Low-Earth-Orbit (LEO) satellite constellations are becoming integral to 6G infrastructure, but increasing in-orbit computation accelerates battery degradation and raises sustainability concerns. Meanwhile, renewable-heavy regions worldwide experience persistent energy curtailment due to transmission bottlenecks, leaving substantial clean energy stranded near generation sites. We identify a satellite-grid co-design opportunity: adaptively offloading task-critical data from satellite to data centers co-located with renewable power plants. However, realizing this vision requires jointly considering intermittent and capacity-limited communication windows, as well as time-varying electricity budgets. In this paper, we propose SQSO, a Sustainable and QoS-aware Satellite Offloading framework that models per-interval task offloading as a constrained optimization over dynamic topology and electricity prices. Under this framework, we design $\text{AO}^2$, an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, $\text{AO}^2$ reduces energy consumption by up to 76.03% and battery life consumption by up to 76.85% compared to state-of-the-art schemes, while also lowering task delay. This work highlights that sustainable scaling of LEO constellations requires co-design of space networking and renewable energy infrastructure, while our solution promotes renewable-aware task offloading and cross-domain collaboration for space-energy integration in the 6G era.

Low Earth Orbit (LEO) satellite networks such as Starlink and Project Kuiper are increasingly integrated with cloud infrastructures, forming an important internet backbone for global web services. By extending connectivity to remote regions, oceans, and disaster zones, these networks enable reliable access to applications ranging from real-time WebRTC communication to emergency response portals. Yet the resilience of these web services is threatened by space radiation: it degrades hardware, drains batteries, and disrupts continuity, even if the space-cloud integrated providers use machine learning to analyze space weather and radiation data. Specifically, conventional fixes like altitude adjustments and thermal annealing consume energy; neglecting this energy use results in deep discharge and faster battery aging, whereas sleep modes risk abrupt web session interruptions. Efficient network-layer mitigation remains a critical gap. We propose RALT (Radiation-Aware LEO Transmission), a control-plane solution that dynamically reroutes traffic during radiation events, accounting for energy constraints to minimize battery degradation and sustain service performance. Our work shows that unlocking space-based web services' full potential for global reliable connectivity requires rethinking resilience through the lens of the space environment itself.

The photon point clouds acquired by ICESat-2/ATLAS offer unprecedented potential for Earth observation but are heavily contaminated by noise photons, posing a significant challenge for downstream applications. Traditional denoising methods, which often rely on local density statistics, struggle with complex terrains and varying signal-to-noise ratios. While deep learning presents a promising alternative, existing approaches often inefficiently process the inherently sparse data via 2D projections or non-optimized 3D networks. To address these limitations, this paper introduces a novel deep learning framework for ICESat-2 photon denoising, termed Sparse Manifold Retrieval Network (SMRNet). We propose a Manifold-Aware Convolution (MAC) module to capture the continuous manifold structures of signal photons through multi-scale dilated sparse convolutions, and a Cross-Scale Pyramid Enhancement (CSPE) module to effectively refine multi-level features extracted from the encoder. Evaluated on a manually annotated dataset covering southeastern coastal regions of China, SMRNet demonstrates superior performance over traditional denoising method and data-driven baselines across multiple metrics. The results underscore the effectiveness of SMRNet in enhancing denoising accuracy, particularly in challenging environments with sparse signals and rugged topography.

The growth of distributed energy resources and local energy markets heightens the need for price formation that is transparent, privacy preserving, and compatible with network constraints. Blockchain provides a trust-minimized substrate for auditable clearing and settlement through consensus, tamperevident ledgers, and smart contracts. This survey organizes blockchain-enabled pricing into three families, namely auction-based, game-theoretic, and optimization-based, and links them to enabling techniques such as metering oracles, secure multiparty computation, zero-knowledge proofs, and verifiable optimality certificates. Applications span wholesale electricity, carbon and green certificates, distributed energy trading, ancillary services, and electric vehicles. Evidence indicates gains in auditability, privacy, network awareness, and automated settlement, alongside challenges in scalability, data protection, grid integration, and regulation. The survey distills design patterns and research directions toward verifiable, interoperable, and governable pricing modules that complement system-operator markets.

