With the rapid development of low-altitude economy, the low-altitude intelligent network (LAIN) serves as a critical infrastructure for supporting diversified aerial activities. However, current LAIN lacks a physical-network synchronization mechanism and fails to handle heterogeneity at the architecture level, which leads to difficulties in real-time adaptive adjustments and efficient unified coordination. To address these issues, this paper proposes the networked embodied intelligence (NEI) paradigm, with a network-level sensing-decision-action-feedback (SDAF) closed-loop mechanism to synchronize physical and network states. Building on this paradigm, we functionally reconfigure LAIN into four collaborative subnetworks: sensing, computing, communication and navigation. As a further step, we propose the NEI-LAIN architecture, where these subnetworks collaborate via the SDAF loop to achieve global collaboration and continuous evolution. Simulation results demonstrate that the proposed NEI-LAIN can significantly enhance the performance of communication robustness, resource utilization and task responsiveness in highly dynamic scenarios. Finally, we discuss its implementation challenges and future research directions.

With the rapid development of low-altitude intelligent networks (LAINs), the growing demand for data services poses significant challenges for the existing networks. At the same time, the airspace becomes increasingly complex due to the escalating count of low-altitude users. Although unmanned aerial vehicles (UAVs) carrying mobile base stations to provide communication services can effectively alleviate pressure on existing network infrastructure, they unfortunately face the dual challenge of sustaining reliable data transmission and guaranteeing UAV flight safety. Therefore, in this paper, we propose a collision-free UAVs communication model specifically designed for the hybrid low-altitude environment, incorporating both static and dynamic, as well as known and unknown obstacles. To efficiently support safe flight operations of UAVs, an artificial potential field (APF)-based collision probability map is constructed, enabling the UAVs to dynamically evaluate and avoid obstacles while maintaining high communication performance constrained by limited energy resources. To maximize energy efficiency in low-altitude environments with hybrid obstacles, an adaptive association multi-agent deep deterministic policy gradient (AA-MADDPG) algorithm is proposed to enable collaborative trajectory planning among multiple UAVs. Simulation results confirm that the proposed strategy enhances energy efficiency by 58.06% and reduces collision probability by 86.18%, achieving significant improvements in both communication performance and flight safety.

Due to the broadcasting characteristics of satellite-terrestrial integrated networks (STINs), security vulnerabilities have emerged as a critical concern requiring urgent mitigation strategies. Unlike traditional security methods, federated learning (FL) enables a large number of participants to collaborate without disclosing actual privacy data. Its potential as a framework that combines collaborative model training and covert payload transmission in STINs represents a significant research gap. This paper proposes FedSAT, a novel FL-based covert communication scheme for STINs, in which each participant in the FL process can utilize the shared learning protocol as a covert medium for transmitting arbitrary information in privacy-preserving framework. Our framework leverages the dual capabilities of FL for collaborative model training and covert payload embedding, utilizing Geostationary Earth Orbit (GEO) satellites and distributed terrestrial nodes to embed sensitive data within FL parameter updates. The system maintains model convergence accuracy while implementing strategic encryption to achieve robust sharing and transmission of payloads within the FL framework. Comprehensive simulation tests demonstrate the framework significant efficacy, achieving a 98.7% communication coverage for covert payload transmission under monitoring by low Earth orbit (LEO) surveillance satellites, with only a 0.8% decrease in model accuracy. This breakthrough achievement paves the way for a transformative paradigm in covert cross-domain communication for next-generation networks.

With the development of unmanned aerial vehicles (UAVs) and the diversification of low-altitude applications, the cooperation among UAVs with different capabilities and objectives offers an exciting prospect for achieving efficient and ubiquitous communication coverage. However, coordinating the cooperation and competition among heterogeneous UAVs is an intractable challenge. In this paper, we propose a novel centralized-distributed heterogeneous-UAVs intelligent communication network system, which addresses the cooperation-competition issue among heterogeneous UAVs through reasonable task allocation. Specifically, a hub UAV makes ground users (GUs) scheduling decisions based on global information and provides backhaul link support through trajectory optimization. Meanwhile, high-mobility distributed UAVs cooperate to ensure fair, efficient communication for assigned GUs. Although centralized user scheduling offers greater flexibility and better performance, it also faces the serious problems which includes time-varying local observation spaces, hybrid action spaces, heterogeneous state spaces, and reward discrepancies. To solve these problems, we propose a convolutional neural network-assisted heterogeneous-UAVs proximal policy optimization algorithm, which aims to jointly optimize UAV trajectories and user scheduling, maximizing the system’s total fair energy efficiency. The simulation results demonstrate that the proposed CNN-HUPPO algorithm outperforms the four multi-agent deep reinforcement learning (MADRL) benchmark algorithms and two baseline algorithms in terms of fairness and accumulative fair energy efficiency.

Intelligent air-ground integration communication is an emerging technology. Uncrewed aerial vehicles (UAVs) serve as mobile edge computing (MEC) servers in large-scale Internet of Things (IoT) applications, alleviating the computational load on ground users. Existing multi-UAV MEC approaches struggle with the complex computation and large data sizes of deep neural network tasks. To address these challenges, we propose a Deep Reinforcement Learning (DRL)-based DNN Partitioning and Dynamic Trajectory Selection (DPDTS) method, which reduces end-to-end latency and system energy consumption through task offloading and collaborative inference. Specifically, we propose an Optimal Partition Point Selection (OPPS) algorithm to minimize transmission overhead by selecting optimal partition points for DNN tasks. Then, we design a fairness-based matching algorithm to optimize user offloading and resource allocation. Finally, OPPS and matching algorithms are integrated to optimize UAV flight trajectories and user transmission power via DRL. The simulation results show that DPDTS outperforms existing benchmark methods in terms of delay and energy efficiency.

The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation con firms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5 dB level.

In this work, a multiple waveguide pinching antenna system (PASS) assisted integrated sensing and communication (ISAC) system is proposed, where the base station (BS) is equipped with transmitting pinching antennas (PAs) and receiving uniform linear array (ULA) antennas. The PASS-transmitting- ULA-receiving (PTUR) BS transmits the communication and sensing signals through the PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramèr–Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraint, and PA deployment constraints. To tackle the resulting non-convex problem, an alternating optimization (AO) framework is developed, which decomposes the problem into a digital beamforming sub-problem and a pinching beamforming sub-problem. The digital beamforming design is optimized via semidefinite relaxation (SDR), while the PA deployment is updated using penalty-based method. Simulation results demonstrate that: 1) the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes; and 2) the PASS assisted ISAC is less affected by stringent communication constraints compared to conventional MIMO-ISAC, and benefits from increasing the number of waveguides and PAs per waveguide.

In the next-generation emergency communication networks, unmanned aerial vehicles (UAVs), serving as aerial base stations, have attracted increasing attention recently due to their high mobility and low cost. Therefore, this paper conceives a heterogeneous UAVs assisted intelligent communication network system, in which tethered UAVs (T-UAV) make ground users (GUs) scheduling decisions to avoid resource competition, while other UAVs cooperate to provide communication services. However, the limited onboard resources of UAVs and cooperativecompetitive problems among heterogeneous UAVs becomes key challenges in UAV-assisted intelligent emergency communication networks. In order to tackle these issues, we propose a heterogeneous multi-agent approximate policy optimization (HMAPPO) algorithm to maximize the total fair energy efficiency by jointly optimizing UAV trajectories and user scheduling. Simulation results demonstrate that HMAPPO outperforms other baseline algorithms in terms of energy efficiency of the system and fairness of GUs. Furthermore, benefit to the partial parameter sharing mechanism, the proposed HMAPPO significantly accelerates the training process compared to other benchmark algorithms.

Autonomous aerial vehicles (AAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety. In order to achieve efficient surveillance and break the limitation of application scenarios caused by single communication, we propose the collaborative surveillance model for hierarchical AAVs based on the cooperation of automatic dependent surveillance-broadcast (ADS-B) and 5G. Specifically, AAVs are hierarchical deployed, with the low-altitude central AAV equipped with the 5G module, and the high-altitude central AAV with ADS-B, which helps automatically broadcast the flight information to surrounding aircraft and ground stations. First, we build the framework, derive the analytic expression, and analyze the channel performance of both air-to-ground (A2G) and air-to-air (A2A). Then, since the redundancy or information loss during transmission aggravates the monitoring performance, the mobile edge computing (MEC) based on-board processing algorithm is proposed. Finally, the performances of the proposed model and algorithm are verified through both simulations and experiments. In detail, the redundant data filtered out by the proposed algorithm accounts for 53.48%, and the supplementary data accounts for 16.42% of the optimized data. This work designs a AAV monitoring framework and proposes an algorithm to enhance the observability of trajectory surveillance, which helps improve the airspace safety and enhance the air traffic flow management.

