6G networks require a sustainable and dependable power supply to ubiquitous space–air–ground connectivity infrastructures. Wireless power transfer (WPT) offers a promising path to sustainable energy delivery; however, concurrent transmitters can experience destructive interference when operating asynchronously. Most existing studies focus on centralized or synchronized WPT systems, leaving the asynchronous regime largely unexplored. In contrast to 5G’s tightly coordinated and slowly varying links, 6G WPT must function under non-stationary mobility, higher carrier frequencies, and dense power transmitter deployments. To bridge this gap, we translate key 6G stressors into design laws and probability guarantees. Specifically, we present a new systematic framework for asynchronous concurrent WPT, featuring a unified kernel that captures frequency, timing, and phase dispersions as a single retention term across instantaneous, short-time, and long-time scales. Further, we provide closed-form ppm/time budgets for a target retention, a retention cumulative distribution function that tightens asO(N−2), and a Doppler time-to-null scheduler coupled to the harvester. Finally, we perform deterministic and Monte Carlo analyses, together with experimental studies.

LoRa, designed for Low-Power, Wide-Area Networks (LPWANs), is widely used in the Internet of Things (IoT). In contrast, Wireless Personal Area Network (WPAN) technologies like ZigBee struggle to connect directly to LPWANs due to their limited communication range and differing modulation schemes. ZigBee uses Offset Quadrature Phase-Shift Keying (OQPSK) modulation, while LoRa employs Chirp Spread Spectrum (CSS) modulation, complicating cross-technology communication. To address this challenge, we propose a novel approach for seamless physical-layer cross-technology communication between ZigBee and LoRa networks, bridging the gap between short-range and long-range communication technologies. We introduce ZigRa, a communication method that leverages neural networks for efficient modulation translation between ZigBee's IEEE 802.15.4 standard and LoRa's CSS modulation. The core of ZigRa is a deep learning model that adapts and optimizes the transformation of ZigBee signals into ultra-narrowband single-tone sinusoidal signals, which can be reliably detected by LoRaWAN base stations. Our solution enables ZigBee devices to seamlessly connect to LoRa-based LPWANs, overcoming modulation mismatches and providing long-range connectivity. Extensive evaluations with both USRP hardware and commercial devices demonstrate that ZigRa achieves a frame reception rate exceeding 85% at distances up to 500 meters, significantly enhancing the interoperability and coverage of heterogeneous IoT networks.

Low-Power Wide-Area Networks (LPWANs) have emerged as a promising communication technology for the Internet of Things (IoT). However, frequency overlap among wireless networks using different radio technologies creates significant interference, compromising communication reliability. This challenge is particularly urgent in LoRa networks, which coexist in the 2.4 GHz ISM band with other IoT transmitters capable of transmitting at much higher power levels. In our study, we begin by providing a comprehensive understanding of the LoRa physical layer (PHY), including insights into modulation and demodulation mechanisms. Leveraging this knowledge, we successfully implemented a real-time LoRa PHY on the GNU Radio Software-Defined Radio platform. To address cross-technology interference during peak detection, we introduce a spectrum merging technique that maintains phase coherence between superimposed peaks, minimizing spectral leakage artifacts. Beyond that, our analysis actively enhances the performance of commercial LoRa devices. Furthermore, we systematically explore the interference dynamics between LoRa and IEEE 802.15.4g networks. Our rigorous investigation reveals LoRa’s ability to achieve high packet reception rates, even in the presence of strong IEEE 802.15.4g interference.

