The widespread and often excessive use of pesticides presents serious risks to human health and environmental safety, calling for IoT-enabled monitoring in real agricultural environments. Current detection methods face challenges in handling diverse pesticide compounds, operating portably, and extracting discriminative signal features. To overcome these limitations, we presentHornbill+, a portable and high-precision electrochemical sensing system. By combining NFC technology with electrochemical biosensing,Hornbill+supports accurate, contactless, and multi-pesticide classification in field-friendly settings. The principle ofHornbill+involves recording electron transfer behaviors of selected biological materials under varying electrode potentials, producing time-variant electrochemical fingerprints that reflect distinct reaction signatures for different pesticides. To implement this approach, we developed a dual-channel fully differential potentiostat integrated into a low-power NFC tag, using DPV as the electrochemical readout method to enhance detection sensitivity. To enhance accuracy in complex real-world scenarios, we integrated a pyramid attention mechanism into a deep learning model for interpreting electrochemical dynamics.Hornbill+achieves over 93% average accuracy across 18 pesticides, five concentrations, and nine mixtures, surpassing existing techniques in both precision and coverage.

Pesticide overuse poses significant risks to human health and environmental integrity. Addressing the limitations of existing approaches, which struggle with the diversity of pesticide compounds, portability issues, and environmental sensitivity, this paper introduces Hornbill. A wireless and battery-free electrochemical bio-tag that integrates the advantages of NFC technology with electrochemical biosensors for portable, precise, and touchless multi-pesticide detection. The basic idea of Hornbill is comparing the distinct electrochemical responses between a pair of biological receptors and different pesticides to construct a unique set of feature fingerprints to make multi-pesticide sensing feasible. To incorporate this idea within small NFC tags, we reengineer the electrochemical sensor, spanning the antenna to the voltage regulator. Additionally, to improve the system's sensitivity and environmental robustness, we carefully design the electrodes by combining microelectrode technology and materials science. Experiments with 9 different pesticides show that Hornbill achieves a mean accuracy of 93% in different concentration environments and its sensitivity and robustness surpass that of commercial electrochemical sensors.

This paper presents the design and implementation of Fusang, a low-barrier system that brings accurate and robust 3D object recognition to Commercial-Off-The-Shelf mmWave devices. The basic idea of Fusang is leveraging the large bandwidth of mmWave Radars to capture a unique set of fine-grained reflected responses generated by object shapes. Moreover, Fusang constructs two novel graph-structured features to robustly represent the reflected responses of the signal in the frequency domain and IQ domain, and carefully designs a neural network to accurately recognize objects even in different multipath scenarios. We have implemented a prototype of Fusang on a commodity mmWave Radar device. Our experiments with 24 different objects show that Fusang achieves a mean accuracy of 97% in different multipath environments. The code, dataset, and trained models of Fusang can be obtained at https://github.com/OpenNISLab/Pro-Fusang.

Long range (LoRa) is an attractive low-power wide-area networks (LPWANs) technology for its features of low power, long range, and support for concurrent transmission. Our study reveals LoRa concurrent transmission suffer from the mismatch between the sender’s reception (RX) and gateway’s transmission (TX) window, which leads to the decline of goodput even the throughput is improved. Our experiment shows that goodput only accounts for two-fifths of the throughput in concurrent transmissions with 48 nodes at a duty cycle of 20%. This article presents a window match scheme named Cantor which improves the goodput of LoRa concurrent transmission by controlling the RX window size. Cantor does not require the frequent exchange of controlling information. Instead, it introduces a novel concurrent transmission model to estimate the downlink packet reception rate (PRR) with different network parameters, and a regression model is used to make the result more realistic. Then, we propose a simple optimization algorithm to select optimal RX window sizes in which nodes are able to receive acknowledgments. We implement and evaluate Cantor with commodity LoRa gateway and nodes, and conduct experiments in different scenarios. The experimental results show that Cantor increases the goodput by 70% and reduces energy consumption by 30% in LoRa concurrent transmissions with 48 nodes operate at a duty cycle of 20%.