The safe and high-efficiency operation of low-altitude vehicles (LAVs) is the key to supporting the rapid development of the low-altitude economy. However, the conventional space-air-ground integrated network (C-SAGIN) mainly focuses on ground users, which is difficult to provide high-quality services for LAVs. In this article, we first propose a novel architecture of low-altitude centric space-air-ground integrated network (LAC-SAGIN), where low-altitude airspace and LAVs become the communication centers. A detailed comparison is further illustrated between these two architectures in terms of their composition and performance metrics. Although LAC-SAGIN can effectively support the low-latency and continuous communication of LAVs, it still faces several key implementation challenges including insufficient low-altitude infrastructures, high mobility of LAVs and its diversified task requirements, and low transmission efficiency and energy efficiency in airspace communication. To address these challenges, the corresponding three countermeasures are proposed, which reveal the future research direction of LAC-SAGIN.

Federated learning (FL), as an innovative paradigm of distributed learning, provides reliable support for the growing edge intelligence (EI). The limitations of traditional FL’s reliance on ground base stations (BSs) make the development of aerial server unmanned aerial vehicles (UAVs) inevitable, thereby developing the air-ground integrated FL (AGIFL). However, current efforts predominately focus on static offline training based on existing datasets and some efforts consider online training in dynamic sample environments, where new samples need to be fully pre-trained to determine sample quality. To this end, we study how to realize high-performance of FL in dynamic environment without training all samples. Specifically, we formulate a joint optimization problem for sample selection, UAV deployment, and resource distribution aiming to minimize the trade-off between the user energy consumption and FL performance. To address the optimization problem without explicit expression, we employ meta-learning to derive an upper bound on the gradient norm of the loss function to evaluate learning performance, and describe how time-varying small-batch ratios affect this bound. Then, we propose an optimization algorithm that ensures convergence, capitalizing on the block coordinate descent techniques. To demonstrate the efficacy of our algorithm, we conduct both extensive simulations and proof-of-concept field experiments. The findings indicate an average improvement of approximately 39% in reducing the objective value when compared to the benchmarks.

Although integrated sensing and communication (ISAC) has an advantage of mutual gain of its dual functions, it is susceptible to be eavesdropped by mobile targets due to the broadcast nature of wireless channels. In this paper, we propose a secure predictive beamforming scheme against a mobile eavesdropping target for ISAC, where the intelligent reflecting surface (IRS) is utilized to assist the sensing and secure transmission. To tackle the mobility of eavesdropping target, we first develop a secure predictive beamforming protocol and formulate a sum secrecy rate maximization problem. However, due to the non-convex objective function and the outdated channel state information (CSI), it is difficult to solve the problem directly. Thus, we develop a deep learning based predictive beamforming scheme, which incorporates the parallel convolutional neural network, the long short-term memory modules and the attention mechanism to learn the features from the historical CSI. It can directly design the beamformings for the next time slot with low computational complexity and bypass the need of CSI prediction. Simulation results show that the proposed scheme can significantly enhance the security of ISAC with low overhead.

With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of channel state information from unpredictable environmental conditions, we formulate the problem to minimize the total energy cost with the chance constraint, which is a mixed-integer nonlinear problem with uncertain parameters and intractable to solve. To tackle this issue, we optimize the UAV deployment via the weighted K-means algorithm. Then, the chance constraint is reformulated via the distributionally robust optimization (DRO). Furthermore, based on the conditional value-at-risk mechanism, we transform the DRO problem into a mixed-integer second order cone programming, which is further decomposed into two subproblems via the primal decomposition. Moreover, to alleviate the complexity of the binary subproblem, we design a binary whale optimization algorithm. Finally, we conduct extensive simulations to verify the effectiveness and robustness of the proposed schemes by comparing with baseline mechanisms.

Unmanned Aerial Vehicle (UAV) clusters are widely used in spectrum reconnaissance, where spectrum samples are transmitted back to the sink through flying ad hoc network (FANET). High sampling frequencies are required, but result in a large amount of data not being transmitted over the wireless network, affecting throughput and reliability of the network. The problem boils down to what a good sampling frequency is and how to find it. There is an unknown dynamic sampling frequency threshold, beyond which high packet loss occurs. Prior work focused on achieving higher frequencies through FANET optimization but neglected methods to find this threshold and achieve a maximum value. We propose an adaptive protocol based on binary search. With limited bandwidth and consistent sampling frequency, the protocol dynamically adjusts sampling frequencies in a phased manner at the application layer to maximize the sampling frequency that the network can handle. In addition to spectrum reconnaissance, this protocol can be extended to other applications with consistent sampling frequencies. We have constructed an application based on the EXata simulation platform to analyze the performance of the protocol in delay, stability, reliability, and search times. Simulation results show the protocol improves the efficiency and survivability of spectrum reconnaissance.

Owing to the ability of reconfiguring wireless channels, intelligent reflecting surface (IRS) can help non-orthogonal multiple access (NOMA) to release its tremendous potential. However, the inter-user interference becomes the bottleneck of IRS-NOMA networks. To tackle this challenge, we propose two constructive interference precoding (CIP) based countermeasures in this paper for interference exploitation in IRS-NOMA networks. Specifically, the first scheme makes the residual interference from higher-order users (HUs) be constructive to lower-order users (LUs), so that the interference-free decoding can be achieved. While the second scheme directly utilizes the interference from LUs for the signal reception of HUs to avoid successive interference cancellation (SIC). The transmit power is minimized by jointly optimizing the BS active beamforming and the IRS passive beamforming for the two schemes, subject to the signal-to-interference-plus-noise ratio (SINR) requirement of each user, SIC decoding constraints, constructive condition and IRS unit-modulus constraint. Due to the coupled variables and non-convex constraints, we first decompose each problem into two subproblems, and then apply successive convex approximation (SCA) to convert them into convex ones. Finally, an alternating optimization (AO) based algorithm is proposed to solve the two convex subproblems for each scheme iteratively. Simulation results are presented to show the superiority and applicability of the proposed schemes compared to benchmarks.

Non-orthogonal multiple access (NOMA) can help integrated sensing and communication (ISAC) to accommodate more users and well manage interference. In this paper, we first propose a NOMA-ISAC scheme, in which a multiantenna base station (BS) transmits ISAC signal to detect a radar user (RU), and provide wireless service to the RU and the communication user (CU) simultaneously. The inter-user interference can be mitigated by the successive interference cancellation (SIC). We further investigate the trade-off between minimizing the beampattern matching error and maximizing the CU’s achievable signal-to-noise ratio (SNR), and propose a penalty-based semi-definite relaxation (SDR) method to solve this non-convex problem. Then, to mitigate the instantaneous NOMA-ISAC beampattern shaking and enhance its stability, we utilize constructive interference precoding (CIP) to assist the NOMA-ISAC beampattern design. Introducing CIP can convert the interference from RU into the beneficial signal to CU and the complex SIC can be avoided. Then, the corresponding trade-off can be transformed into a convex problem by the Taylor-series approximation, and an iterative algorithm is proposed to solve it. Moreover, the Manopt toolbox assisted initialization is utilized to accelerate its convergence speed. Simulation results verify that the proposed CIP-NOMA-ISAC scheme can effectively enhance the stability of instantaneous NOMA-ISAC beampattern over limited time slots, and provide higher SNR for CU.

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature.

Unmanned Aerial Vehicles (UAVs)-assisted networks play a pivotal role in both terrestrial base stations (BSs) and non-terrestrial networks (NTNs) due to their extensive coverage and collaborative decision-making capabilities. However, the presence of diverse node types, rapidly evolving requirements, and dynamic channel conditions poses substantial challenges for ground users (GUs) access, particularly in an unknown environment within BS-UAV-NTN integrated networks. To tackle these challenges, this paper introduces a novel approach-a deep Q-learning network (DQN)-based algorithm for UAVs deployment and an adaptive and load balancing (ALB) scheme for GUs access. This paper formulates the GUs access problem in BS-UAV-NTN networks as a maximization problem, transforming it into a Markov Decision Process (MDP) problem for UAVs deployment in unknown environment. The proposed solution includes a DQN-based UAVs deployment algorithm and an access scheme that prioritizes BSs and UAVs. Simulation results convincingly show that this access scheme outperforms traditional Q-learning and random schemes in terms of rewards and the number of accessed GUs.