Vision language models (VLMs) have opened new avenues for satellite remote sensing image analysis and have shown promise across multiple tasks. However, in the absence of a remote sensing-oriented general VLM, existing approaches rely on retraining generic VLMs with remote sensing datasets to adapt to downstream tasks. This practice is inherently affected by two factors: 1) generic VLMs are pretrained on massive web data containing noise, biases, and misinformation; and 2) many remote sensing image-text datasets use VLM-generated annotations, which can introduce hallucinations and factual errors. Such statistical biases exacerbate the alignment gap of remote sensing VLMs, leading to generation bias and degraded task performance. To address this issue, we propose Geo-Contrastive Decoding (Geo-CD) to enhance remote sensing VLMs. Geo-CD reduces over-reliance on statistical biases by contrasting the output distributions produced from distorted versus original visual inputs. This strategy ensures that the generations of VLMs remain well-grounded in the visual input, thereby improving both reliability and accuracy. Extensive experiments demonstrate that Geo-CD can be applied to diverse remote sensing tasks without additional training or external tools. On the selected base VLM, Geo-CD achieves consistent gains across most remote sensing benchmarks and reaches state-of-the-art performance.

Airdrops are a widely used mechanism in Web3 ecosystems to incentivize early users by distributing governance tokens. However, these mechanisms are increasingly targeted by airdrop hunters—malicious actors who exploit token distribution systems through address farming, automated scripts, and behavioral camouflage. While prior work such as ARTEMIS leverages multimodal features and local transaction patterns to detect such behavior, it lacks a global understanding of wallet influence in the transaction graph. In this paper, we propose an enhanced detection framework that augments the ARTEMIS by incorporating PageRank-based global centrality as an additional structural feature. This allows the model to better distinguish superficially active wallets from those with broader influence in the network. We evaluate our method on real-world Non-Fungible Token (NFT) data from the Blur marketplace and achieve state-of-the-art performance. Furthermore, a feature substitution experiment reveals that simple degree-based features alone can achieve near-perfect performance, even outperforming PageRank, suggesting that the labels are strongly coupled with topological properties. These findings highlight both the effectiveness of structural augmentation and the potential risks of shortcut learning in graph-based detection systems.

Satellite communication plays a crucial role in future networks, but traditional systems face significant challenges in interference management and energy efficiency. To address these issues, this paper proposes a green satellite communication framework that combines Lyapunov optimization with a one-dimensional golden-section search method. The framework builds a two-timescale frame-slot model that jointly captures fast-varying channel fading and slow-varying renewable energy dynamics. By introducing a drift-plus-penalty optimization method, the system ensures queue stability and minimizes long-term grid energy expenditure, while employing the golden-section search to optimize beamforming parameters with reduced computational complexity. Simulation results show that the proposed framework effectively balances energy efficiency and communication performance, demonstrating good scalability for large-scale satellite networks.

Developing compound Large Language Model (LLM) applications is becoming an increasingly prevalent approach to solving real-world problems. In these applications, an LLM collaborates with various external modules, including APIs and even other LLMs, to realize complex intelligent services. However, we reveal that the intrinsic duration and structural uncertainty in compound LLM applications pose great challenges for LLM service providers in serving and scheduling them efficiently. In this paper, we propose LLMSched, an uncertainty-aware scheduling framework for emerging compound LLM applications. In LLMSched, we first design a novel DAG-based model to describe the uncertain compound LLM applications. Then, we adopt the Bayesian network to comprehensively profile compound LLM applications and identify uncertainty-reducing stages, along with an entropy-based mechanism to quantify their uncertainty reduction. Combining an uncertainty reduction strategy and a job completion time (JCT)-efficient scheme, we further propose an efficient scheduler to reduce the average JCT. Evaluation of both simulation and testbed experiments on various representative compound LLM applications shows that compared to existing state-of-the-art scheduling schemes, LLMSched can reduce the average JCT by 14 ~ 79%.

