This paper presents Backscattering Through Cable (BTC), a new backscatter side-channel attack designed for low-cost and effective serial data exfiltration. The BTC attack leverages the impedance variations of a serial port when transmits different bits (‘0’ and ‘1’), which in turn creates fluctuations in the amplitude of the backscattered signal. As a consequence, the sensitive serial data leaks to the backscatter side channel. The serial cable, acting as an unintentional antenna, enables this signal to be intercepted remotely. The BTC attack is notable for its minimal requirements: it does not require any modification to the target device's hardware or software nor any prior knowledge of the target devices or serial communication configurations. Experimental validation shows successful data exfiltration over distances up to 14.5 meters in line-of-sight (LOS) setting and 4.5 meters in nonline-of-sight (NLOS) scenario, even with two wall barriers. The attack is effective at high data rates (1 Mbps and beyond) and operates across various cable types, even with lengths as short as 4 cm. To enhance the understanding of its mechanisms and to facilitate the optimization of attack parameters, a full-wave model was further developed to characterize the impacts of target device cable length and carrier frequency on the attack efficacy. Simulation results indicate that BTC can remain effective with cable lengths as short as 1 cm.

The integration of energy harvesting capabilities into mobile edge computing (MEC) edge servers enables their deployment beyond the reach of electrical grids, expanding MEC services to isolated regions and geographically challenging terrains. However, the fluctuating nature of renewable energy sources, such as solar and wind, necessitates dynamic management of server computing power in response to variable energy harvesting rates. Unlike conventional models that assume predetermined amounts of harvested energy per time period, this study illustrates the complex interdependencies between server power consumption and variable energy harvesting rates due to battery charging characteristics. To address this, we introduce a novel energy harvesting model that comprehensively accounts for the interaction between computing power management and energy harvesting rates. We develop both offline and online offline optimal computing power management strategies aimed at maximizing the average computational capacity of edge servers. An analytical solution to the resulting nonlinear optimization problem is provided to determine the optimal computing power configurations. Simulation results indicate that the proposed strategy effectively balances energy harvesting rates and energy utilization, thereby enhancing computational performance in dynamic energy environments.

This research contributes to long-term structural health monitoring (SHM) by exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in concrete. The RF-SN captures radio energy from a mobile radio transmitter for sensing and communication, which offers a cost-effective solution for consistent in-situ perception. To optimize the system performance across various situations, we’ve explored both active and passive communication methods. For the active RF-SN, we implement a specialized control circuit enabling the node to transmit data through ZigBee protocol at low incident power. For the passive RF-SN, radio energy is not only for power but also as a carrier signal, with data conveyed by modulating the amplitude of the backscattered radio wave. To address the challenge of significant attenuation of the backscattering signal in concrete, we utilize a square chirp-based modulation scheme for passive communication. This scheme allows the receiver to successfully decode the data even under a negative signal-to-noise ratio (SNR) condition. Performance modeling and optimization for both active and passive RF-SNs are provided in this study. The experimental results verify that an active RF-SN embedded in concrete at a depth of 13.5 cm can be effectively powered by a 915 MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32.5 dBm. This setup allows the RF-SN to send over 1 kB of data within 10 seconds, with an additional 1.7 kilobytes every 1.6 seconds of extra charging. For the passive RF-SN buried at the same depth, continuous data transmission at a rate of 224 bps with a 3% bit error rate (BER) is achieved when the EIRP of the transmitter is 23.6 dBm.

This article introduces a practical approach to wirelessly charge underground Internet of Things (IoT) (UIoT) for soil monitoring using ultra-high-frequency (UHF) radio energy. In the proposed system, UIoT nodes do not require batteries or any aboveground attachments (e.g., solar panel). Instead, they harvest 915 MHz radio energy emitted from a UAV for semiperpetual operation. The UIoT nodes utilize the harvested energy to measure soil parameters and transmit data back to the UAV using ZigBee protocol. After collecting the data from UIoTs, the UAV uploads it to a cloud server for online soil quality analysis. The system has been optimized for efficient operation, with a UAV transmit power as low as 2 W. Startup, drive, and power management circuits have been designed to ensure reliable operation of UIoT nodes, even with low incident energy. According to experimental results, the developed radio frequency charging-enabled underground IoT system can successfully power up and transmit more than 1 kB of data within 10 s of wireless charging, followed by an additional 1.7 kB of data every 1.6 s thereafter.

Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of Things (IoT). By collecting renewable energy such as solar or wind energy from the environment, a sustainable cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. In the real world, the density of sustainable energy can vary significantly over time. Therefore, the SEC cloudlet needs to dynamically adjust the clock frequency to balance energy consumption and computing latency. In this paper, we consider the limited energy storage of the cloudlet and the dynamic intensity of renewable energy, and then develop offline optimal CPU frequency scaling policies that (a) maximize the computing power of the cloudlet within a certain period of time, and (b) minimize the execution time given tasks offloaded to the cloudlet. An optimal tightest string policy is proposed to solve the optimization problem. In addition, a dynamic programming (DP) based suboptimal solution is introduced to simplify the practical implementation. How to design an online CPU frequency management strategy is also briefly discussed.

Federated Learning (FL), as an effective decentral-ized approach, has attracted considerable attention in privacy-preserving applications for wireless edge networks. In practice, edge devices are typically limited by energy, memory, and computation capabilities. In addition, the communications be-tween the central server and edge devices are with constrained resources, e.g., power or bandwidth. In this paper, we propose a joint sparsification and optimization scheme to reduce the energy consumption in local training and data transmission. On the one hand, we introduce sparsification, leading to a large number of zero weights in sparse neural networks, to alleviate devices' computational burden and mitigate the data volume to be uploaded. To handle the non-smoothness incurred by sparsification, we develop an enhanced stochastic gradient descent algorithm to improve the learning performance. On the other hand, we optimize power, bandwidth, and learning parameters to avoid communication congestion and enable an energy-efficient transmission between the central server and edge devices. By collaboratively deploying the above two components, the numerical results show that the overall energy consumption in FL can be significantly reduced, compared to benchmark FL with fully-connected neural networks.

In this paper, we propose an edge case-enhanced active noise cancellation (EC-ANC) system that integrates a piezo microphone, a signal processor, and a wireless module into the charging case of TWS earbuds. Considering the fact that sound travels much faster in solid materials than in the air, the piezo microphone on the edge case will pick up a lookahead signal before it reaches the earbuds. The lookahead signal is very useful for real-time noise cancellation, especially for eliminating high-frequency, unpredictable, and high dynamic range noises. Benefiting from the speed advantage of sound traveling in solid materials, EC-ANC performs well when the charging case is either close to the user or near the noise source. This allows EC-ANC to be easily applied to various noise cancellation scenarios. We implemented EC-ANC on Intel MAX 10 FPGA, and then validated its advantages over the popular ANC method adopted by Bose, Sony, and Apple. According to experiment results, EC-ANC can achieve higher noise attenuation in the 4 kHz range compared with Bose QuietComfort 20 (QC20) and Bose QuietComfort Earbuds (QCE). Even without the noise-absorbing material, EC-ANC’s performance at higher frequencies is comparable to QC20 and QCE.

In this paper, we study a new security issue brought by the renewable energy feature in green Internet of Things (IoT) network. We define a new attack method, called the malicious energy attack, where the attacker can charge specific nodes to manipulate routing paths. By intelligently selecting the victim nodes, the attacker can “encourage” most of the data traffic into passing through a compromised node and harm the information security. The performance of the energy attack depends on the charging strategies. We develop two reinforcement-learning enabled algorithms, namely, Q- learning enabled intelligent energy attack (Q-IEA) and Policy Gradient enabled intelligent energy attack (PG-IEA). Through interacting with the network environment, the attacker can intelligently take attack actions without knowing the private information of the IoT network. This can greatly enhance the adaptability of the attacker to different network settings. Simulation results verify that the proposed IEA methods can considerably increase the amount of traffic traveling through the compromised node. Compared with the network without attack, an additional 53.3% data traffic is lured to the compromised node, which is more than 4 times higher than the performance of Random Attack.

