Large language models (LLMs) are increasingly deployed on edge devices. To meet strict resource constraints, real-world deployment has pushed LLM quantization from 8-bit to 4-bit, 2-bit, and now 1.58-bit. Combined with lookup table (LUT)-based inference, CPUs run these ultra-low-bit LLMs even faster than NPUs, opening new opportunities for ubiquitous on-device intelligence.

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%~21% accuracy and presents$1.2\sim 9\times $throughput compared with the state-of-the-art video analytics pipelines.

Large Multimodal Models (LMMs) have shown significant progress in various complex vision tasks with the solid linguistic and reasoning capacity inherited from large language models (LMMs). Low-rank adaptation (LoRA) offers a promising method to integrate external knowledge into LMMs, compensating for their limitations on domain-specific tasks. However, the existing LoRA model serving is excessively computationally expensive and causes extremely high latency. In this paper, we present an end-to-end solution that empowers diverse vision tasks and enriches vision applications with LoRA LMMs. Our system, VaLoRA, enables accurate and efficient vision tasks by 1) an accuracy-aware LoRA adapter generation approach that generates LoRA adapters rich in domain-specific knowledge to meet application-specific accuracy requirements, 2) an adaptive-tiling LoRA adapters batching operator that efficiently computes concurrent heterogeneous LoRA adapters, and 3) a flexible LoRA adapter orchestration mechanism that manages application requests and LoRA adapters to achieve the lowest average response latency. We prototype VaLoRA on five popular vision tasks on three LMMs. Experiment results reveal that VaLoRA improves 24-62% of the accuracy compared to the original LMMs and reduces 20-89% of the latency compared to the state-of-the-art LoRA model serving systems.

In recent years, large language models (LLMs) have shown great potential in many fields. LLMs deployed in cloud data centers are increasingly unable to meet the low-latency inference requirements of massive mobile users. Benefiting from various LLM lightweighting techniques and the continuously improving performance of edge servers, deploying LLMs on edge servers closer to mobile users and executing inference tasks locally can effectively reduce inference latency. However, edge servers have limited storage capacity, and deploying LLMs on edge servers incurs additional deployment overhead. In this paper, we propose an efficient LLM edge collaboration deployment strategy called EdgeColl, aiming to jointly optimize inference latency and LLM deployment costs. Specifically, EdgeColl adopts Low-Rank Adaptation (LoRA) to divide each LLM into a base model and a LoRA matrix. We formulate the LLM edge collaboration deployment problem with LoRA. Then, we present the base model deployment (BMD) strategy to achieve low inference latency and deployment costs. The LoRA deployment (LMD) strategy is also proposed to enable personalized inference. We evaluate the performance of EdgeColl. The experimental results show that EdgeColl effectively reduces LLM inference latency and deployment costs.

Waypoint planning is critical for Unmanned Aerial Vehicle (UAV) operations, particularly for surveillance and monitoring applications. With the rapid development and deployment of UAVs, an increasing number of industrial UAVs are equipped with multiple cameras to enhance monitoring capabilities and operational efficiency. Meanwhile, a new type of camera supporting adjustable pan and zoom is emerging and rapidly being deployed. While UAVs equipped with multi-adjustable cameras enhance flexibility and precision in capturing dynamic scenes, they also introduce new challenges in optimizing waypoints to ensure efficient coverage and accurate data collection. In this paper, we propose WiPlan, which aims to determine the optimal UAV waypoints while dynamically adjusting the pan (horizontal rotation) and zoom (focal length) of cameras to maximize overall monitoring utility. This problem involves two coupled NP-hard problems, making it significantly more complex to solve compared to previous work. In tackling this challenge, we construct WiPlan as a two-level optimization problem. The results show that our algorithm improves monitoring utility by at least$1.73 \times$compared to state-of-the-art algorithms. Moreover, we test WiPlan using a real-world outdoor field, which includes 23 objects and a two-camera UAV with 7 waypoints. The results demonstrate that our algorithm successfully monitors 65 % of the maximum possible monitored targets, outperforming the baselines by factors of$4.25 \times$and$15 \times$, respectively.

