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In the computing world, every company is focusing on the early reservation system. In the early reservation the price, quality of service, time, and everything is maintained and reserved for the user on the due interval. ORRS is a step toward this global vision and specifically designed to act as a flexible reservation supporting multiple services according to user's need and pricing (such as traditional immediate reservation (TIR) request and ORRS reservation. Thus, it is both important and challenging to reserve computing services and the cost of the services in an efficient manner. In the ORRS reservation system, the fuzzy rough set feature selection decreases the dimensional structure and problems of a large database in the computing world. The researcher is now going at a rapid speed in the field of maintaining cost based on decision making like on-demand, reserved, the spot to increase resources utilization and profit. The implementation had done in our research work by the use of cloud simulator.

Adverse childhood experiences (ACEs) are associated with numerous risk behaviors and mental health outcomes among youth. This study examines the relationship between the number of types of exposures to ACEs and risk behaviors and mental health outcomes among reservation-based Native Americans. In 2011, data were collected from Native American (N = 288; 15–24 years of age) tribal members from a remote plains reservation using an anonymous web-based questionnaire. We analyzed the relationship between six ACEs, emotional, physical, and sexual abuse, physical and emotional neglect, witness to intimate partner violence, for those <18 years, and included historical loss associated symptoms, and perceived discrimination for those <19 years; and four risk behavior/mental health outcomes: post-traumatic stress disorder (PTSD) symptoms, depression symptoms, poly-drug use, and suicide attempt. Seventy-eight percent of the sample reported at least one ACE and 40 % reported at least two. The cumulative impact of the ACEs were significant ( p  < .001) for the four outcomes with each additional ACE increasing the odds of suicide attempt (37 %), poly-drug use (51 %), PTSD symptoms (55 %), and depression symptoms (57 %). To address these findings culturally appropriate childhood and adolescent interventions for reservation-based populations must be developed, tested and evaluated longitudinally.

The integration of the low Earth orbit (LEO) satellite and the terrestrial networks has extended the coverage of the Internet of Things (IoT) from densely populated areas to the entire globe. Random access plays an important role in LEO satellite-based IoT (SIoT) since many sensors on the ground need to send the data back to the LEO satellites with a stringent delay requirement. Due to the significant difference in inherent characteristics between the LEO satellite-based systems and the terrestrial networks, the factors of consideration for random access are quite different. First and foremost, a LEO satellite has limited resources, and the coverage is rather dynamic. Secondly, the services provided require scalability and differentiated quality of service (QoS). Thirdly, the received packets are sporadic and sparse at the satellites. In this paper, we propose using a deep neural network box (DNNB) to resolve collisions for resource reservation in the SIoT. An active sensor node sends a reservation packet, which contains a randomly generated ticket number and a password with a checksum. The former is converted into a signature by the mapping of the Finite Projective Plane (FPP). The resource allocator (RA) at the LEO satellite uses the output of the DNNB to determine the active sensor nodes of the reservation packet and assign resources accordingly. The confirmation of resource reservation is doubly checked by the integrity of passwords, placed independently and sequentially in the password section. Through such a dual checking system, the RA at the LEO satellite-based system can take either a conservative policy, an aggressive policy, or a hybrid policy in allocating resources. The reservation-based random access with the assistance of machine learning (ML) can provide high throughput, high scalability, differentiated QoS, and age of information (AoI). In the performance evaluation, we analyze the expected throughput and mean delay for the reservation-based system, and compare the proposed DNNB with CRDSA and IRSA. Lastly, we provide the design of a multi-class QoS mechanism.