In this paper, a semi-passive wake-up radio receiver based time synchronization (WUR-TS) method is developed for energy harvesting wireless networks. In WUR-TS, energy harvesting nodes (EHNs) do not exchange any timing information, but only receive the wake-up signal broadcast periodically by the central node to synchronize time throughout the network. Based on the experimental data, a model is developed to accurately estimate the arrival time of each wake-up signal. By using this model, the EHN can calculate its clock drift in complex radio environments. We have implemented WUR-TS with a minimum number of commercial components. According to the experimental results, WUR-TS can achieve a synchronization accuracy of 3$\mu$s when the power supply voltage is 2.8 V and the received wake-up signal strength is higher than$-$33 dBm. WUR-TS is an ultra-low-power synchronization method. If no wake-up signal is detected, the power consumption of each EHN is 3.2$\mu$W. If the wake-up signal is detected, the EHN consumes only 3.6$\mu$J of energy to complete time synchronization.

In this paper, we identify a new security issue, called the malicious energy attack, in sustainable wireless communication networks (SWCNs). We show that by providing extra energy to specific nodes, a malicious energy source (MES) can intentionally manipulate the routing path of SWCNs. The efficiency of energy attack depends on which nodes to be attacked. To enhance the efficiency of energy attack, a reinforcement learning technique, Q-Learning, is used to develop an intelligent energy attack (Q-IEA) policy for MES. Through interacting with the network environment, the Q-IEA can intelligently take attack actions without having to know the details of the routing method at the network layer. This function can greatly enhance the adaptability of MES to different routing protocols and network topologies. Simulation results verify that Q-IEA can significantly manipulate the routing path of the targeted traffic on demand.

Traditional means of achieving highly reliable wireless communications are to rely on a closed-loop communication methodology, which needs to implement complicated feedback communication mechanisms. Such closed-loop communication means inevitably incur feedback communication latency and thus lead to a fundamental tradeoff problem of simultaneously achieving high reliability and low latency. To avoid encountering this tradeoff problem, in this paper we adopt an open-loop communication methodology in a mobile network and propose a multi-cell association scheme to enhance the reliability of open-loop communication. The multi-cell association scheme helps users connect to multiple base stations (BSs), which form a virtual cell of the user. We first characterize the distribution of the number of the users associating with a BS for the multi-cell association scheme and then use it to establish the accurate models of signal-to-interference ratios (SIRs) in the downlink and uplink. The downlink and uplink communication reliabilities, which are defined based on the SIRs in the downlink and uplink, are accurately analyzed and their explicit upper bounds are found. Our analytical and simulated results show that jointly adopting open-loop communication and multi-cell association is able to significantly improve the communication reliability of users, thereby creating an ultra-reliable mobile network.

Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of things (IoT). By collecting solar or wind energy from the environment, an SEC cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. Considering significant density variation of sustainable energy over time, the SEC cloudlet needs to dynamically adjust the clock frequency of the central processing unit (CPU) to balance energy consumption and computing power. In this paper, we consider the limited energy storage of the cloudlet and develop an offline optimal CPU frequency scaling policy to maximize the overall computing power of the cloudlet within a certain period of time. The tightest string policy that gives a graphical viewpoint of the optimal CPU frequency scaling is found.

Through field experiments, we observed a varying instantaneous charging capacity of the energy buffer with respect to the dynamic intensity of the incident RF signal in the RF energy harvesting system (RF-EHS). The dependency of charging capacity on the incident power of RF signal challenges existing RF energy harvesting models that assume constant charging capacity. In order to accurately describe the energy harvesting process in the real system, we propose a new energy clamp model. The new model reveals that RF intensity higher than the sensitivity of the harvester circuit cannot always guarantee successful energy reception, especially when the energy level of energy buffer is high while the intensity of the incident power is relatively weak. In order to improve the efficiencies of energy harvest and energy utilization in an RF-EHS, we develop new offline (i.e., non-causal) optimal and online (i.e., causal) suboptimal data transmission strategies based on the energy clamp model. Simulation results show that the new strategy can considerably improve the throughput after taking into account the varying instantaneous charging capacity caused by the dynamic RF power density in the air.

