Deployment Location Research Articles

A Decision for Throughput Optimization in UAV-Enabled Emergency Outdoor–Indoor Fairness Communication

This paper investigates the throughput optimization strategy in an unmanned aerial vehicle (UAV)-enabled emergency outdoor–indoor fairness communication scenario, with the UAV as a mobile relay station in the air, to provide outdoor–indoor communication services for users inside buildings. The occurrence of severe signal fading caused by outdoor transmission loss through wall loss as well as indoor transmission loss when the UAV forwards the information to the indoor users reduces the channel gain and degrades the system downlink throughput. To improve the downlink throughput of the system and ensure communication fairness for indoor users, we designed a joint UAV location deployment and resource allocation (JLRO) algorithm that optimized UAV three-dimensional (3D) deployment location, power and bandwidth resource allocation. The simulation results demonstrate the convergence and validity of the proposed JLRO algorithm, as well as its superiority compared to benchmark algorithms.

Charger and receiver deployment for trajectory coverage with delay constraint in mobile wireless rechargeable sensor networks

Wireless Rechargeable Sensor Networks (WRSNs) powered by radio-frequency radiation attract a lot of attention for their sustainability and convenience, etc. One focus is how to rationally deploy the key devices, such as receiver and charger, to improve the network performance and deployment budget. This paper considers a novel deployment problem for WRSNs in a two-dimensional space with static trajectories, deploying the minimum number of wireless chargers and receivers for mobile nodes, subject to the non-overtime updating probability requirement for mobile node at any location of the trajectories. This timeliness communication problem is tricky and NP-hard when involving the influence of three practical delays (retransmission delay, charging delay and competition delay). Therefore, a performance controllable discretization strategy is proposed, based on which the Greedy Heuristic (GH) and Particle Swarm Optimization (PSO) based approximate solutions are introduced. The execution process and time complexity of these two solutions are analyzed, and then the numerical results of extensive simulations are introduced to verified the scalability, accuracy and efficiency of the two solutions. As a result, the GH solution has better deployment success rate and efficiency, and is the recommended solution for the novel problem. Finally, two real-life scenarios (indoor exhibition hall and hospital interior road) are introduced to further verify the robustness of the two solutions under the practical challenges of uneven density of mobile nodes, restricted deployment locations and occlusion of wireless links.

An economic evaluation of Wolbachia deployments for dengue control in Vietnam.

Dengue is a major public health challenge and a growing problem due to climate change. The release of Aedes aegypti mosquitoes infected with the intracellular bacterium Wolbachia is a novel form of vector control against dengue. However, there remains a need to evaluate the benefits of such an intervention at a large scale. In this paper, we evaluate the potential economic impact and cost-effectiveness of scaled Wolbachia deployments as a form of dengue control in Vietnam-targeted at the highest burden urban areas. Ten settings within Vietnam were identified as priority locations for potential future Wolbachia deployments (using a population replacement strategy). The effectiveness of Wolbachia deployments in reducing the incidence of symptomatic dengue cases was assumed to be 75%. We assumed that the intervention would maintain this effectiveness for at least 20 years (but tested this assumption in the sensitivity analysis). A cost-utility analysis and cost-benefit analysis were conducted. From the health sector perspective, the Wolbachia intervention was projected to cost US$420 per disability-adjusted life year (DALY) averted. From the societal perspective, the overall cost-effectiveness ratio was negative, i.e. the economic benefits outweighed the costs. These results are contingent on the long-term effectiveness of Wolbachia releases being sustained for 20 years. However, the intervention was still classed as cost-effective across the majority of the settings when assuming only 10 years of benefits. Overall, we found that targeting high burden cities with Wolbachia deployments would be a cost-effective intervention in Vietnam and generate notable broader benefits besides health gains.

Mobile chargers scheduling algorithm for maximum data flow in wireless sensor networks

Most nodes in wireless sensor networks (WSNs) are battery powered. However, battery replacement is inconvenient, which severely limits the application field of the networks. In addition, the energy consumption of nodes is not balanced in WSNs, nodes with low energy will seriously affect data transmission capability. To solve these problems, we utilize mobile chargers (MCs) in WSNs, which can move by itself and charge low-energy nodes. Firstly, we construct a mixed integer linear programming model (MILP) to solve maximum flow problem, which is proved to be NP-hard problem. To maximize flow to the sink nodes, the BottleNeck algorithm is used to generate the initial population for the genetic algorithm. This algorithm takes path as the unit and schedules MCs to charge the lowest energy node first. Then, the improved adaptive genetic algorithm (IAGA) is utilized to simulate the natural evolution process and search for the optimal deployment location for MCs. The experiment results show that IAGA can effectively improve the maximum flow of sink node compared with other methods.

