters.

With the rapid growth of Lithium-ion (Li-ion) battery usage in modern lifestyles, the increasing risk of fire hazards demands practical temperature monitoring techniques that are massively deployable. The goal of this work is to explore the use of wireless sensing in conjunction with Artificial Intelligence (AI) to enable the detection of the surface temperature of a target object. Our framework for detecting the target surface temperature based on the channel state information (CSI) of WiFi signals utilises a low-complexity classifier algorithm, specifically the linear support vector machine. With a prototype constructed with commodity embedded systems, we experimentally validated the feasibility of our proposed framework for detecting surface temperature levels with more than 93% accuracy at a resolution of 2°C. In addition, we evaluated the robustness of our embedded AI-based WiFI sensing technology. We identified that accuracy is subject to degradation with deployment conditions such as over-range, imbalanced positioning, and inadequate transmit power. Overall, with adequate accuracy, our WiFi CSI-based surface temperature detection framework provides a low-cost, massively deployable, and non-invasive solution for practical temperature surveillance applications involving heat-sensitive products.

With the accelerated global urbanization and industrialization, air pollution due to aerosol has become one of the main challenges facing society. The aim of this work is to explore the use of artificial intelligence (AI) and WiFi-based wireless sensing technologies to enable the detection of aerosols and to assess their potential application in environmental monitoring. We proposed a framework that detects the aerosol concentration levels via the channel state information (CSI) of the ambient WiFi signal with the help of low-complexity machine learning (ML) classifiers that can process CSI data on the embedded devices. We experimentally validated the feasibility of the proposed framework with a prototype system implemented with wide-deployable commodity hardware. We demonstrated empirically that the proposed framework can predict aerosol concentration levels with 99% average accuracy via edge computing on an embedded system with tailored pre-processing. We evaluated the robustness of our proposed technology with practical aerosol materials and achieved more than 97% accuracy for both graffiti paint and air refresher. Overall, our edge-AI-based wireless sensing offers a low-cost, low-complexity, non-invasive solution to practical aerosol monitoring applications, such as indoor air quality monitoring and autonomous graffiti vandalism alerting.

The rapid surge in network sizes, driven by the proliferation of wireless applications, has placed unprecedented demands on wireless systems leading to high interference, spectrum bottlenecks, and increased power utilization. Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for these challenges owing to their passive beamforming action, but have limited gains due to independently functioning elements. Consequently, beyond-diagonal RISs (BDRISs) are proposed as the key-enabler of next-generation systems. In this paper, we propose a fairness-aware design for BDRIS-assisted fluid antenna systems. We jointly optimize the antenna position vector, beamforming vectors, and BDRIS configuration matrix to address service fairness while meeting the power budget in a multi-user downlink system. To tackle this non-convex problem, we utilize proximal policy optimization and obtain a fast-converging solution. Our results demonstrate a 2.8 bps/Hz mean rate improvement as compared to conventional RIS systems.

Wireless Body Area Networks (WBANs) play a paramount role in enhancing the quality of health monitoring and personalized medicine. WBANs frequently necessitate novel green solutions like energy harvesting (EH) to sustain the implants over long periods of time. In this work, we consider an implanted patient present in an indoor environment having an RF power source. The implant can harvest power from the source for which a non-linear EH model is considered. We have first provided a hybrid channel model for off-to-in body communication, thereby calculating the harvested power at the implant. In addition to incorporating the effect of different floors, partition walls in the channel model, we have also investigated the impact of specific-absorption rate on human tissues by providing a novel relation between specific absorption rate and relative permittivity for various layers inside human body. The performance of the proposed model is analyzed in different indoor room scenarios for linear as well as non-linear EH scenarios, and compared with already existing ITU-R path loss models thereby including insights on channel fading. The proposed model is found to outperform the considered benchmark model.

Intelligent reflecting surfaces (IRS) has become a transformative technology to enable smart, reconfigurable, and cost-efficient wireless communication environments. Prior work has primarily focused on distributed IRS models, which require joint IRS design for both energy transfer and information transfer. This approach requires complex phase-shift reconfiguration and leads to suboptimal resource utilization. This work is a novel attempt to consider dedicated IRS for energy harvesting (EH) and information transfer in wireless powered communication networks (WPCN). Specifically, IRS-I is used to assist EH in the downlink (DL) at devices through a power station. A separate IRS-II is installed between devices and the data sink (DS) for information transfer using non-orthogonal multiple access (NOMA). We investigated a non-linear EH model alongside dedicated IRS deployment, surpassing the limitations of the simplified linear EH approach. Our objective is to maximize the sum-rate (SR) problem while optimizing the active beamforming vector, transmit time, power allocation, and IRS phase-shifts considering NOMA-enabled WPCN. This non-convex optimization problem is solved using the proposed alternating optimization (AO) algorithm and simultaneously optimizes the resource allocation and IRS phase-shifts using semidefinite relaxation. The numerical results demonstrate the advantages of integrating dedicated IRS into WPCNs and show the notable performance gains of the proposed algorithm over benchmark schemes.

Non-orthogonal multiple access (NOMA) offers a promising solution to enhance spectral efficiency and support massive connectivity in future wireless systems. However, most NOMA research assumes ideal conditions, neglecting the impact of imperfect successive interference cancellation (SIC) and relying on conventional decoding order based on channel gains. In this work, we address these limitations by considering all possible decoding order combinations for a two-user NOMA system and developing a systematic approach for joint optimization of decoding order and power allocation. Our goal is to maximize the sum-rate and fairness under realistic conditions, including imperfect SIC. We formulate a joint optimization problem to maximize sum-rate while ensuring each user's data rate meets a minimum quality-of-service (QoS) requirement, and tackle the max-min fairness problem to ensure fairness. The optimal decoding order is proven analytically, and global optimal power allocation solutions are derived. Extensive simulations validate our theoretical results, showing that the proposed scheme outperforms conventional methods in sum-rate and fairness.

Backscatter communication (BackCom) is a promising technology that enables ultra-low-power wireless communication by reflecting RF signals. We propose novel Binary polarization Shift Keying (BPolSK) and Differential polarization Shift Keying (DPolSK) in Bistatic BackCom. Here, the backscatter tag modulates the information by changing the polarization state of the incident RF carrier. We derive the closed-form expression for Bit Error Rate (BER) and analyze the performance of BPolSK and DPolSK. Our results compare the performance of BPolSK and DPolSK and verify that they can achieve low BER, demonstrating their reliability for BackCom applications.

Backscatter tag-to-tag networks offer a sustainable and energy-efficient solution for large-scale Internet-of-Things (IoT) applications. In this paper, we propose a novel backscatter tag-to-multiple-tag network protocol based on a dual-phase system, partitioning the operational time into energy-harvesting and backscatter-communication phases. By utilising a multi-antenna reader and jointly optimising the beamforming vectors for the dual-phase problem, our design maximises the sum-throughput and substantially enhances overall system performance. To solve the non-convex transceiver optimisation problem, we derive closed-form solutions for the first phase and employ fractional programming with semidefinite relaxation for the second phase. Simulation results validate the effectiveness of the proposed solution and provide valuable insights for practical deployment, achieving an enhancement of around 3 dB over the benchmarks.

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BSC) offers a low-power option for IoT devices, it can suffer from limited coverage. To overcome this, we leverage unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. Motivated by vehicular communication applications, this paper investigates a NOMA-enabled UAV-assisted BSC framework to maximise system throughput by jointly optimising power allocation and trajectory scheduling. We derive a closed-form solution for the UAV's optimal collection location and apply the Karush-Kuhn-Tucker (KKT) conditions to obtain the power allocation. The numerical and simulation results demonstrate sum-throughput improvements of 620.278% and 7.795% compared to two benchmark schemes, underscoring the potential of our approach for large-scale IoT deployments.

In this paper, we propose a novel phased array with movable antennas (PAMA). By replacing traditional phase shifters with movable antennas, PAMA eliminates insertion loss of phase shifters and enhances antenna performance by leveraging the increased degrees of freedom in antenna positioning. We construct a periodic approximation-based beampattern synthesis (PABS) method. It leverages the approximate periodicity of the PAMA array response to obtain a high-quality initial solution through discrete optimization with quadruple-frequency sampling, which is subsequently refined using projected gradient descent. Simulations demonstrate the effectiveness of the proposed approach for multi-beampattern synthesis and highlight the impact of antenna movement range on synthesis performance. The results show that PAMA-based beampattern synthesis enhances beamforming accuracy and improves efficiency, especially in practical scenarios where digital phase shifters are used.

The advancement of industrial Internet of Things (IIoT) networks has brought challenges in terms of connectivity, efficient spectrum usage, and low latency. To tackle these challenges, advanced multiple access techniques have been developed. Non-orthogonal multiple access (NOMA) is a promising multiple access technique due to its high energy efficiency and fairness for devices. However, NOMA has inherent security issues due to wireless transmission and complex successive interference cancellation (SIC) based decoding, which can negatively impact system performance. Furthermore, achieving perfect SIC is also a challenging task due to implementation complexity. This study investigates the effects of imperfect SIC in a dual-device cooperative NOMA system. Our system includes direct links between the source and devices and uses both decode-and-forward and amplify-and-forward relays. The overall objective is to optimize decoding order and power allocation coefficients in order to maximize the near or priority device’s secrecy rate while meeting the quality-of-service (QoS) requirements of the far or normal device. Observing the underlying optimization problems to be non-convex, a low-complexity algorithm yielding optimal solutions is developed. Our findings reveal how imperfect SIC can impact massive access systems and secrecy performance. Extensive simulations provide novel design insights into the achievable secrecy rate and optimal power allocation coefficients. We also explore the trade-off between the priority device’s secrecy rate and the normal device’s rate, along with the impact of residual interference. Finally, our proposed solution has been shown to significantly improve the QoS-constrained secrecy rate of priority devices when compared to relevant benchmarks.

Abstract.Traffic infrastructure safety is vital to society, and critical traffic corridor breakdowns could halt cities. Particularly, vehicular tunnels are notable due to the constrained space and challenges to emergency response. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of Internet-of-Things (IoT) enabled WiFi sensing technology for environmental safety monitoring using versatile, low-cost embedded devices. As a proof-of-concept, we deployed our WiFi sensing devices in an urban underwater tunnel to monitor the tunnel temperature and fire accidents. Specifically, we set up one long-term deployment for temperature monitoring and conducted two fire detection experiments in the tunnel using a small-scaled bonfire and an actual vehicle fire. Our experiments show that the proposed WiFi sensing system could accurately monitor the ambient tunnel temperature to an error of≤0.4∘C during the long-term deployment. In the smallscaled bonfire experiment, we developed a novel empirical model to characterise fire intensity using WiFi signals. This proposed linear regression model has a coefficient of determination of 0.76. Finally, we validate the potential of our wireless environment vision system for detecting fire events from ambient environments using a low-complexity machine learning classifier enabling in situ processing locally on IoT devices.

Recently, Long-Range (LoRa) based low Earth orbit (LEO) satellite Internet of Things (IoT) has garnered growing interest from both academia and industry, since it can guarantee pervasive connectivity in an energy-efficient and cost-effective manner. In this paper, we provide a novel spherical stochastic geometry (SG) based analytical framework for characterizing the uplink access probability of LoRa-based LEO satellite IoT system. Specifically, multiple classes of LoRa end-devices (EDs) are taken into consideration, where each class of LoRa EDs is modeled by an independent Poisson point process (PPP). Both the channel characteristics of the satellite-to-Earth communications and the unique features of the LoRa network are considered to derive closed-form analytical expressions for the uplink access probability of such a new paradigm. Moreover, the non-trivial impact of the spreading factor, the ED’s density, the orbit altitude, and the satellite effective beamwidth on the system performance is thoroughly investigated. Extensive numerical simulations are conducted, which not only validate the accuracy of our theoretical analysis but also provide useful insights into the practical design and implementation of LoRa-based LEO satellite IoT system.

The rise of Wi-Fi sensing applications leveraging Channel State Information (CSI) from ambient wireless signals has opened up extensive opportunities for human activity and identity recognition. However, this advancement raises serious privacy concerns, as sensitive personal data can be inferred by applying advanced Machine Learning (ML) algorithms to CSI data. In response, researchers have explored adversarial techniques to degrade Wi-Fi sensing accuracy and protect privacy, often by interfering with or corrupting CSI. This paper introduces Wi-Spoof, a novel approach for spoofing CSI to deceive Wi-Fi-based Human Activity Recognition (HAR) systems. Wi-Spoof manipulates Wi-Fi transmission power to inject noise into the CSI and employs a pseudo-Pulse Width Modulation (PWM) scheme to generate controlled, adversarial CSI. Using commercially available hardware, we experimentally demonstrate that Wi-Spoof can achieve targeted misclassification in a state-of-the-art HAR system with a 93% success rate. Our approach is validated on a widely recognised public dataset and further supported by extensive local experiments, underscoring Wi-Spoof’s effectiveness in steering HAR predictions to specified outcomes.

This paper studies backscatter communication (BackCom) systems with a passive backscatter tag. The effectiveness of these tags is limited by the amount of energy they can harness from incident radio signals, which are used to backscatter information through the modulation of reflections. To address this limitation, we adopt a practical Constant-Linear-Constant (CLC) energy harvesting model that accounts for the harvester’s sensitivity and saturation threshold, both of which depend on the input power. This paper aims to maximize this harvested power at a passive tag by optimally designing the underlying M-ary amplitude-shift keying (ASK) modulator in a monostatic BackCom system. Specifically, we derive the closed-form expression for the global optimal reflection coefficients that maximize the tag’s harvested power while satisfying the minimum symbol error rate (SER) requirement, tag sensitivity, and reader sensitivity constraints. We also proposed optimal binary-ASK modulation design to gain novel design insights on practical BackCom systems with readers having superior sensitivity. We have validated these nontrivial analytical claims via extensive simulations. The numerical results provide insight into the impact of the transmit symbol probability, tag sensitivity constraint, and SER on the maximum average harvested power. Remarkably, our design achieves an overall gain of around 13% over the benchmark, signifying its utility in improving the efficiency of BackCom systems. Moreover, our proposed solution methodology for determining the maximum average harvested power is applicable to any type of energy harvesting model that exhibits a monotonic increasing relationship with the input power.