Recent advances in 3D Gaussian Splatting (3DGS) have greatly improved 3D reconstruction. However, its substantial data size poses a significant challenge for transmission and storage. While many compression techniques have been proposed, they fail to efficiently adapt to fluctuating network bandwidth, leading to resource wastage. We address this issue from the perspective of size-aware compression, where we aim to compress 3DGS to a desired size by quickly searching for suitable hyperparameters. Through a measurement study, we identify key hyperparameters that affect the size - namely, the reserve ratio of Gaussians and bit-width settings for Gaussian attributes. Then, we formulate this hyperparameter optimization problem as a mixed-integer nonlinear programming (MINLP) problem, with the goal of maximizing visual quality while respecting the size budget constraint. To solve the MINLP, we decouple this problem into two parts: discretely sampling the reserve ratio and determining the bit-width settings using integer linear programming (ILP). To solve the ILP more quickly and accurately, we design a quality loss estimator and a calibrated size estimator, as well as implement a CUDA kernel. Extensive experiments on multiple 3DGS variants demonstrate that our method achieves state-of-the-art performance in post-training compression. Furthermore, our method can achieve comparable quality to leading training-required methods after fine-tuning.

Personalized Federated Learning (PFL) targets client-specific models under heterogeneous and limited data. However, conventional methods often use heuristic or data-size-based averaging and overlook the true contributions of client updates. We propose a contribution-oriented PFL framework that quantifies client contributions via gradient alignment and prediction discrepancy for informed aggregation. We further develop a parameter-wise personalization mechanism for adaptive local updates and a mask-aware momentum optimizer for stable training. Preliminary results on CIFAR10 validate its effectiveness.

Photovoltaic (PV) defect detection is crucial for preventing power efficiency loss and fire hazards. The industry primarily relies on the fusion of infrared and visible images for defect localization and diagnosis. However, current detection methods exhibit poor generalizability in new site environments or with altered imaging setups. While recent infrared and vision foundation models (FM) facilitate domain-invariant feature maps extraction, directly concatenating them and fine-tuning achieves limited generalizability gain to PV defect detection, due to the asymmetric dual-modal semantics of defects. In this paper, we present the first large infrared-visible model DD-LIVM to enable cross-domain defect detection. The key innovation of DD-LIVM lies in its defect-specific three-step fine-tuning strategy, which utilizes alternating modality masking. Prior to feature fusion and joint fine-tuning, the infrared and visible FM encoders are alternately masked and optimized to enhance their individual semantic utility for defect localization visibility and classification granularity, with feature distances among different defect types regulated through contrastive learning. This approach allows for the extraction of generalizable and defect-specific feature maps. Moreover, for practical employment of DD-LIVM, we propose a domain-agnostic spatial alignment algorithm for infrared-visible images before dual-modal fusion, and develop source data augmentation and adaptive detection head selection schemes based on defects' infrared characteristics to further enhance the generalizability. Extensive experiments on 7,078 dual-modal images from 9 real-world scenarios across 4 cities' PV stations demonstrate that DD-LIVM achieves an accuracy of 87.7% for cross-domain defect detection, surpassing state-of-the-art methods by 17.3%.

To address the growing challenge of energy efficiency in next-generation coordinated multipoint (CoMP) communication systems, this article develops a green CoMP optimization framework that integrates renewable energy harvesting, smart grid interactions, and real-time power control. We formulate a stochastic long-term weighted sum-rate maximization problem, incorporating transmit covariance variables and joint channel-aware precoding. To enable online implementation, we convert the time-averaged problem into an equivalent per-slot formulation and design an online dynamic beamforming and energy management (ODBEM) algorithm. The proposed ODBEM integrates three synergistic mechanisms: 1) dual-driven energy pricing; 2) Lyapunov drift-plus-penalty scheduling; and 3) momentum-based energy smoothing. We further conduct rigorous convexity and Karush–Kuhn–Tucker optimality analysis to ensure algorithmic correctness and convergence. Simulation results demonstrate that ODBEM outperforms baseline strategies in both throughput and energy cost, confirming its effectiveness for sustainable and adaptive CoMP transmission.