Comparing traditional vs. biodegradable seed mussel collectors (SMCs) for seed settlement, seed density, and seed growth: Effect of deployment depth and location

Mussel bottom culture is historically based on transplanting wild mussel seed to designated culture plots. Seed mussel collectors (SMCs) that are deployed in the water column are gradually replacing benthic mussel beds for mussel seed resource provisioning. Traditional SMCs consist of weighted filamentous nylon ropes. The performance of SMCs are promising, but the major disadvantages are the increased cost, effort, and the use of non-sustainable materials. In this study, we developed an innovative SMC: the BioShell-SMC. It consists of a coconut core rope surrounded by empty cockle shells that are held in place by biodegradable socking. The advantage of this system compared to traditional SMCs is that it provides biodegradable and sustainable resource material suitable for on-bottom placement. We compared its relative performance to that of a traditional SMC at different deployment depths and locations used for SMC deployment in the Dutch Wadden Sea and Oosterschelde. The results from this experiment indicated that in six out of nine locations mussel seed biomass was comparable between the two collector types. On both collector types, mussel seed biomass was higher in the Wadden Sea than in the Oosterschelde. We also found that mussel seed biomass development was not affected by deployment depth, though mussels were more numerous and shorter in deep water. The results of the current study provide a promising start toward a more sustainable mussel seed collection for bottom cultivation.

UAV assisted cellular network traffic offloading: Joint swarm, 3D deployment, and user allocation optimization based on a data-aware method

Unmanned Aerial Vehicles (UAVs) have become popular carriers of aerial Base Stations (BSs) to cover temporal hotspots and off-load the transmission requirements of mobile users. Although the UAV-BSs can ignore the terrestrial obstacles, using them to fast deploy for off-loading still faces the time-efficiency problem of jointly optimizing multi-objectives: such as the amount of UAV-BS swarm, 3D deployment locations, and allocating users. This work transforms the above joint optimization problem into a combinatorial problem and proposes data-driven heuristic solutions with both time efficiency and robustness. To alleviate the pressure of collecting data, this paper offers a generative neural network-based framework. Results show that the proposed data-aware method can well address the above problem with less computation iterations compared with benchmark methods.

Advancing the Economic and Environmental Sustainability of the NEWgenerator Nonsewered Sanitation System.

Achieving safely managed sanitation and resource recovery in areas that are rural, geographically challenged, or experiencing rapidly increasing population density may not be feasible with centralized facilities due to space requirements, site-specific concerns, and high costs of sewer installation. Nonsewered sanitation (NSS) systems have the potential to provide safely managed sanitation and achieve strict wastewater treatment standards. One such NSS treatment technology is the NEWgenerator, which includes an anaerobic membrane bioreactor (AnMBR), nutrient recovery via ion exchange, and electrochlorination. The system has been shown to achieve robust treatment of real waste for over 100 users, but the technology's relative life cycle sustainability remains unclear. This study characterizes the financial viability and life cycle environmental impacts of the NEWgenerator and prioritizes opportunities to advance system sustainability through targeted improvements and deployment. The costs and greenhouse gas (GHG) emissions of the NEWgenerator (general case) leveraging grid electricity were 0.139 [0.113-0.168] USD cap-1 day-1 and 79.7 [55.0-112.3] kg CO2-equiv cap-1 year-1, respectively. A transition to photovoltaic-generated electricity would increase costs to 0.145 [0.118-0.181] USD cap-1 day-1 but decrease GHG emissions to 56.1 [33.8-86.2] kg CO2-equiv cap-1 year-1. The deployment location analysis demonstrated reduced median costs for deployment in China (-38%), India (-53%), Senegal (-31%), South Africa (-31%), and Uganda (-35%), but at comparable or increased GHG emissions (-2 to +16%). Targeted improvements revealed the relative change in median cost and GHG emissions to be -21 and -3% if loading is doubled (i.e., doubled users per unit), -30 and -12% with additional sludge drying, and +9 and -25% with the addition of a membrane contactor, respectively, with limited benefits (0-5% reductions) from an alternative photovoltaic battery, low-cost housing, or improved frontend operation. This research demonstrates that the NEWgenerator is a low-cost, low-emission NSS treatment technology with the potential for resource recovery to increase access to safe sanitation.