Orthogonal time frequency space (OTFS) has been widely acknowledged as a promising wireless technology for challenging transmission scenarios, including high-mobility channels. In this paper, we investigate the uplink multi-user OTFS transmission designs based on statistical channel information. Specifically, we investigate the pilot power allocation based on the a priori statistical channel state information (CSI) only, where performance on channel estimation is considered. We first derive the a posteriori Cram$\acute {\text {e}}$r-Rao bound (PCRB) based on the a priori channel information of each user. We unveil that the PCRB only relates to the user’s pilot signal-to-noise ratio (SNR) and the maximum of delay and Doppler shifts under the practical power-delay and power-Doppler profiles. Furthermore, a pilot power allocation scheme is proposed to minimize the average PCRB of different users, whose closed-form optimal allocation solution is derived. Moreover, we study the impact of statistical CSI on transmission rates, where a tight approximation of the sum-rate is derived. Particularly, the approximated sum-rate only relates to the user’s symbol SNR and the maximum of delay and Doppler shifts. More importantly, we propose a power allocation for different users based only on the statistical CSI to maximize the achievable sum-rate while ensuring user fairness. The optimal power allocation solution is obtained by a fractional programming approach. Our numerical results verify the derived PCRB and the sum-rate analysis, where a roughly 3 dB improvement in terms of channel estimation accuracy and a significant rate improvement can be obtained.

The Internet of Things (IoT) applications require uninterrupted network operation which is often hindered by battery energy constraints. Literature suggests that solar energy harvesting is a promising approach to powering IoT devices in a sustainable manner. However, the available literature overlooks key factors of determining effective solar panel size and cost while considering the IoT consumption for sustainable operation. This article tackles these pivotal aspects by investigating viability of commercially available solar panels as a sustainable energy source for IoT applications. A novel stochastic computation model is introduced to characterize the unpredictability of solar irradiance across three different time regions of the day. By employing distribution fitting models, the proposed computation model accurately determines the required solar panel size in cm$^{2}$and panel cost in Indian Rupees for the sustainable operation of the IoT application. Further, the proposed model incorporates the assessment of outage and sustainability probabilities for user-specified solar panel size and cost. These insights are significant in settings where energy efficiency and sustainability are crucial. Numerical results are presented to validate the derived distribution models and performance metrics for sustainable IoT applications. The effectiveness and accuracy of the proposed model are validated by comparing results with baseline model.

Backscatter communication (BackCom) systems play a crucial role in low-cost and low-data-rate Internet of Things (IoT) applications. However, existing research predominantly focuses on idealized scenarios using minimum scattering antennas, resulting in performance discrepancies between simulations and practical implementations. To address this limitation, we investigate the impact of the antenna-dependent parameter associated with structural mode scattering on the amplitude and phase of the backscattered signal.We reveal that the conventional assumption of a minimum scattering antenna negatively affects BackCom system performance. To overcome this challenge, we propose an innovative signal subtraction technique (SST) that effectively mitigates the issues arising from this assumption. Our proposedSSTnot only preserves the minimum scattering antenna assumption but also enhances BackCom systems. Specifically, it improves the signal-to-noise ratio (SNR) in amplitude-shift keying (ASK)-modulated BackCom systems and introduces a trade-off between SNR and bit error rate (BER) in phase-shift keying (PSK)-modulated BackCom systems. Our extensive simulations highlight the practical application ofSST, demonstrating zero performance degradation when applying optimized designs tailored for minimum scattering antennas to any tag antenna. These findings underscore the pivotal role ofSSTin optimizing ASK- and PSK-modulated BackCom systems and offer valuable insights for enhancing overall system performance.

Sensors embedded in mobile smart devices can monitor users’ activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the risk of accessing and potentially disclosing sensitive information of individuals to the apps that may lead to privacy breaches. In this paper, we aim to minimize privacy leakages that may lead to user identification on mobile devices through user tracking and distinguishability while preserving the functionality of apps and services. We propose a privacy-preserving mechanism that effectively handles the sensor data fluctuations (e.g., inconsistent sensor readings while walking, sitting, and running at different times) by formulating the data as time-series modeling and forecasting. The proposed mechanism uses correlated noise-series against noise filtering attacks from an adversary, which aims to filter out the noise from the perturbed data to re-identify the original data. Unlike existing solutions, our mechanism keeps running in isolation without the interaction of a user or a service provider. We perform rigorous experiments on three benchmark datasets and show that our proposed mechanism limits user tracking and distinguishability threats to a significant extent compared to the original data while maintaining a reasonable level of utility of functionalities. In general, we show that our obfuscation mechanism reduces the user trackability threat by 60% across all the datasets while maintaining the utility loss below 0.3 Mean Absolute Error (MAE). More specifically, we observe that 80% of users achieve a 100% untrackability rate in the Swipes dataset across all noise scales. In the handwriting dataset, distinguishability is 17% for 60% of the users. Overall, our mechanism provides a utility error (MAE) of only 0.12 for 60% of users, and this increases to 0.2 for 100% users when correction thresholds are altered.

The Tiered Learning Framework is a multilayered system designed to aid students in assessing their progress and understanding within the learning process. Each layer corresponds to the skills students need to develop, and the framework encourages students to self-assess their current level and identify what they need to progress to higher tiers of learning. This paper explores the application of the Tiered Learning Framework in an Electrical Engineering Design Proficiency course at the University of New South Wales, Sydney. The course encompasses design tasks in critical areas such as electronic circuit design, signal processing design, and power system design. The paper focuses on the design and assessment of the signal processing design task as an example. In a class of 49 students, 96% agreed that the framework helped them achieve their learning goals, and 91% found it challenging yet encouraging for their learning. Compared to a non-tiered framework, 71% of the total students favour a tiered framework.

Road tunnel safety is vital to society, and break-downs due to fire accidents could halt cities. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of our WiFi sensing technology for environmental safety monitoring in a versatile and low-cost manner using commercial WiFi-enabled IoT devices. As a proof-of-concept, we conducted both a small-scale bonfire experiment and a real vehicle fire detection experiment in a traffic tunnel using our proposed WiFi environment vision system. As a result, we verified that WiFi sensing can be used to monitor fire accidents from the bonfire experiment, and developed a novel empirical model to characterise car fire intensity using WiFi signals with a coefficient of determination of 0.319. Additionally, we also demonstrated the potential for detecting fire outbreaks using a low-complexity machine learning-based classifier, capable of identifying the occurrence of fire accidents on the edge.

Thermal source localization is crucial in detecting fires and other anomalies, allowing emergency responders to quickly locate the source of the hazard to minimize damage. With recent advancements in WiFi sensing technology for thermal detection and the emergence of Integrated Sensing and Communication (ISAC) on WiFi systems, we are interested in exploring the potential of using WiFi sensing technology for thermal source localization using commercially available Internet of Things (IoT) WiFi hardware. Our study investigates the potential of using WiFi Channel State Information (CSI) to locate thermal sources. Using the cost-effective and power-efficient ESP32 microcontroller, we propose a WiFi sensing-based system to predict the location of a heat source. We demonstrate that the WiFi CSI signatures can be used to identify localized temperature changes in a timely manner. Our key contribution is effectively filtering out undesired components from the CSI and using smart subcarrier selection to form the feature set for the machine learning algorithm. We present a Support Vector Machine (SVM)–based classifier that achieves up to 99% accuracy in predicting the relevant heat source location. We also identify the optimal WiFi sensing device configuration, demonstrate our technology’s fast response time, and shed insights on the impact of heat source distance on localization performance. Our findings offer promising solutions for low-cost, environmentally friendly monitoring systems.

Backscatter communication (BackCom) is a wireless technology that is highly cost-effective and power-efficient. It uses passive tags to reflect incoming radio frequency (RF) signals to transmit data instead of actively generating their own RF signals. Our study aims to improve the performance of the BackCom system by using Quadrature Amplitude Modulation (QAM), which can make it more useful in various applications. Our primary goal is to increase the distance between the emitter and the tag, which is essential in determining the practicality of BackCom systems. We achieve this by optimizing the reflection coefficients while considering quality of service (QoS) requirements such as tag sensitivity, receiver sensitivity, and the occurrence of symbol error rate. We apply a successive convex approximation method to transform the non-convex problem of maximizing the range into a convex one, enabling us to find the optimal solution using a proposed low-complexity maximization algorithm. The simulation results verify the key analytical claims and provide novel insights into the maximum emitter-to-tag distance for different BackCom applications and tag design specifications, including the near-optimal QAM constellation design.

Rate-splitting multiple access (RSMA) is a technique that is widely used in next-generation wireless systems to efficiently manage multiple users. RSMA splits the transmission into two parts using rate splitting, which increases spectral efficiency. However, this technique also makes a portion of the transmission vulnerable to eavesdropping, which can compromise information security. This vulnerability is not limited to external eavesdroppers but also includes internal eavesdropping among users who share the same transmission. Therefore, there is an increasing need to develop secure RSMA systems to address these critical security issues among untrusted users. This paper focuses on a single-antenna RSMA downlink system with multiple untrusted users undergoing Rician channel fading. In this novel system framework considered for performance analysis, each user interprets its own common and private streams while attempting to eavesdrop on the private streams of other users. We evaluate the system-level performance in practical settings involving line-of-sight (LoS) paths using the Rician channel model and derive a tight closed-form approximate expression of the ergodic secrecy rate. Finally, we conduct numerical simulations to validate the theoretical analysis and provide valuable insights into the critical system parameters that impact the achievable ergodic secrecy performance of the RSMA system with untrusted users.

In traditional Internet-of-Things (IoT) networks, devices generate their own signals to transmit data, which consumes more power. However, monostatic backscatter communications (BSC) can perform modulation and signal processing using an external signal from a reader, rather than generating signals from the device itself. Radio Frequency Identification (RFID) systems operate on this monostatic backscattering technology. Despite its benefits, BSC is vulnerable to exploitation by cyber attackers, primarily due to the limited hardware capabilities of passive RFID tags. Jamming attacks can disrupt legitimate BSC systems, enabling illegal activities or allowing competitors to gain advantages. This novel empirical investigation proposes two methods, power control and location control, to enable a typical RFID system to read data even in the presence of a powerful jamming attack. The study analyzed the relationships among reader power, the reader-to-tag (R2T) distance, the jammer-to-tag (A2T) distance, and power gain. The performance was analytically characterized and evaluated through computer simulations and hardware experiments. To the best of our knowledge, this is the first work to empirically quantify the impact of jamming attacks on RFID read rates at the reader and to assess the efficacy of physical layer security techniques in mitigating their impact. Lastly, experimental validation utilizing commodity hardware provides novel insights into optimal power and topology control for securing passive tags in sustainable IoT environments against jamming attacks.

The modern world is characterized by the Internet of Things (IoT), which requires efficient and eco-friendly energy solutions like backscattering communication. However, wireless communication vulnerabilities and high sensitivity expose backscattering systems to risks, particularly from proactive eavesdropping attacks due to the hardware limitations of the passive tags. This empirical study explores the effects of such attacks on radio frequency identification (RFID) backscattering communication in practical settings using commodity hardware. Specifically, we investigate the two-fold impact of proactive eavesdropping attacks, which decrease legitimate read rates due to jamming and data leakage caused by eavesdropping activities. The novel experimental demonstration proposes two defence mechanisms, power and location control, to protect RFID backscattering against proactive eavesdropping. Our nontrivial findings reveal that the impact of jamming is more challenging to eliminate than eavesdropping in the case of RFID backscattering. Moreover, location control is more effective in reducing data leakage than power control. This innovative research extends its analysis from single-tag scenarios to multiple-tag scenarios by using off-the-shelf hardware, which supports our proposed backscattering framework. Overall, this paper provides substantive insights that contribute to advancing green, energy-efficient security solutions for IoT communication networks.

Backscatter Communication (BackCom) is gaining popularity due to its potential for sustainable and low-cost Internet of Things (IoT) applications. However, due to the limited resources of passive tags, optimizing the backscatter modulation is critical for the widespread use of this technology. Current backscatter modulation designs ignore the impact of the tag’s antenna structure, which we show in this paper to have a negative effect on system performance and lead to design discrepancies. We investigate the impact of the antenna structure parameter on the backscattered signal characteristics. Then, we propose a novel signal subtraction technique that effectively calibrates the received signal based on the tag’s antenna structure to enable accurate detection. Our simulation results demonstrate that different values of this critical parameter result in different backscattered signals, which influence the signal decoding efficiency at the receiver. Furthermore, our work provides insights for optimized system design and enhanced BackCom performance.

Collecting reliable data over extended areas in rural environments for surveillance purposes requires low-cost and effective technologies. This paper proposes a backscattering data collection system that uses unmanned aerial vehicles (UAVs) to overcome wireless coverage challenges in rural areas. The proposed system provides physical-layer security during autonomous data collection, and we optimize the UAV’s trajectory to manage data leakage while taking into account the limited battery of the UAV. Specifically, we aim to maximize the ratio of secrecy across all tags to the UAV’s power consumption while considering constraints such as the UAV’s maximum speed, secrecy rate fairness among the tags and energy budget of the UAV. Since the problem is non-convex, we apply convex transformation by relaxing certain constraints to obtain a locally optimal trajectory with low complexity. We evaluate the performance of our proposed optimization scheme by comparing it with relevant benchmarks and quantify its complexity through simulations.