Unmanned aerial vehicles (UAVs) are widely applied in multiple fields, which emphasizes the challenge of obtaining UAV flight information to ensure the airspace safety. UAVs equipped with automatic dependent surveillance-broadcast (ADSB) devices are capable of sending flight information to nearby aircrafts and ground stations (GSs). However, the saturation of limited frequency bands of ADS-B leads to interferences among UAVs and impairs the monitoring performance of GS to civil planes. To address this issue, the integration of the 5th generation mobile communication technology (5G) with ADS-B is proposed for UAV operations in this paper. Specifically, a hierarchical structure is proposed, in which the high-altitude central UAV is equipped with ADS-B and the low-altitude central UAV utilizes 5G modules to transmit flight information. Meanwhile, based on the mobile edge computing technique, the flight information of sub-UAVs is offloaded to the central UAV for further processing, and then transmitted to GS. We present the deterministic model and stochastic geometry based model to build the air-to-ground channel and air-to-air channel, respectively. The effectiveness of the proposed monitoring system is verified via simulations and experiments. This research contributes to improving the airspace safety and advancing the air traffic flow management.

We consider flying ad hoc networks (FANETs) operating in frequency-sweeping jamming environment, where jamming significantly impairs the performance of routing protocols, leading to unstable network communications and data loss. Currently, jamming is defended at physical and link layer by military radio stations, which focuses on restoring communication of a single link. Existing routing protocols fail to utilize valuable information from these radio stations effectively to restore a multi-hop path. To address this issue, we propose a novel routing protocol optimization method that combines spectrum awareness techniques to further mitigate the adverse effects of jammers on overall network performance. The proposed method utilizes the obtained jammer information and bit error rate to modify various parameters in the routing protocol, enabling it to adapt to jamming scenarios. We implement the optimized protocol on the EXata simulation platform. The simulation results show that, compared with the reference protocols, the proposed protocol can increase the throughput by up to 12% and improve the packet delivery ratio by up to 14% in the presence of frequency-sweeping jammers.

Unmanned Aerial Vehicle (UAV) is frequently used in tactical scenarios due to its flexibility, where networking among UAVs is an enabling technology for cooperation. However, dy-namic location changes and jamming attacks in tactical scenarios pose significant challenges to the stability and reliability of UAV networks. Observing that network conditions change frequently and a single protocol does not work well across different network conditions, we propose a MAC protocol switching strategy, MS-MAC, which is driven by the missions and interference, switching among carrier sense multiple access (CSMA) protocol and time division multiple access (TDMA) protocol. Instead of fixing the number of slots in TDMA, we propose a dynamic variant whose time slot number is determined by traffic load. MS-MAC switches between dynamic TDMA and CSMA based on channel status. We implement the protocol on the EXata platform, and simulations verify the effectiveness of the protocol against existing protocols.

Recent advancements in unmanned aerial vehicle (UAV) technology have propelled the popularity of edge intelligence (EI) applications with deep learning in UAV swarm. Nevertheless, the high computational demands of deep neural networks (DNNs) conflict with the limited computing power and battery capacity of UAV. Furthermore, many UAV applications require real-time performance such as object detection and recognition. In this paper, we study how to achieve fast DNN inference in UAV swarm by the collaboration of multiple UAVs, and formulate the problem of minimizing the completion time of a series of arriving DNN inference tasks, under memory and energy constraints. To solve the aforementioned challenging problem with combinatorial explosion, we propose an efficient solution exploiting deep reinforcement learning (DRL) with action space simplification to find the allocation strategy of each DNN inference task within a resource-constrained UAV swarm. Simulation results validate the effectiveness of the proposed solution compared to five benchmark algorithms.

Nowadays, unmanned aerial vehicle (UAV) clusters are widely used in military and civil scenarios to perform spectrum reconnaissance. In this task, a high sampling frequency is desirable, but it results in a large volume of data that may not be transmitted through the wireless network. The problem boils down to what a good sampling frequency is and how to find it. There is an unknown dynamic sampling frequency threshold exceeding which leads to high packet loss. To find this threshold and achieve the maximum sampling frequency, we propose a distributed protocol based on binary search to find the best sampling frequency, taking consideration of performance metrics like latency, stability, reliability, and search times. Under the premise of limited bandwidth and consistent spectrum sampling frequency of UAVs, our protocol can dynamically adjust the spectrum sampling frequency at the application layer. We have built a network application on the EXata simulation platform to analyze the performance of the proposed protocol. The simulation results show that the protocol improves the efficiency and survivability of spectrum reconnaissance.

Unmanned Aerial Vehicle (UAV) swarm is becoming an important part of the future battlefield. Due to the high confrontation and the multiplicity types of interference in the battlefield environment, ensuring the topology integrity of the UAV formation becomes the premise of achieving the target mission. In this paper, we propose an optimal swarm reconstruction strategy for scenarios where some UAVs are destroyed. Our strategy reorganizes the remaining isolated UAVs into UAV clusters through reconstruction. We formulate the problem as an integer linear programming problem and propose a minimum-cost maximum-flow-based algorithm to solve it to optimality in polynomial time. Numerical results show that the proposed strategy can find the optimal path-cost solution, and the solution also takes into account the remaining energy and topoloaical change,

With the increasing use of autonomous unmanned aerial vehicles (UAVs), it is critical to ensure that they are continuously tracked and controlled, especially when UAVs op-erate beyond the communication range of ground stations (GSs). Conventional surveillance methods for UAVs, such as satellite communications, ground mobile networks and radars are subject to high costs and latency. The automatic dependent surveillance-broadcast (ADS-B) emerges as a promising method to monitor UAVs, due to the advantages of real-time capabilities, easy deployment and affordable cost. Therefore, we employ the ADS-B for UAV trajectory tracking in this work. However, the inherent noise in the transmitted data poses an obstacle for precisely tracking UAVs. Hence, we propose the algorithm of recurrent neural network-enhanced interacting multiple model-Kalman filter (RNN-enhanced IMM-KF) for UAV trajectory filtering. Specifically, the algorithm utilizes the RNN to capture the maneuvering behavior of UAVs and the noise level in the ADS-B data. Moreover, accurate UAV tracking is achieved by adaptively adjusting the process noise matrix and observation noise matrix of IMM-KF with the assistance of the RNN. The proposed algorithm can facilitate GSs to make timely decisions during trajectory deviations of UAVs and improve the airspace safety. Finally, via comprehensive simulations, the total root mean square error of the proposed algorithm decreases by 28.56%, compared to the traditional IMM-KF.

Federated learning (FL) as an emerging distributed machine learning (ML) paradigm enables participants to train their on-device data locally and share model parameters with others by the parameter server. Differing from the centralized ML, FL splits the high requirements of training data and computing power from the server to clients, which is well adapted to unmanned aerial vehicle (UAV) swarms with scattered nodes, heterogeneous data, and limited computing power. However, pre-trained models are unsatisfactory in unfamiliar scenes and most existing approaches fail to concentrate on the communication-sensitivity and real-time requirements in UAV-enabled FL scenarios. To address this problem, this paper proposes participant and sample selection for efficient online federated learning in UAV swarms (FedOL). Through the combination of online learning and FL, UAVs can supplement real-time samples and quickly improve the model accuracy in unfamiliar scenes. Meanwhile, to reduce the training latency with expected model accuracy, FedOL allows the server UAV to select participants with high training utility, while the client UAVs select more important samples. We implement FedOL and deploy it on UAV embedded devices. Experimental results show that compared with existing FL approaches, FedOL speeds up by about 2.61× and reaches the final accuracy about 1.02× higher.

In the cellular Internet of Things (CIoT), it is crucial to ensure the information freshness for status update applications. Considering the centralized access methods could cause large access delay and hamper timely status updates, this paper exploits the random access method and studies decentralized status update schemes to minimize the average age of information (AoI) for CIoT. However, due to the non-cooperation among machine type communication devices (MTCDs) in random access, packet collisions are inevitable, which makes it tricky to improve the AoI performance. In this regard, we design novel age-based status update schemes to control the transmission behavior of MTCDs, where the AoI at the MTCDs and the base station (BS) is used. We first model the AoI minimization problem as a Markov decision process. Then, through variable substitution and linear programming, we get a slightly more computationally complex status update scheme, where the dual threshold structure of the scheme is proved theoretically. To facilitate system design and reduce computational complexity, we further design a low-complexity scheme, where the age thresholds at both the MTCDs and BS are optimized. Simulation results verify that the proposed schemes significantly outperform the common access scheme.