The sustainable Internet of Things (IoT) is becoming a promising solution for green living and smart industries. In this article, we investigate the practical issues in radio energy harvesting and data communication systems through extensive field experiments. A number of important features of energy harvesting circuits and communication modules, including the nonlinear energy consumption of the communication system relative to the transmission power, the wake-up time associated with the payload, and the system power reduction during continuous data transmission, have been studied. In order to improve the efficiency of energy harvesting and energy utilization, we propose a new model to accurately describe the energy harvesting process and the power consumption for sustainable IoT devices. Experiments were conducted using commercial RF transceivers and RF energy harvesters to verify the accuracy of the proposed model. The experiment results show that the new model matches the performance of sustainable IoT devices well in real scenarios.

In this paper, we develop a new time synchronization method, called the energy stimulated time sync (ESTS), for ultra-low-power wireless networks with energy harvesting ability. Compared with existing methods, ESTS does not rely on timestamp exchange between wireless nodes but uses short analog tones to synchronize the time across the network. ESTS can synchronize the time across the entire energy harvesting wireless network with much lower energy consumption than timestampbased synchronization methods. We have implemented ESTS on Microchip ATmega256RFR2 system-on-chip (SoC) and Powercast P1110B radio energy harvester. According to experiment results, ESTS achieves an average of 5 ms time precision with less than 20 μJ of energy consumption.

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies are mainly based on a conventional model, in which the amount of harvested energy is modeled as predetermined random variables and the data transmission is arranged in a fixed feasible energy tunnel. In this paper, we show through the theoretical analysis and experimental results that due to the nonlinear battery charging characteristics, the harvested energy will largely depend on the transmission strategy. The bounds of feasible energy tunnel become dynamic. To describe a practical ambient energy harvesting process more accurately, a new nonlinear recursive model is proposed by adding a feedback loop that reflects the real-time influence of the data transmission on the energy harvesting process. In addition, to improve communication performance, we redesign the optimal transmission scheduling strategy based on the new model. In order to cope with the challenge of the endless loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy harvest and energy utilization regardless of the length of energy packets, thus improving the throughput performance of RF energy harvesting wireless communications.

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies are mainly designed based on a classic model, in which the harvested energy is assumed pre-determined and considered as prior knowledge in offline approaches. In this extended abstract, we challenge this assumption showing that the harvested energy is affected by the transmission scheduling and becomes unknown and not pre-determined. In the new model, we add a feedback line from the data transmission to the harvested energy. It properly indicates the interplay between the energy harvest and the data transmission but challenges the transmission scheduling in the meantime. We formulated the optimal transmission scheduling based on the new model and advocate a recursive solution.

We propose a new energy harvesting (EH) strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency EH powered wireless devices. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the EH device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term.

Energy harvesting is emerging as a promising alternative source to power the next generation of wireless networks. This paper introduces a new energy harvesting strategy that uses a dedicated energy source to optimally replenish energy for radio frequency (RF) based wireless communication systems. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that allows an energy harvesting device to effectively obtain energy from a dedicated energy source. While minimizing the system energy consumption, DTER takes into account the practical constraints on both the energy source and the energy harvesting device. Additionally, the overhead issue and the charge characteristics of an energy storage component are examined to make the proposed strategy practical. To solve the nonlinear optimization problem in DTER, we convert the design of optimal energy requesting problem into a classic shortest path problem and thus enable us to find a global optimal solution through dynamic programming algorithms. Theoretical analysis and simulation study verify that DTER outperforms two other schemes in the literature.