Employing unmanned aerial vehicles (UAVs) for data collection is crucial in facilitating autonomous monitoring applications within wireless sensor networks (WSNs). To enable sustainable WSNs, wireless powering of ground nodes (GNs) from a flying UAV is a promising technique. However, to maximize utility, we need to smartly allocate the limited resources of UAVs. To this end, we propose jointly optimizing the UAV’s trajectory and time allocation per GN to reduce operation time. We first formulate a non-convex optimization problem for data collection that minimizes operation time while satisfying the sum throughput and time constraint. Thereafter, we develop a methodology that decouples the original problem into two sub-problems: time allocation and trajectory planning. Here, the former is solved in semi-closed form, while a genetic algorithm is employed to solve the latter. Simulations confirm the efficiency of our proposed model and unveil an up to 30% improvement in operation time compared to the existing benchmarks.

The Internet-of-things (IoT) for environmental sensing has attracted substantial interest and has great potential in many applications for both industry and lifestyle. Our research aims to sense the wetness of textiles via wireless transmission, which could provide a non-invasive alternative to the existing wearable sensing techniques. We proposed a sensing framework that uses the channel state information (CSI) signature of ambient WiFi signals to reflect the wetness of the targeted textile with a classification-based machine learning approach. As a proof of concept, we experimentally implemented the proposed frame-work with commodity hardware to affirm the wide deployability. Our embedded system-based design empirically demonstrated the proposed WiFi textile wetness sensing was feasible and could predict the wetness levels of Oml to 20ml with a resolution of 5ml for the target textile with more than 99% accuracy for indoor environments. We tested the robustness and scalability of our wireless wetness sensing technology via this real-world implementation and achieved over 90% accuracy with up to a 6m working range in an open outdoor environment. Overall, our prototype implementation provides an affordable, non-invasive, and device-free solution that is perfect for many household healthcare applications, such as monitoring sweat conditions and detecting body fluid leakage for infants and elderly care.

As our cities become more intelligent and more connected with new technologies like 6G, improving communication between vehicles and infrastructure is essential while reducing energy consumption. This study proposes a secure framework for vehicle-to-infrastructure (V2I) backscattering near an eaves-dropping vehicle to maximize the sum secrecy rate of V2I backscatter communication over multiple coherence slots. This sustainable framework aims to jointly optimize the reflection coefficients at the backscattering vehicle, carrier emitter power, and artificial noise at the infrastructure, along with the target vehicle's linear trajectory in the presence of an eavesdropping vehicle in the parallel lane. To achieve this optimization, we separated the problem into three parts: backscattering coefficient, power allocation, and trajectory design problems. We respectively adopted parallel computing, fractional programming, and finding all the candidates for the global optimal solution to obtain the global optimal solution for these three problems. Our simulations verified the fast convergence of our alternating optimization algorithm and showed that our proposed secure V2I backscattering outperforms the existing benchmark by over 4.7 times in terms of secrecy rate for 50 slots. Overall, this fundamental research on V2I backscattering provided insights to improve vehicular communication's connectivity, efficiency, and security.

Non-Orthogonal Multiple Access (NOMA) is a wireless communication technique that uses the Successive Interference Cancellation (SIC) strategy to achieve high efficiency. However, SIC can pose security risks in an untrusted NOMA environment where users can suffer from internal eavesdropping. In this research paper, we propose security solutions for downlink NOMA transmission involving two untrusted user terminals and imperfect SIC. Specifically, we investigate the ergodic secrecy rate performance in NOMA for two user terminals against mutual eavesdropping. To achieve the maximum weighted ergodic sum secrecy rate of the system while meeting the quality of service (QoS) requirements of both user terminals, we formulate an optimization problem for power allocation. Our solution approach considers a decoding order that yields the best possible outcome and employs an iterative method to obtain the optimal power allocation. Simulation results confirm the analytical claims and provide key insights on the optimal design parameters. Lastly, we numerically demonstrate that our proposed power control and decoding order design outperforms the benchmark schemes in terms of weighted ergodic sum secrecy rate performance.

Wireless technology has been used to locate and track people in real time using active sensors attached to the subject. Camera systems can be contact-free, but they are also invasive in terms of privacy. Backscattering and deep learning algorithms have enabled low-cost sustainable tracking, but this also needs to be worn by the subject. We present a novel solution that utilises backscattering from radio frequency identification (RFID) tags placed in the environment, not on the person, to detect the underlying location. These battery-less, sub-dollar energy harvesting tags are sustainable and enable passive contactfree localisation, acting as multiple sensors. To come up with a timely, real-time green, noninvasive wireless localisation system, we use a pre-trained linear machine learning algorithm to predict a person’s location based on a Received Signal Strength Indicator (RSSI) measured from an array of tags placed in the target environment. Our experiments demonstrate that we can accurately predict an individual’s location with high precision on commodity hardware, achieving a resolution of $0.4 \times 0.4 \mathbf{m}^{2}$ in a $6 \mathbf{m}^{2}$ area, with an accuracy of 81.26% in real-time.

Unmanned Aerial Vehicles (UAVs) are pivotal in the real-time and autonomous data collection for the Industrial Internet of Things (IIoT) through wireless sensor networks (WSNs). To ensure the data’s freshness, known as the Age of Information (AoI), we propose an innovative approach to optimize the utility design of limited resources and UAV trajectory. Our research is driven by minimizing the average AoI by optimizing the UAVs’ trajectories and time allocations per energy-harvesting ground node (GN) while meeting their quality of service (QoS) demands regarding the system or sum throughput. We have developed a novel control-communication cross-domain resource allocation strategy that optimizes the interplay between control Key Performance Indicator (KPI) AoI and communication KPI sum throughput to ensure seamless data collection in wireless-powered IIoT. Initially, we formulated a non-convex optimization problem for minimizing the average AoI in a wireless-powered data collection network while complying with the constraints on total throughput and operation time. We then decomposed this challenging problem into two sub-problems: time allocation and trajectory planning. We addressed the former problem using commercial convex optimization tools while we designed the UAV’s trajectory by exploiting a genetic algorithm (GA)-based framework. Our simulations, conducted under various scenarios, have successfully validated the performance of our proposed joint optimization methodology. They showcased a significant reduction of up to 80% in average AoI against the benchmarks, providing robust evidence of the effectiveness of our approach.

Unmanned aerial vehicles (UAVs) are increasingly pivoted in emerging Internet-of-Things (IoT) applications, serving as aerial base stations to provide reliable communication links. These UAVs enable ground users (GUs) to access remote areas where high-altitude platforms (HAPs) are exploited to connect with GUs, UAV relays and form multi-layer non-terrestrial networks to cooperate with other systems. However, UAV-based wireless networks have faced significant challenges in terms of energy consumption and operational duration of aerial devices. To address these challenges, we investigate the optimal resource allocation to maximize energy efficiency in a time-constrained HAP-to-GU communication network via assistance from a UAV relay. We propose a joint optimization method for the UAV’s location, transmit powers of HAP and UAV, and transmission time allocation to maximize energy efficiency while meeting the work time and power budget constraints. Our low-complexity iterative alternating optimization algorithm exploits the individual generalized convexity of the time allocation, power control, and UAV location problems to obtain the individually global optimal solutions. We also derive closed-form expressions for optimal time and power allocation variables to obtain analytical design insights. Finally, we conduct simulations to provide design insights and compare the performance of the jointly optimal and semi-adaptive algorithms against benchmarks.

In this paper, we focus on a UAV-aided Flying Ad hoc Network (FANET) with multiple unmanned aerial vehi-cles (UAVs) deployed for power-efficient data transmission to a ground control station (GCS) through multihopping. Our main objective is to enhance overall network performance by investigating the most effective multihop routing structure for efficient data dissemination. Considering the more realistic Rician fading channel for communication conditions, our goal is to maximize network throughput in a multihop UAV relay network by jointly optimizing multihop routing and power allocation (PA) to each UAV within a specified power budget. We reformulate the original optimization problem into two subproblems and propose an algorithm to derive the optimal solution. Additionally, we analyze the system's reliability in terms of packet loss probability (PLP). Numerical results demonstrate the effectiveness of our approach, offering novel insights in the context of Rician fading channels compared to traditional path loss channel models.

As the Internet of Things (IoT) becomes more integrated with everyday life, Vehicle-to-Vehicle (V2V) communication is becoming increasingly crucial for intelligent transportation systems (ITSs). However, ensuring secure V2V communication is crucial to fully realize the potential and usefulness of IoT-enabled ITS. Therefore, this article proposes a novel joint optimization framework for securing orthogonal frequency division multiple access (OFDMA)-based V2V communications in untrusted vehicle platooning networks. To address this timely nonconvex optimization problem in cooperative vehicular IoT systems, we divide it into two subproblems: 1) power control and 2) subcarrier allocation. For each subproblem, we propose dual solution strategies, prioritizing low-computational cost and emphasizing high accuracy, albeit at a higher computational expense. The low-complexity approach for subcarrier allocation utilizes channel power gains within platoons. On the other hand, the high-accuracy strategy involves a branch-and-bound (BNB) algorithm to obtain the near-global optimal solution for this nondeterministic polynomial-time (NP)-hard problem. Similarly, we introduce a low-complexity strategy based on a high-signal-to-interference-plus-noise ratio (SINR) transformation, enabling closed-form solutions through fractional programming (FP) for optimal power control, which is ideal for automated vehicles. The alternative approach adopts a direct FP transformation for precise power distribution, attaining the near-global optimum numerically but with increased computational demands. Numerical simulations are conducted to validate our theoretical assertions and to offer nontrivial vital design insights. Our policy of jointly allocating BNB subcarriers and directly controlling FP power significantly increases the sum secrecy rate by more than 58% compared to conventional schemes in typical intelligent vehicle settings for IoT environments.

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for enhanced spectral efficiency in the current and next-generation wireless networks. In this paper, we examine a realistic NOMA model where users, assisted by a regenerative relay, cannot be fully trusted. We address the challenge of ensuring secure access for these users while accounting for the error propagation in successive interference cancellation (SIC) during the decoding process. For such, we formulate and solve two optimization problems, viz. maximizing the minimum secrecy rate of the users and maximizing the sum secrecy rate of the users, while accounting for SIC errors and the constraint on the power budget. For each case, we derive the optimal power allocation solution to achieve positive secrecy rates despite imperfect SIC. Simulation results provide key insights on the obtained secrecy rates and power allocations, factoring in residual interference. The joint optimal solution for the decoding order and power allocation is compared with different benchmark schemes: optimal decoding order and equal power allocation, fixed decoding order and equal power allocation, fixed decoding order and optimal power allocation, and optimal decoding order and channel-based power allocation. Our proposed framework demonstrates average performance gains of about 47.62 dB, 50.79 dB, 54.02 dB and 39.83 dB over these schemes and, hence, the fact that the proposed framework can substantially improve the secrecy performance.

The security of confidential information associated with devices in the industrial Internet of Things (IIoT) network is a serious concern. This article focuses on achieving a non orthogonal multiple access (NOMA)-enabled secure IIoT network in the presence of untrusted devices by jointly optimizing resources, such as decoding order and power allocated to devices. Assuming that the devices are resource-constrained for performing perfect successive interference cancellation, we characterize the residual interference at receivers with the linear model. Firstly, considering all possible decoding orders in an untrusted scenario, we obtain secure decoding orders that are feasible to obtain a positive secrecy rate for each device. Then, under the secrecy fairness criterion, we formulate a joint optimization problem of maximizing the minimum secrecy rate among devices. Since the formulated problem is non-convex and combinatorial, we first obtain the optimal secure decoding order and then solve it for power allocation by analyzing Karush–Kuhn–Tucker points. Thus, we provide the closed-form global-optimal solution of the formulated optimization problem. Numerical results validate the analytical claims and demonstrate that the conventional decoding order and assigning more power allocation to weak devices, as presumed in many NOMA works, is not an optimal strategy from the secrecy fairness viewpoint. Also, average percentage gain of about 22.75%, 50.58%, 94.59%, and 98.16%, respectively, is achieved by jointly optimized solution over benchmarks ODEP (optimal decoding order, equal power allocation), ODFP (optimal decoding order, fixed power allocation), FDEP (fixed decoding order, equal power allocation), and FDFP (fixed decoding order, fixed power allocation).

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique to improve the spectral efficiency of the fifth-generation (5G) and beyond networks. However, the successive interference cancellation (SIC) based decoding used at the receivers makes NOMA prone to critical security risks. In this paper, we consider a regenerative relay-assisted dual-user downlink NOMA communication model. To ensure the robustness of the model, we also take into account the error propagation in SIC occurring in the decoding process. Our design goal being to provide security to both users, we propose an optimal power management strategy, so as to maximize the secrecy rate of the users under the impact of imperfect SIC. The optimal power allocation solution is obtained such that positive secrecy rate is achieved at both of the end receivers, while accounting for SIC errors. Analytical expressions of the secrecy rates are derived to analyze the secrecy performance. Simulation results are also presented, and provide key insights on the obtained secrecy rate and power allocation coefficients with residual interference. The achieved gains prove that the proposed model can substantially improve the secrecy performance.

This paper focuses on secure backscatter transmission in the presence of a passive multi-antenna eavesdropper through a symbiotic radio (SR) network. Specifically, a single-antenna backscatter device (BD) aims to transmit confidential information to a primary receiver (PR) by using a multi-antenna primary transmitter's (PT) signal, where the received symbols are jointly decoded at the PR. Our objective is to achieve confidential communications for BD while ensuring that the primary system's quality of service (QoS) requirements are met. We propose an alternating optimisation algorithm that maximises the achievable secrecy rate of BD by jointly optimising primary transmit beamforming and power sharing between information and artificial noise (AN) signals. Numerical results verify our analytical claims on the optimality of the proposed solution and the proposed methodology's underlying low complexity. Additionally, our simulations provide nontrivial design insights into the critical system parameters and quantify the achievable gains over the relevant benchmark schemes.