Unmanned aerial vehicles (UAVs) play an essential role in emergency cases and adverse environments for applications like disaster detection and mine exploration. To process the massive volume of sensing data generated by various sensory payloads in these applications, existing works either compress deep learning (DL) models to conduct onboard computing, or offload raw data back to the resourceful ground station with the help of relay UAVs due to base station damage. However, the former sacrifices the inference accuracy of DL models (up to 10% accuracy loss), while the latter achieves high accuracy at the cost of significant latency, due to limited wireless communication resources in the multi-hop transmission. To address the problem, exploiting the resources of the UAV swarm including both task UAVs and relay UAVs, we build up anall-skyautonomous computing (ASAP) system to autonomously conduct collaborative computing in the swarm, to achieve both high accuracy and low latency of sensing data processing. In detail, we first propose a novel UAV swarm-native collaborative computing architecture, considering the general hierarchy and clustering structure of UAV swarms, as well as the characteristic of DL model execution. We then design an elastic efficient task scheduler to allocate computing tasks for UAVs, and update the scheduling scheme online when some UAVs are unavailable, with the aid of a lightweight and accurate DL inference performance predictor. Finally, we design an adaptive inter-UAV data compressor, to adapt to the limited and dynamic communication resources between UAVs. Experiment results on 24 airborne computers and five real-world UAVs show that, the proposed system can perform collaborative computing in a timely manner and effectively deal with situations when some UAVs become unavailable.

The proliferation of various mobile devices with massive data and improving computing capacity have prompted the rise of edge artificial intelligence (Edge AI). Without revealing the raw data, federated learning (FL) becomes a promising distributed learning paradigm that caters to the above trend. Nevertheless, due to periodical communication for model aggregation, it would incur inevitable costs in terms of training latency and energy consumption, especially in multi-cell edge networks. Thus motivated, we study the joint edge aggregation and association problem to achieve the cost-efficient FL performance, where the model aggregation over multiple cells just happens at the network edge. After analyzing the NP-hardness with complex coupled variables, we transform it into a set function optimization problem and prove the objective function shows neither submodular nor supermodular property. By decomposing the complex objective function, we reconstruct a substitute function with the supermodularity and the bounded gap. On this basis, we design a two-stage search-based algorithm with theoretical performance guarantee. We further extend to the case of flexible bandwidth allocation and design the decoupled resource allocation algorithm with reduced computation size. Finally, extensive simulations and field experiments based on the testbed are conducted to validate both the effectiveness and near-optimality of our proposed solution.

Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

This paper proposes Advanced Fast Recovery OLSR (AFR-OLSR) for Unmanned Aerial Vehicles (UAVs) with sudden link outages, and implements a multipath version of this protocol. AFR-OLSR can maintain high packet delivery ratio and hardly bring extra network delay in the scenario of nodes suddenly offline or moving at high speed in UAV swarm. We improve the route calculation rule by defining a new link state and using penalty function to reduce the priority of selecting the link state. We use a constant bit rate (CBR) application running on selected nodes to simulate traffic in the network and measure the performance of data transmission. Experimental results show that the proposed protocol has a significant performance improvement.

The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.

With the continuous increment of maritime applications, the development of marine networks for data offloading becomes necessary. However, the limited maritime network resources are very difficult to satisfy real-time demands. Besides, how to effectively handle multiple compute-intensive tasks becomes another intractable issue. Hence, in this paper, we focus on the decision of maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs) and vessels. Specifically, we first propose a cooperative offloading framework, including the demands from marine Internet of Things (MIoTs) devices and resource providers from UAVs and vessels. Due to the limited energy and computation ability of UAVs, it is necessary to help better apply the vessels to computation offloading. Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost. Then, we leverage Lyapunov optimization to convert the long-term constraints of the total execution time and energy cost into their short-term constraints, further yielding a set of per-time-slot optimization problems. Furthermore, we propose a Q-learning based approach to solve the short-term problem efficiently. Finally, simulation results are conducted to verify the correctness and effectiveness of the proposed algorithm.

Unmanned Aerial Vehicles (UAVs), now often deployed on battlefields, use wireless networks to communicate with each other. Such wireless communication, however, may become unreliable under the jamming from opponents, and existing UAV routing protocols perform poorly when dealing with the jamming. In this paper, we propose to use the spectrum jamming prediction data from the radio environment map (REM) to update the network topology. The key is to model the network as a weighted graph where the weights of the edges between two neighboring nodes are functions of the location of the jammer. We then update the routing paths accordingly in real-time. The protocol can be incorporated with existing table-driven protocols. We implement the protocol in a commercial network simulator. Simulations show that our routing protocol improves the packet delivery ratio and throughput of existing table-driven routing protocols.

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications, New York City, NY, USA, 17-20 May 2023

Benefit to the advantages of low cost, strong security, flexibility and high line-of-sight (LoS), UAVs constitute a promising platform to accomplish data collection for the IoT networks. However, it has to be admitted that the constrained energy consumption of UAVs has become the main challenge for the UAV-assisted IoT data collection. The existed works are mainly based on the assumption that the amount of data uploaded by different devices are the same, which makes the energy consumption of UAVs deviating from the real situation. Therefore, in this paper, for the sake of practical consideration, the amount of data uploaded varies from different devices in a multi-UAV-assisted IoT data collection scenario. To solve the problem of minimizing the total energy consumption of UAVs, we decouple it into two subproblems, devices clustering and UAVs trajectory planning, and propose an iterative optimization algorithm with energy and data volume constraints. Numerical results show that the performance of the proposed method outperforms the comparison schemes significantly in terms of saving total UAVs energy consumption and reducing the standard deviation of UAVs energy consumption.

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

We study the problem of resilient service provisioning for edge computing (RSPE), i.e., how to determine a service placement strategy to maximize the expected overall utility by service provisioning, in the presence of uncertain service failures. RSPE is extremely challenging to tackle, because the explicit expression of its objective function is difficult to obtain, and it is a resilient max–min problem subject to knapsack constraints, which is unexplored so far and cannot be addressed by existing resilient optimization techniques. We first explore the potential properties of the implicit objective function, and reveal that it is monotone submodular under certain conditions. We further prove that the knapsack constraints form a$q$-independence system constraint, where$q>0$is a constant related to the constraints. We propose two novel solutions for the general RSPE and homogeneous case, respectively. First, for the general problem, we propose a “two-step greedy” algorithm achieving a constant approximation ratio within polynomial time. Second, for the homogeneous case where one of the knapsack constraints reduces to a matroid constraint, we propose an improved “first-greedy-then-local search” polynomial time algorithm achieving better approximation ratio than the previous one. Both extensive simulations and field experiments validate the effectiveness of our proposed algorithms.

Unmanned aerial vehicles (UAVs) have been widely used to perform search and tracking tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple UAVs need to be endowed with intelligence through machine learning (ML). However, the traditional centralized ML cannot be directly applied in UAV networks, since it is challenging to transmit raw data with limited bandwidth and energy budget. As a distributed manner, federated learning (FL) is more suitable for UAV networks than traditional ML schemes in order to boost edge intelligence for UAVs. Considering the limited energy supply of UAVs, we study how to minimize UAVs’ overall training energy consumption by jointly optimizing the local convergence threshold, local iterations, computation resource allocation, and bandwidth allocation, subject to the FL global accuracy guarantee and maximum training latency constraint. The formulated nonconvex mixed-integer programming problem is solved by a joint training and resource allocation optimization algorithm. In addition, we also study how to solve the problem considering fairness among different UAVs by changing the objective to minimizing the maximum energy consumption of UAVs, and extend the aforementioned approach to this problem. Our simulation results show that while satisfying both the training accuracy and latency constraints, the proposed algorithm can reduce more UAVs’ overall training energy consumption and the maximum energy consumption in the UAV swarm than four baseline schemes.

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.

To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating the minimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.

