Retrieval-augmented generation (RAG) has shown strong potential for deploying large language models at the edge, yet existing designs largely rely on generic and monolithic knowledge bases that are poorly matched to the heterogeneous queries and resource-constrained edge computing environments. Through extensive empirical analysis, we find that domain-specialized knowledge bases, when deployed on individual edge servers, deliver substantially higher retrieval accuracy and generation quality than generic knowledge bases under identical resource budgets. Based on this, we propose SRAG, a distributed RAG system that enforces knowledge specialization at the edge. Each edge server maintains a domain-aware specialized knowledge base by retaining domain-aligned knowledge and decoupling out-of-domain content. SRAG uses a buffer-based knowledge migration mechanism to redistribute out-of-domain content to better-matched edge servers, enabling efficient global knowledge utilization without central coordination. To handle domain-mismatched queries, SRAG employs lightweight cross-node routing guided by compact metadata summaries, avoiding full knowledge replication. Together, these mechanisms form an end-to-end workflow for decentralized edge RAG. Experiments show that SRAG improves retrieval relevance, generation quality, and storage efficiency, while reducing end-to-end latency.

Large Language Models (LLMs) increasingly underpin intelligent web applications, from chatbots to search and recommendation, where efficient specialization is essential. Low-Rank Adaptation (LoRA) enables such adaptation with minimal overhead, while federated LoRA allows web service providers to fine-tune shared models without data sharing. However, in privacy-sensitive deployments, clients inject varying levels of differential privacy (DP) noise, creating privacy heterogeneity that misaligns individual incentives and global performance. In this paper, we propose WinFLoRA, a privacy-heterogeneous federated LoRA that utilizes aggregation weights as incentives with noise awareness. Specifically, the noises from clients are estimated based on the uploaded LoRA adapters. A larger weight indicates greater influence on the global model and better downstream task performance, rewarding lower-noise contributions. By up-weighting low-noise updates, WinFLoRA improves global accuracy while accommodating clients' heterogeneous privacy requirements. Consequently, WinFLoRA aligns heterogeneous client utility in terms of privacy and downstream performance with global model objectives without third-party involvement. Extensive evaluations demonstrate that across multiple LLMs and datasets, WinFLoRA achieves up to 52.58% higher global accuracy and up to 2.56× client utility than state-of-the-art benchmarks. Source code is publicly available at https://github.com/koums24/WinFLoRA.git.

Multimodal Retrieval-Augmented Generation (MM-RAG) extends the capabilities of Large Language Models (LLMs) by incorporating external image-text knowledge bases to handle various tasks. However, MM-RAG systems in open environments are highly vulnerable to retrieval poisoning attacks, i.e., adversaries can inject malicious image-text pairs that are retrieved and dominate the generation process, leading to incorrect or harmful outputs. Due to the unique challenges of image-text fusion and cross-modal interference, existing defenses for text-based RAG cannot be directly applied to multimodal scenarios. In this paper, we propose IRAG, the first robust defense framework specifically designed for MM-RAG. The core of IRAG lies in its hazard separation. This structured defense isolates potential contamination sources by leveraging redundancy and consensus, enhancing system robustness and ensuring reliable outputs even when portions of retrieved content are compromised. Extensive experiments conducted under the MMQA and WebQA and the BQI and ROTI poisoning schemes demonstrate that IRAG consistently restores system reliability: the normal answer accuracy improves by 15–30% (restoring it to pre-poisoning levels), while the poisoned answer rate is reduced to below 7%.

A reliable autonomous driving system requires a high-precision perception module. Collaborative perception is emerging as a web-scale information-sharing paradigm for autonomous driving, enabling multiple vehicles to collectively achieve a broader perception field than any single vehicle. However, existing approaches necessitate frequent one-to-many communication, which increases network load and leads to information redundancy. This paper presents Octopus, an innovative vehicle-to-road collaboration framework that leverages the computational capabilities of roadside units. Instead of frequent one-to-many communication, vehicles interact only with roadside units, which significantly reduces communication overhead and improves real-time processing efficiency. While this design alleviates communication burdens, vehicles may still struggle to achieve comprehensive situational awareness in highly dynamic environments. To further address this limitation, our framework incorporates global fusion results as prior knowledge, enabling closed-loop fusion to refine vehicle-side perception. Extensive experiments on OPV2V and V2V4Real datasets demonstrate that Octopus excels at collaborative perception, outperforming the state-of-the-art approach up to 11.58% on [email protected], 12.74% on [email protected] and 5514× reduction in communication volume.