Building an always-on, contextual AI assistant that proactively supports humans remains a central goal in Embodied AI—yet cloud-based pipelines struggle to meet due to delay, bandwidth, and privacy constraints. This demo presents EdgeMind-OS, a fully on-device intelligence system designed for embodied agents operating in real-world scenarios. Edge-Mind-OS features a hierarchical architecture combining a real-time StreamBrain, modular skill experts, and a dynamic scene-episode memory. Achieving up to 7.3× faster local processing, it enables low-latency, privacy-preserving, and plug-and-play deployment across tasks such as semantic navigation, spatial memory recall, and multimodal interaction. We demonstrate how EdgeMind-OS empowers a mobile robot with only basic locomotion capabilities to perform realtime, free-form user-robot interaction through autonomous perception, reasoning and action —without reliance on external cloud infrastructure.

Mixture of experts (MoE) is a popular technique in deep learning that improves model capacity with conditionally-activated parallel neural network modules (experts). However, serving MoE models in resource-constrained latency-critical edge scenarios is challenging due to the significantly increased model size and complexity. In this paper, we first analyze the behavior pattern of MoE models in continuous inference scenarios, which leads to three key observations about the expert activations, including temporal locality, exchangeability, and skippable computation. Based on these observations, we introduce PC-MoE, an inference framework for resource-constrained continuous MoE model serving. The core of PC-MoE is a new data structure,Parameter Committee, that intelligently maintains a subset of important experts in use to reduce resource consumption. To evaluate the effectiveness of PC-MoE, we conduct experiments using state-of-the-art MoE models on common computer vision and natural language processing tasks. The results demonstrate optimal trade-offs between resource consumption and model accuracy achieved by PC-MoE. For instance, on object detection tasks with the Swin-MoE model, our approach can reduce memory usage and latency by 42.34% and 18.63% with only 0.10% accuracy degradation.

For Unmanned Aerial Vehicles (UAVs) monitoring tasks, capturing high quality images of target objects is important for subsequent recognition. Concerning the problem, many prior works study placement/trajectory planning for UAVs to maximize the quality of captured images. However, all of them overlook a fact thatUAV monitoring may cause a huge risk/annoyance on living objects.In this paper, we investigate the novel problem of oPtimizing uncrewed aErial vehicles plAcement byConsidering both monitoring utility and adverseEffects (PEACE). We propose an approach to solve PEACE, which is proved to be NP-hard. Overall, our approach achieves a$1- \frac{1}{e}-\varepsilon$approximation ratio. First, we approximate the original problem of PEACE as a classical problem of Monotone Submodular function Maximization under a Uniform Matroid constraint (MSMUM) with a controlled gap. Then, for MSMUM, we propose a combination of algorithms achieving a$1-\frac{1}{e}$approximation and$O(n\log n)$time complexity considering the correlation among the UAV monitoring strategies. The proposed algorithms outperform existing algorithms for MSMUM through theoretical analysis and experimental results. Extensive simulations and field experiments demonstrate the effectiveness of our approach, achieving performance gains of 9.0% to 1434.5% compared to existing methods.

Rui Kong, Yuanchun Li, Qingtian Feng, Weijun Wang, Xiaozhou Ye, Ye Ouyang, Linghe Kong, Yunxin Liu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

High-definition (HD) cameras for surveillance and road traffic have experienced tremendous growth, demanding intensive computation resources for real-time analytics. Recently, offloading frames from the front-end device to the back-end edge server has shown great promise. In multi-stream competitive environments, efficient bandwidth management and proper scheduling are crucial to ensure both high inference accuracy and high throughput. To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams. The lower-level front-back-end collaborative mechanism (called adaptive hybrid codec) locally optimizes the accuracy and accelerates end-to-end video analytics for a single stream. The upper-level scheduler aims to accuracy fairness among multiple streams via the global bandwidth controller. The evaluation of BiSwift shows that BiSwift is able to real-time object detection on 9 streams with an edge device only equipped with an NVIDIA RTX3070 (8G) GPU. BiSwift improves 10%∼21% accuracy and presents 1.2∼ 9× throughput compared with the state-of-the-art video analytics pipelines.

