Joint Deployment Research Articles

Joint Deployment of Truck-Drone Systems for Camera-Based Object Monitoring

Joint Deployment of Truck-Drone Systems for Camera-Based Object Monitoring

Planning Cost-Efficient FiWi Access Network With Joint Deployment of FWA and FTTH

Planning Cost-Efficient FiWi Access Network With Joint Deployment of FWA and FTTH

Efficient Joint Deployment of Multi-UAVs for Target Tracking in Traffic Big Data

Efficient Joint Deployment of Multi-UAVs for Target Tracking in Traffic Big Data

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation

Abstract. Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of −0.13 and −1.05 m s−1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 m s−1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 m s−1, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 m s−1 and between 7 and 13.3 m s−1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 m s−1 and an error of ∼ 1.5 m s−1, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (−0.18 ± 1.52 m s−1) than in the winter (0.63 ± 2.07 m s−1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

AbstractThis paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

QoE‐driven multi‐UAV deployment scheme for emergency communication networks

AbstractWhen ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)‐mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line‐of‐sight and non‐line‐of‐sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs.

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Joint Deployment and Resource Allocation for Service Provision in Multi-UAV-Assisted Wireless Networks

Joint Deployment and Resource Allocation for Service Provision in Multi-UAV-Assisted Wireless Networks

On Robust Optimal Joint Deployment and Assignment of RAN Intelligent Controllers in O-RANs

On Robust Optimal Joint Deployment and Assignment of RAN Intelligent Controllers in O-RANs

Joint fiber and MEC deployment for sparsely populated areas

The deployment of multi-access edge computing (MEC) networks gives rise to the MEC placement problem, which deals with finding the right server locations to reduce the cost and guarantee network performance. Multiple papers have been presented to solve this problem, but they are usually oriented to urban areas where short distances and high-quality network infrastructure are assumed. When this problem must be solved for sparsely populated areas, like rural environments, the connectivity is not always granted and the deployment of such connectivity using fiber technologies should be included in the problem. In contrast to urban areas, where the density of users is high and therefore the main problem is capacity, in sparsely populated areas, the problem lies in how to cost-effectively plan the MEC sites and the interconnecting network while meeting the delay constraints of the services offered through that network. This paper proposes a technique to solve the MEC placement problem considering the joint deployment of the optical network required to interconnect the base stations and the MEC servers. It consists of a three-phase scheme, which combines a spanning tree topology, for fiber deployment, with the use of mixed integer linear programming (MILP) formulations to minimize MEC servers and MEC data centers (MEC-DCs). We have applied the technique in a case study for a province in Spain (Valladolid, 8110km2 ), obtaining a reduction of around 50% of the total cost when compared to a previous work. In addition, a clustering method is proposed to improve the scalability of the model for large scenarios. A simulation study is also presented to demonstrate the performance of the proposal assuming a 94,226km2 region (Castilla y León) with 1576 base stations.

Enhanced performance of an indoor non-line-of-sight VLC system utilizing a multi-pixel photon counter and interleaved single-carrier FDM scheme

A non-line-of-sight (NLOS) visible light communication (VLC) system, utilizing a multi-pixel photon counter (MPPC) as the detector and interleaved single-carrier frequency division multiplexing (I-SC-FDM) scheme, was first proposed and experimentally investigated in this work. Specifically, the I-SC-FDM scheme exhibits a significantly lower peak-to-average power ratio (PAPR) than the original orthogonal frequency division multiplexing (OFDM) and widely used discrete Fourier transform spread (DFTs)-OFDM schemes, thereby necessitating a much lower power gain, which is greatly beneficial for indoor power-sensitive VLC systems. Meanwhile, the implementation of an MPPC with high sensitivity enables the detection of faintly scattered light and thus facilitates the possibility of omnidirectional optical transmission based on indoor NLOS links. Following this, an MPPC-based omnidirectional VLC system was developed and successfully achieved a net data rate up to 18 Mbps in a 3.6 m × 3 m × 3 m room, with a joint deployment of the I-SC-FDM scheme and hybrid time-frequency domain equalization (TFD-EQ) strategy. Such results can be viewed as an expansion of indoor VLC systems beyond line-of-sight (LOS) links and serve as a reference for the practical application of Light-Fidelity (LiFi) in future optical wireless access.