In the context of big data and intelligent connectivity, optimizing scheduled bus dispatch can enhance urban transit efficiency and passenger experience, which is vital for the sustainable development of urban transportation. This paper, based on existing fixed bus stops, integrates traditional demand-responsive transit and travel booking models, considering the spatiotemporal variations in scheduled travel demands and passenger flows and addressing the combined scheduling issues of fixed-capacity bus models and skip-stop strategies. By leveraging intelligent connected technologies, it introduces a dynamic grouping method, proposes an intelligent connected bus dispatching model, and optimizes bus timetables and dispatch control strategies. Firstly, the inherent travel characteristics of potential reservation users are analyzed based on actual transit data, subsequently extracting demand data from reserved passengers. Secondly, a two-stage optimization program is proposed, detailing passenger boarding and alighting at each stop and section passenger flow conditions. The first stage introduces a precise bus–traveler matching dispatch model within a spatial–temporal–state framework, incorporating ride matching to minimize parking frequency in scheduled travel scenarios. The second stage addresses spatiotemporal variations in passenger demand and station congestion by employing a skip-stop and bus operation control strategy. This strategy enables the creation of an adaptable bus operation optimization model for temporal dynamics and station capacity. Finally, a dual-layer optimization model using an adaptive parameter grid particle swarm optimization algorithm is proposed. Based on Beijing’s Route 300 operational data, the simulation-driven study implements contrasting scenarios of different bus service patterns. The results demonstrate that this networked dispatching system with dynamic vehicle grouping reduces operational costs by 29.7% and decreases passenger waiting time by 44.15% compared to baseline scenarios.

This paper develops a reservation-based dedicated lane (RDL) mechanism for mixed autonomous and human-operated vehicles at intersections in connected vehicle environment. The RDL mechanism is proposed to coordinate hybrid transport at intersections under the condition of dedicated land, improve the capacity of intersections, reduce delay and the number of stops. The overall RDL architecture consists of two essential sub-modules: a connected and autonomous vehicles (CAVs) speed guidance application, a signal-control application for human-operated vehicles (HVs). Thus, the proposed RDL strategy enables HV/CAV/signal cooperation in a connected vehicle environment. A set-projection algorithm and a three-segment linear speed profile are employed to control the trajectories of the CAV. The performance of the proposed method is evaluated by simulating various traffic conditions on an actual urban network. The simulation evaluation results show that, compared with the traditional signal control method, the model can significantly reduce the travel delay. The simulation experiments indicate that the penetration rate of CAV reaches 30%-40%, and proposed method can achieve considerable results.

Urban air mobility (UAM) represents a promising concept for 3-D transportation, as it leverages a new generation of vertical takeoff and landing (VTOL) aircraft. However, UAM currently lacks a comprehensive origin-to-destination service. In this study, we propose integrating fly-drive vehicles (FVs) into the existing road infrastructure and connected vehicle (CV) surface traffic environment through a dynamic reservation-based traffic control system, aiming to enhance travel time for high-priority FVs and provide door-to-door service. Landing and takeoff operations are provided on a first-come, first-served basis while taking into consideration the downwash that VTOL FVs can produce. In addition, the proposed approach incorporates the use of fly-drive vehicles that employ the same propulsion system for both drive and flight modes. We simulate such FVs in custom-made microsimulation tool, which includes dynamic reservation-based traffic control for FVs. Our results demonstrate that the proposed traffic control increases delays of surface vehicles when compared to base case without FVs in the network. However, the number of denied takeoff and landing requests is significantly reduced when compared to a previous study, showing that this concept indeed provided priority service to FVs. Notably, since FVs are scarce in the network and are not intended for mass use, the delay increase for surface vehicles is relatively insignificant, especially for low and moderate surface traffic conditions. Furthermore, a noise assessment showed that the proportion of FVs, if this concept is to be employed, should be minimal or FVs should produce much less noise than contemporary helicopters.

We present a three-dimensional (3D) intersection traffic management platform for small autonomous Unmanned Aerial Vehicles (UAVs), particularly quadcopters, in urban airspace. Assuming many autonomous UAVs are approaching a shared airspace, where UAVs have varying sources and destinations, we propose a system model for a 3D intersection that aims to provide safe and systematic management of UAVs. We also devised a scheduling scheme to ensure that the intersection is efficiently utilized and that there are no collisions among the UAVs in the intersection. The scheduling scheme applies the reservation-based approach, which is sensitive to the sequence of the UAVs in scheduling, thus genetic algorithm is used to determine the best sequence of the UAVs. Simulations were performed to evaluate the efficiency of the system. We also show through the simulations that our scheduling scheme reduces the UAVs’ average time in the system by 27 percent compared with when the UAVs are scheduled in a first-come, first-served manner for the highly crowded intersection.

