Deployment Plan Research Articles

SDN-enabled deployment and path planning of aerial base stations

Aerial Base Stations (ABSs) play a central role in mission critical environments due to the fast deployment, flexible reconfiguration, low cost. In this manner, through the controllable ABSs that are directed to the disaster region, users can continuously remain connected with ABSs. However, ABS deployment and path planning face several key challenges to provide on-demand communication and enable “Quality of Experience (QoE)-guaranteed”service to the users. For example, energy consumption, maximizing the number of covered users and scheduling the ABS’s operations are important issues that need to be considered. Thus, this paper jointly presents a Software Defined Networking (SDN) enabled solution to efficiently deploy ABSs which maximizes the covered users and communication quality, and provides a path planning strategy which decreases the energy consumption. Our simulation is managed with SITL (Software In The Loop) simulator, which allows to create and test drones without a real vehicle using C++ compiler and we confirm the improvement in terms of connectivity and energy consumption when compared to a recent state-of-the-art approach.

Are parents ready to use autonomous vehicles to transport children? Concerns and safety features

Introduction: This study addressed a gap in the literature – the potential of using autonomous vehicles (AV) to enhance children’s mobility. Prior studies documented the perceived benefits and concerns about this prospect, but did not examine the features in AV and support mechanisms that are desired by potential users. Method: An on-line survey was used to collect public opinions within the United States. In the survey, willingness to use AVs for this use case was asked twice to assess if participants changed their mind after being asked about concerns related to this prospect and importance of car features. A combination of statistical and machine-learning methods were used to profile individuals with high versus low post-willingness and to identify variables that differentiated the two groups. Results: Results indicated that respondents who were lower on their post-willingness to use AVs to transport children were more concerned about how AVs would protect children, how someone could harm the children inside, and whether there would be someone at the destination. In addition, they were less in favor of technology, older in age, and rated car features such as having a designated adult waiting at destination, a camera, and a microphone as relatively required (as opposed to optional). These results highlight potential users’ needs and requirements as they think about AVs in the context of parent–children mobility practices. Practical Applications: Relevant stakeholders should develop deployment and implementation plans while taking into account ridership contexts and vulnerable road users who can benefit from enhanced mobility.

Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic

In the foreseeable future, the traffic stream will be likely mixed with connected automated vehicles (CAVs) and regular vehicles (RVs). In the mixed traffic environment, when following a RV, due to the lack of vehicle-to-vehicle communications, it may take longer time for a CAV to sense and react than a human driver, which results in longer time headway and the loss of highway throughput. To address such a connectivity gap, this paper investigates an infrastructure-based solution, i.e., the deployment of roadside units to help CAVs in the heterogeneous traffic stream. Specifically, it is envisioned that these roadside units can sense vehicles in their coverage areas and provide the beyond-line-of-sight motion information to CAVs to empower them to react proactively, as they would do when following other CAVs. This paper is devoted to the analysis of the impacts of this type of roadside units at a strategic planning stage. In doing so, we first derive an analytical link performance function to capture their impact on the link capacity and travel time, and then develop a network equilibrium model to gauge their effect on travelers’ route choices and thus the flow distribution of both RVs and CAVs across the whole network. This modeling development will allow us to conduct a cost-benefit analysis for a given deployment plan of roadside units. For fair analyses, we further develop an optimization model to determine the optimal deployment plan for a given budget, while focusing on the worst case of its impact, because the flow distribution resulting from our network equilibrium model is not unique. Such a model provides a conservative estimate of the benefit brought by roadside units. Lastly, we offer case studies to demonstrate the models and unveil the potential of such an infrastructure-based solution.

Accuracy Assessment and Cross-Validation of LPWAN Propagation Models in Urban Scenarios

With the proliferation of machine-to-machine (M2M) communication in the course of the last decade, the importance of low-power wide-area network (LPWAN) technologies intensifies. However, the abundance of accurate propagation models proposed for these systems by standardization bodies, vendors, and research community hampers the deployment planning. In this paper, we question the selection of accurate propagation models for Narrowband IoT (NB-IoT), LoRaWAN, and Sigfox LPWAN technologies, based on extensive measurement campaign in two mid-size European cities. Our results demonstrate that none of the state-of-the-art models can accurately describe the propagation of LPWAN radio signals in an urban environment. For this reason, we propose enhancements to the selected models based on our experimental measurements. Performing the fine-tuning of the propagation models for one of the cities, we select Ericsson Urban (NB-IoT, LoRaWAN) and 3GPP (Sigfox) models as the ones providing the closest match. Finally, we proceed to perform cross-validation of the propagation models using the data set for another city. The tuned models demonstrate an excellent match with the real data in the cross-validation phase. They outperform their competitors by at least 20–80% in terms of relative deviation from the measured signal levels presenting the accurate option for NB-IoT, LoRaWAN, and Sigfox deployments planning in mid-size cities.