Vehicle-to-Vehicle (V2V) cooperative perception has become increasingly popular in the field of autonomous driving, effectively overcoming the inherent limitations of single-vehicle perception systems, such as limited range and susceptibility to occlusions. In a V2V system, vehicles in close proximity can share perception data. To fuse this data, which is collected from different viewpoints by each vehicle, accurate pose information (including position and heading direction) is essential to transform the received data to the receiving vehicle's viewpoint. However, pose errors, often caused by measurement noise or sensor failures, can lead to severe misalignment during data fusion, resulting in incorrect object detections and potentially hazardous decisions in autonomous driving systems. To address this challenge, we present BB-Align, a lightweight pose recovery framework that utilizes Lidar Bird's-eye View (BV) images and object bounding Boxes for relative pose estimation. Designed as a plug-and-play solution, the proposed method requires no additional model training, enabling effortless integration into existing V2V systems. Our approach uses Lidar-derived BV images with a Log-Gabor filter-based feature map for effective image matching despite image sparsity. To reduce errors from self-motion distortion, we also integrate object bounding boxes for finer alignment. The proposed method is rigorously evaluated on the V2V 4Real dataset-currently the only real-world V2V dataset. Our approach demonstrates high pose estimation accuracy, outperforming an existing graph-matching method. It achieves translation and rotation errors of less than 1 m and 1°, respectively, in 80 % of cases within a 70 m range between vehicles. Furthermore, by integrating the proposed framework into cooperative object detection models under serious pose error, the result shows up to a 2x increase in Average Precision (AP) compared to those without pose recovery, with more pronounced improvements in the short range.

Federated Learning is a promising technique that facilitates cloud–edge–terminal collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without centralizing data, addressing privacy, and security concerns. However, when applied to AIoT, this technique faces several challenges, such as low communication efficiency among terminal devices, edges, and cloud platforms. In this article, we propose a novel approach called asynchronous federated learning with quantization (QuAsyncFL), which combines asynchronous federated learning with an unbiased nonuniform quantizer to address the issue of low communication efficiency. Moreover, we provide a detailed theoretical analysis of convergence with quantized gradients proving that the model could converge to a certain bound. Our experiments demonstrate that QuAsyncFL outperforms the original approach, achieving significant improvements in terms of communication efficiency. The research results represent a further step toward developing cloud–edge–terminal collaboration enabled AIoT.

Cross-technology communication is essential for the Internet of Multimedia Things (IoMT) applications, enabling seamless integration of diverse media formats, optimized data transmission, and improved user experiences across devices and platforms. This integration drives innovative and efficient IoMT solutions in areas like smart homes, smart cities, and healthcare monitoring. However, this integration of diverse wireless standards within cross-technology multimedia communication increases the susceptibility of wireless networks to attacks. Current methods lack robust authentication mechanisms, leaving them vulnerable to spoofing attacks. To mitigate this concern, we introduce DeepSpoof, a spoofing system that utilizes deep learning to analyze historical wireless traffic and anticipate future patterns in the IoMT context. This innovative approach significantly boosts an attacker's impersonation capabilities and offers a higher degree of covertness compared to traditional spoofing methods. Rigorous evaluations, leveraging both simulated and real-world data, confirm that DeepSpoof significantly elevates the average success rate of attacks.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

Noise pollution is a pressing concern in urban areas, exacerbated by the rapid pace of urbanization, industrialization, and high population density. It poses significant risks to human health and disrupts ecosystems. While noise pollution monitoring and E-health technologies have individually made substantial contributions to their respective fields, their integration has been largely overlooked in existing research. This oversight has resulted in missed opportunities for valuable insights and fragmented data analysis. To bridge this gap, we present the development of a collaborative sensing network that simultaneously monitors noise pollution and manages E-health. Our proposed solution, AntiNoise, employs a novel architecture that leverages smart devices and data mules to record noise levels and health statuses, transmitting this information to a cloud platform. To optimize the performance of the system, we design an integrated deep reinforcement learning framework for data mule trajectory planning and employ a deep Q-network algorithm for trajectory control. Through extensive evaluation, we demonstrate the efficiency of our approach, surpassing conventional schemes, particularly in scenarios with strict transmission power budgets.

