We propose a new convex programming relaxation for the weighted Nash social welfare (NSW) problem that achieves a matching (e^{1/e} ≈ 1.445)-approximation via the rounding algorithm of Feng and Li. Unlike the exponential-size configuration LP used in prior work, our formulation can be converted into a compact linear program of polynomial size, incurring only an additive loss of ln(1+ε) in the objective. This allows the program to be solved directly using standard LP solvers, without the ellipsoid method or dual separation oracles. In the unweighted case, we show that our convex program is equivalent to the restricted-spending Fisher market convex program of Cole and Gkatzelis, yielding a constructive proof that its integrality gap is exactly e^{1/e}. With a minor modification, our analysis also gives a simple proof of the e^{1/e} EF1 gap for the identical agent setting. Finally, we show that our convex programming technique extends to two unrelated machine scheduling problems, recovering the best-known approximation ratios with simpler analyses.

We study the problem of allocating items to agents with submodular valuations with the goal of maximizing the weighted Nash social welfare (NSW). The best-known results for unweighted and weighted objectives are the (4+є) approximation given by Garg, Husic, Li, Végh, and Vondrák [STOC 2023] and the (233+є) approximation given by Feng, Hu, Li, and Zhang [STOC 2025], respectively.

We give the first $O(1)$-approximation for the weighted Nash Social Welfare problem with additive valuations. The approximation ratio we obtain is $e^{1/e} + ε\approx 1.445 + ε$, which matches the best known approximation ratio for the unweighted case. Both our algorithm and analysis are simple. We solve a natural configuration LP for the problem, and obtain the allocation of items to agents using a randomized version of the Shmoys-Tardos rounding algorithm developed for unrelated machine scheduling problems. In the analysis, we show that the approximation ratio of the algorithm is at most the worst gap between the Nash social welfare of the optimum allocation and that of an EF1 allocation, for an unweighted Nash Social Welfare instance with identical additive valuations. This was shown to be at most $e^{1/e} \approx 1.445$ by Barman, Krishnamurthy and Vaish, leading to our approximation ratio.

Accurate measurements of soil macronutrients (i.e., nitrogen, phosphorus, and potassium) and moisture play a key role in smart agriculture. However, existing commodity soil sensors are often expensive and the achieved accuracy is unsatisfactory. To address these issues, we present SoilCares, a low-cost soil sensing system enabling accurate and simultaneous monitoring of the concentration levels of soil moisture and macronutrients. SoilCares overcomes key challenges of accommodating diverse soil types and soil textures by introducing a novel membrane-based scheme. For moisture sensing, SoilCares leverages the multi-modal fusion of RF and NIR signals to significantly increase the sensing accuracy. Through delicate hardware design, we enable negligible-cost sensor data transmission using the existing sensing hardware, building up a complete end-to-end soil sensing system. SoilCares is cost-effective ($63.5), portable (0.5 kg), and low-power (236 μW), making it suitable for insitu deployment. On-site experimental results show that SoilCares achieves high macronutrient sensing accuracy with a low RMSE of 0.138, and extremely low moisture estimation error of 1%, outperforming the state-of-the-art research and expensive commodity moisture sensors on the market.

Soil moisture sensing is a basic function required by applications like precision irrigation. Recently, RF based soil moisture sensing solutions [10, 43] have been proposed, which, however, can hardly support large scale deployment in challenging outdoor environments, since they must have dedicated signal emitters and also require power supply for either the signal emitters (WiFi or RFID reader) or both the transceivers (WiFi AP and client). LTE signal provides a unique opportunity for soil moisture sensing as the ubiquitously deployed base stations are naturally always-on signal emitters, eliminating the need for deploying extra hardware. In this paper, we implement a low-cost LTE based soil moisture sensor using commercial off-the-shelf hardware. We also realize duty-cycled soil sensing by automatically self-calibrating the phase offset after powering on the devices, significantly reducing the overall power consumption of the sensor. Extensive experiments show that our low-cost sensor ($55) achieves a high accuracy (3.15%) which is comparable to high-end soil moisture sensors ($850), wide coverage (2.4 km from the base station) and low power consumption (lasting 16 months using batteries).

A long-standing challenge in radios for wearables is to design ultra-low power, yet high performance receivers with good sensitivity and spectral efficiency while being compatible with WiFi. The vanilla envelope detector used in standard UHF RFID is the most popular receivers on backscatter tags since they are passive but suffer from poor sensitivity and cannot decode complex modulations, which makes them a poor choice for directly decoding data from WiFi packets.