Operating in unlicensed ISM bands, ZigBee devices often yield poor performance due to the interference from ever increasing wireless devices in the 2.4 GHz band. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to quantify and trace the relationship between interference and link qualities of the sender's outbound links. With such a link model, Smoggy-Link can translate low-cost interference information to the fine-grained spatiotemporal link state. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferers.

Wireless sensor networks (WSNs) have been used for many long-term monitoring applications with the strip topology that is ubiquitous in the real-world deployment, such as pipeline monitoring, water quality monitoring, vehicle monitoring, and Great Wall monitoring. The efficiency of routing strategy has been playing a key role in serving such monitoring applications. In this article, we first present a robust geographic opportunistic routing (GOR) approach—LIght Propagation Selection (LIPS)—that can provide a short path with low energy consumption, communication overhead, and packet loss. To overcome the complication caused by the multi-turning point structure, we propose the virtual Plane mirror (VPM) algorithm, inspired by the light propagation, which is to map the strip topology into the straight one logically. We then select partial neighbors as the candidates to avoid blindly involving all next-hop neighbors and ensure the data transmission along the correct direction. Two implementation problems of VPM—transmission spread angle and the communication range—are thoroughly analyzed based on the percolation theory. Based on the preceding candidate selection algorithms, we propose a GOR algorithm in the strip topology network. By theoretical analysis and extensive simulation, we illustrate the validity and higher transmission performance of LIPS in strip WSNs. In addition, we have proved that the length of the path in LIPS is two times the length of the shortest path via geometrical analysis. Simulation results show that the transmission success rate of our approach is 26.37% higher than the state-of-the-art approach, and the communication overhead and energy consumption rate are 33.11% and 40.23% lower, respectively.

Network coding is emerging as a promising technique that can provide significant improvements in the throughput of Internet of Things (IoT). Previous network coding schemes focus on several nodes, regardless of the topology and communication range in the whole network. Consequently, these schemes are greedy. Namely, all opportunities of combinations of packets in these nodes are exploited. We demonstrate that there is still room for whole network throughput improvement for these greedy design principles. Thus, in this paper, we propose a novel network coding scheme, ECS (Efficient Coding Scheme), which is designed to achieve a higher throughput improvement with lower computational complexity and buffer occupancy compared to current greedy schemes for wireless mesh networks. ECS utilizes the knowledge of the topologies to minimize interference and obtain more throughput. We also prove that the widely used expected transmission count metric (ETX) in opportunistic listening has an inherent error ratio that would lead to decoding failure. ECS therefore exploits a more reliable broadcast protocol to decrease the impact of this inherent error ratio in ETX. Simulation results show that ECS can greatly improve the performance of network coding and decrease buffer occupancy.

Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.

Vehicle-to-Vehicle (V2V) communication is a vital component of vehicular ad-hoc networks (VANET) under the situation that infrastructure for vehicle-to-infrastructure (V2I) has not been well deployed due to its cost and suffering. However, messages transmission path is so challenge to be found without infrastructures supporting that packets are inevitably spread in a sparsely or competitive area, in which they should avoid being trapped because of its poor communication links between vehicles, which eventually lowers down the performance of messages propagation. In this paper, we analyze the relationship between vehicular geographical distribution and packets propagation of VANET in a realistic large-scale urban scenario. It is demonstrated that, from a macro perspective, we could guide the path selection of data propagation between source and destination through V2V communication on the basis of the feature of vehicle density in different geographic locations. Furthermore, to be further close to the actual traffic environment, we model the vehicular geographical distribution by four typical real scenes to present the real environment and develop appropriate messages propagation strategies respectively.

In this paper, we propose a novel clock calibration approach, which addresses two key challenges for clock calibration in Wireless Sensor Networks: excessive communication overhead and the trade-off between accuracy and cost. To achieve this, our approach leverages the fact that the clock skew is highly correlated to temperature, which can serve as both an assistant for clock skew estimation and a regulatory factor for the duty-cycled design. Our approach is one order of magnitude more power-efficient than communication based approaches since the calibration largely relies on local temperature information. In addition, our approach provides a nice feature of self-adaptive period, which can substantially promote the system flexibility. We present the theory behind our approach, and provide preliminary results of a simulated comparison of our approach and some recent approaches.