When UAVs are sent out for investigation such as battlefield reconnaissance, data from different UAVs should be transmitted to a central station, forming convergecast communication traffic. When infrastructure support is not available, data travels along a multi-hop routing path to the destination, and a routing protocol is needed to find these paths. In this work, we evaluate one commonly used protocol, BATMAN-adv, on commercially available Wi-Fi chipsets in embedded systems. We find that BATMAN-adv can form the desired routing paths, but the efficiency can be improved. In particular, this protocol does not consider traffic from other sources, and may lead to crowded paths. In addition, TCP works mostly well on the found routing paths, and its performance is comparable to UDP, but its built-in resource allocation mechanism may not meet an application's need. Consequently, both routing protocols and transport layer protocol should be revised to better suit convergecast applications.

Spectrum map is of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, the existing spectrum map construction schemes mainly depend on spatial interpolation and cannot construct the spectrum map when the measurement data of the target frequency are not obtained. In order to overcome this challenge, an accurate spectrum map construction scheme is proposed by using an intelligent frequency-spatial reasoning approach. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct the highly accurate spectrum maps of the frequencies without spectrum data. A novel autoencoder adapting to the three-dimensional (3D) spectrum data is proposed. Simulation results demonstrate that our proposed scheme is superior to the benchmark schemes in terms of the construction accuracy. Moreover, it is shown that our proposed autoencoder network has a fast convergence speed.

Recently, unmanned aerial vehicles (UAVs) are gathering increasing attentions from both the academia and industry. The ever-growing number of UAV brings challenges for air traffic control (ATC), and thus trajectory prediction plays a vital role in ATC, especially for avoiding collisions among UAVs. However, the dynamic flight of UAV aggravates the complexity of trajectory prediction. Different with civil aviation aircrafts, the most intractable difficulty for UAV trajectory prediction depends on acquiring effective location information. Fortunately, the automatic dependent surveillance-broadcast (ADS-B) is an effective technique to help obtain positioning information. It is widely used in the civil aviation aircraft, due to its high data update frequency and low cost of corresponding ground stations construction. Hence, in this work, we consider leveraging ADS-B to help UAV trajectory prediction. However, with the ADS-B information for a UAV, it still lacks efficient mechanism to predict the UAV trajectory. It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data. As above, in this work, we design a system of UAV trajectory prediction with the ADS-B information, and propose the recurrent LSTM (RLSTM) based algorithm to achieve the accurate prediction. Finally, extensive simulations are conducted by Python to evaluate the proposed algorithms, and the results show that the average trajectory prediction error is satisfied, which is in line with expectations.

This demonstration shows a working prototype of IDEA, Intelligent Divine Eye on Air through multi-UAV collaborative inference, to improve the throughput and accuracy of onboard object detection. Different from most existing UAV airborne object detection systems relying single UAV to run the convolutional neural networks (CNN)-based inference independently, IDEA leverages the abundant resources of multiple UAVs in a swarm, and collaboratively executes the inference task. Specifically, IDEA divides the CNN model into multiple submodels (each consisting of several successive layers), and distributes each submodel to a UAV, where the execution sequence of the submodels is coordinated to output the final prediction. The prominent advantage of IDEA lies in that, it can not only run highly accurate complex CNN models, but also perform the object detection tasks in a pipeline manner, which thus boosts high detection accuracy and frame rate. IDEA prototype with three self-constructed real-world UAVs shows ~2.6× frame rate improvement over that with one single UAV, while achieving higher detection accuracy.

Nonorthogonal multiple access (NOMA) and cognitive integrated satellite terrestrial relay networks are the promising and key part for the next-generation wireless networks. This article researches the joint effects of channel estimation errors (CEEs) and hardware impairments on the secrecy performance of cognitive integrated satellite terrestrial relay networks. The noncolluding eavesdropping scheme is applied in the multiple eavesdroppers, where the eavesdropper with the highest eavesdropping capacity is selected to overhear the legitimate transmission signal. Moreover, the detailed analysis for the secrecy outage probability (SOP) is obtained based on the utilized partial terrestrial relay selection strategy. To obtain the insightful conclusions, the asymptotic analysis along with the secrecy coding gain and secrecy diversity order for the SOP are further derived, which gives the effective methods to valuate the impacts of CEEs and hardware impairments on the considered system with the NOMA scheme in high signal-to-noise ratio regime. Moreover, simulations are derived for the secrecy energy efficiency. Finally, Monte Carlo simulations are given to prove the correctness of the theoretical SOP analysis.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm is proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

In this paper, we study the problem ofRobust offloading schEduling for mobIle edge computiNg (REIN), i.e., in the presence of uncertain offloading failures, how to determine an offloading schedule to minimize the overall latency of all computation-intensive tasks. We mathematically formulate the problem in the form of min-max robust optimization, based on the twice equivalent transformations of the scheduling problem that originally does not consider robustness. REIN is challenging to solve because the min-max robust objective is computationally intractable with existing approaches, and the monotonicity of the objective function is uncertain, even if we transform the objective into the popular max-min form by introducing an appropriate constant upper bound. To solve the above challenges, we first construct a constant upper bound and a monotone modular function to approximate the transformed max-min objective function, and then propose a computationally feasible solution with provable performance bound. Moreover, given the fact of the weak computation ability of users in practical, we construct a tighter constant upper bound and a monotone submodular approximation function, and propose a feasible solution with possibly improved performance bound. Extensive results show that, given a maximum number of offloading failures, our proposed algorithms outperform three benchmark algorithms, and approach the optimum at small time costs.

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.

With the envision of sixth generation (6G) network technology, varied artificial intelligence (AI) services gradually develop from the network center to the edge, which makes unmanned aerial vehicle (UAV) a hot spot to provide auxiliary services of machine learning (ML) to empower terrestrial users intelligence. However, due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, fed-erated learning (FL) could be more suitable. Meanwhile, unlike conventional FL working on terrestrial networks, applying FL in UAV-assisted networks should strictly consider the impact of air-ground wireless channel caused by the maneuverability of UAVs, as well as the allocation of various network resources, including frequency and latency. To address these challenges, we propose to jointly optimize the UAV location and resource allocation, subject to the constraints of learning accuracy and training latency to minimize the energy consumption of terrestrial users. The for-mulated complicated non-convex problem is efficiently solved by an alternating optimization algorithm based on successive convex approximation (SCA) approaches after problem decomposition. Simulations results show that our proposed algorithm can reduce more overall users' energy consumption than three benchmarks while guaranteeing the learning accuracy within the maximum training latency.

In case of energy depletion at some nodes, a sensor network may become disconnected, and data from live nodes may not be sent to the sink. This problem can be solved by a UAV acting as a mobile data collector. However, the traditional approach is to treat live sensor nodes as independent data sources. In this paper, we consider a cooperative approach where live sensors form sub-networks, instead of acting indepen-dently. Sensors within a subnetwork can send data to each other. A UAV only needs to visit subnetworks, instead of individual sensor nodes, to collect data. We formulate the problem and propose an iterative optimization algorithm to find the shortest data collection trajectory. Given any trajectory, our algorithm iteratively merges nodes within the same subnetwork and adjusts hovering locations to minimize trajectory length. Simulations show that, compared with the traditional TSP formulation, our approach can significantly reduces the length of the trajectory.

Area coverage is one of the most common and important tasks for flying ad hoc networks (FANETs). The increasingly large scale of FANETs brings challenges in communication and coverage. Clustering is an effective technique for networking and management for large-scale ad hoc networks. Meanwhile, some applications, i.e., face recognition, need to perform intensive computation after unmanned aerial vehicles (UAVs) perform area coverage. Due to long response delay in transferring data to the cloud, it becomes a trend to use mobile-edge computing (MEC) for processing data in FANETs, which selects the node of rich computing resources, i.e., cluster head (CH), as MEC server, thus the delay performance of the edge node to the server is particularly critical. However, there is a conflict between area coverage efficiency and delay performance. Area coverage expects UAVs to spread as widely as possible, which may lead to a longer delay. In this article, we consider maximizing coverage efficiency under delay constraints. We define the coverage efficiency and propose an iterative coverage-efficient clustering algorithm (CECA) by applying penalty and block coordinate descent methods. Specifically, the CHs, positions and transmit powers are alternately optimized in each iteration. In addition, CECA can adjust delay constraints according to task requirements. Extensive simulation results show that our proposed approach is superior to other approaches in terms of coverage efficiency and delay.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.