Backscatter communication (BackCom) has been gaining a lot of interest as a low-energy consumption energy harvesting solution. Here, the limited transmission range of BackCom systems constraint can now be resolved by using mobile data collectors or readers such as unmanned aerial vehicles (UAVs). In this study, we investigate a monostatic UAV-assisted BackCom system where backscatter devices (BDs) are served in a time-division multiple access (TDMA) fashion. Here we solve an energy efficiency (EE) maximization problem by jointly optimizing the transmit power allocation and the UAV's trajectory while adhering to the quality of service constraints. Since the problem is non-convex and combinatorial in nature, we employ a reinforcement learning framework that utilizes a finite-state Markov decision process. We introduce a low-complexity Ex-pected State-Action-Reward-State-Action (ESARSA) algorithm to determine the UAV's optimal trajectory with power allocation. A closed-form solution is proposed for global power optimization. In simulations, we compare the implemented ESARSA against the State-Action-Reward-State-Action (SARSA) and Q-learning algorithms and show that the proposed ESARSA algorithm can provide a 24% gain over the fixed allocation benchmark.

Reconfigurable intelligent surface (RIS) is considered a promising technology in sixth-generation (6G) networks due to its ability to configure the phase of signals using low-cost reflecting elements. An RIS-aided device-to-device (D2D) communication system operating in underlay mode is considered in this work. Assuming the presence of a direct link between the D2D pair, we present a closed-form expression of signal-to-interference and noise ratio (SINR) at the D2D user. We aim to solve the SINR maximisation problem by jointly optimising the power allocated to the D2D source and the RIS placement. A closed-form global optimal solution to the proposed optimisation problem has been obtained. Numerical results are presented to validate the analytical framework. Our proposed solution can yield a significant gain of around 4.65 dB over the benchmark.

Due to high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) are emerging as an alternative for reliable wireless communications. The main challenge of integrating UAVs with cellular networks is their limited on-board energy capacity, which restricts their operation period. Hence, this article examines the energy-efficiency (EE) maximization under the constraint of UAV’s propulsion and data reception energy. Specifically, the formulated problem optimizes the user associations with UAV or base station, their respective transmit power allocations, and UAV’s trajectory subject to the user data rate requirements. As this joint optimization problem is combinatorial and involves multiple variables, we have reduced it into an equivalent tractable form using the Markov decision process (MDP). Later we leverage the deep reinforcement learning (DRL) framework based on a deep deterministic policy gradient (DDPG) algorithm to learn the UAV’s trajectory. The proposed green DRL algorithm improves the total EE of the system by 15.63% compared to the benchmark particle swarm optimization.

With the increasing popularity of WiFi in recent years, WiFi-based wireless sensing technologies have attracted tremendous research. As commercial WiFi networks transition from the 2.4GHz band into the 5GHz band, no prior work has explicitly investigated the impact that this choice of frequency has on sensing outcomes. On both frequency bands, this paper uses the frequency selective behaviour of CSI and a support vector machine classifier to verify the accuracy of human surveillance applications. These experiments demonstrate that 5GHz WiFi offers superior sensing outcomes, with a 6% increase in human occupancy counting accuracy. By restricting the domain of Orthogonal Frequency Division Multiplexing (OFDM) subcarriers, we perform further experiments to conclude that the increase in accuracy is primarily a result of having the higher number of subcarriers or the larger bandwidth for 5GHz WiFi. To corroborate the robustness of our sensing system, we conduct the experiments over multiple trials for two very different environments; one being a controlled elevator setup, while the other being a more realistic and uncontrolled, office space. This novel experimental investigation motivates the need for conducting more comparison studies across the electromagnetic spectrum to identify the application specific best frequency spectrum for the next generation of wireless sensing technologies.

Non-orthogonal multiple access (NOMA) has emerged as an enabling solution for 5th generation and beyond networks, but the often-neglected issues due to successive interference cancellation (SIC) based decoding might seriously hamper its performance. In this paper, we study a downlink NOMA system with cooperative half-duplex relaying with imperfect SIC, where the base station is communicating with two untrusted users with the aid of a trusted decode-and-forward relay while also considering the availability of direct links from the source. An optimization problem is formulated for maximizing the secrecy rate of the near user while fulfilling the quality-of-service requirements of the far user. The optimal power allocation solution is derived while considering the impact of SIC error. The simulation results illustrate the exactness of the theoretical analysis alongside insightful discussions to investigate the impact of imperfect SIC.

This paper proposes a novel cooperative transmission protocol for a three-user backscatter communication (BackCom) in the Internet of Things. Specifically, the optimal time-division multiple access based transmission protocols for the 2-hop and 3-hop cooperative schemes in the BackCom system have been developed to maximize the underlying system throughput. In the investigated monostatic BackCom system, a reader simultaneously transmits the radio frequency signal to the backscatter tags in the downlink and receives the backscattered signals in the uplink. In the 2-hop cooperative scheme, either one of the near-apart tags serves as a cooperative decode-and-forward agent that relays the far-apart tag’s information to the reader. On the other hand, the farthest tag collaborates with the two near-apart tags to relay its information to the reader in the 3-hop cooperative scheme. Since we aim to maximize the system throughput of the cooperative BackCom system, we formulate the maximization problem of minimum throughput among the tags in both 2-hop and 3-hop cooperative schemes. Then, we investigate the optimal time allocation for maximizing the minimum system throughput for both 2-hop and 3-hop cooperative schemes. Subsequently, we introduce auxiliary variables to the original problems and convert them into equivalent linear programming problems. The numerical results verify the utility of the cooperative schemes in different transmission channels and tags’ placement. In our investigation, the proposed 3-hop cooperative scheme obtained an average gain above 30% over the non-cooperative scheme.

Mobile-edge computing (MEC) and intelligent reflecting surface (IRS) are envisioned as two promising technologies that enable massive connectivity in the future Internet of Things (IoT) networks. MEC allows IoT devices (IDs) to offload their computation intensive tasks and, thus, can prolong their lifespan. In contrast, the IRS can enhance the channel condition between IDs and the access points (APs), which are co-located with the MEC server. Wireless power transfer technique enabling energy harvesting for IDs helps realizing sustainable IoT network. This article applies IRS in a multi-ID MEC system for better latency performance. We first propose a multiple access scheme with hybrid frequency-division and nonorthogonal access technologies and then design a timing protocol for the IDs. Based on the above design, we study the latency optimization problem with the joint optimization of power allocation, the IRS phase shift matrix, and uplink and downlink beamformer under maximum power constraint for the IDs and AP. To tackle the formulated multivariable nonconvex problem, we split the target problem into several subproblems and provide a near-optimal low-complexity ID clustering scheme. Afterward, we derive optimal solutions to these subproblems, and a low-complexity fast-convergence alternating algorithm is proposed to minimize the overall latency. Presented simulation results verify the convergence of the alternating algorithm, and its superiority over the benchmarks.

The usage of passive intelligent surface (PIS) is emerging as a low-cost green alternative to massive antenna systems for realizing high-energy beamforming (EB) gains. Considering the limited computational capability and constant-envelope precoding for PIS, we propose three novel low-complexity passive EB designs for optimizing the efficacy of PIS-assisted energy transfer (PET) from a multiantenna power beacon (PB) to a single-antenna energy harvesting (EH) user. The first EB design involves solving a univariate equation, and closed forms expressed are presented for the other two. Further, to maximize the practical utility of PET, we introduce a novel channel estimation (CE) protocol for obtaining least-squares estimators for the channels as required for EB designing. Using them, we also derive closed-form expressions for optimal PIS location and optimal time allocation between CE and PET within each coherence block to maximize the user’s net harvested energy. Numerical results verify the CE analysis and validate the novel analytical bound derived for received power during PET and proposed PIS designs’ quality against existing benchmarks. We show that the proposed jointly optimal design for PET can yield a significant improvement of about 15 dB, and a reduced active array size at PB can achieve the desired EB gain with sufficient passive elements at PIS. Finally, we also briefly discuss how the proposed CE and EB designs can be extended to the multiuser settings.

Unmanned aerial vehicles (UAVs) have emerged as a specular technology that can assist the terrestrial base stations. However, the battery limitation of UAV inhibits the system performance by decreasing the overall lifespan of coverage provided by the UAV, driving the necessity of replacement and recharging. Thus, the energy-depleted UAV must be returned to a charging station and be replaced by a fully charged UAV to increase the service span. Therefore, this paper presents a novel framework of UAV replacement to maintain coverage continuity in a UAV-assisted wireless communication system when a serving UAV runs out of energy. Our objective during this replacement process is to maximize the minimum achievable throughput to the UAV-served ground users by jointly optimizing the three-dimensional (3D) multi-UAV trajectory and resources allocated to the users from the individual UAVs. The formulated problem is non-convex for which an efficient algorithm based on successive convex approximation and alternating optimization is proposed. Numerical results provide insights into the UAV trajectories and the effectiveness of the proposed scheme compared to the existing benchmark schemes.

WiFi sensing has gained tremendous traction due to its inherent advantages in terms of privacy and ubiquity. Recent work has shown the ability to sense physical environments, such as counting the number of human occupants. These results have traditionally been achieved using statistical features on WiFi Channel State Information (CSI) amplitude, however more recently there has been interest in exploiting Image based Machine Learning (ML) techniques to achieve better outcomes. In this work, we produce Probability Mass Function (PMF) Images on WiFi CSI, to create spectral maps which clearly distinguish between different human occupancies. We validate our PMF images with common default CNN architectures such as GoogleNet, ResNet and ShuffleNet. By changing the filter size and training parameters, we improve the performance of ShuffleNet from 84% to 98%. Furthermore, we demonstrate how the PMF images can be optimised for sensing outcomes, by controlling the image resolution.

With the emergence of next generation networks, Network Traffic Classification (NTC) has seen greater importance in network management and security. Recently, Channel State Information (CSI) based WiFi sensing techniques have shown their potential for NTC applications [1], [2] as a privacy-preserving yet effective tool. As CSI could be prone to interference, this paper examines the performance of CSI-based NTC models under interference-induced adversarial attacks. Specifically, the impact of spectral allocation of the interference, underlying interfering network traffic type, and physical location of the interference are studied and quantified. We conducted experiments using off-the-shelf devices to test the NTC performance, with and without the adversarial interference attack of ping, buffered video streaming, and live video streaming network traffics. Subsequently, we found that spectral allocation of the attacking interference and the underlying traffic types of interference could be used to deceive the established NTC model, and different network traffic types show different robustness across the interference cases. Namely, ping suffers the most in the spectrally manipulated attack, with the classification accuracy down to as low as 23.2%, whereas Twitch might be completely misidentified as other traffic in underlying traffic type controlled attack.

In this paper, we present a novel framework to maintain coverage continuity in a unmanned aerial vehicle (UAV)-assisted wireless communication system, when a serving UAV runs out of energy. Service continuity is maintained by launching another fully charged UAV to replace the existing serving UAV. This replacement process must ensure maximal coverage to all ground users. Our objective during this replacement process is to maximize the achievable sum rate of all ground users by jointly optimizing the three-dimensional (3D) multi-UAVs trajectory and resource allocation to the users from the individual UAVs. This is carried out in the presence of the UAV’s constraints on velocity, collision avoidance, and energy availability while considering a more practical and accurate probabilistic line-of-sight (LoS) channel model. This results in a non-convex optimization problem for which an efficient iterative algorithm based on successive convex approximation and alternating optimization is proposed. Numerical results are provided to obtain insights on the UAV trajectories and the effectiveness of the proposed scheme compared to the existing benchmark schemes is shown.

With the Internet of Things becoming an essential part of our lives, Vehicle-to-vehicle (V2V) communication is becoming very popular to meet the growing demands of having an Intelligent Transportation System (ITS). However, ensuring secure V2V communication is the key to realizing the full potential and utility of ITS. Therefore, this paper develops a secure framework for orthogonal frequency division multiple access (OFDMA) based V2V communication among the untrusted platoons. Specifically, we propose optimal subcarrier and power allocation policies to maximize the sum secrecy rate across the vehicles which share the subcarriers with the vehicles outside their platoons. We start with allocating sub-carriers to the vehicles inside each platoon based on their underlying channel power gains. After that, noting the non-convexity of the power allocation problem, we propose a high signal-to-interference-plus-noise ratio-based transformation to develop two optimal power control policies. While both are based on fractional programming (FP), one yield closed-form power allocation and the other provides the optimal global solution numerically at a higher computational complexity. Lastly, the numerical simulations verify the analytical claims, provide key design insights and demonstrate that our proposed power control policies can improve the sum secrecy rate of benchmark schemes by over 4dB for the eight vehicles setting.

Non-orthogonal multiple access (NOMA) has been considered as a promising solution to provide spectrally efficient communications in the 5th generation and beyond networks. Cooperative communication, on the other hand, helps in improving the coverage and reliability of the communications. In this paper, we consider a cooperative NOMA communication system with two untrusted users and a trusted amplify-and-forward relay. Maximization of the secrecy rate of the near user while considering the service requirements of the far user is tackled. Closed-form expressions are obtained for the optimal power sharing between the source and the relay, along with the optimal power allocation for both users. Numerical results are also provided, which verify the exactness of the theoretical analysis and provide insights on the design of secure NOMA-based cooperative communication networks.

Backscatter communication (BSC) has the potential to realise the ubiquitous proliferation of internet-of-things (IoT) technology. Bistatic BSC offers greater scalability and range over conventional BSC, making it a competent contender for its implementation in the IoT. We propose a robust, novel two-phase channel estimation (CE) protocol for bistatic BSC, which involves finding the required channel estimates at the multiantenna emitter and multiantenna reader and the appropriate pilot signal designs for each phase. We derived closed-form expressions for the emitter-to-tag channel estimate at the emitter and the tag-to-reader channel estimate at the reader without prior knowledge of channel and noise statistics. After that, we explore the utility of the proposed assessments by using them for transceiver (emitter-reader) design at the emitter and reader. Specifically, an optimal design maximising the reader signal-to-noise (SNR) is investigated. Finally, extensive numerical simulations verify the accuracy and utility of the proposed estimates.