Multi-access edge computing (MEC) has garnered increasing attention from users due to low web service latency. Edge servers deployed near base stations have limited resources and can only serve users within their coverage areas. Therefore, efficiently allocating users to the appropriate edge servers is crucial for significantly enhancing system performance. Traditional edge user allocation (EUA) strategies often rely on precise user location, leading to significant privacy leakage risks and weakening users' trust. To tackle this challenge, this paper presents Disguiser, a novel privacy-preserving scheme designed to achieve efficient edge user allocation while safeguarding user location privacy. Disguiser employs a Laplace noise-based location obfuscation mechanism to ensure users' privacy. To resolve the user allocation problem after location obfuscation, which involves balancing real-time performance and accuracy, we design a two-stage user allocation algorithm, TEUA, consisting of the LR-EUA initial allocation algorithm and the MR-EUA reallocation algorithm. First, a novel EUA algorithm, LR-EUA, is integrated into Disguiser, innovatively combining linear relaxation with a greedy approach. Additionally, Disguiser further minimizes resource wastage in MEC systems caused by location obfuscation by employing the MR-EUA algorithm at base stations. Experimental results show that Disguiser effectively balances privacy protection with system performance, substantially outperforming existing state-of-the-art methods.

As a promising distributed computing paradigm, edge computing (EC) enhances service quality and reduces service latency by deploying computational and storage resources at the network edge. However, due to edge servers' geographic distribution and resource constraints, EC is challenged by edge denial-of-service (EDDoS) attacks. Although various approaches have been proposed to mitigate EDDoS attacks, the impact of capacity uncertainties in edge server processing capacity and transmission latency has been largely overlooked. This limits the adaptability and robustness of mitigation strategies in edge computing environments, which could result in overload and paralysis of edge servers. To address this issue, this paper introduces uEDDoS-D, an uncertainty-aware EDDoS mitigation approach based on an improved deep deterministic policy gradient algorithm. uEDDoS-D models the uncertainties of computing capacities and transmission latency of edge servers in EC environments and leverages the collective computational resources of edge servers to mitigate EDDoS attacks without relying on traditional attack detection mechanisms. uEDDoS-D aims to minimize the impact of uncertainties in edge server processing capacity and transmission latency on mitigation decisions while effectively reducing service latency. Experimental results demonstrate that uEDDoS-D significantly reduces service latency, outperforming the state-of-the-art approaches by an average of 16.75%, and enhances system robustness in EDDoS attack scenarios.

As an emerging distributed computing paradigm, edge computing (EC) brings computing and storage capabilities to the network edge to support low-latency services. Service providers can cache popular data on edge servers to greatly reduce service latency and improve service quality. However, data popularity in EC environments is often dynamic and stochastic. Given the limited storage resources of edge servers, when data popularity changes, data may need to be migrated from edge servers with lower data popularity to those with higher data popularity. Data migration between edge servers can effectively reduce the high transmission costs associated with transferring data from the cloud to the edge. Most existing data migration approaches are designed for static EC environments, assuming that user access patterns and data demands remain constant over time. Even in dynamic EC environments, many approaches oversimplify data popularity by treating it as static, failing to model its fluctuations over time. In realistic EC scenarios, unexpected situations, such as sudden shifts in user preferences for specific data at certain times, can cause significant fluctuations in data popularity, leading to increased data retrieval latency and inefficient storage management. To address this uncertainty, this paper proposes a popularity uncertainty-aware online data migration approach that combines distributed robust optimization and Lyapunov optimization to minimize the data retrieval latency and migration cost for service providers. Specifically, it handles data popularity uncertainty in a data-driven manner through robust optimization. Then, it employs Lyapunov optimization to decompose the continuous optimization problem into multiple single-slot online optimization problems. Extensive experiments on a widely used real-world dataset confirm the effectiveness of this approach and its significant advantages over state-of-the-art approaches.