In this paper, we consider the problem of area charging, that is, assuming that there is a mobile charger (MC) equipped with a directional wireless charger whose charging area is in the shape of a sector, and the MC can only recharge sensors on the boundary of an Area of Interest (AOI), how to design an efficient charging scheme for the MC to recharge any sensor inside the AOI with efficient energy while its overall charging time is minimized. To address this problem, we first partition the AOI into two types of subareas, i.e., rectangle-like subareas and sector-like subareas, based on the Medial Axis of the AOI. Second, we propose rectangle-based moving strategy for the rectangle-like subareas, and sector-based rotating strategy for the sector-like subareas, respectively, for the MC. Finally, we calculate charging time of all subareas based on the above two moving strategies, and prove that our algorithm achieves a constant approximation ratio. We evaluate our algorithm by conducting extensive simulation. The results show that on average, other comparison algorithms require at least 18:70 times of charging time of that of our algorithm.

In this work, we study the problem of Robust Server Placement (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 RSP is a robust max-min problem with a matroid constraint and a knapsack constraint, which is still an unexplored problem in the literature. To address the above challenges, we first investigate the special properties of the problem, and reveal that the objective function is monotone submodular. We then prove that the involved constraints form a p-independence system constraint, where p is a constant value related to the ratio of the coefficients in the knapsack constraint. Finally, we propose an algorithm that achieves a provable constant approximation ratio in polynomial time. Both synthetic and trace-driven simulation results show that, given any maximum number of server failures, our proposed algorithm outperforms three state-of-the-art algorithms and approaches the optimal solution, which applies exhaustive exponential searches.

Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.

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%.

Due to the flexibility and affordability, multi-target positioning based on cooperation of Unmanned Aerial Vehicles (UAVs) becomes attractive in recent years. Trilateration is popular and easy to implement, but still faces challenges in multi-UAV scenario. First, multiple distance measurements from a single UAV on same targets will lead to large accumulated errors. Second, the time interval between successive distance measurements on the same target cannot be long due to the mobility of the target. Finally, UAVs have limited onboard energy which constrains the flight duration and the mission will fail when some UAVs reach the limitation. In this paper, to complete multi-target positioning mission, we aim at minimizing the maximum energy consumption among all UAVs, which can be decomposed into two subproblems after dividing all UAVs into groups of three. Then we propose a two-stage heuristic algorithm, in which we first use adjusted Genetic Algorithm (GA) to plan the trajectories of all groups with bounded maximum energy consumption and then we pursue to minimize the maximum energy consumption among UAVs in a group. Compared to two other algorithms, extensive simulations show that the proposed algorithm can reduce up to 24.9% and 11.8% in terms of maximum and average energy consumption, respectively.

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 addresses the fundamental problem of identifying driver talking directions using a single smartphone, which can help drivers by warning distraction of having conversations with passengers in a vehicle and enable safety enhancement. The basic idea of our system is to perform talking status and direction identification using two microphones on a smartphone. We first use the sound recorded by the two microphones to identify whether the driver is talking or not. If yes, we then extract the so-called channel fingerprint from the speech signal and classify it into one of three typical driver talking directions, namely, front, right and back, using a trained model obtained in advance. The key novelty of our scheme is the proposition of channel fingerprint which leverages the heavy multipath effects in the harsh in-vehicle environment and cancels the variability of human voice, both of which combine to invalidate traditional TDoA, DoA and fingerprint based sound source localization approaches. We conducted extensive experiments using two kinds of phones and two vehicles for four phone placements in three representative scenarios, and collected 23 hours voice data from 20 participants. The results show that our system can achieve 95.0% classification accuracy on average.

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%.

Nowadays, Flying Ad hoc NETworks (FANETs) which consists of multiple Unmanned Aerial Vehicles (UAVs) are widely used in various military and civilian applications which need different Quality of Service (QoS) guarantees, for example, low delay for safety packet and high throughput for service packet. Meanwhile, to provide high bandwidth and spatial reuse, directional antennas are equipped on the UAVs more and more. However, due to the high mobility of UAVs, how to support different QoS with directional antenna is challenging for Media Access Control (MAC) protocol of FANETs. Recently, multi-channel MAC protocol has been proved to be effective to support different QoS. In this paper, we propose a FANETs multi-channel MAC protocol called FM-MAC, which combines the advantages of multi-channel and directional antenna to provide different QoS guarantees. Firstly, a reservation scheme based on mobile prediction is proposed to address the link interruption issue brought by high mobility of UAVs. Secondly, we propose a preemption mechanism to provide priority for service packets. Simulation results show that compared with other two representative protocols, FM-MAC not only improves the throughput of service packets, but also achieves lower delay and higher reliability for safety packets.

Area coverage is one of the most common and critical applications for Flying Ad hoc Networks (FANETs), which typically involves a host of small or mini Unmanned Aerial Vehicles (UAVs). The increasingly large scale of FANETs brings challenges of networking and management, for which clustering scheme is an effective technique. However, there is a conflict between area coverage and network connectivity which is required for clustering and data transmission among UAVs. In this paper, we propose a MObility cOntrol based Clustering (MOOC) scheme for area coverage in FANETs. In MOOC, a mobility control strategy is designed for different types of nodes based on virtual forces, considering both reducing overlapped coverage and maintaining connections to improve clustering performances. The coverage and clustering performances of MOOC are analyzed and compared with other approaches through extensive simulations. The results show that MOOC outperforms the compared approaches by at least 37% and 235% respectively in terms of coverage efficiency while maintaining a high network connectivity. Meanwhile, MOOC also outperforms the other clustering algorithms by at least 503% on average in terms of cluster head duration.

In this paper, we investigate the effect of Multiple- User Multiple-Input Multiple-Output (MU-MIMO) enabled WiFi Access Point (AP) on the performance of traffic offloading in LTE-Unlicensed (LTE-U) networks. We first derive closed-form expressions for the downlink rates of both cellular user and offloaded user under limited Channel State Information (CSI) feedback, and obtain the sum-rate of the system. Then, we validate our analysis by numerical simulations and evaluate the system performance under different number of offloaded users and various CSI feedback lengths. The evaluation illustrates that there is a trade-off between the number of users offloaded to WiFi AP and the sum-rate of the system, and performance inflection point lies on the SNR conditions. Meanwhile, increasing CSI feedback length for the cellular Base Station (BS) or the WiFi AP alone helps increase the sum-rate of the system and rate of corresponding users, while it sacrifices the rate of users in the other network. These results suggest that adaptive utilization of antenna numbers on the MU-MIMO enabled AP based on SNR, and deciding CSI feedback length according to practical traffic demand of corresponding users are critical to achieve high rate performance for traffic offloading in LTE-U networks.

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.

CSMA/CA based MAC protocols, i.e., IEEE 802.11, make use of control messages and backoff scheme to avoid collision in distributed wireless networks. However, the channel utilization of 802.11 decreased dramatically with the development of advanced physical layer techniques, such as OFDM, since increasing data rate makes the control messages transmission and backoff time occupy larger proportion of the transmission period. To address this, we propose PD-MAC. Instead of control message, PD-MAC uses pulse signals to express the control information. Pulses can be transmitted and detected concurrently with the user data, thus the control information transmission and backoff time can be overlapped with user data transmission. Moreover, to make the pulse signal express enough information, PD-MAC utilizes the 2-dimensional feature of OFDM signals, that is, position of pulses in both time domain and frequency domain. We implement the physical layer prototype of PD-MAC with USRP N210 and evaluate MAC layer performance with trace-driven simulation. PD-MAC can achieve 95.4% channel efficiency and provide throughput gains of up to 71.6%, 33.4%, and 11.2% compared with 802.11 DCF, 802.11ec and back2F, respectively.

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.

The benefits of network coding on multicast in traditional multihop wireless networks have already been extensively demonstrated in previous works. However, most existing approaches cannot be directly applied to multihop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel's available bandwidth is time-varying and uncertain. Accordingly, the capacity of the link using that channel is also uncertain, which can significantly affect the network coding subgraph optimization and may result in severe throughput loss if not properly handled. In this paper, we study the problem of network coding-based multicast in multihop CRNs while considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computational intractability of the above-mentioned program, we then transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that the proposed algorithm achieves higher multicast rates, compared with a state-of-the-art non-network coding algorithm in multihop CRNs, and a conservative robust network coding algorithm that treats the link capacity as a constant value in the optimization.

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.