To understand the potential threat of the energy-efficient jamming attacker in a secure orthogonal frequency division multiple access (OFDMA) based industrial internet of things (IIoT) network, in this work, we investigate the optimal jamming to IIoT users under limited power constraint. In particular, we design an optimization problem to maximize the attacker energy-efficiency (AEE) by jointly optimizing the power allocation over the subcarriers of the IIoT users. To realize its globally optimal solution, first, we formulate an equivalent optimization problem by converting the original fractional objective function into a parametric subtractive concave function. Thereafter, the optimal point is obtained using a superlinear fast converging iterative algorithm based on Dinkelbach method that exploits Karush-Kuhn-Tucker (KKT) conditions. Via numerical results, we obtain various insights on the system performance with respect to the system parameters and lastly, the jointly optimal scheme is compared with other benchmark jamming strategies to quantity the performance.

Internet of Things (IoT) is a cyber-physical architecture bringing a new revolution by developing an intelligent environment and incorporating trillions of sensing, processing, and transmission units. Due to battery energy constraints, it is critical to sustain many IoT devices’ network operations in diverse applications of weather monitoring, smart agriculture etc. In this regard, solar energy harvesting has emerged as a potential solution in developing a net-zero energy IoT (NZ-IoT) architecture to quantify sustainability. For NZ-IoT, this article examines the practical viability of a solar energy harvesting model for empirically characterizing the solar panel dimension (PD) in cm2and panel outlay (PO) in INR (Indian Rupee). Furthermore, the sensing outlay and processing outlay (which include both the memory and transmission outlay of sensed data) are also determined. It is accomplished by first developing an IoT-based consumption model and then establishing an empirical relationship between panel dimension and panel outlay. Finally, the simulation results of solar panel dimensions and outlay are computed for a net-zero energy IoT system.

Tag-to-tag backscattering is emerging as a promising technology to realise cooperative symbiotic radio communications. This paper aims to maximise the system throughput and backscattering range for multiple and single antenna readers. Specifically, we consider optimal transceiver designs for the multiantenna reader and inter-node location between the tags to maximise the sum of tag-to-tag (T2T) and monostatic backscattering throughput. Whereas, for the single-antenna scenario, we obtain the quality-of-service-aware maximum backscattering range that satisfies the minimum throughput requirements for T2T and tag-to-reader communications. For both scenarios, we have proved the global optimality of the underlying proposed numerical solutions. Apart from numerical solutions, we also proposed low complexity semi-closed-form solutions for sum throughput maximisation in multiantenna reader case and analytical approximations for range maximisation in single antenna case. Finally, simulations validate the proposed solution’s effectiveness and provide nontrivial design insights while demonstrating around 90% improvement over the benchmark.

This paper explores a novel cooperative timing protocol in two-user backscatter communication (BSC) network, where one Reader transmits a wireless energy signal to two collaborative backscatter tags. These tags modulate the incident signal and backscatter its information to the Reader. Specifically, the tag closer to the Reader uses its resources to help relay the far tag’s information to the Reader to reduce the effect of the doubly near-far problem in BSC. We aim to maximize the transmission range of the farther tag while satisfying the Quality of Service (QoS) requirement in terms of throughput and energy threshold. First, we derive the necessary conditions for the proposed problem’s feasibility for throughput and energy constraints. Then, we solve this non-convex range maximization problem by alternatively optimizing the time allocation for maximizing minimum throughput and energy, respectively, and the transmission range. The numerical simulations have been conducted to provide insight into the impact of energy and throughput base QoS demand on the achievable transmission range. The average gain of the proposed cooperative BSC over the non-cooperative one is $\gt 20$%.

In an unmanned aerial vehicle (UAV)-enabled Inter-net of Thing (IoT) network, the transmission rate of an IoT node from the aerial base station (ABS) is determined by the signal-to-noise ratio (SNR). In general, the characterization of the ABS-to-node (A2N) channel is determined by its instantaneous SNR. The previous works reported in this direction have considered the average SNR. This study fills the gap in understanding how fading affects the network’s performance. In this paper, we consider a UAV-enabled IoT network, where multiple ABSs are deployed to serve the IoT nodes. We model the A2N channel based on the instantaneous channel; we consider a generalized fading model, the Nakagami-m model for the A2N link, to provide a more generic analysis. Through analytical insights, we obtain the exact and closed-form approximate expression of the association probability of IoT node with the ABS and infer the capacity enhancement obtained by considering fading compared to the average SNR scenario. The average improvement in ergodic capacity is around 10% compared to the average fading scenario. Furthermore, the results show that considering fading is beneficial for low-altitude scenarios, as it provides a significant increase in the node association probability compared to the average fading scenario.

Graphene-based intelligent reflecting surface (GIRS) has been proved to provide a promising propagation environment to enhance the quality of high frequency terahertz (THz) wireless communication. In this paper, we characterize GIRS for THz communication (GITz) using material specific parameters of graphene to tune the reflection of the incident wave at IRS. In particular, we propose a GITz design model considering the incident signal frequency material level parameters like conductivity, Fermi-level, patch width to control the reflection amplitude (RA) at the communication receiver. We have obtained the closed-form expression of RA for an accurate design and characterization of GIRS, which is incomplete in the existing research due to the inclusion of only phase-shift. The numerical simulation results demonstrate the effectiveness of the proposed characterization by providing key insights.

Non-orthogonal multiple access (NOMA) has been recognized as one of the key enabling technologies for future generation wireless networks. Sharing the same time-frequency resource among users imposes secrecy challenges in NOMA in the presence of untrusted users. This paper characterizes the impact of user-pair selection on the secrecy performance of an untrusted NOMA system. In this regard, an optimization problem is formulated to maximize the secrecy rate of the strong user while satisfying the quality of service (QoS) demands of the user with poorer channel conditions. To solve this problem, we first obtain optimal power allocation in a two-user NOMA system, and then investigate the user-pair selection problem in a more generalized four user NOMA system. Extensive performance evaluations are conducted to validate the accuracy of the proposed results and present valuable insights on the impact of various system parameters on the secrecy performance of the NOMA communication system.

To develop a cyber-physical artificial intelligence enabled wireless network, it is essential to support unprecedented high throughput and efficient spectrum utilization in a practically unknown channel. In this regard, we need to investigate the design aspects of the network exploiting deep learning-based non-orthogonal multiple access (NOMA) for a model-free environment. In this work, a model-free deep learning algorithm based on deep deterministic policy gradient is proposed that provides a continuous course of actions under the optimal policy for an untrusted NOMA network. Utilizing the concept of physical layer security, we focus on maximizing the sum secrecy rate of the system in terms of decoding order and transmitting power allocation to users under the limited energy constraint at the base station. Via extensive simulations, while training, we measure the performance of the deep learning algorithm in terms of cumulative sum secrecy rate, convergence rate and stability. Also, after the training, we obtain various insights on the performance of the obtained optimal policy by varying the independent system parameters and compare the algorithm against a benchmark that provides the improvement of nearly 55% in the noisy channel.

Cooperative relaying in RF powered communication solves the problems related to long range energy and information transfer. However, there is a necessity of learning based algorithms for incorporating composite processes in an unknown environment, to get the optimal policy for efficient energy and information transfer. In this paper, we propose a deep learning algorithm based on deep deterministic policy gradient (DDPG), providing continuous course of actions under optimal online policy for selection based integrated information and energy relaying (Si2ER) network. The designed problem defined is a nonconvex problem where the end-to-end average net bit rate is maximized in the four phases of operations under the given constraints on the harvested energy at relay and source nodes. Via extensive simulations, more insights are obtained on the performance of the proposed algorithm in different used modulation for transmission and learning rate while and after learning. Finally, the achieved bit rate in the Si2ER network is compared with the performance of a greedy benchmark scheme and get an improvement upto 70.72%.

Intelligent reflecting surface (IRS) has emerged as a transforming solution to enrich wireless communications by efficiently reconfiguring the propagation environment. In this paper, a novel IRS circuit characterization model is proposed for practical beamforming design incorporating various electrical parameters of the meta-surface unit cell. Specifically, we have modelled the IRS control parameters, phase shift (PS) and reflection amplitude (RA) at the communication receiver, in addition to the circuit level parameter, variable effective capacitance C of IRS unit cell. We have obtained closed-form expressions of PS, RA and C in terms of transmission frequency of signal incident to IRS and various electrical parameters of IRS circuit, with a novel touch towards an accurate analytical model for a better beamforming design perspective. Numerical results demonstrate the efficacy of the proposed characterization thereby providing key insights.

Non-orthogonal multiple access (NOMA) has come to the fore as a spectral-efficient technique for fifth-generation and beyond communication networks. We consider the downlink of a NOMA system with untrusted users. In order to consider a more realistic scenario, imperfect successive interference cancellation is assumed at the receivers during the decoding process. Since pair outage probability (POP) ensures a minimum rate guarantee to each user, it behaves as a measure of the quality of service for the pair of users. With the objective of designing a reliable communication protocol, we derive the closed-form expression of POP. Further, we find the optimal power allocation that minimizes the POP. Lastly, numerical results have been presented which validate the exactness of the analysis, and reveal the effect of various key parameters on achieved pair outage performance. In addition, we benchmark optimal power allocation against equal and fixed power allocations with respect to POP. The results indicate that optimal power allocation results in improved communication reliability.

In this article, we aim to study an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication system where a rotary-wing UAV travels from the initial to the final location to communicate with multiple IoT ground devices. The limited onboard energy of the UAV poses a constraint to the overall system’s performance. UAV’s energy consumption is majorly based on its kinematics, i.e., UAV’s velocity and acceleration. Therefore, in this work, we maximize the sum user throughput by jointly optimizing the 3-D UAV trajectory, and velocity-time profile in the presence of onboard energy, velocity, acceleration, and completion time constraints. Noting the nonconvexity of the optimization problem, the original problem is decoupled into two subproblems. First, the trajectory is optimized considering the velocity constraint, while in the second subproblem, the velocity and time optimization in each time slot is carried out. Simulation results show insights on the UAV trajectory and velocity-time profile with the variation in the onboard energy availability. In addition, we demonstrate the superior performance of the proposed approach in comparison to the benchmark schemes.

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Beamforming technology can enhance the throughput of a multi-antenna secondary user (SU) system. However, its performance depends on the accuracy of channel estimation (CE) that is influenced by CE duration. In this work, we present a three-phase transmission approach for a multi-antenna SU, where, in the first phase, the SU performs spectrum sensing, thereafter, the channel is estimated in the second phase followed by the data is transmitted using beamforming in the third phase. Consequently, based on it, we define a frame structure and closed-form expressions for SU's average throughput as well as interference energy received at the primary user's (PU's) receiver are derived. Numerical results validate the analysis and provide insights on the impact of frame duration and CE duration on SU's average throughput. Lastly, we investigate the throughput-interference tradeoff problem numerically with different values of CE duration.

In this paper, we study and optimize the trajectory for unmanned aerial vehicle (UAV) to provide fifth-generation (5G) cellular service to users in a given area. We consider a single UAV launched from the fixed initial to the final location, during which it serves the ground users that are distributed in a circular field. We introduced an optimization problem to minimize the average outage probability of the system by optimizing the three-dimensional (3D) trajectory of the UAV. As this problem is nonconvex, we proposed an efficient approach that involves two steps, firstly it frames a new problem to obtain a globally optimal location in 3D space for minimal average outage probability. This problem is shown to be conditionally convex and an efficient algorithm is proposed to obtain a globally optimal location within an acceptable tolerance. Thereafter, a sub-optimal solution is proposed for the original problem. Simulation results provide useful insights into the sub-optimal trajectory of UAV and show that our proposed approach on an average provides 24% outage improvement over the benchmark scheme. This improvement is further enhanced with the increase in the velocity of UAV.

To provide seamless services by low powered small devices having stringent energy constraint in Internet of Things (IoT) networks, it is highly sought over the past few decades to design a competent energy-aware network. In that sense, thereof, the strong minimum energy topology (SMET) is investigated in the existing works to minimize the total power consumption while maintaining the strong connectivity between any pair of nodes (small IoT devices) in the network consisting only bidirectional links. Nevertheless, to significantly improve it, in this paper, we further explore the SMET with respect to insertion of a new node among the existing nodes which is defined as node insertion problem (NIP) for SMET (NIP-SMET). It has been proved that the NIP-SMET is NP-complete, therefore, we propose a heuristic based on Prim-incremental power greedy heuristic to solve it in polynomial time. Also, analytically, it has been shown that the NIP-SMET can provide significant energy-aware improvement over SMET. Via obtained numerical results, we find that the proposed heuristic can reduce the total power dissipation by 50% against the existing heuristic for SMET.

This paper presents a novel joint transmit power sharing and relay placement scheme for decode-and-forward relay assisted secure communication to a legitimate user in presence of external eavesdropper. Observing that secrecy outage minimization problem for the trusted user is non-convex, key insights on optimal power sharing between source and relay are first presented to derive an equivalent single variable problem. Next, tight analytical bounds for optimal relay placement are discoursed to ultimately come up with a computationally-efficient jointly global optimization algorithm. Finally, selected numerical results validate analysis, present key insights, and demonstrate performance gains of around 30dB over benchmark schemes.

WiFi Sensing has received tremendous attention in Recent Literature, demonstrating the ability to leverage ubiq-uitous commercial WiFi devices to sense Human activities and environmental occupancy. We identify that in all environments fire-safety is vital, and this paper demonstrates the suitability for using WiFi to sense fire. Using commodity Raspberry Pi devices on the 5GHz WiFi band we demonstrate a temporal shift in WiFi Channel State Information (CSI) Amplitude, before, during, and after the ignition of a flame. We further emphasise the presence of fire by observing the spread of CSI Amplitudes, noting that CSI takes much more diverse values in the presence of fire. This result is exacerbated by the frequency selective behaviour of OFDM subcarriers, where some subcarriers displayed larger variation in CSI amplitude due to the fire. The WiFi Fire sensing model was evaluated in an ideal setup with a gas flame to remove material deformation as a variable, and subsequently in a real-world scenario with the ignition of building cladding.