Data deduplication, originally designed for cloud storage systems, is increasingly popular in edge storage systems due to the costly and limited resources and prevalent data redundancy in edge computing environments. The geographical distribution of edge servers poses a challenge in aggregating all data storage information for global decision-making. Existing edge data deduplication (EDD) methods rely on centralized cloud control, which faces issues of timeliness and system scalability. Additionally, these methods overlook data popularity, leading to significantly increased data retrieval latency. A promising approach to this challenge is to implement distributed EDD without cloud control, performing regional deduplication with the edge server requiring deduplication as the center. However, our investigation reveals that existing distributed EDD approaches either fail to account for the impact of collaborative caching on data availability or generate excessive information exchange between edge servers, leading to high communication overhead. To tackle this challenge, this paper presents EdgeDup, which attempts to implement effective EDD in a distributed manner. Additionally, to ensure data availability, EdgeDup aims to maintain low data retrieval latency. EdgeDup achieves its goals by: 1) identifying data redundancies across different edge servers in the system; 2) deduplicating data based on their popularity; and 3) reducing communication overheads using a novel data dependency index. Extensive experimental results show that EdgeDup significantly enhances performance, i.e., reducing data retrieval latency by an average of 47.78% compared to state-of-the-art EDD approaches while maintaining a comparable deduplication ratio.

Mobile edge computing (MEC) is an emerging distributed paradigm where edge servers are deployed near base stations or access points to support low-latency services. The edge user allocation (EUA) problem, which aims to minimize system cost while meeting constraints like data transmission latency, has become a critical challenge for service providers. Existing studies typically focus on static MEC scenarios, assuming predictable latency between users and edge servers. However, network congestion introduces latency uncertainty, which, if not addressed, increases the risk of allocation failures or excessive service latency. This paper addresses the latency uncertainty-aware edge user allocation (uEUA) problem. We model uEUA as an integer programming problem and apply chance-constrained programming to convert uncertain latency into probabilistic constraints, which we then transform into deterministic conditions using Chebyshev's inequality. We propose two methods to solve the problem: BD-uEUA, an exact approach based on Benders decomposition, and LR-uEUA, an approximate method based on linear relaxation. Extensive experiments on a real-world dataset show that BD-uEUA reduces system costs by 19.70% compared to the state-of-the-art method, while LR-uEUA achieves a 3.05% reduction with just 0.09% of the system overhead.

KV cache technology, by storing key-value pairs, helps reduce the computational overhead incurred by *large language models* (LLMs). It facilitates their deployment on resource-constrained edge computing nodes like edge servers. However, as the complexity and size of tasks increase, KV cache usage leads to substantial GPU memory consumption. Existing research has focused on mitigating KV cache memory usage through sequence length reduction, task-specific compression, and dynamic eviction policies. However, these methods are computationally expensive for resource-constrained edge computing nodes. To tackle this challenge, this paper presents Sim-LLM, a novel inference optimization mechanism that leverages task similarity to reduce KV cache memory consumption for LLMs. By caching KVs from processed tasks and reusing them for subsequent similar tasks during inference, Sim-LLM significantly reduces memory consumption while boosting system throughput and increasing maximum batch size, all with minimal accuracy degradation. Evaluated on both A40 and A100 GPUs, Sim-LLM achieves a system throughput improvement of up to 39.40\% and a memory reduction of up to 34.65%, compared to state-of-the-art approaches. Our source code is available at https://github.com/CGCL-codes/SimLLM.

Multimodal Knowledge Graphs (MMKG) models integrate multimodal contexts to improve link prediction performance. All existing MMKG models follow the transductive setting with a fixed predefined set, meaning that all the entities, relations, and multimodal information in the test graph are observed during training. This hinders their generalization to real-world MMKG with unseen entities and relations. Intuitively, a MMKG model trained on DBpedia cannot infer on Freebase. To address above limitations, we make the first attempt towards inductive learning for MMKG and propose a multimodal Inductive MMKG model (IndMKG) that is universal and transferable to any MMKG. Distinct from existing transductive methods, our model does not rely on specific trained embeddings; instead, IndMKG generates adaptive embeddings conditioned on any new MMKG via multimodal prototypes. Specifically, we construct class-adaptive prototypes to appropriately characterize the multimodal feature distribution of the given graph and equip IndMKG with robust adaptability to multimodal information across MMKGs. In addition, IndMKG learns non-specific structural embeddings based on meta relations. Such strategies tackle the challenge of notable multimodal feature discrepancies in cross-graph induction and allow the pre-trained IndMKG model to effectively zero-shot generalize to any MMKG. The strong performance in both inductive and transductive settings, across more than 20+ different scenarios, confirms the effectiveness and robustness of IndMKG. Our code is released at https://github.com/MMKGer/IndMKG/.