We recently proposed a concept, called Combined Alternate-Direction Lane Assignment and Reservation-based Intersection Control (CADLARIC), for organizing directionally unrestricted traffic flows in automated vehicle environment. The conflicts between through movements are handled by a reservation-based algorithm while the turning conflicts at the intersections are avoided altogether. This paper extends this research by analyzing the impacts that CADLARIC’s parameters, used to control the conflict-resolution processes, have on the efficiency and surrogate safety indicators. The investigated parameters include: (i) buffer time in cell’s reservation schedule; (ii) allowed speed to cross the reserved cell; (iii) distance from intersection from which a vehicle can make reservation, and (iv) duration of the lane-change process. For most of the investigated parameters, the numerical results show that less efficient operations lead not just to an increase in delay time and number of stops but also increase number of conflicting situations, because of vehicular queues formed within the intersection zone.

High data rate communication systems usually implement Orthogonal Frequency Division Multiplexing (OFDM) to face frequency selectivity. High Peak to Average Power Ratio (PAPR) is an OFDM disadvantage that causes Bit Error Rate (BER) degradation and out-of-band (OOB) radiation when OFDM signal pass through nonlinear Power Amplifier (PA). In order to overcome this problem larger Input Back-Off (IBO) is required. However, large IBO decreases the PA efficiency. PAPR reduction techniques are used to reduce the required IBO, so that PA efficiency is saved. Several PAPR reduction methods are introduced in literature, among them Tone Reservation based on Null Subcarriers (TRNS) is downward compatible version of Tone Reservation (TR) with small excess in the average power and low computational complexity compared to others. As will be shown, TRNS is the best practical one of the four downward compatible techniques. Performance of TRNS is controlled by three parameters; number of peak reduction tones (PRTs), predefined threshold (Amax), and number of iterations (Itr). In order to increase PAPR reduction gain, enhance BER performance, and reduce the required IBO to follow the given power spectral density (PSD), we have to choose the values of these parameters adequately. Results showed that, we have to reduce the threshold value to the average (i.e. Amax =0 dB). Also, we have to increase number of PRTs. However, we have to maintain the spectrum shape. Finally, we have to choose moderate number of iterations (e.g. Itr ≈50), as excessive increase in number of iterations is not useful, especially at high PAPR values.

In 4G cellular networks, call admission control (CAC) has a direct impact on quality of service (QoS) for individual connections and overall system efficiency. Reservation-based CAC schemes have been previously proposed for cellular networks where a certain amount of system bandwidth is reserved for high-priority calls, e.g., hand-off calls and real-time new calls. Traditional reservation-based schemes are not efficient for 4G vehicular networks, as the reserved bandwidth may not be utilized effectively in low hand-off rates. We propose a channel borrowing approach in which new best effort (BE) calls can borrow the reserved bandwidth for high-priority calls. Later, if a hand-off call arrives and all the channels are busy, it will pre-empt the service of a borrower BE call if there exists any. The pre-empted BE calls are kept in a queue and resume their service whenever a channel becomes available. The analytical model for this scheme is a mixed loss-queueing system for which it is difficult to calculate call blocking probability (CBP) and call dropping probability (CDP). Our focus in this paper is on the system modeling and performance evaluation of the proposed scheme. We present two system models that approximate the operation of the proposed scheme. For these models, we derive the CBP and CDP analytically. It is shown that our analytical results are very close to the ones obtained from simulations. Furthermore, it is observed that our channel borrowing approach decreases the CBP considerably while increases the CDP slightly over a large range of hand-off rates.