Besides the communication function, wireless signals are recently exploited for sensing purposes, enabling diverse applications. However, designing a wireless sensing system that provides truly pervasive coverage at city or even national scale and at the same time does not affect ongoing data communication is still challenging. In this work, we propose to involve the pervasive LTE signals into the ecosystem of wireless sensing. Although LTE sensing solves the coverage issue and does not compromise the communication function, it brings unique challenges. Due to the long distance between LTE base stations and terminals, the LTE signal interacts with diverse objects during the propagation process which causes severe interference in sensing. We enable LTE sensing by designing delicate signal processing schemes to combat against the severe interference. We demonstrate the advantages of LTE sensing using two typical applications, indoor respiration sensing and outdoor traffic monitoring. Extensive experiments show that the proposed system can achieve highly accurate respiration sensing with the blind spot and orientation-sensitive issues greatly mitigated. For traffic monitoring, the error of car speed estimation is lower than 2 mph, as good as commercial devices on the market.

Visible light communication (VLC) is gaining a significant amount of interest as a new paradigm to meet rapidly increasing demands on wireless capacity required by a digitalized world. VLC is considered as a secure wireless communication scheme because VLC signals can be easily constrained within physical boundaries. In this paper, for the first time, we show that VLC is not as secure as people thought: VLC can be sniffed through walls! The key principle behind this is that in VLC transmissions, a VLC transmitter not only emits visible light signals but also leaks out 'side channel RF signals'. The leaked RF signals can be sniffed by a receiver to decode the VLC transmissions even the receiver is blocked (e.g., by walls) from the VLC transmitter. In this work, we establish a theoretical model to quantify the amplitude of the leaked RF signal and verify the model with comprehensive experiments. We design and implement a VLC sniffing system including receiver coil design, signal processing and frame decoding, spanning across hardware and software. Field studies show that with a cheap receiver design, our system can simultaneously sniff transmissions from multiple VLC transmitters 6.4 meters away with a 14 cm concrete wall in between, where the distance exceeds the communication range of most state-of-the-art VLC systems. By simply twining a wired earphone on the arm, we can sniff the VLC transmission 1.9 meters away.

Recently proposed OFDMA backscatter could improve both concurrency and spectrum allocation flexibility for backscatter systems based on OFDM. However, we find that it is remarkably inefficient for the existing design to scale up in prototyping: it requires one-by-one offline computation to obtain tags' operating parameters, in order to ensure orthogonality among subcarriers in the system; moreover, the tag hardware has to be dedicatedly modified offline before being assigned multiple subcarriers. The inefficiency is caused by the current analog frequency synthesis design for the tag. This paper proposes DigiScatter, an OFDMA backscatter system realizing digital frequency synthesis, which provides an efficient prototyping approach for large-scale OFDMA backscatter networks. In DigiScatter, we for the first time integrate IDFT into the tag design; such a simple but effective improvement enables the system to support high concurrency and flexible spectrum resource allocation through pure software configurations in an online manner. We build a prototype and conduct comprehensive experiments to validate our design. DigiScatter physically realizes 100 and 300 concurrent OFDMA backscatter transmissions in 2.4GHz and 900MHz respectively, and provides frequency synthesis capability for supporting 1019 concurrent transmissions.

In this paper, we for the first time demonstrate how to enable OFDMA in Wi-Fi backscatter for capacity and concurrency enhancement. With our approach, the excitation signal is reflected, modulated and shifted to lie in the frequency band of the OFDM subcarrier by the tag; OFDMA is realized by coordinating tags to convey information to the receiver with orthogonal subcarriers concurrently through backscatter. The crux of the design is to achieve strict synchronization among communication components, which is more challenging than in regular OFDMA systems due to the more prominent hardware diversity and uncertainty for backscattering. We reveal how the subtle asychnronization scenarios particularly for backscattering can incur system offsets, and present a series of novel designs for the excitation signal transmitter, tag, and receiver to address the issue. We build a prototype in 802.11g OFDM framework to validate our design. Experimental results show that our system can achieve 5.2-16Mbps aggregate throughput by allowing 48 tags to transmit concurrently, which is 1.45-5x capacity and 48x concurrency compared with the existing design respectively. We also design an OFDMA tag IC, and the simulation and numerical analysis results show that the tag's power consumption is in tens of μW.