Charger placement is an important problem in improving the quality of service in wireless rechargeable sensor networks. This paper studies the problem ofWireless ChArger PlacemeNt with Multiple (Directional) Antennas (WANDA). The problem is described as follows: given a set of wireless chargers equipped with multiple directional antennas and a set of wireless rechargeable sensors, determine the chargers' positions and orientations to maximize the overall charging utility. According to the relative positional relationship between the antennas, the problem is classified into Relative Orientation Fixed (WANDA-ROF) and Relative Orientation Unfixed (WANDA-ROU) situations. To address WANDA, we present a piecewise constant function to approximate the nonlinearity of charging power and propose an area discretization technique to reduce the infinite solution space to a limited one without performance loss. Then, we prove the monotonic submodularity of WANDA, and present a$\frac{1}{2}-\epsilon$approximation algorithm for the ROF situation and a$\frac{1}{2}-\epsilon$approximation algorithm for the ROU situation, all run in polynomial time. Finally, we conduct extensive simulation and experiments to show that our algorithms outperform comparison algorithms by at least 16% for ROF situation and 12% for ROU situation.

Truck-drone systems, wherein trucks carrying drones drive to pre-planned positions and then free drones equipped with cameras to monitor a known number of objects with reported positions, have been used for various scenarios. An object's quality of monitoring (QoM) by a camera is defined as a function of camera focal length and monitoring distance. Improving the QoM would help downstream tasks, including object detection and recognition. The monitoring utility is the fusion of all the QoMs of an object from multiple cameras. This paper optimizes theDeploymentOfTrucksAndDrones forObject monitoring (DOTADO) problem,i.e., deploying a truck-drone system, where each drone is equipped with a varifocal camera, to maximize the overall monitoring utility for all objects. Firstly, we model the hybrid system and define monitoring quality and utility. Then, we discretize the solution space into deployment strategies with performance bound. To select deployment strategies, we prove the submodularity of the problem and propose a two-level greedy algorithm with a bounded approximation ratio. Finally, we devise an optimal method to adjust the strategy for energy saving and communication improvement without losing monitoring utility. We perform both simulations and field experiments to verify the proposed framework.

The quality of the video stream is the key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor-quality cameras or over-compressed/pruned video streaming protocols, e.g., as a result of upstream bandwidth limit. To address this issue, existing studies use quality enhancers (e.g., neural super-resolution) to improve the quality of videos (e.g., resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural-enhanced video analytics, selects a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality and inference then reuse on the unselected ones. Next, we extend AccDecoder to AccDecoder$+$by formulating the resolution-involved Markov decision process (MDP) to achieve resolution adaptation; it aims to trade accuracy and latency corresponding under various video resolutions. Proved by experiments, AccDecoder provides efficient inference capability via filtering important frames using DRL for DNN-based inference and reusing the results for the other frames via extracting the reference relationship among frames and blocks, which contributes 6-21% accuracy improvement and a latency reduction of 20-80% than baselines. Compared with AccDecoder, AccDecoder$+$achieves an additional 2-7% accuracy improvement.

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

The quality of the video stream is key to neural network-based video analytics. However, low-quality video is inevitably collected by existing surveillance systems because of poor quality cameras or over-compressed/pruned video streaming protocols, e.g., as a result of upstream bandwidth limit. To address this issue, existing studies use quality enhancers (e.g., neural super-resolution) to improve the quality of videos (e.g., resolution) and eventually ensure inference accuracy. Nevertheless, directly applying quality enhancers does not work in practice because it will introduce unacceptable latency. In this paper, we present AccDecoder, a novel accelerated decoder for real-time and neural-enhanced video analytics. AccDecoder can select a few frames adaptively via Deep Reinforcement Learning (DRL) to enhance the quality by neural super-resolution and then up-scale the unselected frames that reference them, which leads to 6-21% accuracy improvement. AccDecoder provides efficient inference capability via filtering important frames using DRL for DNN-based inference and reusing the results for the other frames via extracting the reference relationship among frames and blocks, which results in a latency reduction of 20-80% than baselines.

In this work, we study the problem ofRobustServerPlacement (RSP) for edge computing, i.e., in the presence of uncertain edge server failures, how to determine a server placement strategy to maximize the expected overall workload that can be served by edge servers. We mathematically formulate the RSP problem in the form of robust max-min optimization, derived from two consequentially equivalent transformations of the problem that does not consider robustness and followed by a robust conversion. RSP is challenging to solve, because the explicit expression of the objective function in RSP is hard to obtain, and it is a robust max-min problem with knapsack constraints, which is still an unexplored problem in the literature. We reveal that the objective function is monotone submodular, and propose two solutions to RSP. First, after proving that the involved constraints form a$p$-independence system constraint, where$p$is a parameter determined by the coefficients in the knapsack constraints, we propose an algorithm that achieves a provable approximation ratio in polynomial time. Second, we prove that one of the knapsack constraints is a matroid contraint, and propose another polynomial time algorithm with a better approximation ratio. Furthermore, we discuss the applicability of the aforementioned algorithms to the case with an additional server number constraint. Both synthetic and trace-driven simulation results show that, given any maximum number of server failures, our proposed algorithms outperform four state-of-the-art algorithms and approaches the optimal solution, which applies exhaustive exponential searches, while the proposed latter algorithm brings extra performance gains compared with the former one.

Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which are more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements into several sub-ranges, and thus the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. Finally, we set up a testbed to profile the functional relationship between the resource and the throughput for different types of VNFs, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by 27.33% on average in terms of the number of servers required. Furthermore, SAFE has marginal overheads, around 7.22%, compared to the instance consolidation strategy without VNF backup consideration.

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to optimize multiple users’ experiences jointly, but users often suffer short connection durations and frequent handoffs because of their high mobility. This paper proposes an end-to-end scheme, VSiM, to support mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement on average of the averaged QoE.

The limitation on the flight range motivates a hybrid monitoring system, wherein trucks carrying drones drive to pre-planned positions and then free drones for task execution. While the flight range limitation is mitigated, it is challenging to determine the destination of trucks and drones and set airborne cameras. This paper optimizes the joint Deployment of trUcks and dronEs for objecT monitoring (DUET), that is, deploy a set of trucks where each truck carries drones, and each drone is equipped with a varifocal camera such that the overall monitoring utility for target objects is maximized. To tackle the DUET problem, we first model the hybrid system and monitoring utility; then, discretize the solution space of DUET with performance bound. In this way, the problem is transformed into a two-level combinatorial optimization problem satisfying submodularity. To address it, a two-level greedy algorithm with $\frac{{{{(e - 1)}^2}}}{{e(2e - 1)}} \cdot (1 - \varepsilon )$ approximation ratio is proposed to select deployment strategies. After the strategy selection, an optimal method is devised to carefully adjust the strategy for energy saving and communication improvement without loss of monitoring utility. Both simulations and field experiments are conducted to evaluate the proposed framework, which outperforms baseline algorithms on monitoring utility by at least 28.4% and 40%, respectively.

Unmanned aerial vehicle (UAV) has attracted much attention due to its excellent ability to collect visual information of surroundings. In this paper, we investigate a new monitoring model to focus on sizes and shapes of objects, and occlusion between objects, and then study the placement of a set of UAVs to monitor polygonal sizable objects. Our aim is to maximize the overall monitoring utility of all objects by determining the positions and orientations of UAVs, given a set of polygonal sizable objects with fixed coordinates and shapes on a$2\mathbf{D}$plane. We study two typical scenarios of the problem: the former stipulates that a line segment is effectively monitored only when it is completely monitored by a single UAV, and the latter allows multiple UAVs to cooperatively monitor a line segment and then integrate their image information. The problem is proved to be NP-hard with infinite continuous solution space. For the first scenario, we propose a$(1-1/e)$-approximation algorithm. For the second one, we first propose a 1/2-approximation algorithm to address its simple version, and then propose a heuristic solution. Numerical evaluations validate the effectiveness of our proposed algorithms.

This paper studies the problem of Wireless ChArger PlacemeNt with Multiple (Directional) Antennas (WANDA). Given a set of wireless chargers wherein each charger is equipped with multiple directional antennas and a set of wireless rechargeable sensors, determining the chargers' positions and the antennas' orientations, such that the overall charging utility is maximized. To address WANDA, we first present a piecewise constant function to approximate the nonlinear relationship between charging power and charging distance. Then, we propose an area discretization technique to reduce the infinite solution space to a limited one without performance loss. Next, we present approximation algorithms for both Relative Orientation Fixed (WANDA-ROF) and Relative Orientation Unfixed (WANDA-ROU) situations. For WANDA-ROF, we propose a Maximum Coverage Set extraction method that transforms WANDA-ROF into the problem of maximizing a monotone submodular function subject to a partition matroid constraint and then present a$1/2 -\epsilon$approximation algorithm. For WANDA-ROU, we construct a candidate position set for each charger to limit the searching space. Then, we propose a novel two-level submodular optimization scheme to address it, which achieves an approx-imation ratio of 1/6 - ∊. Simulation and experimental results show that our algorithms outperform comparison algorithms by at least 22%.