This paper presents a low overhead multimedia data compression method for Solar Insecticidal Lamps Internet of Things (SIL-IoTs), achieving efficient audio data transmission. First, the audio and video data generated by working solar insecticidal lamps are collected to form an original dataset in the solar insecticidal lamps applications. Then, we propose SILGAN, a generative adversarial network to compress multimedia audio data in the SIL-IoTs. Specifically, a depth-wise separable convolution is adopted to reduce the computational resources for training networks and running programs. Moreover, the network parameters are optimized so that obtaining a better neural network model with stable operation on the nodes of the SIL-IoTs. Finally, experimental evaluation is conducted, showing the effectiveness of the proposed SILGAN approach.

Backscatter communication (BackCom), one of the core technologies to realize zero-power communication, is expected to be a pivotal paradigm for the next generation of the Internet of Things (IoT). However, the “strong” direct link (DL) interference (DLI) is traditionally assumed to be harmful, and generally drowns out the “weak” backscattered signals accordingly, thus deteriorating the performance of BackCom. In contrast to the previous efforts to eliminate the DLI, in this paper, we exploit the constructive interference (CI), in which the DLI contributes to the backscattered signal. To be specific, our objective is to maximize the received signal-to-noise ratio (SNR) by jointly optimizing the receive beamforming vectors and tag selection factors under different detection error probability (DEP) requirements, which leads to two different optimization problems. However, the resulting problems are non-convex and unanalyzable due to constraints on the DEP. To solve these problems, the Kullback-Leibler divergence is first applied to transform the DEP into a tractable form. Then, inspired by the alternating optimization, we respectively propose two successive convex approximation (SCA)-based algorithms to solve the corresponding sub-problems with beamforming design, and a greedy algorithm to solve the sub-problem with tag selection. In order to gain insight into the CI, we consider a special case with the single-antenna reader to reveal the channel angle between the backscattering link (BL) and the DL, in which the DLI will become constructive. Simulation results show that significant performance gain can always be achieved with the proposed algorithms compared to the traditional algorithms without the CI in terms of the received SNR. The derived constructive channel angle for the BackCom system with a single-antenna reader is also confirmed by simulation results.

This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still commu-nicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a sep-arate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modu-lar framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by com-paring the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU's ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWan, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWan gateway, enabling new use cases and expanding the applicability of LoRa technology.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

The connectivity and intelligence of Internet of Things (IoT) equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. However, an important aspect that is often overlooked in security literature is IoT equipment’s physical security and safety, namely, preventing IoT equipment from vandalism and theft. Therefore, this article provides an overview of IoT equipment’s physical security and safety to draw attention to new research opportunities in this area. Afterward, we discuss, among other aspects, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Besides, we summarize the artificial intelligence solutions for the physical security and safety of IoT equipment. Finally, we conclude with four future research opportunities.

Recently, several datasets shedding light on connectivity aspects in real-world LoRa networks have been provided to the community. However, they typically only involve a limited number of nodes, deal with unidirectional communication only, or focus on very specific physical layer settings. More importantly, existing datasets typically lack fine-grained environmental information such as the temperature in the surroundings of each node, which is known to have a strong impact on communication performance. In this work, we provide the community with a comprehensive dataset that fills all these gaps. We have collected detailed connectivity information in an outdoor LoRa network composed of 21 nodes for more than four months. Our dataset does not only focus on network-level performance (e.g., the average number of correctly-exchanged packets), but sheds light on link-level information such as the received signal strength, signal-to-noise ratio, and the number of available neighbours over time. We further collect environmental information from an online weather site, as well as the on-board temperature of each node in the network, which varies considerably across the deployed locations. We collect all this information while perpetually changing physical layer settings such as the spreading factor and the RF channel. A preliminary analysis of our dataset, which is available in Zenodo1, reveals that temperature has a significant correlation with the link quality and connectivity in the outdoor LoRa network, confirming the findings of earlier studies.