Cellular networks operators is facing one great challenge on how to manage the exponential data traffic increase. Mobile data offloading can be a low-cost and efficient way to relieve the pressure of cellular networks. Among various offloading techniques, device- to-device (D2D) communications has gained more and more attention. However, current studies mainly concentrated on traffic offloading between two user equipments directly, i.e., single-hop D2D and most of them are theoretic works. In this paper, with smartphones we build a multihop D2D communications platform which can expand the application scenarios of single-hop D2D communications. We utilize the platform to measure and analyze some important performances of multihop D2D communications and experiment results demonstrate the importance of multihop to D2D communications. The performances include energy consumption, network delay, coverage and link quality. We believe our work can provide a valuable reference to the works of multihop D2D communications based mobile data offloading and other related ones.

UAVs Ad-Hoc Networks has earned more and more attentions recently. How to design a collision-free MAC protocol which allows UAVs to access the networks rapidly and reliably is a crucial challenge. Many MAC protocols proposed for VANETs or UAVs Ad-Hoc Networks have some limitations, i.e., some ones need full duplex technique which is not very practical now. This paper propose a collision-free MAC protocol CF-MAC which allows the UAVs with half-duplex radio and omnidirectional antenas to rapidly access the networks and utilizes a region marking scheme to reduce the collision probability to near zero. Compare with VeMAC which is an outstanding MAC protocol for VANETs, simulations show that CF-MAC can get a 20% improvement in efficiency of channel access, and more importantly it reduce the collision probability to near zero.

We study the problem of supporting simultaneous query in WSNs. For such networks, the network efficiency can be significantly improved if the poller can obtain information via a simultaneous query. Two fundamental cases of such queries are studied in this paper: counting and identifying active neighboring nodes. We propose two mechanisms, Power based counting (Poc) and Power based identification (Poid), which achieve the goals by allowing neighbors to respond simultaneously to a poller. A key observation that motivates our design is that the power of a superposed signal increases with the number of component signals under the condition that the component signals are synchronized precisely. However, such high precision of synchronization is rather difficult to achieve in WSNs. To address this challenge, we design delicate delay compensation methods to reduce the phase offset of each component signal. Moreover, we propose a novel probabilistic estimation technique to overcome the hardware limitations on the observed received power. Poid currently works only in sparse networks though, we discuss and analyze the time complexity of applying Poid in dense networks. Experimental results show that the average accuracy of Poc and Poid is above 95 and 91 percent, respectively. In addition, our methods achieve substantially lower energy consumption and estimation delay compared with the state-of-the-art solutions.

Abstract The past few years have witnessed a surge of wireless mesh networks (WMNs)‐based applications and heterogeneous WMNs are taking advantage of multiple radio interfaces to improve network performance. Although many routing protocols have been proposed for heterogeneous WMNs, most of them mainly relied on hierarchical or cluster techniques, which result in high routing overhead and performance degradation due to low utilization of wireless links. This is because only gateway nodes are aware of all the network resources. In contrast, a unified routing protocol (e.g., optimal link state routing (OLSR)), which treats the nodes and links equally, can avoid the performance bottleneck incurred by gateway nodes. However, OLSR has to pay the price for unification, that is, OLSR introduces a great amount of routing overhead for broadcasting routing message on every interface. In this paper, we propose unified routing protocol (URP), which is based on passive bandwidth measurement for heterogeneous WMNs. Firstly, we use the available bandwidth as a metric of the unification and propose a low‐cost passive available bandwidth estimation method to calculate expected transmission time that can capture the dynamics of wireless link more accurately. Secondly, based on the estimated available bandwidth, we propose a multipoint relays selection algorithm to achieve higher transmission ability and to help accelerate the routing message diffusion. Finally, instead of broadcasting routing message on all channels, nodes running URP transmit routing message on a set of selected high bandwidth channels. Results from extensive simulations show that URP helps improve the network throughput and to reduce the routing overhead compared with OLSR and hierarchical routing. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Network coding (NC) can greatly improve the performance of wireless mesh networks (WMNs) in terms of throughput and reliability, and so on. However, NC generally performs a batch‐based transmission scheme, the main drawback of this scheme is the inevitable increase in average packet delay, that is, a large batch size may achieve higher throughput but also induce larger average packet delay. In this work, we put our focus on the tradeoff between the average throughput and packet delay; in particular, our ultimate goal is to maximize the throughput for real‐time traffic under the premise of diversified and time‐varying delay requirements. To tackle this problem, we propose DCNC, a delay controlled network coding protocol, which can improve the throughput for real‐time traffic by dynamically controlling the delay in WMNs. To define an appropriate control foundation, we first build up a delay prediction model to capture the relationship between the average packet delay and the encoding batch size. Then, we design a novel freedom‐based feedback scheme to efficiently reflect the reception of receivers in a reliable way. Based on the predicted delay and current reception status, DCNC utilizes the continuous encoding batch size adjustment to control delay and further improve the throughput. Extensive simulations show that, when faced with the diversified and time‐varying delay requirements, DCNC can constantly fulfill the delay requirements, for example, achieving over 95% efficient packet delivery ratio (EPDR) in all instances under good channel quality, and also obtains higher throughput than the state‐of‐art protocol. Copyright © 2014 John Wiley & Sons, Ltd.

The benefits of network coding on multicast in traditional multi-hop wireless networks have already been demonstrated in previous works. However, most existing approaches cannot be directly applied to multi-hop cognitive radio networks (CRNs), given the unpredictable primary user occupancy on licensed channels. Specifically, due to the unpredictable occupancy, the channel's bandwidth is uncertain and thus the capacity of the link using this channel is also uncertain, which may result in severe throughput loss. In this paper, we study the problem of network coding-based multicast in multi-hop CRNs considering the uncertain spectrum availability. To capture the uncertainty of spectrum availability, we first formulate our problem as a chance-constrained program. Given the computationally intractability of the above program, we transform the original problem into a tractable convex optimization problem, through appropriate Bernstein approximation together with relaxation on link scheduling. We further leverage Lagrangian relaxation-based optimization techniques to propose an efficient distributed algorithm for the original problem. Extensive simulation results show that, the proposed algorithm achieves higher multicast rates, compared to a state-of-the-art non-network coding algorithm in multi-hop CRNs, and a conservative robust algorithm that treats the link capacity as a constant value in the optimization.

Network coding has the potential to provide powerful support to transmit real-time traffic in wireless network. In this paper, we utilize pairwise coding to schedule the flows which have heterogeneous delay constraints and weights. Our goal is to maximize the weighted sum of scheduled packets that satisfy the delay constraints. We formulate the problem as an integer linear programming problem, and propose two algorithms to solve it. The first algorithm drops the integral constraints and rounds the fractional solutions in such a way that the rounded solution is also optimal. Inspired by the first algorithm and for better running time, we propose the second algorithm based on a minimum cost flow formulation. The formulation is proved to be equivalent to the original integer linear programming formulation. Simulations are conducted to show the effectiveness of our approach over two greedy algorithms.

Anypath routing can improve the end-to-end throughput by exploiting the broadcast nature of wireless communication media in wireless networks. Although anypath routing has been extensively studied in recent years, the problem of anypath routing in wireless networks with directional antennas has not been fully investigated. To the best of our knowledge, we are the first to study the joint problem of antenna direction scheduling and transmission rate selection for anypath routing in wireless networks with directional antennas. In this paper, we present a Directional antenna based Shortest Multirate Anypath routing algorithm (DSMA), which is proven to compute the optimal anypath routing strategy. We integrate our algorithm with a well-known opportunistic routing protocol MORE, and extensively evaluate its performance. Our evaluation results show that our algorithm can achieve at least 34.0% higher end-to-end throughput, in the median case; and at least 29.9% shorter mean end-to-end packet delay, than the single-rate anypath routing protocol.

In cognitive radio networks (CRNs), secondary users (SUs) may employ network coding to pursue higher throughput. However, because SUs should not interfere with high-priority primary users (PUs), the available transmission time of SUs is usually uncertain, i.e., SUs do not know how long the idle state can last. Meanwhile, existing network coding strategies generally adopt a block-based transmission scheme, implying that all packets in the same block can be decoded simultaneously only with enough coded packets collected. Therefore, the gain induced by network coding may be dramatically decreased once a block cannot be decoded due to the arrival of PUs. In this paper, for the first time, we develop an efficient network coding strategy for SUs while considering the uncertain idle durations in CRNs. To handle the uncertainty of SUs' available transmission time, we first consider how to estimate the length of idle duration. For the case where the length of idle duration is stochastic, we employ confidential interval estimation (CIE) method to estimate the expected length of the idle duration. For the non-stochastic case, we utilize multi-armed bandit (MAB) to determine the idle durations sequentially. After obtaining the estimated length, we further adopt systematic network coding (SNC) in the data transmission of SUs. We find that SNC is more suitable for SUs' transmission than the general block-based network coding in the sense that it can reduce average perpacket delay without decreasing the throughput gain. However, the block size (also the proportion of uncoded packets to be sent) of SNC is hard to determine, due to the complicated correlation among the receptions at different receivers. To solve this problem, we propose an optimal block size selection algorithm for SNC (OSNC) to determine the transmission proportion of uncoded packets, under a given idle duration length. Due to its low computational complexity, OSNC can be used to make an online decision on the optimal block size with small delay. Simulation results show that, compared to traditional block-based network coding and plain retransmission schemes, our proposed scheme achieves highest performance for both stochastic and non-stochastic idle durations.