Unlocking the nexus potential: A techno-economic analysis of joint deployment of minigrids with smallholder irrigation

Sub-Saharan Africa (SSA) has the lowest rate of electricity access and irrigation in the world, at 45% and 5% respectively. The problem is in part due to the low economic viability of rural electrification, particularly through grid extension, lack of low-cost irrigation energy, and limited access to finance for the high cost of irrigation infrastructure. Despite their promising potential, minigrids’ deployment has been rather slow in SSA, partly due to the lack of viable business models for the dispersed low-income rural population. In this paper, we present an integrated model that couples a minigrid model with a crop model to explore the nexus potential of joint deployment of minigrids with smallholder irrigation to alleviate barriers to both minigrid scale-up and irrigation adoption. We apply the proposed model to three rural case studies in different geographical contexts in SSA. Our simulation results show the potential of irrigation loads to lower the unit cost of minigrid energy (LCOE) by up to 47%, depending on the quantity of irrigable land and its spatial distribution. A further scenario-based sensitivity analysis of the minigrid with and without aggregate irrigation load shows wide variations in LCOE–between a minimum of $0.24/kWh and a maximum of $1.2/kWh–with changes in the cost of capital and technology cost. Regarding irrigation viability, our model results show that irrigation profitability is a function of a complex interplay of factors, including crop type, soil fertility, infrastructure costs, energy prices, fertiliser application rate, and crop farm gate prices. We further test the effects of pay-as-you-go (PAYGo) business model incentives on the profitability of irrigation with minigrids; our results show that various incentives that alleviate either all or a fraction of upfront capital cost do not only mitigate irrigation adoption barrier but also enhances its profitability on the part of the farmer. Finally, our model results underscore that the economic viability of joint deployment of minigrids with smallholder irrigation depends on the optimal combination of agro-ecological factors of production (location suitability), type of crop (on a descending order, onion, tomato, and potato are the most profitable crops in our model), fertiliser application rate (near-optimal to optimal soil fertility is recommended) and cost scenarios (low cost of capital and technology preferred). In the main, our model and the results thereof highlight the potential benefits of co-planning minigrids and irrigation projects. Our model and results presented in this paper can be used as high-level decision-making pointers on the nexus planning of minigrids with smallholder irrigation farming.

USV Search Mission Planning Methodology for Lost Target Rescue on Sea

Quick and efficient mission planning is essential in maritime search and rescue (SAR). This includes defining the search area and developing an effective strategy. The task is fraught with challenges due to the difficulty of determining location information and the impact of complex meteorological environments. The primary objective of SAR mission planning is the rapid deployment of unmanned surface vehicles (USVs) to the incident area. While many planning algorithms prioritize the shortest route, there’s a lack of mission planning measures that maximize SAR effectiveness. In addition, the joint deployment of USVs increases the success rate compared to individual operations. Therefore, this paper presents a task assignment framework for USVs in SAR missions that considers the probability of success and time constraints. USVs are used to search for lost targets, and the framework consists of the following three modules: (1) a module for predicting the location of the overboard target to be rescued; (2) a module for modeling the probability of mission success; (3) a module for assigning search tasks to USVs. The framework first analyzes the search area. Then, it predicts the target location with a stochastic particle method, which incorporates marine environment forecast data to update the mission target location. To improve the scientific nature of USV search and rescue mission plans, an evaluation model is developed to assess mission capability. Simulation experiments and task scheme analysis validate its effectiveness.