Extensive literature has shown the possibility of using WiFi to sense large scale environmental features such as people, movement, and human gestures. To our best knowledge, there has been no investigation on identifying the microscopic changes in a channel due to atmospheric temperature variations. We identify this as a real world use case, since there are scenarios such as Data Centres where WiFi traffic is omnipresent and temperature monitoring is important. We develop a framework for sensing temperature using WiFi Channel State Information (CSI), proposing that the increased kinetic energy of ambient gas particles will affect the wireless link. To validate this, our paper uses low wavelength 5GHz WiFi CSI from commodity hardware to measure how the channel changes as the ambient temperature is raised. Empirically, we demonstrate that the CSI amplitude value drops at a rate of 13 per degree Celsius rise in the ambient temperature based on the testing platform, and developed regressions models with ± 1°C accuracy in the majority of cases. Moreover, we have shown that WiFi subcarriers exhibit a frequency-selective behaviour in their varying responses to the rise in ambient temperature.

Energy Borrowing (EB) aided Energy harvesting (EH) systems provide a greener alternative to self-sustaining electronic devices in a complex, unprecedented environment by borrowing energy from a supplementary source to regulate the data transmission flow. We propose a reinforcement learning-based algorithm for energy scheduling policy which jointly optimizes the EB and utilizes harvested energy for efficient data transfer at every time instant. As the exact pattern of harvested energy and channel conditions at any time slot is unknown, the proposed algorithm, BEAR (Borrowing Energy with Adaptive Rewards), based on actor-critic architecture, learns the optimal power allocation policy for the transmission node. Our designed reward function accommodates the concept of adaptive penalty to punish the transmission node for selecting unfavourable actions. Our simulations show that the BEAR algorithm providing efficient energy management with a focus on throughput maximization yields a 35.45% enhancement in sum throughput over a typical non-borrowing system. Lastly, nontrivial design insights are outlined via numerical results to quantify the practical efficacy of BEAR for EH systems.

Non-orthogonal multiple access (NOMA) serves multiple users simultaneously via the same resource block by exploiting superposition coding at the transmitter and successive interference cancellation (SIC) at the receivers. Under practical considerations, perfect SIC may not be achieved. Thus, residual interference (RI) occurs inevitably due to imperfect SIC. In this work, we first propose a novel model for characterizing RI to provide a more realistic secrecy performance analysis of a downlink NOMA system under imperfect SIC at receivers. In the presence of untrusted users, NOMA has an inherent security flaw. Therefore, for this untrusted users' scenario, we derive new analytical expressions of secrecy outage probability (SOP) for each user in a two-user untrusted NOMA system by using the proposed RI model. To further shed light on the obtained results and obtain a deeper understanding, a high signal-to-noise ratio approximation of the SOPs are also obtained. Lastly, numerical investigations are provided to validate the accuracy of the desired analytical results and present valuable insights into the impact of various system parameters on the secrecy rate performance of the secure NOMA communication system.

Unmanned Aerial Vehicle (UAV) deployment as an aerial base station in fifth-generation (5G) communication system has emerged as a promising technology to provide seamless communication in a geographical region. The UAV’s high mobility potential offers additional degrees of freedom for effective deployment. Therefore, the three-dimensional (3D) deployment of UAV is one of the key challenges in UAV-assisted communication systems. In this paper, we address the problem of UAV deployment in 3D space to provide on-demand coverage to the ground users to maximize the sum rate. We consider realistic UAV-ground channel model derived from extensive experiments. The problem formulated is non-convex. To obtain the optimal location, we approximate the rate expression to identify the concave regions and propose a low-complexity solution by applying alternating optimization. Through simulation results, we provide valuable insights into the low-complexity solution.

In this paper, we have proposed an energy-efficient unmanned aerial vehicle (UAV) assisted Internet of things (IoT) network where a low altitude UAV is employed as a mobile data collector. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, device association and respective transmit power allocation at every time slot while ensuring that every device should achieve a given transmission rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed as a Markov decision process (MDP) where UAV learns its trajectory according to its current state and corresponding action aiming to maximize the reward under the current policy. Finally, we conceive state-action-reward-state-action (SARSA), a low complexity iterative algorithm for updating the current policy in the case of randomly deployed IoT devices which achieves good computational complexity-optimality tradeoff via numerical results. We find that the proposed methodology reduces the total energy consumption of all devices by 9.23%, 14.06%, and 15.87% in the case of 80, 100, and 120 available time slots of UAV respectively.

Intelligent reflecting surface (IRS) is an emerging technology for beyond fifth-generation (B5G) networks conceived from metamaterials that enhances the communication channel through controllable passive reflecting of transmit signals. However, the IRS-assisted communication model and optimization of available resources need to be improved further from existing works. In this paper, we obtain the expression of reflection coefficient of IRS panel by exploiting the given data of radar communications. And, using the reflection coefficient, we derive the expression of received power that incorporates the free space loss, reflection loss factor, physical dimension of the IRS panel, and radiation pattern of the transmit signal. Moreover, to maximize the received power, we jointly optimize the reflective phase shift and location of the IRS panel. To obtain more pursuits, we also investigate the semi-adaptive schemes where the phase shift and location are individually optimized while keeping other at a fixed value and examine the global optimality of obtained solutions. Lastly, via obtained numerical results, we get key insights on proposed analysis and optimal solution for different schemes.

We study a wireless powered network, including a power beacon (PB), an energy-harvesting (EH)-based source, and multiple users. To improve the spectrum efficiency of the network as well as for practical implementation consideration, two users are paired to perform nonorthogonal multiple access (NOMA) transmission. Specifically, the NOMA-based transmission protocol consists of two phases, where the source harvests energy from the PB in the first phase, and then sends a superimposed signal to the paired users in the second phase. We derive exact and asymptotic closed-form expressions of the average throughput for each paired user. Then, the joint optimization problem for the time and power allocation is investigated to achieve the optimal fair performance of the paired users. To provide a benchmark, optimal resource allocation strategy for an orthogonal multiple access (OMA)-based transmission protocol is also studied. Simulation results confirm the validity of our analytical derivations and show that the considered network with NOMA transmissions is superior to that with OMA transmissions, especially when the transmit power of the PB is low and the paired users have significant differences in channel gain.

Secure communication with low computational resources is a critical issue in the Internet-of-Things (IoT) implementations. It is more challenging in the presence of hybrid adversary enabled with full-duplex (FD) capability to perform eavesdropping and jamming simultaneously. In this work we aim to address this issue through optimal subcarrier allocation towards combating the FD hybrid attacker. We begin with secrecy performance analysis in a multi-user IoT system considering statistical channel state information only of all attacker links. Novel analytical expression for the exact intercept probability is derived and a closed-form approximation is also provided. We further propose an optimisation framework for fair subcarrier allocation with a novel objective of minimising maximum intercept probability among multiple users. Considering the proposed optimisation framework as a non-convex combinatorial, we propose a low-complexity sub-optimal solution by leveraging the integer linear program (ILP) structure of the problem. To reduce the complexity further, the original problem is mapped to the assignment model and solved by exploiting its special structure with graph theory tools providing an optimal solution in polynomial time. Comprehensive investigations, conducted to verify the analysis and quantify the secrecy performance, demonstrate that proposed optimal solutions yield significant enhancement in secrecy performance over relevant schemes.

Advent of unmanned aerial vehicle (UAV) as relay in ad-hoc networks has offered numerous promising communication solutions with both military and civilian applications. Additionally with more degrees of freedom, mobile UAV relaying provides better susceptibility to changing environment conditions thereby enhancing the network performance. However, optimization of UAV altitude is a primary concern in such flying ad hoc networks (FANETs). In this work, we consider a three node decode-and-forward (DF) FANET, and the aim is to minimize the average network outage probability subject to practical mobility constraints of maximum UAV velocity and minimum allowable UAV height, thereby finally optimizing the overall UAV trajectory (UT). In particular, for pre-determined initial and final relay positions, we first prove convexity of the optimization problem and then obtain the semi-closed-form globally optimal UT solution. The analysis is numerically validated along with the discussion of multiple optimal design insights. Results reveal the respective average performance gain of 56% and 37% over the two benchmark schemes.

Due to the increased proliferation of WiFi in public and private spaces, there is interest in exploiting WiFi for spatial monitoring. In this paper, we aim to characterize movement or objects in a channel using Channel State Information (CSI). Channel state information represents the degree to which a wireless signal has been attenuated and delayed, and hence we hope to characterize different objects and multipath channel characteristics from CSI. We place different static objects and moving humans in a channel and inspect the CSI for each channel condition. From the variations in CSI Amplitude we can accurately distinguish between a person walking, squatting, or standing still in the channel. To identify static objects, we present a novel approach by inspecting the CSI of different Orthogonal Frequency Division Multiplexing (OFDM) subcarriers. This paper makes a novel contribution, by observing frequency selective behavior of CSI for different channel stimuli. This can be used to improve channel detection accuracy.

Lately, Passive Intelligent Surfaces (PIS) are being recognized to play an important role in meeting the timely demand of low-cost green sustainable Internet of Things (IoT). In this paper, we focus on maximizing the sum received power among the energy harvesting IoT users by jointly optimizing the active precoder for multi-antenna power beacon and the passive constant-envelope precoding based phase shifters (PS) design for PIS. Here, a multiuser channel estimation protocol is first introduced to obtain the least-squares estimators for the underlying effective cascaded channel links involved in the PIS assisted multi-antenna wireless power transfer as desired for the optimal precoder and PS designing. Thereafter, new semi-closed-form expressions for the proposed optimal active and passive beamforming design are derived so as to meet the low-complexity requirements of IoT communications. Finally, the numerical results are presented to validate the key nontrivial analytical claims and demonstrate the significant performance enhancement in terms of sum harvested energy among IoT users over conventional designs.

We present a novel monostatic backscatter communication (BSC) protocol for multiple-input-multiple-output (MIMO) reader to detect signals from a single-antenna tag. Understanding tags resource constraints, it involves new preamble designing that neither requires pilot transmission from tag nor channel estimation at reader. So, considering the maximum likelihood detector, we derive closed-form expressions for the optimal detection threshold and bit error rate (BER) by exploiting practically-motivated approximations for short-range BSC channels. Thereafter, analytical insights on the globally-optimal backscattering coefficients (BC) at tag for minimizing BER are provided. Lastly, the analysis is validated via simulations, while shedding key insights on impact of the reader’s array-size, rice factor, and BSC channel gains on optimal BC and achievable BER.

To address the growing concern of security in advance communication networks like Internet of things (IoT), this work aims to propose an optimisation framework for fair subcarrier allocation to encounter a full duplex (FD) attacker. We first analyse the performance of secure communication in a multi-user IoT system against an FD hybrid attacker possessing the capability of concurrent eavesdropping and jamming. Specifically, a novel analytical expression has been derived for the exact intercept probability considering incomplete information of all ungoverned communication links that include self-interference link along with eavesdropping and jamming ones. The optimisation problem of subcarrier allocation is then formulated as an integer linear program. Finally, a low-complexity suboptimal solution is proposed to circumvent the need for high computational resources for finding the optimal solution to the problem in practice. Numerical results, validating the analytical framework, report the average improvement of more than 25% in secrecy performance over relevant benchmarks. This establishes that the proposed scheme has a potential to mitigate the secrecy outage efficiently for secure and reliable communication in IoT networks.

This paper studies a two-user cooperative non-orthogonal multiple access network with a novel cooperation scheme, where the source communicates with the near user directly or through the help of K relays, while the communication to the far user relies on the help of the near user or the K relays. We propose a new amplify-and-forward (AF)-based transmission protocol in which the near user sends a 1-bit feedback to inform relays of its detection results at the end of the first phase. Based on the 1-bit feedback and the available channel state information at each relay, optimal relay selection strategy encompassing two different selection criteria is designed to minimize the system outage probability (SOP). Tight-approximated as well as asymptotic closed-form expressions of the SOP are derived. Asymptotic results illustrate that the proposed relay selection strategy can achieve the full diversity order of K + 1. Simulation results are finally provided to validate our analytical results and the superiority of the new cooperation scheme.

Energy-efficient resource allocation to achieve desired throughput over energy-aware power line communications (PLCs) has gained a growing interest from the past few years. In order to improve it further, we propose a joint solution methodology for efficient utilization of available resources to minimize energy-aware outage probability in an amplified-and-forward (AF) relay-assisted PLC. In this regard, first, we derive a closed-form expression for energy-efficiency based outage probability. Using the statistical properties of the outage probability, an equivalent problem, more tractable for optimizations, is formulated and respective closed-form solutions are obtained for individual and joint optimization of relay location and power allocation (PA) over the transmit modems. Using the numerical investigations, we validate our outage analysis and describe the design insights on the obtained optimal solution. Finally, it is shown that the joint optimization provides an outage improvement of around 55% against a benchmark scheme.

The amalgamation of non-orthogonal multiple access (NOMA) and physical layer security is a significant research interest for providing spectrally-efficient secure fifth-generation networks. Observing the secrecy issue among multiplexed NOMA users, which is stemmed from successive interference cancellation based decoding at receivers, we focus on safeguarding untrusted NOMA. Considering the problem of each user’s privacy from each other, the appropriate secure decoding order and power allocation (PA) for users are investigated. Specifically, a decoding order strategy is proposed which is efficient in providing positive secrecy at all NOMA users. An algorithm is also provided through which all the feasible secure decoding orders in accordance with the proposed decoding order strategy can be obtained. Further, in order to maximize the sum secrecy rate of the system, the joint solution of decoding order and PA is obtained numerically. Also, a sub-optimal decoding order solution is proposed. Lastly, numerical results present useful insights on the impact of key system parameters and demonstrate that average secrecy rate performance gain of about 27 dB is obtained by the jointly optimized solution over different relevant schemes.

