Accurate radio source localization on resource-constrained hardware presents a primary challenge in wireless networks. We introduce EfficientLocNet, a novel architecture achieving superior accuracy with exceptional computational efficiency, driven by two distinct design choices. Its efficiency stems from lightweight Depthwise Separable Convolutions, while its accuracy is enhanced by two components working in tandem: Atrous Spatial Pyramid Attention to capture long-range spatial features, and a Self-Attention module to refine the latent representation. Evaluated against state-of-the-art (SOTA) methods, EfficientLocNet outperforms the top-performing model, DSLoc, on all key metrics: it reduces the mean localization error by at least 5%, possesses a 40x smaller model size, and requires over 100x fewer computations. This compelling combination of performance and efficiency validates EfficientLocNet as a powerful solution for deployment in edge computing environments.

Accurate traffic forecasting is crucial for Intelligent Transportation Systems (ITS) but is significantly challenged by non-periodic external events that disrupt regular traffic patterns. While Graph Neural Networks (GNNs) excel at modeling periodic traffic, they often falter in predicting event-driven dynamics. Existing event-aware methods either rely on manually engineered features with limited generalization or depend on curated textual event datasets that are costly to maintain and incomplete. The advent of Large Language Models (LLMs) offers new avenues for understanding and integrating event information. However, directly applying LLMs for all spatio-temporal reasoning can be inefficient, and effectively leveraging their event understanding capabilities within structured forecasting workflows remains a challenge. This paper introduces FUSE-Traffic, a framework which synergizes the dynamic event querying and understanding prowess of LLMs with the spatio-temporal modeling capabilities of GNNs. FUSE-Traffic features an on-demand event information extraction module using LLM prompting and a cross-attention based multimodal fusion mechanism to integrate rich event semantics with traffic flow features. This design enables the model to dynamically perceive and adapt to event-triggered traffic pattern changes. Comprehensive experiments on the METR-LA and PEMS datasets demonstrate that FUSE-Traffic significantly outperforms state-of-the-art models, especially under high-impact event conditions, showcasing robust predictive accuracy and resilience where traffic patterns are most disrupted. Code available at https://github.com/GeoAICenter/FUSE-Traffic_Sigspatial2025

Geo-obfuscation is a Location Privacy Protection Mechanism used in location-based services that allows users to report obfuscated locations instead of exact ones. A formal privacy criterion, geoindistinguishability (Geo-Ind), requires real locations to be hard to distinguish from nearby locations (by attackers) based on their obfuscated representations. However, Geo-Ind often fails to consider context, such as road networks and vehicle traffic conditions, making it less effective in protecting the location privacy of vehicles, of which the mobility are heavily influenced by these factors.

Understanding and predicting Origin-Destination (OD) flows is crucial for urban planning and transportation management. Traditional OD prediction models, while effective within single cities, often face limitations when applied across different cities due to varied traffic conditions, urban layouts, and socio-economic factors.

Customizing the location obfuscation functions generated by existing systems can result in weakening the privacy guarantees offered by these functions as they are not robust against such updates. In this demo, we present a new framework called, CORGI, i.e., CustOmizable Robust Geo Indistinguishability. The demonstration platform is a web application which is built on top on a real world dataset (Gowalla). The user-friendly interface of the demo allows participants to easily specify their customization preferences and generate a customizable and robust location obfuscation function. They can also examine the trade-offs among privacy, utility, and customization; visualized on a map for comparison between CORGI and a state of the art baseline.

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure.

To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers? mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers? mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers? overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP's constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the real-world experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency.

Crowdsourcing leverages the diverse skill sets of large collections of individual contributors to solve problems and execute projects, where contributors may vary significantly in experience, expertise, and interest in completing tasks. Hence, to ensure the satisfaction of its task requesters, most existing crowdsourcing platforms focus primarily on supervising contributors' behavior. This lopsided approach to supervision negatively impacts contributor engagement and platform sustainability.

Crowdsourcing allows many people to complete tasks of various difficulty with minimal recruitment and administration costs. However, the lack of participant accountability may entice people to complete as many tasks as possible without fully engaging in them, jeopardizing the quality of responses. In this paper, we present a dynamic and time efficient solution to the task assignment problem in crowdsourcing platforms. Our proposed approach, CrowdSelect, offers a theoretically proven algorithm to assign workers to tasks in a cost efficient manner, while ensuring high accuracy of the overall task. In contrast to existing works, our approach makes minimal assumptions on the probability of error for workers, and completely removes the assumptions that such probability is known apriori and that it remains consistent over time. Through experiments over real Amazon Mechanical Turk traces and synthetic data, we find that CrowdSelect has a significant gain in term of accuracy compared to state-of-the-art algorithms, and can provide a 17.5\% gain in answers' accuracy compared to previous methods, even when there are over 50\% malicious workers.

Functioning as an intermediary between tenants and cloud providers, cloud service brokerages (CSBs) can bring about great benefits to the cloud market. CSBs buy the cloud resources, i.e., servers, with lower prices from cloud providers and sell the resources to the tenants with higher prices. To maximize its own profit, a CSB may distribute tenants' requests to the clouds that waste energy resources. However, as energy costs of cloud computing have been increasing rapidly, there is a need for cloud providers to optimize energy efficiency while maintain high service level performance to tenants, not only for their own benefit but also for social welfares (e.g., protecting environment). Thus, for green cloud companies, two questions have arisen: 1) under what pricing policies from the cloud providers to the CSB, a profit-driven CSB is willing to minimize the total cloud energy cost while satisfy tenant demands and 2) how should a CSB distribute tenants' demands to achieve this objective? To address question 1), we find a pricing policy for cloud providers such that maximizing CSB's profit is equivalent to minimizing cloud providers' energy cost. To address question 2), we first devise a greedy solution, and then propose an approximation algorithm with a constant approximation ratio. Both simulation and real-world Amazon EC2 experimental results demonstrate the effectiveness of our pricing policy to incentivize CSBs to save energy for cloud providers and the superior performance of our algorithms in energy efficiency and resource utilizations in comparison with the previous algorithms.