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existingedge data caching(EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enableuncertainty-aware edge data caching(uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive applications. An increasingly large number of data is being generated at the edge, stretching the limited capacity of edge storage systems. Improving resource utilization for edge storage systems has become a significant challenge in recent years. Existing solutions attempt to achieve this goal through data placement optimization, data partitioning, data sharing, etc. These approaches overlook the data redundancy in edge storage systems, which produces substantial storage resource wastage. This motivates the need for an approach for data deduplication at the edge. However, existing data deduplication methods rely on centralized control, which is not always feasible in practical edge computing environments. This article presents Ripple, the first approach that enables edge servers to deduplicate their data in a decentralized manner. At its core, it builds a data index for each edge server, enabling them to deduplicate data without central control. With Ripple, edge servers can 1) identify data duplicates; 2) remove redundant data without violating data retrieval latency constraints; and 3) ensure data availability after deduplication. The results of trace-driven experiments conducted in a testbed system demonstrate the usefulness of Ripple in practice. Compared with the state-of-the-art approach, Ripple improves the deduplication ratio by up to 16.79% and reduces data retrieval latency by an average of 60.42%.

The emergence ofmobile edge computing(MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data requests for data storage, making the reduction of data storage costs a critical objective. Despite the extensive studies of edge data deduplication as a data reduction technique, existing deduplication methods encounter numerous challenges in MEC environments. These challenges stem from disparities between edge servers and cloud data center edge servers, as well as uncertainties such as user mobility, leading to insufficient robustness in deduplication decision-making. Consequently, this paper presents a robust optimization-based approach for the edge data deduplication problem. By accounting for uncertainties including the number of data requirements and edge server failures, we propose two distinct solving algorithms: uEDDE-C, a two-stage algorithm based on column-and-constraint generation, and uEDDE-A, an approximation algorithm to address the high computation overhead of uEDDE-C. Our method facilitates efficient data deduplication in volatile edge network environments and maintains robustness across various uncertain scenarios. We validate the performance and robustness of uEDDE-C and uEDDE-A through theoretical analysis and experimental evaluations. The extensive experimental results demonstrate that our approach significantly reduces data storage cost and data retrieval latency while ensuring reliability in real-world MEC environments.

Edge computing allows app vendors to store popular data on edge servers, enabling users to retrieve data with low latency. However, edge servers may become unavailable at runtime due to expected exceptions. Data requests are routed to cloud servers, resulting in increased data retrieval latency. To address this issue,erasure coding(EC) has been employed to improve data availability, aiming to ensure full data access for all the users in anedge storage system(ESS). However, in real-world scenarios, data popularity differs and varies. Existing approaches for edge data placement place coded blocks across the entire system without considering data popularity. As a result, they often suffer from high data retrieval latency. In addition, they are designed to process data items individually. Data placed earlier will limit the placement options for subsequent files because edge servers with the most neighbors in the system can be easily exhausted. Some files cannot be placed properly to accommodate user demands. This increases users' data retrieval latency further. This paper tries to study the placement of multiple files in an edge storage system, considering their popularity. We first model theedge data placement(EDP) problem as a mixed-integer programming problem and prove its$\mathcal {NP}$-hardness. Then, we present an optimal algorithm named EDP-O, decoupling the EDP problem into three convex optimization subproblems for solving with an iterative algorithm. In addition, we propose an approximation algorithm named EDP-A that quickly solves the EDP problem in large-scale scenarios with a guaranteed approximation ratio of$\ln N$. The results of experiments conducted on a real-world dataset show that EDP-O and EDP-A reduce the average data retrieval latency against four representative approaches by an average of 18.4% and 15.6% in small-scale scenarios. EDP-A reduces the average data retrieval latency against four representative approaches by an average of 54.7% and reduces the data discard rate by an average of 34.9% in large-scale scenarios.

Edge computing, as an emerging paradigm in distributed computing, introduces a novel data access framework for latency-sensitive applications, enabling data retrieval from edge servers situated closer to users, rather than the remote cloud. This significantly reduces data retrieval latency, thereby enhancing the quality of experience. However, the resources of edge servers are highly constrained. Recent studies in erasure coding-based edge data storage have effectively sealed the gap between storage cost and data retrieval latency. Despite these advantages, the highly dynamic edge computing environments and frequent data updates introduce significant challenges in cache replacement for erasure coding-based edge storage systems. Specifically, direct data replacement, similar to traditional replica-based storage methods, may result in insufficient encoded blocks for reconstructing the original data during retrieval, which can increase data retrieval latency and even compromise data availability. In this paper, we identify and address, for the first time, the cache replacement problem in erasure coding-based edge storage systems. We propose a novel cache replacement algorithm, named ECCR, based on Lyapunov optimization, which effectively solves the cache replacement problem in dynamic edge computing environments. Theoretical analysis and extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method, which outperforms two state-of-the-art approaches and achieves an average system cost reduction of 49.78%.