Securing wireless communication, being inherently vulnerable to eavesdropping and jamming attacks, becomes more challenging in resource‐constrained networks such as internet‐of‐things. Towards this, physical layer security (PLS) has gained significant attention due to its low complexity. The authors address the issue of random inter‐node distances in secrecy analysis and develop a comprehensive quality‐of‐service (QoS) aware PLS framework for the analysis of both eavesdropping and jamming capabilities of the attacker. The proposed solution covers the spatially stochastic deployment of nodes. The secrecy performance is characterised against both attacks using inter‐node distance‐based probabilistic distribution functions. The model takes into account the practical limits arising out of underlying QoS requirements, which include the maximum distance between legitimate users driven by transmit power and receiver sensitivity. A novel concept of eavesdropping zone is introduced, and the relative impact of jamming power is investigated. Closed‐form expressions for asymptotic secrecy outage probability are derived offering insights into the design of optimal system parameters for desired security level against the attacker's capability of both attacks. The analytical framework, validated by numerical results, establishes that the proposed solution offers a potentially accurate characterisation of the PLS performance and key design perspective from point‐of‐view of both legitimate user and attacker.

We consider a relay-assisted non-orthogonal multiple access (NOMA) network consisting of a source (S), an energy-constrained relay (R), and two users, where a hybrid power-splitting (PS) and time-splitting (TS) scheme is applied at R for energy harvesting. We provide optimal transmission designs for such a network by jointly optimizing the transmit power of S, the TS and PS ratios, the power allocation ratios at S and R, and the user ordering (indicating which user should apply the successive interference cancellation). In particular, when full channel state information at the transmitters (CSIT) is available, our design can minimize the energy consumption while ensuring that two users correctly receive the desired information. When only partial CSIT is available, our design can minimize the system outage probability. We analytically show that the joint optimal solution for a given TS ratio can be derived in a closed form for both CSIT cases. The optimal TS ratio can be found either in a closed form or using a bisection method for the full CSIT case, while it can be found using a one-dimension search for the partial CSIT case. Finally, numerical results are provided to validate the analytical results and to evaluate the performance advantage of the hybrid PS/TS scheme with the proposed optimal designs.

In this paper, we consider a UAV-assisted small-cell having heterogeneous users with different data rate and coverage demands. Specifically, we propose a novel utility-aware resource-allocation protocol to maximize the utility of UAV by allowing it to simultaneously serve the highest possible number of heterogeneous users with available energy resources. In this regard, first we derive a closed-form expression for rate-coverage probability of a user considering Rician fading to incorporate the strong line of sight (LoS) component in UAV communication. Next since this UAV utility maximization problem is non-convex and combinatorial, to obtain the global optimal resource allocation policy we propose an iterative feasibility checking method for fixed integers ranging from lower to upper bound on the number of users that can be served by UAV. To further reduce the complexity, we formulate an equivalent problem aimed at minimizing per user energy consumption, where tight analytical relaxation on rate-coverage probability constraint is used along with semi-closed expressions for joint-optimal power and time allocation. Lastly, via detailed numerical investigation, we validate our analytical claims, present insights on the impact of key system parameters, and demonstrate that 60% more users can be served using the proposed scheme as compared to relevant benchmarks.

Spectrally-efficient secure non-orthogonal multiple access (NOMA) has recently attained a substantial research interest for fifth generation development. This work explores crucial security issue in NOMA which is stemmed from utilizing the decoding concept of successive interference cancellation. Considering untrusted users, we design a novel secure NOMA transmission protocol to maximize secrecy fairness among users. A new decoding order for two users' NOMA is proposed that provides positive secrecy rate to both users. Observing the objective of maximizing secrecy fairness between users under given power budget constraint, the problem is formulated as minimizing the maximum secrecy outage probability (SOP) between users. In particular, closed-form expressions of SOP for both users are derived to analyze secrecy performance. SOP minimization problems are solved using pseudoconvexity concept, and optimized power allocation (PA) for each user is obtained. Asymptotic expressions of SOPs, and optimal PAs minimizing these approximations are obtained to get deeper insights. Further, globally-optimized power control solution from secrecy fairness perspective is obtained at a low computational complexity and, asymptotic approximation is obtained to gain analytical insights. Numerical results validate the correctness of analysis, and present insights on optimal solutions. Finally, we present insights on global-optimal PA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%, respectively are achieved, compared to fixed PA and individual users' optimal PAs.

In resource allocation for secure cooperative communication systems, along with power allocation, relay placement also has equal importance in improving the system performance. In this paper, we study the joint optimization of relay placement and power allocation to minimize the secrecy outage probability (SOP) for a four-node cooperative system with a trusted randomize-and-forward relay in the presence of an external eavesdropper. Initially, we derive the expression of SOP, and then formulate an optimization problem to minimize SOP under given power budget and relay placement (RP) constraints. By providing analytical insights on power allocation (PA) between source and relay, we obtain closed form expressions of optimal PA (OPA) for a given RP that minimizes the SOP. Next, we propose a low complexity algorithm to obtain the near-optimal RP (ORP) for a given PA. Finally, we obtain optimal SOP with joint RP and PA. Numerical results present validation of analytical SOP through Monte Carlo simulations, optimal PA for a given RP, optimal RP for a given PA, and joint PA and RP for a given secrecy threshold rate. Finally, we highlight the significant performance gains achieved by the joint design over the conventional scheme.

Usage of passive intelligent surface (PIS) is emerging as a low-cost green alternative to massive antenna systems for realizing high energy beamforming (EB) gains. To maximize its realistic utility, we present a novel channel estimation (CE) protocol for PIS-assisted energy transfer (PET) from a multiantenna power beacon (PB) to a single-antenna energy harvesting (EH) user. Noting the practical limitations of PIS and EH user, all computations are carried out at PB having required active components and radio resources. Using these estimates, near-optimal analytical active and passive EB designs are respectively derived for PB and PIS, that enable efficient PET over a longer duration of coherence block. Nontrivial design insights on relative significance of array size at PIS and PB are also provided. Numerical results validating theoretical claims against the existing benchmarks demonstrate that with sufficient passive elements at PIS, we can achieve desired EB gain with reduced active array size at PB.

Backscatter communication (BSC) is emerging as the core technology for pervasive sustainable internet-of-things applications. However, owing to the resource-limitations of passive tags, this work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the full-duplex multiantenna reader and backscattering coefficients (BC) at the single antenna tags. Despite this joint optimization problem being non-convex, we present low-complexity joint TRX-BC designs by exploring the asymptotically-optimal solutions in low and high signal-to-noise-ratio regimes. We discourse that with precoder and detector designs at the reader respectively targeting downlink energy beam-forming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Selected computer simulations are presented to validate the analytical claims, shed optimal-design insights, and demonstrate the throughput enhancement of around 20% over the relevant benchmark schemes.

Physical layer security (PLS) in wireless communication has gained recent attention due to the emergence of new technological breakthroughs in this space. Since the internode distances have been noted to play a key role in the desired security performance, we propose a novel quality-of-service-aware PLS model that incorporates the random spatial deployment of the legitimate users and a potential attacker. This proposed model considers practical constraints like maximum separation between legitimate users and eavesdropping capability of attacker. In this regard, a novel concept of eavesdropping zone is also introduced. Eventually, closed-form expressions are derived for secrecy outage probability using the probabilistic inter-node distance distributions between the legitimate users and attacker to shed key analytical insights like optimal parameter designing to achieve a desired secrecy performance. Lastly, specific simulation results, presented to validate the analytical claims and provide key secured system designing perspectives, corroborate the potential of the proposed framework for more accurately characterizing the desired PLS performance from both the legitimate users' and attackers point-of-view.

In this paper, we consider a UAV-assisted communication system comprising of single UAV serving to heterogeneous users having different data rate and coverage demands. Specifically, we propose a novel utility-aware transmission protocol to maximize the UAV utility by allowing it to simultaneously serve the highest possible number of users with available energy resources. In this regard, first we derive a closed-form expression for rate-coverage probability of a user considering Rician fading to incorporate the strong line of sight (LoS) component in UAV communication. Next, we formulate an optimization problem P to maximize the UAV utility under energy resources and rate-coverage constraints. Since, P is non-convex and combinatorial in nature, to provide global optimal solution, an equivalent distributed problem is formulated and a joint optimization algorithm is proposed which provide closed-form solution for joint-optimal power and time allocation. With the help of numerical investigation, we validate our coverage analysis and discuss the design insights on the optimal solution. We observe that the proposed joint-optimal resource allocation scheme can yield a significant gain in the UAV utility by making it to serve 60% more users as compared to benchmark fixed allocation scheme.

Due to increasing quality-of-service (QoS) demand in already congested radio spectrum, there is a need for designing energy-efficient free space optical (FSO) communication networks. Considering a realistic fading model incorporating the fluctuations in angle-of-arrival, we minimize the outage probability for error free transmission of high data volumes through optimizing the power allocation (PA) and relay placement (RP) in a dual-hop decode-and-forward (DF) relay-assisted cooperative FSO communication with coherent detection and direct link unavailability. As this problem is nonconvex, first the optimal PA between source and relay is obtained using a global optimization algorithm. Also, a closed form for the solution is obtained using a tight analytical approximation with the assumption that atmospheric turbulence over both the links is nearly same. Next, we optimize the RP followed by the outage probability is jointly minimized using alternating optimization algorithm. Numerical results validate the outage analysis and provide key insights on optimal PA and RP yielding an outage enhancement of around 37% over the benchmark scheme.

Hybrid energy beamforming (HEB) can reduce the hardware cost, energy consumption, and space constraints associated with massive antenna array transmitter (TX). With a single radio frequency chain having N digitally controlled phase shifter pairs, one per antenna element, theoretically achieving the same performance as a fully digital beamforming architecture with N RF chains, this letter investigates the practical efficacy of the HEB. First adopting the proposed analog phase shifter impairments model and exploiting the channel reciprocity along with the available statistical information, we present a novel approach to obtain an accurate minimum mean-square error estimate for the wireless channel between TX and energy receiver (RX). Then, tight analytical approximation for the global optimal time allocation between uplink channel estimation and downlink energy transfer operations is derived to maximize the mean net harvested energy at RX. Numerical results, validating the analysis and presenting key design insights, show that with an average improvement of 58% over the benchmark scheme, the optimized HEB can help in practically realizing the fully digital array gains.

Usage of low-cost hardware in large antenna arrays and low-power wireless devices in Internet of Things (IoT) has led to the degradation of practical beamforming gains due to the underlying hardware impairments, such as in-phase and quadrature-phase imbalance (IQI). To address this timely concern, we present a new nontrivial closed-form expression for the globally optimal least squares estimator (LSE) for the IQI-influenced channel between a multiantenna transmitter and single-antenna IoT device. Thereafter, to maximize the realistic transmit beamforming gains, a novel precoder design is derived that accounts for the underlying IQI for maximizing received power in both single and multiuser settings. Finally, the simulation results, demonstrating a significant -8 dB improvement in the mean squared error of the proposed LSE over existing benchmarks, show that the optimal precoder designing is more critical than accurately estimating IQI-impaired channels. Also, the proposed jointly optimal LSE and beamformer outperforms the existing designs by providing 24% enhancement in mean signal power received under IQI.

Smart multiantenna wireless power transmission can enable perpetual operation of energy harvesting (EH) nodes in the Internet-of-Things. Moreover, to overcome the increased hardware cost and space constraints associated with having large antenna arrays at the radio frequency (RF) energy source, the hybrid energy beamforming (EBF) architecture with single RF chain can be adopted. Using the recently proposed hybrid EBF architecture modeling the practical analog phase shifter impairments (API), we derive the optimal least-squares estimator for the energy source to an EH user channel. Next, the average harvested power at the user is derived while considering the nonlinear RF EH model and a tight analytical approximation for it is also presented by exploring the practical limits on the API. Using these developments, the jointly global optimal transmit power and time allocation for channel estimation (CE) and EBF phases, that maximizes the average energy stored at the EH user is derived in closed form. Numerical results validate the proposed analysis and present nontrivial design insights on the impact of API and CE errors on the achievable EBF performance. It is shown that the optimized hybrid EBF protocol with joint resource allocation yields an average performance improvement of 37% over benchmark fixed allocation scheme.

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This paper targets at maximizing the achievable sum-backscattered throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BCs) at the tags. Since this joint problem is nonconvex, we first present individually optimal designs for the TRX and BC. We show that with precoder and combiner designs at the reader, respectively, targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically optimal joint-TRX-BC designs are proposed for both low- and high-signal-to-noise ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings, such as wireless powered communication networks and BSC with imperfect channel state information. Finally, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.

Green low power networking with the least requirement of dedicated radio resources is need of the hour which has led to the upsurge of backscatter communication (BSC) technology. However, this inherent potential of BSC is challenged by hardware constraints of the underlying tags. We address this timely concern by investigating the practical efficacy of multiple-input-multiple-output (MIMO) technology in overcoming the fundamental limitations of BSC. Specifically, we first introduce a novel least-squares based channel estimation (CE) protocol for multi-tag BSC settings that takes care of both the unintended ambient reflections and the inability of tags in performing estimation by themselves. Then using it, a nontrivial low-complexity algorithm is proposed to obtain the optimal transceiver designs for the multiantenna reader to maximize the minimum value of the lower-bounded backscattered throughput among the single-antenna semi-passive tags. Additional analytical insights on both individually and jointly-optimal precoding vector and detector matrix at the reader are provided by exploring the asymptotically-optimal transceiver designs. Lastly detailed numerical investigation is carried out to validate the theoretical results and quantify the practically realizable throughput fairness. Specifically, more than seven-fold increase in the common-backscattered-throughput among tags as achieved by the proposed designs over the relevant benchmarks corroborates their practical significance.