Joint Deployment and Request Routing for Microservice Call Graphs in Data Centers

Joint Deployment and Request Routing for Microservice Call Graphs in Data Centers

Joint Deployment and Beamforming Design for STAR-RIS Aided Communication

Joint Deployment and Beamforming Design for STAR-RIS Aided Communication

Joint Deployment Design and Phase Shift of IRS-Assisted 6G Networks: An Experience-Driven Approach

The performance of wireless networks is constrained by the dynamic and random nature of the wireless channels. Intelligent reflecting surface (IRS) is a promising approach that can smartly reconfigure wireless propagation environment to increase the spectral efficiency in 6G networks. However, IRS deployment optimization in a complex and random 6G environment remains a limiting factor in improving the performance. To address the issue, we propose a deep reinforcement learning (DRL) network empowered by a generative adversarial network (GAN) to jointly optimize the IRS placement and reflecting beamforming matrix of IRS as well as the transmit beamforming at the base station (BS) in an IRS-assisted wireless network. Simulation results show that the proposed technique outperforms the benchmark scheme in terms of achievable rate and signal-to-noise ratio (SNR) by learning the optimal IRS locations in an IRS-aided wireless network.

Integration of hybrid score functions to design new adaptive EWMA control chart to monitor the process mean shift

Small-to-moderate shifts in the process parameters can be detected using the traditional Exponentially Weighted Moving Average (EWMA) control chart. The classical EWMA control chart's parameter helps only to identify the specific size of the shift. However, various shifts occur in real-world processes, so the conventional EWMA control chart performs inefficiently for off-target shifts. This problem has been confronted by applying an adaptive EWMA (AEWMA) control chart, an advanced version of the classical EWMA control chart. The score functions are utilized to design the AEWMA control chart. Generally, the Huber and Bi-square (score) functions have unique characteristics, such as monotone and redescended, respectively.There has not been any investigation into the simultaneous and/or joint deployment of score functions. So, by taking this point as motivation, this study aims to propose a new score function by blending the Huber and Bi-square characteristics into one hybrid formulation and then utilize it to design a new AEWMA control chart to monitor different sizes of the shift in the process location parameter. To evaluate the proposed control chart performance against other control charts, Monte Carlo simulation is applied to obtain the run length properties under zero-state and steady-state environments. Performance measures, such as average run length, standard deviation of run length, and median of run length, are considered to evaluate the performance at a single shift. In contrast, extra quadratic loss, relative average run length, and performance comparison index are utilized to evaluate effectiveness for a specific range of shifts. The comparison based on performance measures and visual presentation reveals the superiority of the proposed control chart. Besides, the proposed control chart is employed in the real-world chemical engineering process industry problem, demonstrating excellent detection ability of anomalies in air pollutant data. The comparison based on performance measures and visual presentation reveals the good performance of the proposed control chart.

Joint Deployment and Coverage Path Planning for Capsule Airports with Multiple Drones

Due to the advantages of low cost and high flexibility, drones have been applied to urban surveillance, vegetation monitoring, and other fields with the need for coverage of regions. To expand UAVs’ coverage, we designed the Capsule Airport (CA) to recharge and restore drones and provide take-off and landing services. Meanwhile, the combination of drones’ coverage path planning (CPP) and the deployment of CAs is a crucial problem with few relevant studies. We propose a solution approach to the CPP problem based on selecting scanning patterns and trapezoidal decomposition. In addition, we construct a 0–1 integer programming model to minimize the cost of the distance between CAs and the scanning missions. Specifically, a solution approach based on greedy and clustering heuristics is designed to solve this problem. Furthermore, we then develop a local-search-based algorithm with the operators of CA location exchange and drone scanning mission exchange to further optimize the solution. Random instances at different sizes are used to validate the performance of proposed algorithms, through which the sensitivity analysis is conducted with some factors. Finally, a case study based on the Maolichong forest park in Changsha, China, is presented to illustrate the application of the proposed method.

Joint Trajectory Planning, Service Function Deploying, and DAG Task Scheduling in UAV-Empowered Edge Computing

Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0–1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings.