This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), in which one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC) and then DC transmits the processed data back to a group of ground users to fulfill their diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Sensor Association Information (AomaI) metric by incorporating the multi-source and up-downlink aspects. Under this framework, we formulate the optimization problem aiming to minimize the average AomaI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose parallel optimization and primal-dual methods to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, then develop the data processing and results distribution scheduling strategies for the DC, and lastly propose the task-associated genetic algorithm (TAGA) and the improved Nawas-Enscore-Ham (INEH) algorithm to design the UAV’s visiting order. Simulation results demonstrate that uplink and downlink AoI influence each other, and the consideration of up-downlink AoI can effectively enhance the freshness of AomaI.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), where one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC), intending to fulfill the users’ diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Source Information (AomsI) metric. Under this framework, we formulate the optimization problem aiming to minimize the average AomsI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose the parallel optimization method to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, and then propose the task-associated genetic algorithm (TAGA) to design the UAV’s visiting order. Simulation results demonstrate that the UAV tends to prioritize the collection of all SN data required by the same user during data collection.

Recent research in price-based Mobile Edge Computing (MEC) has predominantly aimed at maximizing edge server revenue by efficiently allocating computing resources. While this operational approach has certainly strengthened the edge computing industry, the practical deployment scenario's impact on server hardware lifespan has not been fully explored. In this paper, we introduce a novel server pricing strategy that accounts for the influence of CPU temperature on the server's longevity, all while ensuring the quality of service requirements for time-sensitive User Equipments (UEs). Leveraging these insights, we establish an MEC system model with a single MEC server and multiple UEs, which we then formulate as a Stackelberg game. We derive two closed-form solutions considering various UEs' conditions, closely aligning with the expectations of time-sensitive UEs. Our simulation results showcase the effectiveness of our proposed method in safeguarding hardware equipment while minimizing revenue loss.

In response to the escalating data volumes and the pressing need for reduced network latency in Mobile Edge Computing (MEC), this paper delves into the sphere of edge computing. Many MEC platforms are turning to container-based virtualization and leveraging image layering to cut down transmission costs. Amid this shift, applications are growing more complex, composed of multiple tasks and demanding diverse execution environments. However, existing research has mainly concentrated on task scheduling within edge systems, sidelining the vital aspect of preparing runtime environments on MEC servers. To bridge this gap, our paper tackles task scheduling complexities, including data and layer dependencies. It offers an integrated solution that optimizes task scheduling and layer loading across MEC servers, all with the goal of minimizing the total computation completion time. To address this NP-hard problem, we present a heuristic task-scheduling algorithm rooted in the genetic algorithm (GA) and complement it with an in-depth exploration of layer-loading policies. Our experiments conclusively demonstrate the substantial reduction in total computation completion time achievable through this approach.

Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.

The giant crab fishery in southeast Australia currently suffers from a lack of accurate size and sex data to establish population dynamics essential for the management of the industry. Determining these traits manually on boats or from observers using video would be time-consuming and prone to observer error. This research aims to find an efficient, accurate, and fast way to identify giant crabs' gender to eliminate the manual cost and operation time. This problem can be solved by artificial intelligence technologies, particularly Convolutional Neural Networks (CNN). However, CNNs can detect the crabs but find it challenging to identify their genders from the top view (carapace). Other issues include the lack of training data and the demand for compact system to be deployed on boat easily. With such constraints of effectiveness and efficiency, we address the problem of crab gender classification by proposing a cascading architecture. First, we simplify a light-weight object detection model (MobileNet) for carapace localisation. After that our model extract the carapace area on crab images for classification modelling. According to our experiments' results, with the use of light-weight CNNs for our cascading architecture, we achieved the highest accuracy of up to 96.34% with an efficiency of around 2 frames per second in a Raspberry Pi V4.