We investigate the practical realization of energy beamforming gains in the downlink wireless power transfer from a massive antenna radio frequency (RF) source to multiple single antenna energy harvesting (EH) users. Assuming channel reciprocity for the uplink and downlink channels undergoing Rician fading, we first obtain the least-squares and linear-minimum-mean-square-error channel estimates using the energy-constrained pilot signal transmission from EH users. Due to the usage of low cost hardware at the users and for realizing massive antenna system at the RF source, these estimates are strongly influenced by the transmitter and receiver in-phase-and-quadrature-phase imbalance (IQI). Using these channel estimates, we next derive the harvested power at each user by applying the source transmit precoding that maximizes the sum harvested power among the users. Selected results generated considering practical RF EH system parameters show that IQI and channel estimation errors can lead to about 30% degradation in the sum EH performance.

In this paper, we consider a Multiple Input Single Output (MISO) multicasting Internet of Things (IoT) system comprising of a multiantenna Transmitter (TX) that simultaneously transfers information and power to low power and data hungry IoT devices. Each IoT device is assumed to be equipped with Power Splitting (PS) hardware that enables Energy Harvesting (EH) and imposes an individual Quality of Service (QoS) constraint to the downlink communication. We study the joint design of TX precoding and IoT PS ratios for the considered MISO Simultaneous Wireless Information and Power Transfer (SWIPT) multicasting IoT with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements. In our novel EH fairness maximization formulation, we adopt a generic Radio Frequency (RF) EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem. For this problem, we first present an equivalent Semi- Definite Relaxation (SDR) for the considered design problem and prove that it possesses unique global optimality. Then, capitalizing on our derived tight upper and lower bounds on the optimal solution, we present an efficient algorithmic implementation for the jointly optimal TX precoding and IoT PS ratio parameters. Insights on the optimal TX precoding structure are also presented. Representative numerical results including comparisons with benchmark schemes corroborate the usefulness of the proposed design and provide useful insights on the interplay of critical system parameters on the optimized power vs achievable rate trade off.

Self-sustainability of wireless nodes in Internet-of-Things applications can be realized with the help of controlled radio frequency energy transfer (RF-ET). However, due to significant energy loss in wireless dissipation, there is a need for novel schemes to improve the end-to-end RF-ET efficiency. In this paper, first we propose a new channel model for accurately characterizing the harvested dc power at the receiver. This model incorporates the effects of nonline of sight (NLOS) component along with the other factors, such as radiation pattern of transmit and receive antennas, losses associated with different polarization of transmitting field, and efficiency of power harvester circuit. Accuracy of the model is verified via experimental studies in an anechoic chamber (a controlled environment). Supported by experiments in controlled environment, we also formulate an optimization problem by accounting for the effect of NLOS component to maximize the RF-ET efficiency, which cannot be captured by the Friis formula. To solve this nonconvex problem, we present a computationally efficient golden section-based iterative algorithm. Finally, through extensive RF-ET measurements in different practical field environments we obtain the statistical parameters for Rician fading as well as path loss factor associated with shadow fading model, which also asserts the fact that Rayleigh fading is not well suited for RF-ET due to presence of a strong line of sight component.

Enabling technologies for energy sustainable Internet of Things (IoT) are of paramount importance since the proliferation of high data rate demands of low power network devices. In this paper, we consider a multiple input single output (MISO) multicasting system comprising of a multiantenna transmitter (TX) simultaneously transferring information and power to data hungry IoT nodes. Each IoT device is assumed to be equipped with power splitting (PS) hardware that enables energy harvesting (EH) and imposes an individual quality of service (QoS) constraint to the downlink communication. We study the joint design of TX precoding and IoT PS ratios for the considered MISO simultaneous wireless information and power transfer multicasting system with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements. In our novel EH fairness maximization formulation, we adopt a generic EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem. For this problem, we first present an equivalent semi-definite relaxation formulation and then prove it possesses unique global optimality. We also derive tight upper and lower bounds on the globally optimal solution that are exploited in obtaining low complexity algorithmic implementations for the targeted joint design. Analytical expressions for the optimal TX beamforming directions, power allocation, and PS ratios are also presented. Representative numerical results including comparisons with benchmark designs corroborate the utility of proposed framework and provide useful insights on the interplay of key system parameters.

Doubly-near-far problem in RF-powered networks can be mitigated by choosing appropriate device-to-device (D2D) communication mode and implementing energy-efficient information transfer (IT). In this work, we present a novel RF energy harvesting architecture where each transmitting-receiving user pair is allocated a disjoint channel for its communication which is fully powered by downlink energy transfer (ET) from hybrid access point (HAP). Considering that each user pair can select either D2D or cellular mode of communication, we propose an optimized transmission protocol controlled by the HAP that involves harvested energy-aware jointly optimal mode selection (MS) and time allocation (TA) for ET and IT to maximize the sum-throughput. Jointly global optimal solutions are derived by efficiently resolving the combinatorial issue with the help of optimal MS strategy for a given TA for ET. Closed-form expressions for the optimal TA in D2D and cellular modes are also derived to gain further analytical insights. Numerical results show that the joint optimal MS and TA, which significantly outperforms the benchmark schemes in terms of achievable RF-powered sum-throughput, is closely followed by the optimal TA scheme for D2D users. In fact, about 2/3 fraction of the total user pairs prefer to follow the D2D mode for efficient RF-powered IT.

Performance of RF powered communication network is bottlenecked by short downlink energy transfer range and doubly-near-far problem faced in uplink information transfer to Hybrid Access Point (HAP). Theses problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma of R by proposing a novel integrated information and energy relaying (i2ER) protocol. It also provides insights on the relay positions suited for either energy relaying (ER), or information relaying (IR), or where neither ER nor IR will be useful. In this regard, while considering Rician fading channels, we derive the expression for mean harvested dc power at S via energy transfer from HAP and ER from R. We also derive the closed-form outage probability expression for decode-and-forward relaying with maximal-ratio-combining at HAP over Rician channels. Finally numerical results validating the analytical results and providing insights on the relaying role (ER and/or IR) of RF harvesting helping node are presented.

To address the ever-increasing data traffic demand, there is a need for novel cost-efficient network deployment schemes. In this work, we investigate the joint optimization of base station (BS) location, its density, and transmit power allocation to minimize the overall network operational cost required to meet an underlying coverage constraint at each user equipment (UE), which is randomly deployed following the binomial point process. As the joint optimization problem is nonconvex and combinatorial in nature, we propose a non-trivial solution methodology that effectively decouples it into three individual optimization problems. Firstly, by using the distance distribution of the farthest UE from the BS, we present novel insights on optimal BS location for a given number of BSs and sectoring type. After that we provide a tight approximation for the optimal transmit power allocation to each BS. Lastly, using the latter two results, the optimal number of BSs that minimize the operational cost is obtained. Numerical results validate the analysis and provide practical insights on optimal BS deployment. We observe that the proposed joint optimization framework, that solves the coverage probability versus operational cost tradeoff, can yield a significant reduction of about 65% in the operational cost as compared to the benchmark fixed allocation scheme.

To overcome finite lifetime bottleneck in the ubiquitous deployment of low-power wireless devices in Internet-of-Things, we propose a novel integrated information relay and energy supply (i2RES)-assisted RF harvesting co-operative communication model. i2RES aids the communication between two distant energy-constrained wireless nodes by: 1) RF energy transfer to the source and 2) relaying source data along with supplying energy to the destination. To enable efficient i2RES-powered information transfer to the destination, we first derive and then maximize the delay-limited achievable throughput over Rician channels by jointly optimizing time allocation for information and energy transfer along with relative position of i2RES between source and destination. Although the throughput maximization problem is nonconvex and highly nonlinear, we prove its generalized-convexity and obtain the global-optimal numerical solutions. To gain analytical insights, we also derive tight closed-form approximation for the optimized solutions. Numerical results validate the analysis and demonstrate significant gain in throughput performance via our proposed optimization schemes under practical hardware constraints. Finally, we discuss how the analysis and optimization results can be extended to general RF-EH system settings with relaxed constraints.

The performance of RF powered communication networks is bottlenecked by the short downlink energy transfer range and the doubly near-far problem faced in uplink information transfer to hybrid access point (HAP). These problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma at R by providing insights into its optimal positions suited for either energy relaying (ER) or information relaying (IR). It also investigates the possibilities of integrated ER and IR along with the regions where neither ER nor IR will be useful. In this regard, while considering Rician fading channels and practical nonlinear RF energy harvesting model, the expression for mean harvested dc power at S via energy transfer from HAP and ER from R is first derived. The closedform outage probability expression is also derived for decodeand-forward relaying with maximal-ratio-combining at HAP over Rician channels. Using these expressions, insights into optimal relaying mode is obtained along with global-optimal utilization of harvested energy at R for ER and IR to maximize the delaylimited RF-powered throughput. Numerical results validate the analysis and provide insights into the optimal relaying mode.

We propose a relay-assisted RF harvesting sensor network (R2FSN) model, where the field nodes having finite battery capacity are powered by RF energy transfer (RFET) from a single relay, thus leading to perpetual sensor network operation. The relay node also forwards the field data to the information sink in a two-hop, half-duplex decode-and-forward fashion. We derive closed-form expression of the average end-to-end signal-to-noise ratio (SNR) for each source-destination link in cooperative R2FSN. The performance improvement provided by R2FSN over conventional RF-powered networks is numerically investigated along with the impact of RFET time and relay location on the maximum and minimum average end-to-end SNR over all links.

In this paper, we develop an optimization framework to jointly optimize the efficiency of wireless information and RF power transfer to the destination of a two hop decode-and-forward source-relay-destination network. In particular, we investigate the performance of three optimization schemes: (i) optimal power allocation (PA) with fixed relay placement (RP), (ii) optimal RP with fixed PA, and (iii) joint optimization of PA and RP, for minimizing the outage probability under the harvested power constraint at the destination. In absence of direct source to destination reachability, closed-form global-optimal solutions are obtained for all three optimization schemes. Numerical results show that the optimized schemes significantly outperform fixed allocation and the joint optimal PA and RP provides an outage improvement of about 35%. Also, a tradeoff exists between minimized outage probability and minimum required average harvested power at destination for its uninterrupted operation.

Simultaneous wireless information and power transfer (SWIPT) can lead to uninterrupted network operation by integrating radio frequency (RF) energy harvesting with data communication. In this paper, we consider a two-hop source-relay-destination network and investigate the efficient usage of a decode-and-forward (DF) relay for SWIPT toward the energy-constrained destination. In particular, by assuming a Rician fading environment, we jointly optimize power allocation (PA), relay placement (RP), and power splitting (PS) so as to minimize outage probability under the harvested power constraint at the destination node. We consider the two possible cases of source-to-destination distance: (1) small distance with direct information transfer link; and (2) relatively large distance with no direct reachability. Analytical expressions for individual and joint optimal PA, RP, and PS are obtained by exploiting convexity of outage minimization problem for the no direct link case. In case of direct source-to-destination link, multipseudoconvexity of joint-optimal PA, RP, and PS problem is proved, and alternating optimization is used to find the global optimal solution. Numerical results show that the joint optimal solutions, although strongly influenced by the harvested power requirement at the destination, can provide respectively 64% and 100% outage improvement over the fixed allocation scheme for without and with direct link.

Recently, wireless radio frequency energy transfer (RFET) has emerged as an effective technology for prolonging lifetime of the energy-limited wireless sensor networks. However, low RFET efficiency is still a fundamental bottleneck in its widespread usage. Multi-hop RF energy transfer (MHET) can improve the RFET efficiency by deploying relay nodes that scavenge the dispersed energy and transfer it to the nearby sensor node. The efficiency of MHET is strongly influenced by the relay node's placement. To maximize the RFET efficiency for a two-hop scenario, in this paper a novel optimization model is proposed to determine the optimal relay placement (ORP) on an Euclidean x-y plane. Nontrivial tradeoff between the energy scavenged at the relay versus the effective energy delivered by the relay to the target node is investigated. Due to the nonconvex and highly nonlinear nature of the optimization problem, an α-based branch and bound algorithm has been used. The proposed optimization model is further extended by incorporating distributed beamforming to enhance the RFET efficiency. Numerical results illustrate that the proposed algorithm provides convergence to the ϵ-global optimal solution in a few iterations, and ORP provides significant energy saving over arbitrary relay positions for commercial RF energy harvesting systems.

Harvesting energy from radio frequency (RF) waves brings us closer to achieving the goal for perpetual operation of a wireless sensor network (WSN) by replenishing the batteries of the sensor nodes. However, due to restrictions on the maximum transmitted power, path loss, and receiver sensitivity, only a small amount of energy can be harvested. While a dedicated RF source alleviates the problem to some extent, novel techniques are required to boost the energy transfer efficiency of the source. In this paper, we provide the first experimental demonstration of multi-path energy routing (MPER) for the case of a sparsely distributed WSNs and show its improved performance over direct energy transfer (DET). In addition, we extend this concept to the case of densely distributed WSNs and experimentally demonstrate and compare the gains obtained by 2- and 3-path energy routing over DET. Our experimental results show that significant energy gains can be achieved in a dense network deployment even when the node to be charged is partially blocked by the neighboring nodes.

Batteries of field nodes in a wireless sensor network pose an upper limit on the network lifetime. Energy harvesting and harvesting aware medium access control protocols have the potential to provide uninterrupted network operation, as they aim to replenish the lost energy so that energy neutral operation of the energy harvesting nodes can be achieved. To further improve the energy harvesting process, there is a need for novel schemes so that maximum energy is harvested in a minimum possible time. Multi-hop radio frequency (RF) energy transfer is one such solution that addresses these needs. With the optimal placement of energy relay nodes, multi-hop RF energy transfer can save energy of the source as well as time for the harvesting process. In this work we experimentally demonstrate multi-hop RF energy transfer, wherein two-hop energy transfer is shown to achieve significant energy and time savings with respect to the single-hop case. It is also shown that the gain obtained can be translated to energy transfer range extension.