This paper considers the fundamental problem of deployment of Unmanned AerialVehIcles for aniSotropic monItoringTasks (VISIT), that is, given a set of objects with determined coordinates and directions in 2D area, deploy a fixed number of UAVs by adjusting their coordinates and orientations such that the overall monitoring utility for all objects is maximized. We develop a theoretical framework to address VISIT problem. First, we establish monitoring model whose quality of monitoring (QoM is anisotropic with monitoring angle and varying with various monitoring distance. To the best of our knowledge, we are the first considering the anisotropy of monitoring angle. Then, we propose a framework consisting of area discretization and Monitoring Dominating Set (MDS) extraction to reduce the infinite solution space of VISIT to a limited one with performance bound. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint, and present a greedy algorithm with$1-1/e-\epsilon$approximation ratio. We conduct both simulations and field experiments to evaluate our framework, and the results show that our algorithm outperforms comparison algorithms by at least 41.3 percent.

Many organizations nowadays have multiple sites at different geographic locations. Typically, transmitting massive data among these sites relies on the interconnection service offered by ISPs. Segment Routing over IPv6 (SRv6) is a new simple and flexible source routing solution which could be leveraged to enhance interconnection services. Compared to traditional technologies, e.g., physical leased lines and MPLS-VPN, SRv6 can easily enable quick-launched interconnection services and significantly benefit from traffic engineering with SRv6-TE. To parse the SRv6 packet headers, however, hardware support and upgrade are needed for the conventional routers of ISP. In this paper, we study the problem of SRv6 incremental deployment to provide a more balanced interconnection service from a traffic engineering view. We formally formulate the problem as an SRID problem with integer programming. After transforming the SRID problem into a graph model, we propose two greedy methods considering short-term and long-term impacts with reinforcement learning, namely GSI and GLI. The experiment results using a public dataset demonstrate that both GSI and GLI can significantly reduce the maximum link utilization, where GLI achieves a saving of 59.1% against the default method.

Existing Internet routing protocols much focus on providing interconnection service for independent autonomous systems (ASes) rather than end-to-end low latency transmission. Nowadays, a growing number of applications and platforms have high requirements for low latency. However, developing new routing protocols in the wide area network that provides low latency routing service is very challenging, and remains an open problem due to the obstacles of compatibility, feasibility, scalability and efficiency. On the other hand, the ignorance of latency performance results in triangle inequality violations (TIV). In this paper, we leverage TIV and a new routing technology, SRv6, to build a new distributed routing protocol, SRv6 underlay federation (SRUF), which aims to provide low-latency routing services in network core. We design a novel method to find alternative paths with lower latency between any pair of ASes in SRUF. This method can achieve high scalability as it incurs only$O(n)$bandwidth overhead in each member of SRUF. SRv6 is then employed to steer the flows along the selected indirect low-latency paths, while keeping compatibility to legacy routing systems. The experimental results with realworld datasets demonstrate that SRUF can effectively reduce the average end-to-end delay by 5.4% ~ 58.9%.

This paper considers the problem of practical Heterogeneous wireless charger Placement with Obstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies, on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of 1/2 - ε. We conduct experiments to evaluate the performance. Results show that our algorithm outperforms the comparison algorithms by at least 33.49 percent on average.

This paper considers the fundamental problem of Placement of unmanned Aerial vehicles achieviNg 3D Directional coverAge (PANDA), that is, given a set of objects with determined positions and orientations in a 3D space, deploy a fixed number of UAVs by adjusting their positions and orientations such that the overall directional coverage utility for all objects is maximized. First, we establish the 3D directional coverage model for both cameras and objects. Then, we propose a Dominating Coverage Set (DCS) extraction method to reduce the infinite solution space of PANDA to a limited one without performance loss. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with 1- 1/e approximation ratio to address this problem. We conduct simulations and field experiments to evaluate the proposed algorithm, and the results show that our algorithm outperforms comparison ones by at least 75.4%.