Cognitive acoustic (CA) is emerging as a promising technique for environment-friendly and spectrum-efficient underwater communications. Due to the unique features of underwater acoustic networks (UANs), traditional spectrum management systems designed for cognitive radio (CR) need an overhaul to work efficiently in underwater environments. In this paper, we propose a receiver-initiated spectrum management (RISM) system for underwater cognitive acoustic networks (UCANs). RISM seeks to improve the performance of UCANs through a collaboration of physical layer and medium access control (MAC) layer. It aims to provide efficient spectrum utilization and data transmissions with a small collision probability for CA nodes, while avoiding harmful interference with both “natural acoustic systems”, such as marine mammals, and “artificial acoustic systems”, like sonars and other UCANs. In addition, to solve the unique challenge of deciding when receivers start to retrieve data from their neighbors, we propose to use a traffic predictor on each receiver to forecast the traffic loads on surrounding nodes. This allows each receiver to dynamically adjust its polling frequency according to the variation of a network traffic. Simulation results show that the performance of RISM with smart polling scheme outperforms the conventional sender-initiated approach in terms of throughput, hop-by-hop delay, and energy efficiency.

In recent years, the underwater cognitive acoustic network (UCAN) has been advocated as an efficient technique to enhance the utilization of acoustic channel, while not interrupting the activity of marine mammals, sonars and other acoustic users. In cognitive radios, the common control channel (CCC) based media access control (MAC) protocols are very popular for their high reliability, easy implementation and low overhead. However, due to the severe frequency-dependent attenuation of acoustic waves, a UCAN may not have enough bandwidth for CCC. How to prevent the control channel from congesting in a UCAN with heavy traffic should be investigated carefully. With this in mind, we propose a dynamic control channel MAC (DCC-MAC) for distributed UCANs. Nodes in DCC-MAC could adjust the bandwidth of their control channel adaptively based on the situation of network traffic. Whenever acoustic nodes detected the congestion of CCC, they could flexibly select proper data channels to extend the bandwidth of their control channel, and return excessive frequency bands back when the control channel becomes idle. Simulation results show that DCC-MAC could reduce the collision probability among control messages significantly, thereby providing a better network performance in terms of throughput and energy efficiency than conventional cognitive MAC protocols.

Cognitive acoustic (CA) is emerging as a promising technique for environment-friendly and spectrum-efficient underwater acoustic networks (UANs). Due to the unique features of UANs, traditional spectrum management systems used for radio networks need an overhaul to work efficiently in underwater environments. In this paper, we propose a receiver-initiated spectrum management (RISM) system for underwater cognitive acoustic networks (UCANs). RISM seeks to significantly improve the performance of UANs through a collaboration of the physical layer and medium access control (MAC) layer. This system features collaborative spectrum sensing, efficient spectrum sharing and advanced spectrum decision algorithms. It aims to provide collision-free data transmissions and efficient spectrum utilization for CA users, while avoiding harmful interference with both “natural acoustic systems”, such as marine mammals, and “artificial acoustic systems”, like sonar users and other UANs. Simulation results show that, RISM can effectively operate in both tree topology and partially connected mesh topology networks and achieve collision-free data transmissions.

Cooperative communication has been studied extensively as a promising technique for improving the performance of terrestrial wireless networks. However, in underwater cooperative acoustic networks, long propagation delays and complex acoustic channels make the conventional relay selection schemes designed for terrestrial wireless networks inefficient. In this paper, we develop a new best relay selection criterion, called COoperative Best Relay Assessment (COBRA), for underwater cooperative acoustic networks to minimize the one-way packet transmission time. The new criterion takes into account both the spectral efficiency and the underwater long propagation delay to improve the overall throughput performance of the network with energy constraint. A best relay selection algorithm is also proposed based on COBRA criterion. This algorithm only requires the channel statistical information instead of the instantaneous channel state. Our simulation results show a significant decrease on one-way packet transmission time with COBRA. The throughput and delivery ratio performance improvement further verifies the advantages of our proposed criterion over the conventional channel state based algorithms.