Impacts of photovoltaic and energy storage system adoption on public transport: A simulation-based optimization approach

Photovoltaic and energy storage system (PESS) adoption in public transport (PT) can offer a promising alternative towards reducing the charging and carbon emission costs of transit agencies. However, the quantitative impacts of PESS on operational cost, carbon emission cost, bus scheduling, and energy management in PT remain unclear. This study is performed with the aim of filling this research gap by presenting an impact analysis framework incorporating multisource datasets and a simulation-based optimization approach. A simulation is designed to capture bus scheduling and energy management on a day-to-day transit operation. A surrogate-based optimization method is adopted to determine an optimal PESS configuration for battery electric bus (BEB) charging stations. A case study is performed by introducing PESS at BEB charging stations in Beijing, China, leveraging historical weather and bus operational data. Compared with conventional charging stations, the novel transit system with PESS reduces the annual charging costs and carbon emissions of a single bus route in this case study by 17.6% and 8.8% on average, respectively. The results suggest that the recycling electricity price of PV generation is the key to influencing charging costs and carbon emissions. Battery capacities also impact the scheduling pattern of PESS considerably. Further comparison across five bus routes in Beijing reveals that the deployment locations of PESS are key decisions influencing the operational cost and carbon emissions in PT at the network level when the investment budget is limited.

Numerical analysis on gas production performance by using a multilateral well system at the first offshore hydrate production test site in the Shenhu area

Multilateral well technique is a promising approach to enhance gas recovery, especially in heterogeneous hydrate reservoirs with underlying free gas. However, little is known about the optimal deployment for multilateral wells on hydrate production. Herein, a 3D numerical model for predicting the combined production performance of hydrate reservoirs by using multilateral wells is proposed. The results indicate that the horizontal branch deployed at the interface between three-phase layer and free gas layer shows the best production potential when one branch is available. Then, effects of geometric parameters including branch deployment location, spacing, length, phase angle, and branch number on production behaviors are analyzed through orthogonal designs. The analysis shows dual-branch and quad-branch scenarios are most influenced by deployment location and branch length, and the corresponding optimal combinations are suggested respectively. By contrast, dual-lateral wells have the most favorable yield-increasing effects due to the synergistic depressurization, of which total gas production and productivity index are 123% and 81% higher than those of vertical wells. Additionally, well interference may exist between branches, which is unfavorable for gas recovery between the lateral branches and exacerbates with an increase in branch number. This research gives an in-depth insight into the industrialized co-production of hydrate and free gas.

Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment

D-band millimeter-wave, a key wireless technology for beyond 5G networks, promises extremely high data rate, ultra-low latency, and enables new Internet of Things applications. However, massive signal attenuation, complex response to building structures, and frequent non-availability of the Line-Of-Sight path make D-band picocell deployment challenging. To address this challenge, we propose a deep learning-based tool, that allows a network deployer to quickly scan the environment from a few random locations and predict Signal Reflection Profiles everywhere, which is essential to determine the optimal locations for picocell deployment.

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions.

Due to its flexibility, cost-effectiveness, and quick deployment abilities, unmanned aerial vehicle-mounted base station (UmBS) deployment is a promising approach for restoring wireless services in areas devastated by natural disasters such as floods, thunderstorms, and tsunami strikes. However, the biggest challenges in the deployment process of UmBS are ground user equipment's (UE's) position information, UmBS transmit power optimization, and UE-UmBS association. In this article, we propose Localization of ground UEs and their Association with the UmBS (LUAU), an approach that ensures localization of ground UEs and energy-efficient deployment of UmBSs. Unlike existing studies that proposed their work based on the known UEs positional information, we first propose a three-dimensional range-based localization approach (3D-RBL) to estimate the position information of the ground UEs. Subsequently, an optimization problem is formulated to maximize the UE's mean data rate by optimizing the UmBS transmit power and deployment locations while taking the interference from the surrounding UmBSs into consideration. To achieve the goal of the optimization problem, we utilize the exploration and exploitation abilities of the Q-learning framework. Simulation results demonstrate that the proposed approach outperforms two benchmark schemes in terms of the UE's mean data rate and outage percentage.