The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and partic-ipating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging.

Utilizing compound Large Language Model (LLM) systems, instead of a monolithic LLM model, is gradually becoming a practical solution to realize a diverse range of industry applications. In compound LLM systems, an LLM collaborates with other external tools, APIs, or LLMs to offer intelligent services. In this poster, we identify the unique challenges, namely temporal and topological uncertainty, brought about by compound LLM systems in system serving. We then propose a priority-based scheduling policy to schedule different stages in DAG-represented compound LLM systems. The preliminary results show promising performance of uncertainty-aware scheduling policies.

Today's electrical grid is experiencing a fast transition toward a smart infrastructure. Modern smart grid is expected to integrate Artificial Intelligence of Things (AIoT)-empowered energy management systems (EMS) to sense, analyze, and optimize the power consumption and QoS of diverse end users. Non-Intrusive Load Monitoring (NILM) plays a key role in this transition, particularly considering that many legacy devices/appliances may not have built-in sensors. Yet most of the NILM solutions rely on large (often impractical) datasets for training. In this paper, we address this challenge through a meta learning-inspired approach, which implements a hierarchical architecture with a “meta-learner” to supervise the training of each appliance. Current EMS also relies on a central controller to access long-term information across all participants, which mismatches their distributed nature, and so often with slow responses. To this end, we develop a deep reinforcement learning based controller to make dynamic decisions for each component in the system. The experiment results based on real-world data sets and simulation data show that applying the meta learning approach can greatly improve the performance of NILM and the QoS of the whole system.

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving maneuvers only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving maneuvers calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving maneuvers for driving maneuver prediction. We accordingly present GazMon, an active driving maneuver monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Both our on-road experiments and driving simulator based evaluations demonstrate the superiority of our GazMon on predicting driving maneuvers as well as other distracted behaviors. It is readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.

The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac, an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.

Base stations have been widely deployed to satisfy the service coverage and explosive demand increase in today's cellular networks. Their reliability and availability heavily depend on the electrical power supply. Battery groups are installed as backup power in most of the base stations in case of power outages due to severe weathers or human-driven accidents, particularly in remote areas. The limited numbers and capacities of batteries, however, can hardly sustain a long power outage without a well-designed allocation strategy. As a result, the service interruption occurs along with an increasing maintenance cost. Meanwhile, a deep discharge of a battery in such case can also accelerate the battery degradation and eventually contribute to a higher battery replacement cost. In this paper, we closely examine the base station features and backup battery features from a 1.5-year dataset of a major cellular service provider, including 4,206 base stations distributed across 8,400 square kilometers and more than 1.5 billion records on base stations and battery statuses. Through exploiting the correlations between the battery working conditions and battery statuses, we build up a deep learning based model to estimate the remaining lifetime of backup batteries. We then develop BatAlloc, a battery allocation framework to address the mismatch between the battery supporting ability and diverse power outage incidents. We present an effective solution that minimizes both the service interruption time and the overall cost. Our real trace-driven experiments show that BatAlloc cuts down the average service interruption time from 4.7 hours to nearly zero with only 85 percent of the overall cost compared to the current practical allocation.

Deep learning has been applied in many recent advanced applications in the field of transportation, finance and medicine. These applications require significant computation resources and large-scale training samples. Cloud becomes a natural choice for conducting these learning tasks due to its abundant resources. However, deeper penetration of deep learning techniques in mission critical applications, like driverless car, calls for stricter time requirement to guarantee its interaction and larger amount of dataset for training to guarantee its accuracy, which cannot be easily satisfied by the cloud and makes the network transmission become the bottleneck. Edge learning emerges to be a promising direction to reduce data transmission time by processing and compressing the raw data at the edge of the network, while brings the concern of accuracy reduction at the meantime. To balance this tradeoff under cloud-edge architecture, we study a task scheduling problem for reducing weighted transmission time which takes learning accuracy into consideration. We also propose efficient scheduling algorithms which are able to achieve up to 50% reduction in makespan with extensive trace-driven simulations.