Workload management is a critical concern in shared control of unmanned ground vehicles. In response to this challenge, prior studies have developed methods to estimate human operators’ workload by analyzing their physiological data. However, these studies have primarily adopted a single-model-single-feature or a single-model-multiple-feature approach. The present study proposes a Bayesian inference model to estimate workload, which leverages different machine learning models for different features. We conducted a human subject experiment with 24 participants, in which a human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) with the help from an autonomy while performing a surveillance task simultaneously. Participants’ eye-related features, including gaze trajectory and pupil size change, were used as the physiological input to the proposed Bayesian inference model. Results show that the Bayesian inference model achieves a 0.823 F1 score, 0.824 precision, and 0.821 recall, outperforming the single models.

Edge computing (EC) enables low-latency services by pushing computing resources to the network edge. Due to the geographic distribution and limited capacities of edge servers, EC systems face the challenge of edge distributed denial-of-service (DDoS) attacks. Existing systems designed to fight cloud DDoS attacks cannot mitigate edge DDoS attacks effectively due to new attack characteristics. In addition, those systems are typically activated upon detected attacks, which is not always realistic in EC systems. DDoS mitigation needs to be cohesively integrated with workload migration at the edge to ensure timely responses to edge DDoS attacks. In this paper, we present EdgeShield, a novel DDoS mitigation system that leverages edge servers’ computing resources collectively to defend against edge DDoS attacks without the need for attack detection. Aiming to maximize system throughput over time without causing significant service delays, EdgeShield monitors service delays and migrates workloads across an EC system with adaptive mitigation strategies. The experimental results show that EdgeShield significantly outperforms state-of-the-art solutions in both system throughput and service delays.

In themobile edge computing(MEC) environment, edge servers with storage and computing resources are deployed at base stations within users’ geographic proximity to extend the capabilities of cloud computing to the network edge.Edge storage system(ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers’ limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers’ geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus,balanced edge data deduplication(BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its$\mathcal {NP}$-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

Edge computing, as a new computing paradigm, brings cloud computing’s computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constituteedge storage systems(ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this article, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novelErasure Coding based Edge Data Placement(EC-EDP) problem as an integer linear programming problem and prove its$\mathcal {NP}$-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

As autonomous systems become more complicated, humans may have difficulty deciphering autonomy-generated solutions and increasingly perceive autonomy as a mysterious black box. The lack of transparency contributes to the lack of trust in autonomy and suboptimal team performance. In response to this concern, researchers have proposed various methods to enhance autonomy transparency and evaluated how enhanced transparency could affect the people’s trust and the human-autonomy team performance. However, the majority of prior studies measured trust at the end of the experiment and averaged behavioral and performance measures across all trials in an experiment, yet overlooked the temporal dynamics of those variables. We have little understanding of how autonomy transparency affects trust, dependence, and performance over time. The present study, therefore, aims to fill the gap and examine such temporal dynamics. We develop a game Treasure Hunter wherein a human uncovers a map for treasures with the help from an intelligent assistant. The intelligent assistant recommends where the human should go next. The rationale behind each recommendation could be conveyed in a display that explicitly lists the option space (i.e., all the possible actions) and the reason why a particular action is the most appropriate in a given context. Results from a human-in-the-loop experiment with 28 participants indicate that by conveying the intelligent assistant’s decision-making rationale via the display, participants’ trust increases significantly and becomes more calibrated over time. Using the display also leads to a higher acceptance of recommendations from the intelligent agent.