Analysing the influence of power take-off adaptability on the power extraction of dense wave energy converter arrays

The aim of this work is to assess the influence of different degrees of adaptability of the power take-off (PTO) system on the power absorption of dense wave energy converter (WEC) arrays. The adaptability is included in simulations through a transmission ratio that scales the force actuating the PTO relative to the force generated by the motion of a floater. A numerical model is used in which hydrodynamic interactions between floaters and nonlinearities in the PTO are considered. The lower computational cost of this numerical model makes it possible to study the power extraction of a dense WEC array in irregular waves to easily create power matrices and other performance metrics. The methodology is applied to the case study of the Ocean Grazer WEC to showcase the potential performance improvements achieved through the inclusion of a transmission ratio. The analysis shows that including a high degree of adaptability and choosing WEC array configurations and PTO designs specific to potential deployment locations early in the design process can lead to an increase in extracted power.

Geospatial Analysis of Technical U.S. Wave Net Power Potential

Developing new technologies to utilize natural renewable energy resources and fluxes is critical to mitigate climate change and energy crisis. As a renewable energy resource, wave energy is more predictable and has a higher power density compared to solar and wind. However, the technology for wave energy harvesting is still relatively nascent. One of the major challenges as the sector developers is the lack of spatial–temporal representation of wave resources, or wave power generation, from a generic WEC.This paper proposes a novel Wave Net Power Assessment (WNPA) method by utilizing the theoretical Wave Energy Converter (WEC) power limits (including the radiation power limits, Budal’s limit, and gross wave power), and applies it to a decade of wave conditions across the USA East Coast, West Coast and Hawaiian Islands. The WNPA method assesses the wave resource from device point of view (unlike traditional gross wave resource assessment) and is generally applicable to WECs regardless of dimension, size, and degrees of freedom (DoFs). The geospatial application of the WNPA method allowed for the analysis of wave power production potential along U.S. coastlines and provides WEC developers meaningful data to make decisions about the best spatial region to deploy certain WECs. Numerical simulations are conducted for WECs with 5 different sizes, 3 different operating modes across the West Coast, East Coast, and Hawaii over 10 years. In addition, productivity, efficiency and gross power quality comparisons are made in terms of the size and degree-of-freedom for WECs deployed at certain ocean sites (e.g., PacWave).A few of the most important findings: (1) The net wave power is dramatically smaller than the gross wave power, which indicates the new WNPA method has a more conservative assessment of the wave power potential; (2) A heaving 10 m WEC is found to have the highest (compared to other sizes and degrees of freedom) absorption efficiency on the West Coast and Hawaii; (3) For smaller 2 m surging/pitching WECs, ocean sites on the East Coast (e.g., Diamond Shoals, NC) maximize power production. (4) There are distinct trade-offs between maximizing power or minimizing power quality and dependencies exists between the location of interest, the WEC size, and the priority degree of freedom. The proposed methodology can be utilized by WEC technology and project developers to conduct apriori preliminary analysis of WEC deployment locations and device characteristics.

Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries

Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1–2 h forecast errors—resulting in early or delayed battery discharging relative to the onset of a reliability event—lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery’s availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.

UAV Trajectory and Energy Efficiency Optimization in RIS-Assisted Multi-User Air-to-Ground Communications Networks

An air-to-ground downlink communication network consisting of a reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) is proposed. In conjunction with a resource allocation strategy, the system’s energy efficiency is improved. Specifically, the UAV equipped with a RIS starts from an initial location, and an energy-efficient unmanned aerial vehicle deployment (EEUD) algorithm is deployed to jointly optimize the UAV trajectory, RIS phase shifts, and BS transmit power, so as to obtain a quasi-optimal deployment location and hence improve the energy efficiency. First, the RIS phase shifts are optimized by using the block coordinate descent (BCD) algorithm to deal with the nonconvex inequality constraint, and then integrated with the Dinkelbach algorithm to address the resource allocation problem of the BS transmit power. Finally, for solving the UAV trajectory optimization problem, the complex objective function is transformed into a convex function, and the optimal UAV flight trajectory is obtained. Our simulation results show that the quasi-optimal deployment location obtained by the EEUD algorithm is superior to other deployment strategies in energy efficiency. Moreover, the instantaneous energy efficiency of the UAVs along the trajectory of searching the deployment location is better than other comparison trajectories. Furthermore, the RIS-assisted multi-user air-to-ground communication network can offer up to 145% improvement in energy efficiency over the traditional amplify-and-forward (AF) relay.

Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs.

Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs' reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs' reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs' reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.

Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication

This paper studies the unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC), in which UAVs are dispatched as aerial dual-functional access points (APs) that can exploit the UAV maneuver control and strong line-of-sight (LoS) air-to-ground (A2G) links for efficient communication and sensing. In particular, we consider that one UAV-AP, equipped with a vertically placed uniform linear array (ULA), sends combined information and sensing signals to communicate with multiple users and at the same time sense potential targets at interested areas on the ground. Under this setup, we consider two scenarios with quasi-stationary and fully mobile UAVs, in which the UAV is deployed at an optimizable location over the whole ISAC mission period and can fly over different locations during the ISAC mission period, respectively. For the two scenarios, our objective is to jointly design the UAV maneuver (deployment location or flight trajectory) and the transmit beamforming, for maximizing the weighted sum-rate throughput of communication users, while ensuring the sensing beampattern gain requirements, subject to the transmit power and flight constraints. However, due to the ULA consideration at the UAV, the two formulated problems are highly non-convex and very difficult to be optimally solved, as the UAV’s location/trajectory variables are involved on the exponent parts of each entry in the steering vectors, and are closely coupled with the transmit beamforming vectors. To tackle this issue, we propose efficient algorithms to find their suboptimal but high-quality solutions, by using various techniques from convex and non-convex optimization. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmark schemes with heuristic maneuver designs. It is shown that the joint maneuver and transmit beamforming design efficiently balances the inherent tradeoff between sensing and communication with regards to different beampattern gain thresholds.

Joint Location and Beamforming Design for STAR-RIS Assisted NOMA Systems

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

Mathematical modeling and design of RIS deployment in a RIS-assisted metal cuboid for WAIC systems

Wireless avionics intra-communications (WAIC) is an emergent research topic, since it can improve fuel efficiency and enhance aircraft safety significantly. However, there are numerous baffles in an aircraft, e.g., seats and cabin bulkheads, resulting in serious blockage and even destroying wireless communications. Thus, this paper focuses on the reconfigurable intelligent surface (RIS) deployment issue of RIS-assisted WAIC systems, to solve the blockage problem caused by baffles. We first propose the mirror-symmetric imaging principle for mathematically analyzing electromagnetic (EM) wave propagation in a metal cuboid, which is a typical structure of WAIC systems. Based on the mirror-symmetric imaging principle, the mathematical channel model in a metal cuboid is deduced in detail. In addition, we develop an objective function of RIS's location and deduce the optimal RIS deployment location based on the geometric center optimization lemma. A two-dimensional gravity center search algorithm is then presented. Simulation results show that the designed RIS deployment can greatly increase the received power and efficiently solve the blockage problem in the aircraft.

Throughput properties and optimal locations for limited deployment of Max-pressure controls

Max-pressure (MP) control is one of the few traffic signal controllers proven to maximize network throughput or maximally stabilize the network. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections. Previous work did not consider a limited number of MP controlled intersections while proving the stability properties. Therefore, it is not clear whether a network can still be stabilized with a limited deployment of MP control. Using Lyapunov drift techniques this paper proves that even with a limited deployment, MP control can stabilize a network within feasible demand. Then we present an optimization formulation to find the optimal intersections to install MP control given a limited budget. We also present a greedy efficient algorithm to solve that optimization problem and prove that the algorithm solves the problem to optimality. Numerical results from simulations conducted on the downtown Austin network using an in-house custom simulator called AVDTA are then presented. The change in theoretical maximum servable demands for different amounts of deployments obtained from the optimization problem seemed to match with simulation results most of the times. We found that limited deployment of MP control almost always performed better than random deployment of MP control in terms of servable stable demand. Average total queue length and link density were observed to decrease as the number of MP controls increased which indicates better network performance. Average travel times per vehicle also decreased with installations of MP controls which shows how the travelers would benefit from more MP controls.