Ear-worn devices (earables) are increasingly central to smart system interactions involving privacy-sensitive data, yet secure user authentication on these devices remains a challenge. Existing methods often depend on auxiliary sensors like in-ear microphones or accelerometers, which are absent in many commercial earables. This paper introduces a novel biometric approach leveraging natural head gestures. We observe that head gestures generate unique air friction patterns detectable by out-ear microphones, producing sonic signatures shaped by the head and neck's musculoskeletal dynamics. These signatures serve as a robust basis for earable authentication. We propose HMPrint, a system that captures air-friction-induced sonic effects (AFiSe) from head gestures via outear microphones for authentication. HMPrint incorporates advanced spectral analysis, synthetic data generation using variational autoencoders, and a contrastive continual learning framework to enhance robustness against inconsistent wearing postures, varied movement patterns, and environmental noise. A proof-of-concept prototype was tested with 30 participants and 15 commercial earable models across diverse conditions. Results show that HMPrint achieves high authentication accuracy (97.49% recall), a low FAR (2.34%), and strong resistance to spoofing (98% success rate).

Many constrained multimodal multi-objective evolutionary algorithms (CMMEAs) can effectively solve constrained multimodal multi-objective optimization problems (CMMOPs) with multiple equivalent constrained Pareto optimal solution sets (CPSs) mapped to the same constrained Pareto optimal front (CPF). Due to the existence of the constraint-first mechanism, these CMMEAs will delete solutions that can improve convergence of the CPS but violate the constraints to a large extent and even ignore the CPS with high constraint strength when encountering the CPS with the imbalance between convergence and constraints. To solve the above problems, this paper proposes a co-evolutionary algorithm with a local auxiliary population. The main population searches for all equivalent CPSs through the constraint-based global Pareto dominance principle. The local auxiliary population weakens the mutual influence of convergence and diversity between regions with different constraint strengths through the local Pareto dominance principle and provides more solutions around CPSs with higher constraint strengths to the main population. The proposed method is compared with five state-of-the-art CMMEAs on 31 CMMOPs, and the experimental results show that the proposed method is competitive.

Balancing convergence and diversity in the decision space is essential in solving multimodal multi-objective optimization problems (MMOPs), which have multiple equivalent Pareto optimal sets (PSs) with the same Pareto optimal front (PF). For MMOPs with an imbalance between convergence and diversity in the decision space (MMOP-ICD), numerous efficient multimodal multiobjective evolutionary algorithms (MMEAs) avoid premature convergence and search for the imbalanced PS by relaxing the traditional convergence-first selection mechanism. Unfortunately, existing MMEAs suffer from convergence degradation due to excessive relaxation of the convergence-first selection mechanism. Therefore, this paper proposes an improved convergence-relaxed mechanism that includes an enhanced local convergence indicator and a two-stage mating selection. The enhanced local convergence indicator introduces the global convergence indicator into the local convergence indicator. The local convergence indicator can locate more equivalent PSs and prevent premature convergence caused by the global convergence indicator. The global convergence indicator can improve the convergence quality of the solution selected by the local convergence indicator. Then, the two-stage mating selection is used to enhance the diversity in the decision space and balance the improved convergence. Experimental results and statistical analysis show that the proposed algorithm is significantly superior to other state-of-the-art MMEAs.

Generative agents, capable of simulating human behavior and collaborating on complex tasks, have the potential to revolutionize the investigation of traffic accidents. In this study, we designed TAA (Traffic Accident Agents), an advanced framework based on extended large language models (LLMs) to digitally model urban accident handling procedures and stakeholder interactions. TAA formalizes the roles, responsibilities, and interactions of all stakeholders through natural language encoding, utilizing this knowledge base to orchestrate its execution workflows. Facilitates trusted agent interactions, generates comprehensive reports, and employs memory mechanisms to plan and optimize subsequent actions. We evaluated TAA performance across multiple versions of ChatGPT, focusing on its capabilities to generate reliable interactions, make context-sensitive decisions, maintain extended dialogues, and produce accurate reports in streamlined accident resolution scenarios. Our analysis included evaluations of token consumption and economic costs to ensure scalability and practicality, with TAA achieving 87.9% effectiveness on the GPT 4omini benchmark. Experimental results demonstrate TAA’s successful execution of urban accident handling workflows with maintained informational consistency. The framework shows broad applicability to accident investigation, reconstruction, and archival documentation. This work pioneers the use of generative agents as collaborative human proxies, offering a transformative pathway to advance the future of traffic accident management and investigation.