RFID-based human activity identification has become a key component in today's Internet-of-Things applications. State-of-the-art solutions mostly focus on the simple scenario with a single person in the open space. Extension to the more realistic realworld scenarios with multiple persons however is non-trivial. Given the much richer interactions among them, the backscattered signals will inevitably mixed, obscuring the information of individual activities. This is further complicated with multi-path in a common indoor environment. In this paper, we however argue that, though often considered harmful, the rich interactions combined with multi-path indeed offer more observable data. After careful processing the raw signals, critical information about the activities can be unveiled through modern learning tools. We present M2AI, which for the first time accommodates both multi-path and multi-object for activity identification. M2AI incorporates a phase calibration mechanism to automatically eliminate the frequency hopping offsets, and a novel decoupling mechanism for the periodogram and pseduospectrum in the raw signal mixture. The refined data are then fed into an advanced deep-learning engine that integrates a Convolutional Neural Network and a Long Short Term Memory network, which examines both spatial and temporal information in realtime for activity identification. Our M2AI is readily deployable using off-the-shelf RFID readers. We have implemented an M2AI prototype with Impinj UHF passive tags and a Speedway R420 reader. Experiments with multiple objects in a multipath-rich indoor environments report an activity identification accuracy of 97%, a significant gain (27%) over state-of-art solutions.

Automobiles have become one of the necessities of modern life and deeply penetrated into our daily activities. They unfortunately also introduce numerous social problems, among which traffic accidents are most notoriously threatening automobile drivers and other road users. Advanced driver-assistance systems (ADAS) are under rapid development in recent years, which can necessarily reduce or even eliminate the driver errors, significantly relieving on drivers suffering or stress. These state-of-the-art ADAS mainly rely on built-in cameras, radars and ultrasound sensors to provide road sensing services for object detection, which are further advanced by recent explosion of vision and neural network technologies.

The emerging 5G mobile networking promises ultrahigh network bandwidth and ultra-low communication latency (100ms), due to its store-and-forward design and the physical barrier from signal propagation speed, not to mention congestion that frequently happens. Caching is known to be effective to bridge the speed gap, which has become a critical component in the 5G deployment as well. Besides storage, 5G base stations (BSs) will also be powered with strong computing modules, offering mobile edge computing (MEC) capability. This paper explores the potentials of edge computing towards improving the cache performance, and we envision a learning-based framework that facilitates smart caching beyond simple frequency- and time-based replace strategies and cooperation among base stations. Within this framework, we develop DeepCache, a deep-learning-based solution to understand the request patterns in individual base stations and accordingly make intelligent cache decisions. Using mobile video, one of the most popular applications with high traffic demand, as a case, we further develop a cooperation strategy for nearby base stations to collectively serve user requests. Experimental results on real-world dataset show that using the collaborative DeepCache algorithm, the overall transmission delay is reduced by 14%~22%, with a backhaul data traffic saving of 15%~23%.

MapReduce effectively partitions and distributes computation workloads to a cluster of servers, facilitating today's big data processing. Given the massive data to be dispatched, and the intermediate results to be collected and aggregated, there have been a significant studies on data locality that seeks to co-locate computation with data, so as to reduce cross-server traffic in MapReduce. They generally assume that the input data have little dependency with each other, which however is not necessarily true for that of many real-world applications, and we show strong evidence that the finishing time of MapReduce tasks can be greatly prolonged with such data dependency. In this paper, we present Dependency-Aware Locality for MapReduce (DALM) for processing the real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework, organically synthesizing the key components from data reorganization, replication, placement. Beside algorithmic design within the framework, we have also closely examined the deployment challenges, particularly in public virtualized cloud environments, and have implemented DALM on Hadoop 1.2.1 with Giraph 1.0.0. Its performance has been evaluated through both simulations and real-world experiments, and compared with that of state-of-the-art solutions.