Unsegmented network coding (UNC) is a promising technology to overcome poor source information scheduling of segmented network coding (SNC) in large scale networks, where unresponsive feedback slacks the scheduling. However, three unsolved problems limit its applicability. First, UNC employs ACK on witness as the feedback. The frequently triggered witness-ACK will introduce considerable overhead and depress throughput. Second, a new constraint from practical decoding requirement on the slide window has recently been proved. The additional constraint will make UNC much different, which has not been studied. Third, although UNC may outperform SNC in large scale networks, it does not work well in small and moderate-sized networks, exhibiting poor universality. In this paper, we address these problems and propose the Adaptive unsegmented Network Coding (ANC). ANC applies technologies for improvements, which is derived through reinvestigating UNC (solve the second problem), to improve achievable throughput, and save a majority of control overhead (solve the first problem). In addition, ANC incorporates a novel hybrid source packets admission scheme and can well adapt to various network conditions (solve the third problem). Simulation results show that ANC outperforms both SNC and UNC in universal network conditions, and the throughput gain over both can be up to 22%.

One fundamental question in Wireless Rechargeable Sensor Networks (WRSNs) is the energy provisioning problem, i.e., how to deploy energy sources in a network to ensure that the nodes can harvest sufficient energy for continuous operation. Though the potential mobility of nodes has been exploited to reduce the number of sources necessary in energy provisioning problem in existing literature, the non-negligible impacts of the constraints of node speed and battery capacity on energy provisioning are completely overlooked, in order to simplify the analysis. In this paper, we propose a new metric - Quality of Energy Provisioning (QoEP) - to characterize the expected portion of time that a mobile node can sustain normal operation in WRSNs, which factors in the constraints of node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. We investigate the upper and lower bounds of QoEP in one-dimensional case with one single source and multiple sources respectively. For single source case, we prove the tight lower bound and upper bound of QoEP. Extending the results to multiple sources, we obtain tight lower bound and relaxed upper bound in normal cases, together with tight upper bound for one special case. Moreover, we give the tight lower bounds in both 2D and 3D cases. Finally, we perform extensive simulations to verify our findings. Simulation results show that our bounds perfectly hold, and outperform the former works.

Wireless mesh networks (WMNs) are taking advantage of multiple radio interfaces to improve network performance, and numerous routing protocols have been proposed for WMNs. However, these routing protocols can not perform well in the heterogeneous multi-radio environment with distinct bandwidth difference, for little attention has been paid to routing overhead balancing. To address this issue, we present a routing protocol called URP (unified routing protocol) which cooperates with distinct bandwidth difference and makes appropriate use of networks to achieve the best performance. In WMNs, a large number of routing messages spread in different channels are duplicated, considering this nodes running URP select a high bandwidth channel set to forward routing messages rather than broadcasting them in each channel. On the one hand, this helps reduce the routing overhead and economize the resource of low bandwidth channels. On the other hand, the low bandwidth channels can contribute their limited resource to improving the throughput. In addition, an advanced expected transmission time (ETT) estimation method accounting for node mobility is implemented in URP, it helps URP considers both the variation of link quality and channel bandwidth to select a route. Simulations show that URP considerably reduces the routing overhead by 25% in heterogeneous WMNs with distinct bandwidth difference compared with OLSR. Moreover, the reduced resource consumption and the advanced ETT estimation method help improve the network throughput.

Unsegmented network coding was incorporated into opportunistic routing to improve inefficient schedule of segmented network coding due to delayed feedback. However, most unsegmented network coding schemes fail to constrain the size of decoding window. Large decoding window not only challenges limited computational ability and decoding memory in practical environments, but also introduces large decoding delay, which is undesirable in delay sensitive applications. In this paper, we prove that the possibility of unacceptably large decoding window is innegligible, and verify the necessity of handling decoding window. To solve the issue, we argue that the number of unknown packets injected into the network must be strictly constrained at the source. Based on the idea, we improve unsegmented network coding scheme for opportunistic routing. Simulation results shows that the solution is robust to losses. In addition, to achieve optimal throughput, the redundancy factor should be selected larger than the reciprocal of end-to-end delivery ratio.

In the past few decades, wireless networks based on different standards have been developed quickly, and the coexistence of multiple communication systems has turned into reality. Due to the need for mobile computing or pervasive computing, quick and accurate protocol identification and analysis technology is required. Rather than employing the traditional demodulation-based method that requires to implement all known protocol modules in one single device, in this paper, we fully analyze the features in both time domain and frequency domain of physical (PHY) layer signals that can be used to describe a protocol, and present a new method of identifying and analyzing protocols without demodulation, which uses PHY layer signals only. This method can be used in battlefield and other situations where demodulation is impractical. To validate the feasibility of our method, we implement a system using GNU Radio and Universal Software Radio Peripheral (USRP). The results show that the system can successfully identify three different signals (including Wi-Fi, Bluetooth and Zigbee signals) with frequency domain features, and detect the period of beacons in Wi-Fi networks with the help of time domain features.

A Flow Driven MAC Protocol(FD-MAC) is a slot based MAC protocol which is designed for long distance wireless multihop transmission. With the flow-driven resource reservation mechanism, FD-MAC protocol is especially suitable to be used for wireless ad hoc network with dynamic traffic pattern. We argue that the Flow Classification Threshold(FCTs) of the protocol are the key parameters to the performance of the protocol,which optimal values are not yet been fully investigated. Then the value of the parameters are analyzed theoretically, and the optimal values are given in turn. Simulation result validates our analysis, best performance of the protocol would be anticipated when optimal FCT values are chosen.

Cyber-Physical Systems require the integration of various heterogeneous networks. To evaluate proposed algorithms in network research, real-world test beds are an indispensable complement to simulations. In this paper, we present a flexible heterogeneous mesh networking platform (FHMESH) which interconnects various heterogeneous networks, i.e. wireless sensor network, wireless mesh network, FM radio network and the Internet. FHMESH builds agile and universal gateways using software defined radio technology, and the gateways form mesh backbone to interconnect those heterogeneous networks. Then, we present the platform architecture and the specific implementation details for FHMESH, and carry out some preliminary experiments. Even though the testing results of our system are not so perfect, we believe that the integration of various heterogeneous networks makes FHMESH work as a prototype for heterogeneous network research, and the flexible and universal gateway can facilitate the cross-layer research.

WANETs is a recent network architecture where the nodes are spread all over the world but behave exactly as if they are part of a single-hop or multi-hop wireless networks at the PHY and MAC layers. Without distinguishing data packets and noise, the Software Defined Access Point (SoDA) samples the wireless channel for the uplink and multicasts the sampled data via Internet to other SoDAs. This leads to tremendous traffic load on the Internet. In this paper, we use energy detection to address this issue. Specifically, we propose EDDD to aim at reducing the traffic load on the Internet under the condition that dropping data packet as few as possible. Through extensive experiments on IEEE 802.11 and IEEE 802.15.4, we validate the feasibility and effectiveness of EDDD.

Quality-based routing protocols are proposed to restrict message flooding within only high quality nodes in delay tolerant networks (DTNs). However, different quality threshold mechanisms have diverse impact on the network and we investigate this issue in the paper. Through theoretical analysis we show that heterogeneous threshold mechanism suffers a severe nodal cost imbalance problem. Furthermore, for both homogeneous and heterogeneous mechanisms, routing cost depends on the total number of nodes, and becomes significant in large-scale networks. To address these issues, we propose Constant Cost Quality-based Routing (CCQR) protocol, which not only retains the favorable features of quality-based routing protocols, but also achieves a constant routing cost. More importantly, thanks to the wise message distribution rules, CCQR protocol is capable of alleviating nodal cost imbalance problem effectively. We also conduct extensive simulations to verify our conclusions and the efficacy of the new protocol.