As an important physical control technology, solar insecticidal lamp (SIL) can effectively prevent and control the occurrence of pests. The combination of SILs and wireless sensor networks (WSNs) initiates a novel agricultural Internet of Things (IoT), i.e., SIL-IoTs, to simultaneously kill pests and transmit pest information. In this article, we study the weighted SIL deployment problem (wSILDP) in SIL-IoTs, where weighted locations on ridges are prespecified and some of them are selected to deploy SILs. Different from the existing studies whose optimization objective is to minimize the deployment cost, we consider the deployment cost and the total weight of selected locations jointly. We formulate the wSILDP as the weighted set cover (WSC) problem and propose a layered deployment method based on greedy algorithm (LDMGA) to solve the defined optimization problem. The LDMGA is composed of two phases. First, SILs are deployed layer by layer from the boundary to the center until the entire farmland is completely covered. Second, on the basis of three design operations, i.e., substitution, deletion and fusion, the suboptimal locations obtained in the first phase are fine-tuned to achieve the minimum deployment cost together with the maximum total weight for meeting the coverage and connectivity requirements. Simulation results clearly demonstrate that the proposed method outperforms three peer algorithms in terms of deployment cost and total weight.

The three previous industrial revolutions profoundly transformed agriculture industry from indigenous farming to mechanized farming and recent precision agriculture. Industrial farming paradigm greatly improves productivity, but a number of challenges have gradually emerged, which have exacerbated in recent years. Industry 4.0 is expected to reshape the agriculture industry once again and promote the fourth agricultural revolution. In this article, first, we review the current status of industrial agriculture along with lessons learned from industrialized agricultural production patterns, industrialized agricultural production processes, and the industrialized agri-food supply chain. Furthermore, five emerging technologies, namely the Internet of Things, robotics, artificial intelligence, big data analytics, and blockchain, toward Agriculture 4.0 are discussed. Specifically, we focus on the key applications of these emerging technologies in the agricultural sector and corresponding research challenges. This article aims to open up new research opportunities for readers, particularly industrial practitioners.

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This paper fills this gap and provides the first extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by de-synchronization and beating on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based flooding protocols in the presence of radio interference on a large-scale, and derive important insights on how the used physical layer affects their dependability.

The increasing congestion of the RF spectrum is a key challenge for low-power wireless networks using concurrent transmissions. The presence of radio interference can indeed undermine their dependability, as they rely on a tight synchronization and incur a significant overhead to overcome packet loss. In this paper, we present Harmony, a new data collection protocol that exploits the benefits of concurrent transmissions and embeds techniques to ensure a reliable and timely packet delivery despite highly congested channels. Such techniques include, among others, a data freezing mechanism that allows to successfully deliver data in a partitioned network as well as the use of network coding to shorten the length of packets and increase the robustness to unreliable links. Harmony also introduces a distributed interference detection scheme that allows each node to activate various interference mitigation techniques only when strictly necessary, avoiding unnecessary energy expenditures while finding a good balance between reliability and timeliness. An experimental evaluation on real-world testbeds shows that Harmony outperforms state-of-the-art protocols in the presence of harsh Wi-Fi interference, with up to 50% higher delivery rates and significantly shorter end-to-end latencies, even when transmitting large packets.

Solar insecticidal lamp (SIL) is a green prevention and control technology for pests. With the development of wireless sensor networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things-SIL Internet of Things (SIL-IoTs). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this article, we study the SIL deployment problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary, and partition structure. According to the partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with the partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on the genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. The experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.

Energy efficient sensing is one of the main objectives in the design of networked embedded monitoring systems. However, existing approaches such as duty cycling and ambient energy harvesting face challenges in railway bridge health monitoring applications due to the unpredictability of train passages and insufficient ambient energy around bridges. This paper presents eco-friendly vibration (ECO VIBE), an on-demand sensing system that automatically turns on itself when a train passes on the bridge and adaptively powers itself off after finishing all tasks. After that, it goes into an inactive state with near-zero power dissipation. ECO VIBE achieves these by: first, a novel, fully passive event detection circuit to continuously detect passing trains without consuming any energy. Second, combining train-induced vibration energy harvesting with a transistor-based load switch, a tiny amount of energy is sufficient to keep ECO VIBE active for a long time. Third, a passive adaptive off control circuit is introduced to quickly switch off ECO VIBE. Also this circuit does not consume any energy during inactivity periods. We present the prototype implementation of the proposed system using commercially available components and evaluate its performance in real-world scenarios. Our results show that ECO VIBE is effective in railway bridge health monitoring applications.