The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time.

With detailed digital map of the transport network and even real-time traffic, today's navigation services provide good quality routes in the major route level. Once entering the last mile near the destination, they unfortunately can be ineffective and, instead, local drivers often have a better understanding of the routes there. With the deep penetration of 3G/4G mobile networks, drivers today are well connected anytime and anywhere; they can readily access information from the Internet and share information to the driver's community. This motivates our design of CrowdNavi, a complementary service to existing navigation systems, seeking to combat the last mile puzzle. CrowdNavi collects the crowdsourced driving information from users to identify their local driving patterns, and recommend the best local routes for users to reach their destinations. In this paper, we present the architectural design of CrowdNavi and identifies the unique challenges therein, particularly on identifying the last segment in a route from the crowdsourced driving information and navigate drivers through the last segment. We offer a complete set of algorithms to identify the last segment from the drivers' trajectories, scoring the landmark, and locating best routes with user preferences. We then present effective navigation algorithm to locate the best route along the landmarks for the last segment. We further realize the potential risks of attacks in crowdsourced systems and develop a multisensor cross-validation method against them. We have implemented the CrowdNavi app on Android mobile OS, and have examined its performance under various circumstances. The experimental results demonstrate its superiority in navigating drivers in the last segment toward the destination.

Many radio frequency identification (RFID) applications, such as virtual shopping cart and tag-assisted gaming, involve sensing and recognizing tag mobility. However, existing RFID localization methods are mostly designed for static or slowly moving targets (less than 0.3m/sec). More importantly, we observe that prior methods suffer from serious performance degradation for detecting real-world moving tags in typical indoor environments with multipath interference. In this article, we present i 2 tag, an intelligent mobility-aware activity identification system for RFID tags in multipath-rich environments (e.g., indoors). i 2 tag employs a supervised learning framework based on our novel fine-grain mobility provile, which can quantify different levels of mobility. Unlike previous methods that mostly rely on phase measurement, i 2 tag takes into account various measurements, including RSSI variance, packet loss rate, and our novel relative phase--based fingerprint. Additionally, we design a multidimensional dynamic time warping--based algorithm to robustly detect mobility and the associated activities. We show that i 2 tag is readily deployable using off-the-shelf RFID devices. A prototype has been implemented using a ThingMagic reader and standard-compatible tags. Experimental results demonstrate its superiority in mobility detection and activity identification in various indoor environments.

Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain high service availability, backup battery groups are usually installed on base stations and serve as the only power source during power outages, which can be prevalent in rural areas or during severe weather conditions such as hurricanes or snow storms. Therefore, being able to understand and predict the battery group working condition is of immense technical and commercial importance as the first step towards a cost-effective battery maintenance on minimizing service interruptions.

Recent years have witnessed the prevalence of MapReduce-based systems, e.g., the Apache Hadoop, in large-scale distributed data processing. Fetching data from remote servers across multiple network switches is known to be costly. Hence, it is highly desirable to co-locate computation with data. State-of-the-art popularity-based replication achieves data locality through replicating popular files and spreading the replicas over multiple servers. While working well for independent files, they can store highly dependent files in different servers, resulting in excessive remote data accesses exchanges and consequently prolonging the job completion time. In this paper, we develop DALM (Dependency-Aware Locality for MapReduce), a novel replication strategy for general real-world input data that can be highly skewed and dependent. DALM accommodates data-dependency in a data-locality framework that comprehensively weights such key factors as popularity and storage budget. We extensively evaluate DALM through both simulations and real-world implementations, and have compared with state-of-the-art solutions, including the Hadoop system and the popularity-based Scarlett. The results show that DALM can significantly improve data locality for different inputs. For a popular iterative graph processing application on Hadoop, our prototype implementation of DALM reduces the remote data access and job completion time by 34.3% and 9.4%, respectively.