Dynamic Adaptive Streaming over HTTP (DASH) stands as a leading streaming technology embraced by major video platforms and smart TV manufacturers worldwide. Despite its widespread use, the inherent diversity in both the video content and the client devices poses challenges, hindering DASH from consistently delivering top-notch playback quality for all users. This oversight often leads to network congestion, compromising the playback quality for users. To tackle these issues, we propose a Two-stage Stackelberg Game (TSBG) algorithm for personalized video streaming transmission in Edge Computing (EC) environments. The TSBG algorithm aims to optimize the Quality of Experience (QoE) of users by tailoring video streaming services between EC servers and clients. Initially, we establish the system model and define the video stream transmission problem as a multi-objective optimization problem, balancing server downlink resource scheduling and client adaptive bit rate. Subsequently, we design the TSBG algorithm, where an edge server allocation mechanism is adopted in the first stage to maximize overall user QoE, while users adjust their video bit rates based on the edge server's distribution plan to enhance their individual QoE in the second stage. We prove the existence and uniqueness of the equilibrium solution of the two-stage Starkelberg game and design an optimal pricing algorithm to maximize the benefits of edge servers. Extensive simulation experiments validate the effectiveness of the TSBG algorithm, showcasing its superiority in achieving enhanced QoE, network efficiency, and fairness compared to alternative approaches.

Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO , a novel user- P erception-based video E xperience O ptimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between a device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of the on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.

Taxi business, with its ubiquitous availability, route flexibility and comfortable travel experience, offers a complementary service for the public transportation system. Among the existing methods of doing taxi business, an meaningful issue is to mine efficient seeking strategies for taxi drivers, in order to improve transportation efficiency. Recent efforts have been made mainly on the individual recommendation with respect to shorter seeking time and higher seeking efficiency, whereas the global transportation efficiency will greatly be reduced once each driver only pays attention to his local optimization. Rather than the individual recommendation, in this paper we conduct research on mining the taxis mobility from large-scale taxi data, thereby proposing a novel solution, namely GREEN (short for A Global RoutEs rEcommeNdation), to improve the seeking strategies and optimize the global transportation situation. Specifically, we first investigate how the drop-off information affects seeking strategies and conduct quantitive analysis, revealing the impact of seeking efficiency, passenger density and top drivers’ experience. Moreover, to deal with the conflict between local optimization and global optimization, we dynamically adjust the weights of road segments based on the number of vacant taxis passing through each road segment. Also, to well evaluate the transportation efficiency, we define the seeking efficiency, net revenue and operation efficiency. Extensive experiments on the real-world dataset demonstrate that our scheme can work well, which not only improves the overall seeking efficiency by reducing total vacant driving time, but also increases the global operation efficiency, thereby optimizing the global transportation efficiency.

Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem. Most of the recent analytical approaches on this topic only considered how to maximize the pickup chance, energy efficiency, or profit for the immediate next trip when recommending seeking routes, therefore may not be optimal for the overall profit over an extended period of time due to ignoring the destination choice of potential passengers. To tackle this issue, we propose a novel Spatial Network-based Markov Decision Process (SN-MDP) with a rolling horizon configuration to recommend better driving directions. Given a set of historical taxi records and the current status (e.g., road segment and time) of a vacant taxi, we find the best move for this taxi to maximize the profit in the near future. We propose statistical models to estimate the necessary time-variant parameters of SN-MDP from data to avoid competition between drivers. In addition, we take into account fuel cost to assess profit, rather than only income. A case study and several experimental evaluations on a real taxi dataset from a major city in China show that our proposed approach improves the profit efficiency by up to 13.7 percent and outperforms baseline methods in all the time slots.

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

The communication infrastructures are likely to fail, in the case of disasters like earthquakes and debris flow, resulting in blind areas and the inconvenience of residents' communication. In this paper, we propose a novel scheme connecting these infrastructure failure areas, namely, an energy-aware device-to-device communication scheme (NEED). Our proposed scheme, taking advantage of clustering technology, connects users within the infrastructure failure areas that often have no direct access to the cellular network. Compared with the clustering used in traditional cases, we add the process of determining candidate cluster heads (CHs) before determining final CHs. Based on location and residual energy, the final CHs are selected in the candidate CHs, and dual CHs in the cluster run alternately to share the communication cost. Besides, a modified ant colony algorithm (MACA) is developed to increase routing efficiency. The simulation results show the effectiveness of our proposed NEED scheme in terms of energy consumption and energy balance, and demonstrate that the scheme significantly extends the lifetime of the whole network.

Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem since it could improve the income of the drivers and reduce gas emissions and fuel consumption. The recent research on seeking strategies may not be optimal for the overall revenue over an extended period of time as they ignored the important impact of passengers' destinations on future passenger seeking. To address these issues, this paper investigates how to increase the revenue efficiency (revenue per unit time) of taxi drivers, and models the passenger seeking process as a Markov Decision Process (MDP). For each one-hour time slot, we learn a different set of parameters for the MDP from data and find the best move for a vacant taxi to maximize the total revenue in that time slot. A case study and several experimental evaluations on a real dataset from a major city in China show that our proposed approach improves the revenue efficiency of inexperienced drivers by up to 15% and outperforms a baseline method in all the time slots.