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless sensor network, in which one UAV flies in the sky to collect the data transmitted from a set of ground nodes (GNs) via distributed beamforming. We consider two scenarios with delay-tolerant and delay-sensitive applications, in which the GNs send the common/shared messages to the UAV via adaptive- and fixed-rate transmissions, respectively. For the two scenarios, we aim to maximize the average data-rate throughput and minimize the transmission outage probability, respectively, by jointly optimizing the UAV’s trajectory design and the GNs’ transmit power allocation over time, subject to the UAV’s flight speed constraints and the GNs’ individual average power constraints. However, the two formulated problems are both non-convex and thus generally difficult to be optimally solved. To tackle this issue, we first consider the relaxed problems in the ideal case with the UAV’s flight speed constraints ignored, for which the well-structured optimal solutions are obtained to reveal the fundamental performance upper bounds. It is shown that for the two approximate problems, the optimal trajectory solutions have the same multi-location-hovering structure, but with different optimal power allocation strategies. Next, for the general problems with the UAV’s flight speed constraints considered, we propose efficient algorithms to obtain high-quality solutions by using the techniques from convex optimization and approximation. Finally, numerical results show that our proposed designs significantly outperform other benchmark schemes, in terms of the achieved data-rate throughput and outage probability under the two scenarios. It is also observed that when the mission period becomes sufficiently long, our proposed designs approach the performance upper bounds when the UAV’s flight speed constraints are ignored.

Different from public 4G/5G networks that are dominated by downlink (DL) traffic, emerging 5G non-public networks (NPNs) need to support significant uplink (UL) traffic to enable emerging applications such as industrial Internet of things (IIoT). The UL-DL spectrum sharing is becoming a viable solution to enhance the UL throughput of NPNs, which allows NPNs to perform the UL transmission over the time-frequency resources configured for DL transmission in coexisting public networks. To deal with the severe interference from the DL public base station (BS) transmitter to the coexisting UL non-public BS receiver, we propose an adaptive asymmetric successive interference cancellation (SIC) approach, in which the non-public BS is enabled to have the capability of decoding the DL signals transmitted from the public BS and cancelling them for interference mitigation. In particular, this paper studies a basic UL-DL spectrum sharing scenario when a UL non-public BS and a DL public BS coexist in the same area, each communicating with multiple users via orthogonal frequency-division multiple access (OFDMA). Under this setup, we aim to maximize the common UL throughput of all non-public users, under the condition that the DL throughput of each public user is above a certain threshold. The decision variables include the subcarrier allocation and user scheduling for both non-public and public BSs, the receiver mode of the non-public BS over subcarriers, as well as the rate and power control. Numerical results show that the proposed design significantly improves the common UL throughput as compared to benchmark schemes without such consideration.

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, in which the UAV is placed at a fixed but optimizable location during the communication period. In this scenario, we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, in which the UAV has a mission to fly from an initial location to a final location during the communication period. In this scenario, we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors, e.g., under colluding eavesdroppers, the UAV should fly farther apart from them (than that under the non-colluding ones) for avoiding their collaborative interception.

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi- point (CoMP) reception, in which one UAV sends confidential messages to a set of distributed ground nodes (GNs) that can cooperate in signal detection, in the presence of several colluding suspicious eavesdroppers. Different from prior works considering the two-dimensional (2D) horizontal trajectory design in the non-CoMP scenario, this paper additionally exploits the UAV's vertical trajectory (or altitude) control for further improving the secrecy communication performance with CoMP. In particular, we jointly optimize the three dimensional (3D) trajectory and transmit power allocation of the UAV to maximize the average secrecy rate at GNs over a particular flight period, subject to the UAV's maximum flight speed and maximum transmit power constraints. To solve the non-convex optimization problem, we propose an alternating-optimization-based approach, which optimizes the transmit power allocation and trajectory design in an alternating manner, by convex optimization and successive convex approximation (SCA), respectively. Numerical results show that in the scenario with CoMP reception, our proposed 3D trajectory optimization significantly outperforms the conventional 2D horizontal trajectory design, by exploiting the additional degree of freedom in vertical trajectory.

This paper considers the secrecy transmission in a large-scale unmanned aerial vehicle (UAV)-enabled wireless network, in which a set of UAVs in the sky transmit confidential information to their respective legitimate receivers on the ground, in the presence of another set of randomly distributed suspicious ground eavesdroppers. We assume that the horizontal locations of legitimate receivers and eavesdroppers are distributed as two independent homogeneous Possion point processes (PPPs), and each of the UAVs is positioned exactly above its corresponding legitimate receiver for efficient secrecy communication. Furthermore, we consider an elevation-angle-dependent line-of-sight (LoS)/non-LoS (NLoS) path-loss model for air-to-ground (A2G) wireless channels and employ the wiretap code for secrecy transmission. Under such setups, we first characterize the secrecy communication performance (in terms of the connection probability, secrecy outage probability, and secrecy transmission capacity) in mathematically tractable forms, and accordingly optimize the system configurations (i.e., the wiretap code rates and UAV positioning altitude) to maximize the secrecy transmission capacity, subject to a maximum secrecy outage probability constraint. Next, we propose to use the secrecy guard zone technique for further secrecy protection, and analyze the correspondingly achieved secrecy communication performance. Finally, we present numerical results to validate the theoretical analysis. It is shown that the employment of secrecy guard zone significantly improves the secrecy transmission capacity of this network, and the desirable guard zone radius generally decreases monotonically as the UAVs’ and/or the eavesdroppers’ densities increase.