A New Probability Density Function for Minimizing Geometric Dilution of Precision in Location-Aware Wireless Communications

Cellular site planners have long used propagation models for optimizing an architecture deployment strategy. The foundation of these models are a plethora of theoretical results that all contribute to a probabilistic understanding of this phenomenon. In lower-band frequencies, coverage and propagation phenomena were sufficient considerations for legacy infrastructure deployment; however, as cellular technology probes uncharted millimeter wave spectra and beyond, it is natural to inquire as to whether other metrics could contribute to an informed infrastructure deployment. As the benchmark goals for positioning accuracy grow more ambitious and location-aware communications becomes a reality, we argue that localization accuracy should also play a prominent role in cellular infrastructure deployment planning. To this end, we submit a new closed-form probability density function (PDF) to characterize the angular difference of a pair of base stations and a mobile terminal. The importance of the angular difference is demonstrated by showing that the Cramer-Rao lower bound for localization is solely a function of it and measurement accuracy. Further, we submit a computationally tenable algorithm for producing the required PDF. To demonstrate the power of the density, we show some base station deployments that are guaranteed to yield geometrically favorable environments for positioning. Finally, we demonstrate how this new distribution outperforms numerical analysis when planning wireless network architecture deployment for location-aware communications.

A Coverage-Based Location Approach and Performance Evaluation for the Deployment of 5G Base Stations

It has become a strategic consensus of the international community for accelerating the deployment of 5G network. This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem for the site selection and location of 5G macro and micro base stations. An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base stations. A series of numerical examples are solved in the paper to demonstrate the proposed approach, and a cost-benefit analysis is also conducted to determine the optimal deployment plan for the number of macro and micro base stations. In the conclusion, a balanced executable solution is presented to make the signal strength of all demand points in the studied 5G network reach the strongest under the budget constraint.

Lithium Battery Model Development and Application in Simulation of the Energy Consumption of Electric Bus Running

Battery electric buses take an increasingly larger market share and attract much attention from bus fleet operators to undercut urban emissions limits. But meanwhile it also becomes a challenge to operators to determine the required battery capacity to be sufficient for the specific transport operations. The deployment planning includes to select appropriate bus model specifications, battery characteristics, charging parameters, timetable schedules and further dependencies under the aspect of ownership costs. Regarding battery characteristics however, most electrochemical battery models focus more on the internal structure, which have a worse compatibility on a system level. This paper aims to build up a lithium-battery model based on the third-Thevenin equivalent circuit, considering both complexity and accuracy. Firstly, this battery model is implemented with lookup tables to represent the values of each circuit component, which results in a mean error of 1.98 mV or 0.06%, compared with the original measurement data. Then a general control model of electric vehicles is introduced to cooperate with this battery model in a system, combined by the current flowing between these two models. Finally a simulation is proceeded, employing the real data from Solaris® Urbino 12 Electric Bus, which provides a reliable SOC estimation for the full day operation.

Border Control Morphing Attack Detection with a Convolutional Neural Network De-morphing Approach

Currently, the use of biometric identification, automated or semiautomated, is a reality. For this reason, the number of attacks has increased in such systems. One of the most common biometric attacks is the presentation attack (PA) because it is relatively easy to perform. Automated border control (ABC) is a clear target for phishers. Concerning biometric attacks, morphing is one of the most threatening attacks because authentication systems are usually unable to correctly detect them. In this attack, a fake face is generated with the morphing and blending of two different subjects (genuine and phisher), and the image result is stored in the passport. These attacks can generate risky situations in cases of border crossings where an ABC system should perform identification tasks. This research work proposes a de-morphing architecture that is founded on a convolutional neural network (CNN) architecture. This technique is based on the use of two images: the potentially morphed image stored in the passport, and the snapshot of the person located in the ABC system. The goal of the de-morphing process is to unravel the chip image. If the chip image is a morphed one, the revealing process between the in vivo image and the morphed chip image will return a different facial identity to the person located in the ABC system, and the impostor will be uncovered in situ . If the chip image is a non-morphing image, the resulting image will be similar to a genuine passenger. Therefore, the information obtained is considered at the border crossing. The equal error rate (EER) achieved is very low compared to the literature values published to date. The accomplished outcomes endorse a robust method that provides high accuracy rates without taking into account the quality of images used. This key point is crucial to plausible deployment plans in areas such as ABC.

Dynamic Joint Allocation of EV Charging Stations and DGs in Spatio-Temporal Expanding Grids

The number of electric vehicles (EVs) and the size of smart grid are witnessing rapid expansion in both spatial and temporal dimensions. This requires an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs). Such an allocation strategy should provide acceptable charging services at different deployment stages while meeting financial and technical constraints. As new CSs get allocated, distributed generation (DG) units need to be also dynamically allocated in both space and time to compensate for the increment in the loads due to the EV charging requests. Unfortunately, existing power grid models are not suitable to reflect such spatio-temporal evolution, and hence, new models need to be developed. In this paper, we propose a spatio-temporal expanding power grid model based on stochastic geometry. Using this flexible model, we perform a dynamic joint allocation of EV CSs and DG units based on a constrained Markov decision process. The proposed dynamic allocation strategy accounts for charging coordination mechanism within each CS, which in turn allows for maximal usage of deployed chargers. We validate the proposed stochastic geometry-based power grid model against IEEE 123-bus test system. Then, we present a case study for a 5-year CSs deployment plan.

Does having a seasonal influenza program facilitate pandemic preparedness? An analysis of vaccine deployment during the 2009 pandemic

BackgroundNational seasonal influenza programs have been recommended as a foundation for pandemic preparedness. During the 2009 pandemic, WHO aimed to increase Member States’ equitable access to influenza vaccines through pandemic vaccine donation. MethodsThis analysis explores whether the presence of a seasonal influenza program contributed to more rapid national submission of requirements to receive vaccine during the 2009 influenza pandemic. Data from 2009 influenza vaccine donation, deployment, and surveillance initiatives were collected during May-September 2018 from WHO archival material. Data about the presence of seasonal influenza vaccine programs prior to 2009 were gathered from the WHO-UNICEF Joint Reporting Form. Cox proportional hazards models were used to assess the relationship between presence of a seasonal influenza program and time to submission of a national deployment and vaccination plan and to vaccine delivery. FindingOf 97 countries eligible to receive WHO-donated vaccine, 83 (86%) submitted national deployment and vaccination plans and 77 (79%) received vaccine. Countries with a seasonal influenza vaccine program were more likely to submit a national deployment and vaccination plan (hazards ratio [HR] 2.1; 95% confidence interval [CI]. Countries with regulatory delays were less likely to receive vaccine than those without these delays (HR 0.4, 95% CI: 0.2–0.6). InterpretationDuring the 2009 pandemic, eligible countries with a seasonal influenza vaccine program weremore ready to receive and use donated vaccines than those without a program. Our findings suggest that robust seasonal influenza vaccine programs increase national familiarity with the management of influenza vaccines and therefore enhance pandemic preparedness. FundingN/A.

MAPLE: Mixed Path Calculation in Tile-Based 3D Maps

This letter proposes a new model for estimating mixed path wireless signal propagation characteristics of new deployment areas, utilizing signal propagation training from other areas that have prior signal measurements. Using 3D city maps, the training and the deployment areas can be divided into geographical grid tiles, each one having distinct propagation channel characteristics. The similarity between the training area tiles and the deployment area tiles can be used to estimate the potential propagation characteristics on the deployment area, thereby increasing the efficiency of the infrastructure deployment planning process.

Design and Analysis Sensor Deployment Scheme for Underwater Communication

At the point while setting up the submerged acoustic sensor system (UASN), hub deployment will be the above all else errand, whereupon numerous basic system administrations, for example, system topology control, directing, & limit recognition, will be manufactured. Where hub deployment at 2-D physical remote sensor systems had been broadly concentrated, little consideration must be gotten through the 3-D partners.This paper goes for examining the effects for hub deployment procedures at localization exhibitions at the 3-D environment. All the more particularly, these re-enactments directed at this project uncover which of general tetrahedron deployment plan outflanks these irregular deployment plan & this 3D shape deployment plan as far as decreasing localization blunder & expanding localization proportion by keeping up these normal number for neighbouring anchor hubs & system availability. Given this way which arbitrary deployment will be essential decision for greater part of viable applications for date, the outcomes will be relied upon for reveal some insight onto the outline UASNs sooner rather than later.

Time series behavior modeling with digital twin for Internet of Vehicles

Electric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles.

