Powerline inspection with UAVs significantly improves efficiency and safety in remote areas. However, the lack of cellular infrastructure necessitates the use of UAV-mesh networks, whose deployment presents challenges in jointly optimizing coverage, node load, robustness, and cost under complex terrain constraints. In this paper, we investigate the computational complexity of this deployment problem by formulating it as a multi-objective optimization task and proving its NP-hardness. To address this, we presentU-Mesh+, aterrain-aware, robust, and cost-efficientdeployment framework that integrates four key components: (i) identifying line-of-sight and non-line-of-sight links to model terrain-induced communication constraints; (ii)NetConsfor cost-effective coverage and connectivity network topology construction; (iii)NetOptfor network resilience and balance node-level load improvement without extra cost; and (iv)NetEnhfor service availability enhancement via targeted local refinements. We implementU-Mesh+in a real-world 270km2mountainous forest with 174 power towers and 48km of transmission lines. Extensive experiments demonstrate its efficacy in terms of deployment cost and network performance. On-site network data from the deployed wireless network further validate its effectiveness and scalability under real-world conditions.

UAV-assisted task execution is a promising approach to powerline inspections in remote areas where no cellular network infrastructure exists for inspection data transmission. In this paper, we investigate UAV-mesh network deployment in remote areas to empower UAV-assisted powerline inspection, which is challenging considering a mountainous environment with no power supply. Particularly, given the locations of a set of power towers, we first formulate the UAV-mesh network deployment problem with connectivity and coverage constraints, which is NP-hard. Then, we propose U-Mesh, which is a cost-effective and load-balanced deployment scheme. To be specific, U-Mesh integrates three components, i.e., link identification: identifying the link conditions between power towers based on geographical barriers, NetCons: conducting local search to gradually obtain a cost-effective initial mesh nodes with connectivity and coverage constraints, and NetOpti: optimizing the initial mesh node positions to improve the mesh load and robustness from the perspectives of overall network structure. Finally, we implement U-Mesh in a 270 km2mountain forest area, and demonstrate its efficacy in terms of both deployment cost and network performance via extensive evaluations.

Unmanned sanitation vehicles (USVs) promise cleaner cities, yet efficiently coordinating multiple USVs in large urban areas remains challenging due to constraints such as limited waste capacity and battery life. In this demo, we present MRTC, a multi-robot task cooperation system. First, Dynamic Task Assignment employs an Actor-Critic policy within a Markov decision framework to allocate cleaning tasks and decide the required number of USVs. Second, Single-USV Path Planning refines each route via a fast two-layer iterative search. Over an eight-month real-world deployment in three urban testbeds, our MRTC system markedly improved cleaning efficiency while lowering operating costs. Operating over a combined 10,775 km of routes per month, the system achieved average monthly savings of 20,575 kWh of energy and 2,744 labour hours. A demonstration video is available at https://llq978.github.io/Demo/.

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method namedMoCo. InMoCoframework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority ofMoCoin comparison with state-of-the-art baselines. Robust experiments show thatMoCocan work efficiently in different transportation modes and urban densities.

Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, namedMOTO(i.e.,Mobility-awareOnlineTaskOffloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve theToCandSeGsub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness ofMOTOin reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

In distributed IoT data systems, full-size data collection is impractical due to the energy constraints and large system scales. Our previous work has investigated the advantages of integrating matrix sampling and inference for compact distributed IoT data collection, to minimize the data collection cost while guaranteeing the data benefits. This paper further advances the technology by boosting fast and accurate inference for those distributed IoT data systems that are sensitive to computation time, training stability, and inference accuracy. Particularly, we proposeCODE$^{+}$+, i.e.,Compact Distributed IOTData CollEction Plus, which features a cluster-based sampling module and a Convolutional Neural Network (CNN)-Transformer Autoencoders-based inference module, to reduce cost and guarantee the data benefits. The sampling component employs a cluster-based matrix sampling approach, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. The inference component integrates a CNN-Transformer Autoencoders-based matrix inference model to estimate the full-size spatio-temporal data matrix, which consists of a CNN-Transformer encoder that extracts the underlying features from the sampled data matrix and a lightweight decoder that maps the learned latent features back to the original full-size data matrix. We implementCODE$^{+}$+under three operational large-scale IoT systems and one synthetic Gaussian distribution dataset, and extensive experiments are provided to demonstrate its efficiency and robustness. With a 20% sampling ratio,CODE$^{+}$+achieves an average data reconstruction accuracy of 94% across four datasets, outperforming our previous version of 87% and state-of-the-art baseline of 71%.