This paper studies the design and secrecy performance of linear multihop networks, in the presence of randomly distributed eavesdroppers in a large-scale 2-D space. Depending on whether there is feedback from the receiver to the transmitter, we study two transmission schemes: an ON–OFF transmission (OFT) and a non-ON–OFF transmission (NOFT). In the OFT scheme, transmission is suspended if the instantaneous received signal-to-noise ratio (SNR) falls below a given threshold, whereas, there is no suspension of transmission in the NOFT scheme. We investigate the optimal design of the linear multiple network in terms of the optimal rate parameters of the wiretap code as well as the optimal number of hops. These design parameters are highly interrelated, since more hops reduce the distance of per-hop communication, which completely changes the optimal design of the wiretap coding rates. Despite the analytical difficulty, we are able to characterize the optimal designs and the resulting secure transmission throughput in mathematically tractable forms in the high SNR regime. Our numerical results demonstrate that our analytical results obtained in the high SNR regime are accurate at practical SNR values. Hence, these results provide useful guidelines for designing linear multihop networks with targeted physical layer security performance.

In this paper, we consider the secure connection problem in multihop wireless networks with full-duplex (FD) jamming relaying, where the colluding eavesdroppers are randomly distributed following a homogeneous Poisson point process (PPP). By applying FD, each legitimate node (including relay and destination) jams the eavesdroppers when it receives the desired signal from transmitter. We adopt the end- to- end secure connection probability (SCP) as a secrecy metric to characterize the physical layer security performance. We first derive the exact expression of SCP for any given path. Then, an approximation of the SCP is proposed to facilitate efficient secure routing by using a revised Bellman- Ford algorithm. We show that a notable performance gain can be achieved by the proposed scheme compared to the half-duplex (HD) scheme, if the self- interference can be well canceled. Simulation results verify our theoretical analysis.

In this paper, we study the problem of secure routing in a multihop wireless ad-hoc network in the presence of randomly distributed eavesdroppers. Specifically, the locations of the eavesdroppers are modeled as a homogeneous Poisson point process (PPP) and the source-destination pair is assisted by intermediate relays using the decode-and-forward (DF) strategy. We analytically characterize the physical layer security performance of any chosen multihop path using the end-to-end secure connection probability (SCP) for both colluding and noncolluding eavesdroppers. To facilitate finding an efficient solution to secure routing, we derive accurate approximations of the SCP. Based on the SCP approximations, we study the secure routing problem, which is defined as finding the multihop path having the highest SCP. A revised Bellman–Ford algorithm is adopted to find the optimal path in a distributed manner. Simulation results demonstrate that the proposed secure routing scheme achieves nearly the same performance as exhaustive search.

UWB over fiber (UWBoF) technique has been proposed to increase the area of coverage for UWB communication systems. In this paper, the transmission performance of impulse UWB signals over optical fiber is analyzed. Three types of UWB signals generated based on three different techniques are considered. Since optical signals with different optical spectra would have different tolerances to fiber dispersion, the transmission performance of the three types of UWB signals is studied. First, the impact of fiber chromatic dispersion on UWB waveforms and their spectra is evaluated. Then, the transmission performance of data-modulated UWB signals in an optical fiber is investigated, with a general model to analyze the signal power spectral density (PSD) being developed. The PSD of an UWB signal with on-off keying (OOK), bi-phase modulation (BPM) and pulse position modulation (PPM) schemes is calculated. Evolution of the PSD as a function of transmission distance is then performed. The suitability of the three types of UWB signals for UWBoF applications is also evaluated. The study provides a guideline for the design and development of a practical UWBoF system.

This paper gives an introduction to three perspectives on formal specification review. These three perspectives on specification review are: (1) data-oriented review, (2) process-oriented review, (3) scenario-oriented review. For every perspective, the critical properties to review and the corresponding review methods are put forward. This technique is applied to the SOFL specification language, which is an integrated formalism of VDM, Petri nets, and data flow diagrams, to discuss how to review each of these perspectives.