This paper considers the fundamental problem of Placement of unmanned Aerial vehicles achieviNg 3D Directional cover Age (PANDA), that is, given a set of objects with determined positions and orientations in a 3D space, deploy a fixed number of UAVs by adjusting their positions and orientations such that the overall directional coverage utility for all objects is maximized. First, we establish the 3D directional coverage model for both cameras and objects. Then, we propose a Dominating Coverage Set (DCS) extraction method to reduce the infinite solution space of PANDA to a limited one without performance loss. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint, and present a greedy algorithm with 1 -1 /e approximation ratio to address this problem. We conduct simulations and field experiments to evaluate the proposed algorithm, and the results show that our algorithm outperforms comparison ones by at least 75.4%.

This paper considers the fundamental problem of placement of Unmanned Aerial VehIcles for aniSotropic monItoring Tasks (VISIT). That is, given a set of objects on 2D area, place a fixed number of UAVs by adjusting their coordinates and orientations subject to Gaussian bias, such that the overall monitoring utility for all objects is maximized. We develop a theoretical framework to address VISIT. First, we establish the monitoring model whose quality of monitoring (QoM) is anisotropy with respect to monitoring angle and monitoring distance. To the best of our knowledge, we are the first to consider anisotropic QoM. Then, we propose an algorithm consisting of area discretization and Monitoring Dominating Set (MDS) extraction, to reduce the infinite solution space to a limited one without performance loss. Finally, we prove that the reformulated problem can be modeled as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with 1−1/e−ϵ approximation ratio to address it. We conduct both simulations and field experiments to evaluate our algorithm, and the results show that our algorithm outperforms comparison algorithms by at least 41.3%.

This paper considers the problem of Heterogeneous wIreless charger Placement with Obstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies, on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of 1/2 -- ϵ. We conduct both simulations and field experiments to evaluate the performance of our algorithm and other five comparison algorithms. The results show that our algorithm outperforms the comparison algorithms by at least 33.49% on average.

Due to the agility, low-cost and robustness, UAV (Unmanned Aerial Vehicle) Ad Hoc Networks formed by small UAVs have popular application in the battlefield. Considering the high mobility of UAV which may exit and join in the networks frequently, random access is critical for UAV Ad Hoc Networks. Due to the complex and serious electromagnetic environment in the battlefield, how to identify the MAC protocol when demodulation is unrealistic and switch to this MAC protocol adaptively is challenging. In this paper, we propose Demodulation-free Random Access (DFRA) scheme which can help UAVs join in the UAV ad hoc networks without demodulating the property field of MAC protocol header. First, we propose an adaptive feature extraction algorithm and use it for machine learning based MAC protocol identification. Then, DFRA adopts an adaptive MAC switching framework to access the networks. We implement DFRA with USRP N210 and evaluate the performance by experiments. The results show that DFRA can guarantee access accuracy rate over 95% when demodulation is unrealistic.

In this paper, we intend to study the optimal deployment density of drone small cells (DSCs) to achieve maximum coverage considering the inter-cell interference. Due to the high altitude, the air-to-ground channel of DSCs consist of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) links, causing computational difficulties in performance analysis. To accurately analyze coverage performance, we calculate the cumulative inter-cell interference considering both LoS and NLoS links. And we derive an approximate and closed-form expression for it to facilitate the computation of the optimal deployment density in a tractable way. Given the altitude, the optimal deployment density is obtained by determining the optimal coverage radius of a DSC. And numerical results show that, increasing the altitude of DSCs does not necessarily improve coverage performance.

Due to the agility and low-cost, small Unmanned Aerial Vehicle (UAV) has recently captured great attention of academia and industry. However, since the capability limitation of single device, an ad hoc network formed by small UAVs is very promising. But compared to ordinary ad hoc networks, because of the unmanned characteristic and the diversity of missions, the protocols of UAV ad hoc networks require higher adaptive ability, i.e., the MAC protocol. In this paper, first, we verify that different MAC protocols have respective performance advantage under various network scenarios during the UAV reconnaissance mission. Then, we propose an adaptive MAC framework which allows multiple MAC protocols to switch mutually based on some kind of information you want. After that, in order to demonstrate this framework we design an adaptive MAC protocol called CT-MAC following the proposed framework. CT-MAC allows UAVs to switch between CSMA and TDMA based on their own positions when performing reconnaissance mission. Finally, we implement CT-MAC with Raspberry Pi and MDS Radio. The experiment results show that CT-MAC can always keep desirable performance compared to single MAC protocol through the fast and transparent MAC switching and the proposed adaptive MAC framework is feasible and effective.