Map matching is a fundamental component for location-based services (LBSs), such as vehicle mobility analysis, navigation services, traffic scheduling, etc. In this paper, we investigate federated learning augmented map matching based on heterogeneous cellular moving trajectories from different operator systems, the goal of which is to improve matching accuracy without violating the user privacy. First, we develop a data collection platform with one Android-based application, and conduct rigorous data collection campaigns. Second, we perform systematic data analytics to reveal the data-driven technical challenges, including the impact of sampling rate, high location error of cellular moving data, and poor heterogeneous matching performance. Third, we propose an augmented map matching model, named FL-AMM, i.e.,FederatedLearningAugmentedMapMatching, in which we i) adopt the vertical federated learning framework to achieve data collaboration and privacy protection for heterogeneous operators; ii) devise a data augmentation component to enhance the capability of representing the raw cellular data; and iii) design a map matching model to further learn the mapping function from cellular trajectory points to road segments. Finally, we conduct extensive data-driven experiments to corroborate the efficiency and robustness of the proposed FL-AMM.

It is unpractical to conduct full-size data collection in ubiquitous IoT data systems due to the energy constraints of IoT sensors and large system scales. Although sparse sensing technologies have been proposed to infer missing data based on partial sampled data, they usually focus on data inference while neglecting the sampling process, restraining the inference efficiency. In addition, their inferring methods highly depend on data linearity correlations, which become less effective when data are not linearly correlated. In this paper, we propose, Compact IOT Data CollEction, namely CODE, to conduct precise data matrix sampling and efficient inference. Particularly, CODE integrates two major components, i.e., cluster-based matrix sampling and Generative Adversarial Networks (GAN)-based matrix inference, to reduce the data collection cost and guarantee the data benefits, respectively. In the sampling component, a cluster-based sampling approach is devised, in which data clustering is first conducted and then a two-step sampling is performed in accordance with the number of clusters and clustering errors. For the inference component, a GAN-based model is developed to estimate the full matrix, which consists of a generator network that learns to generate a fake matrix, and a discriminator network that learns to discriminate the fake matrix from the real one. A reference implementation of CODE is conducted under three operational large-scale IoT systems, and extensive data-driven experiment results are provided to demonstrate its efficiency and robustness.

Edge computing servers (ECSs) have been widely deployed in large-scale mobile edge computing (MEC) systems, which can provide nearby computing services by charging users a price. Service pricing schemes can regulate user task offloading and affect the total revenue of service providers. Investigating how to maximize the revenue of service provider and improve the utilization of edge computing resources becomes crucial while is challenging, considering the users mobility and the uncertainty of users service requests. In this paper, we model the dynamic pricing process of ECS as a Markov decision process and propose a dynamic pricing approach based on Dueling Double Deep Q Network (D3QN) by using the current load conditions and user characteristics, the goal of which is to maximize the revenue of service provider. In addition, considering more ECSs in the MEC system, with the dynamic variations of ECSs loads and the different arrival rate of user tasks, we propose a joint scheduling approach based on D3QN (called RLJS) to collectively improve the total service revenue of service providers. Specifically, we first use a data-driven method to group the ECSs and then devise a D3QN-based task scheduling scheme to distribute tasks among ECS groups by considering the load and price conditions in real time. Simulation results demonstrate the efficacy of RLJS in improving the total revenue of the system provider and reducing the user delays.

Monitoring the performance of a large network is very costly. Instead, a subset of paths or time intervals of the network can be measured while inferring the remaining network data by leveraging their spatiotemporal correlations. The quality of missing data recovery highly relies on the inference algorithms. Tensor completion has attracted some recent attentions with its capability of exploiting the multi-dimensional data structure for more accurate missing data inference. However, current tensor completion algorithms only model the three-order interaction of data features through the inner product, which is insufficient to capture the high-order, nonlinear correlations across different feature dimensions. In this paper, we propose a novel Neural Tensor Completion (NTC) scheme to effectively model three-order interaction among data features with the outer product and build a 3D interaction map. Based on which, we apply 3D convolution to learn features of high-order interaction from the local range to the global range. We demonstrate this will lead to good learning ability. We conduct extensive experiments on two real-world network monitoring datasets, Abilene and WS-DREAM, to demonstrate that NTC can significantly reduce the error in missing data recovery. When the sampling ratio is low at 1%, the recovery error ratios on the testing data are around 0.05 (Abilene) and 0.13 (WS-DREAM) when using NTC, but are 0.99 (Abilene) and 0.99 (WS-DREAM) using the best current tensor completion algorithms, which are 21 times and 8 times larger.