Water quality data is incredibly important and valuable, but its acquisition is not always trivial. A promising solution is to distribute a wireless sensor network in water to measure and collect the data; however, a drawback exists in that the batteries of the system must be replaced or recharged after being exhausted. To mitigate this issue, we designed a self-sustained water quality sensing system that is powered by renewable bioenergy generated from microbial fuel cells (MFCs). MFCs collect the energy released from native magnesium oxidizing microorganisms (MOMs) that are abundant in natural waters. The proposed energy-harvesting technology is environmentally friendly and can provide maintenance-free power to sensors for several years. Despite these benefits, an MFC can only provide microwatt-level power that is not sufficient to continuously power a sensor. To address this issue, we designed a power management module to accumulate energy when the input voltage is as low as 0.33V. We also proposed a radio-frequency (RF) activation technique to remotely activate sensors that otherwise are switched off in default. With this innovative technique, a sensor’s energy consumption in sleep mode can be completely avoided. Additionally, this design can enable on-demand data acquisitions from sensors. We implement the proposed system and evaluate its performance in a stream. In 3-month field experiments, we find the system is able to reliably collect water quality data and is robust to environment changes.

Non-destructive concurrent transmissions recently attract widespread attention in wireless sensor networks research community, and many studies demonstrate that non-destructive interference in concurrent transmissions enables routing-free packet transmission with low latency and increased reliability. In this paper, we present a new MAC protocol, called Non-Destructive Interference MAC (NDI-MAC), that integrates non-destructive simultaneous transmissions into receiver-initiated protocols, achieving energy efficient and low latency data transmission under a variety of traffic loads. The capture effect is exploited in NDI-MAC to finish rendezvous between multiple senders and receivers. NDI-MAC also relies on triggercast, a distributed middleware to trigger synchronous packet transmissions with constructive interference. So as to ensure single-hop reliability, backcast primitive is used under unicast traffic. Evaluation results show that NDI-MAC achieves high performance in terms of energy consumption and data delivery latency under data dissemination and collection traffic.

Multi-channel media access control (MAC) is important in wireless sensor networks because it allows parallel data transmissions and resists external wireless interference. Existing multi-channel MAC protocols, however, do not efficiently support delay-sensitive applications that require reliable and timely data transmissions. In addition, multi-channel operation is inherently deficient for supporting multi-hop broadcasting, due to independent waking-up schedules on sensors. To address these issues, we present a routing-enhanced multi-channel MAC (RM-MAC) which allows nodes to coordinately select their channel polling times based on cross-layer routing information. RMMAC also supports a ripple broadcast mechanism which achieves efficient multi-hop broadcast among sensors. Simulation results show that RM-MAC provides significant improvement over the MuCHMAC [1], in terms of end-to-end delay, under a wide range of traffic loads including both unicast and broadcast traffic.

Event-driven applications in wireless sensor networks feature correlated traffic bursts: after a period of idle time with light traffic loads, multiple sensors that have detected the same event have to transmit large amounts of data simultaneously to sink node or cluster head. The demand for simultaneous data transmission often causes severe collision, which is one of the most significant sources of energy consumption in wireless sensor networks. In this paper, we propose SC-MAC (sender-centric MAC), a new asynchronous duty cycle MAC protocol designed for burst traffic loads. SC-MAC achieves collision-free environment while do not introduce extra overhead. In order to minimize delivery latency in tree structure or other multi-hop networks, SC-MAC also introduces a latency optimization mechanism. We show the performance of SC-MAC through ns-2 simulation and compare it to PW-MAC, the state-of-the-art asynchronous MAC protocol. The simulation results show that SC-MAC significantly minimizes energy consumption and delivery latency.