Optimal UAV Base Station Trajectories Using Flow-Level Models for Reinforcement Learning

Cellular base stations (BS) and remote radio heads can be mounted on unmanned aerial vehicles (UAV) for flexible, traffic-aware deployment. These UAV base station networks (UAVBSN) promise an unprecendented degree of freedom that can be exploited for spectral efficiency gains as well as optimal network utilization. However, the current literature lacks realistic radio and traffic models for UAVBSN deployment planning and for performance evaluation. In this paper, we propose flow-level models (FLM) for realistically characterizing the UAVBSN performance in terms of a broad range of flow- and system-level metrics. Further, we propose a deep reinforcement learning (DRL) approach that relies on the UAVBSN FLM for learning the optimal traffic-aware UAV trajectories. For a given user traffic density and starting UAV locations, our RL approach learns the optimal UAV trajectories offline that maximizes a cumulative performance metric. We then execute the learned UAV trajectories in a discrete event simulator to evaluate online UAVBSN performance. For ${M}\pmb {=}$ 9 UAVs deployed in a simulated Downtown San Francisco model, where the UAV trajectories are defined by ${N}\pmb {=}$ 20 discrete actions, our approach achieves approximately a three-fold increase in the average user throughput compared to the initial UAV placement, while simultaneously balancing traffic loads across the BSs.

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme.

Data Mining Using Clustering Methods to Identify The Type of Lung Disease (Case Study: Siti Hajar Hospital)

In the process of grouping types of lung disease, doctors and staff Siti Hajar hospital experience the various obstacles encountered, including difficulties in grouping the data types of lung disease. This deployment planning analysis will help the hospital in analyzing data classification type of lung disease. This method of making use of data collection by taking data at the hospital.

The Effect of Drifter GPS Errors on Estimates of Submesoscale Vorticity

AbstractDifferential kinematic flow properties (DKP), such as vertical vorticity, have been estimated from surface drifters. However, previous DKP error estimates were a posteriori and did not include correlated errors across drifters. To accurately estimate submesoscale (≤1 km) DKPs from drifters, errors must be better understood. Here, the a priori vorticity standard error is derived that depends upon the number of drifters in the cluster, the drifter cluster major and minor axes lengths, the instrument velocity error, and the cross-drifter error correlation. Two stationary GPS experiments, with zero vorticity, were performed at separations of O(101–103) m to understand vorticity error and test the derivation using 1 Hz position differences and Doppler shift velocities. Vorticity errors of ±5f (where f is the local Coriolis parameter)were found for ≈40 m separations. The frequency-dependent velocity variances and GPS-to-GPS correlations are quantified. Vorticity estimated with a “blended” velocity has reduced error. The stationary vorticity error can be well predicted given velocity error, correlation, and minor axis length. Vorticity error analysis is applied to submesoscale-sampling in situ GPS drifters near Point Sal, California. The derivation predicts when large high-frequency vorticity fluctuations (indicating noise) occur. Previously, cluster area or ellipticity were used as criteria to distinguish error. We show that the drifter cluster minor axis (narrowness) is a key time-dependent factor affecting vorticity error, and even for velocity errors <0.004 m s−1, the vorticity error exceeds ±5f when cluster minor axis <50 m. These results will aid submesoscale drifter deployment planning.

Relay Placement for Reliable Ranging in Cooperative mm-Wave Systems

We study millimeter wave-based ranging of randomly located terminal nodes (TNs) using fixed relay nodes (RNs) deployed around a central node (CN). This setting may correspond to a disaster-relief scenario where the rescuers require positioning information in the absence of a global positioning system (GPS). We derive the Bayesian Cramer-Rao lower bound (BCRLB) for the TNs range estimation from the CN as well as from the RNs in this network using a stochastic geometry framework. Contrary to existing studies, we take the effect of link-blockages into account while deriving the BCRLB, and thereby present a more accurate bound on the ranging error. For the special case of no blockages, we formulate a convex problem for obtaining the optimal relay positions. Our results provide the operator a guideline for initial deployment planning, in terms of number and location of RNs to be deployed in order to achieve an accurate ranging.

Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semi-definite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.

A REPRESENTATION OF MLS DATA AS A BASIS FOR TERRAIN NAVIGABILITY ANALYSIS AND SENSOR DEPLOYMENT PLANNING

Abstract. Recording an ever-changing urban environment in a structured manner requires sensor deployment planning. In case of mobile sensor platforms, this also includes verifying the terrain navigability. Solving both tasks would usually require different application-specific data structures and tools. In this work, we propose a theoretical framework that provides a uniform representation for spatial information as well as the tools required to combine, manipulate and visualize it. We provide an efficient implementation of the framework utilizing octree-based evidence grids. Our approach can be used to solve complex tasks by combining simple spatial information sources, which we demonstrate by providing simple solutions to the aforementioned applications. Despite the use of a volumetric approach, our runtimes are within the range of minutes.