Processing-In-Memory (PIM) has emerged as a high-performance and energy-efficient computing paradigm for accelerating convolutional neural network (CNN) applications. Resistive random access memory (ReRAM) has been widely used in PIM architectures due to its extremely high efficiency for accelerating matrix-vector multiplications through analog computing. However, because CNN training usually requires high-precision computation in the backward propagation (BP) stage, the limited precision of analog PIM accelerators impedes their adoption in CNN training. In this paper, we propose ReHy, a hybrid PIM accelerator to support CNN training in ReRAM arrays. It is composed of Analog PIM (APIM) and Digital PIM (DPIM) modules. ReHy uses APIM to accelerate the feed-forward propagation (FP) stage for high performance, and DPIM to process the BP stage for high accuracy. We exploit the capability of ReRAM for Boolean logic operations to design the DPIM architecture. Particularly, we design floating-point multiplication and addition operators to support matrix multiplications in ReRAM arrays. We also propose a performance model to offload high-precision matrix multiplications to DPIM according to the data parallelism. Experimental results show that ReHy can speed up CNN training by 18.8 and 2.3, and reduce energy consumption by 21.2 and 3.2, compared with CPUs and state-of-the-art FloatPIM, respectively.

Mobile edge computing(MEC) deploys edge servers at the base station in the proximity of users to provide cloud computing-like computing and storage functionalities, which can achieve applications’ low latency requirement at the network edge. Theedge server network(ESN), constituted by edge servers in an area and the links between them, can host app vendors’ services for serving nearby users. Many existing studies have demonstrated that a high ESN density allows for high service performance because edge servers can communicate and share resources with each other effectively over the ESN. However, in the real-world MEC environment, constructing a high-density ESN may incur high construction costs. The trade-off between construction cost and network density plays a vital role in the design of an ESN. Unfortunately, existing studies of MEC have commonly and simply assumed the densities of the ESNs in their experiments. In this paper, we make the first attempt to study the design of cost-effective ESNs with the aim to trade off between the network construction cost and the network density. We model this novelEdge Server Network Design(ESND) problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness. ESND-O as an optimal approach is proposed based on integer programming to solve small-scale ESND problems. Another approximation approach named ESND-A is designed to solve large-scale ESND problems efficiently. We conduct extensive experiments to test the performance of ESND-O and ESND-A on a real-world dataset, and the experimental results demonstrate their effectiveness and efficiency against four representative approaches.

This paper presents an approach to formulating the cost function for a motion planner intended for human-robot collaboration on manipulation tasks in a shared workspace. To be effective for human-robot collaboration a robot should plan its motion so that it is both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot's motion planner: (1) Avoidance of the workspace previously-occupied by the human, so that the motion is as safe as possible, and (2) Consistency of the robot's motion, so that the motion is as predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human's previous motion. We find that using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms.

This paper focuses on recognition and prediction of human reaching motion in industrial manipulation tasks. Several supervised learning methods have been proposed for this purpose, but we seek a method that can build models on-the-fly and adapt to new people and new motion styles as they emerge. Thus, unlike previous work, we propose an unsupervised online learning approach to the problem, which requires no offline training or manual categorization of trajectories. Our approach consists of a two-layer library of Gaussian Mixture Models that can be used both for recognition and prediction. We do not assume that the number of motion classes is known a priori, and thus the library grows if it cannot explain a new observed trajectory. Given an observed portion of a trajectory, the framework can predict the remainder of the trajectory by first determining what GMM it belongs to, and then using Gaussian Mixture Regression to predict the remainder of the trajectory. We tested our method on motion-capture data recorded during assembly tasks. Our results suggest that the proposed framework outperforms supervised methods in terms of both recognition and prediction. We also show the benefit of using our two-layer framework over simpler approaches.

Dynamic assignment and re-assignment of large number of simple and cheap robots across multiple sites is relevant to applications like autonomous survey, environmental monitoring and reconnaissance. In this paper, we present supervisory control laws for cost-effective (re)-distribution of a robotic swarm among multiple sites. We consider a robotic swarm consisting of tens to hundreds of simple robots with limited battery life and limited computation and communication capabilities. The robots have the capability to recognize the site that they are in and receive messages from a central supervisory controller, but they cannot communicate with other robots. There is a cost (e.g., energy, time) for the robots to move from one site to another. These limitations make the swarm hard to control to achieve the desired configurations. Our goal is to design control laws to move the robots from one site to another such that the overall cost of redistribution is minimized. This problem can be posed as an optimal control problem (which is hard to solve optimally), and has been studied to a limited extent in the literature when the cost objective is time. We consider the total energy consumed as the cost objective and present a linear programming based heuristic for computing a stochastic transition law for the robots to move between sites. We evaluate our method for different objectives and show through Monte Carlo simulations that our method outperforms other proposed methods in the literature for the objective of time as well as more general objectives (like total energy consumed).