Fixed Time Slot Research Articles

An adapted savings algorithm for planning heterogeneous logistics with uncrewed aerial vehicles

This paper proposes a new extension to the Sustainable Specimen Collection Problem (SSCP), where medical specimens are transported by vans, bikes, and uncrewed aerial vehicles (UAVs, or drones) from local medical practices/offices to a central hospital laboratory for analysis, employing a two-echelon collection approach. Time restrictions from existing operations and literature are also introduced, with the study being formulated as a weighted multi-objective problem seeking to minimise (i) operating costs; (ii) transit times; and (iii) energy/environmental impacts. A new adaptation of the Clarke and Wright Savings Algorithm is subsequently presented to create collection rounds that leverage each mode’s strengths. Subsequently, routes are compiled into workable fixed shifts using a modified bin-packing algorithm in each iteration.The approach of this study is based on a case study of the UK’s National Health Service (NHS), involving the collection of pathology samples using traditional vans operating within fixed time slots. Using case study data from the Solent region (England), a novel test instance generation methodology was also developed, whereby realistic site positioning and origin-destination travel data are captured to enable effective algorithm experimentation. The findings from applying the proposed algorithm to a set of test instances based on this methodology are subsequently discussed, where it was found that the adapted savings and bin-packing approach produced effective solutions quickly, with 90% of all large instances (200 sites) being solved within 15 min. Further algorithm developments and the application of the devised problem/methodologies are also discussed.

Improving performance metrics in WBANs with a dynamic next beacon interval and superframe duration scheme

The advancement of networking, information, and communication technologies has fueled the popularity of Wireless Body Area Networks (WBANs) in both medical (remote patient monitoring) and non-medical sectors. Due to low, medium, and high data traffic requirements, WBAN performance suffers during the synchronization process that generates periodic beacon frames between sensor nodes and the coordinator. It also suffers when sensor nodes implicitly send data to the coordinator during the fixed time slot using the Contention Access Period (CAP). In this study, we propose a solution called Dynamic Next Beacon Interval and Superframe Duration Scheme (DNBISD) to tackle these issues of the IEEE 802.15.4 standard. This standard relies on a Beacon Interval (BI) and CAP for synchronization and data transmission between sensor nodes and the coordinator. However, the standard must adapt to BI and CAP's changing traffic load requirements, resulting in drawbacks such as prolonged packet delays, increased energy consumption, and potential data loss, particularly in real-time patient monitoring applications. In order to overcome these challenges, our DNBISD scheme employs a fuzzy approach to adapt the BI and CAP based on requested synchronization and data considering input parameters like packet received ratio and buffer ratio. The inference system utilizes the Takagi, Sugeno, and Kang (TSK) fuzzy model for rational quantitative analysis. Simulations demonstrate that our proposed scheme significantly enhances data transmission, boosts the average packet delivery ratio and throughput, and reduces the coordinator's average packet loss ratio and energy consumption. Consequently, this improvement allows for more efficient data transfer among numerous nodes within the specified superframe structure.

Hybrid traffic scheduling in time‐sensitive networking for the support of automotive applications

AbstractTime‐sensitive networking (TSN) is considered one of the most promising solutions to address real‐time scheduling in in‐vehicle network due to its capabilities for providing deterministic service. The TSN working group proposed various traffic shaping mechanisms, while deterministic scheduling of hybrid traffic is still not effectively solved since the traffic requirements are difficult to satisfy by standalone or combined mechanisms with fixed time slot divisions. This article presents a time‐aware multi‐cyclicqueuing and forwarding scheduling model, that integrates the no‐wait enabled time‐aware shaper and multi‐cyclic queuing and forwarding shaping models. Then, a scheduling solution, dubbed “TSN scheduling optimizer” (TSO) is proposed that combines optimization methods and incremental techniques. TSO aims to balance the load to maximize flow schedulability while guaranteeing the service requirements of hybrid traffic. Simulation evaluations through OMNeT++ provide a performance assessment of this proposed scheduling model, which can satisfy multiple types of traffic transmission requirements. Furthermore, TSO is compared with other baseline scheduling solutions, and TSO shows efficacy regarding execution time and schedulability.

A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method

Each other, leading to issues such as interference, limited bandwidth, and varying channel conditions. These challenges require specialized optimization techniques tailored to the wireless environment. In wireless communication networks is to maximize the overall system throughput while ensuring fairness among users and maintaining quality of service requirements. This objective can be achieved through resource allocation optimization, where the available network resources such as bandwidth, power, and time slots are allocated to users in an optimal manner. Optimization-based approaches in wireless resource allocation typically involve formulating the resource allocation problem as an optimization problem with certain constraints.. These techniques provide practical solutions with reduced computational complexity, although they may not guarantee optimality. In summary, optimization-based approaches have been instrumental in studying resource allocation problems in communication networks, including the wireless domain. While techniques from the Internet setting have influenced the understanding of congestion control and protocol design, specific challenges in wireless networks necessitate tailored optimization techniques that account for interference, limited bandwidth, and varying channel conditions. power allocation problem in wireless ad hoc networks Cross-layer optimization refers to the process of jointly optimizing the allocation of resources across different layers of wireless networks, the interactions between different layers become more complex due to the shared medium and time-varying channel conditions. Nash equilibrium, where no user can unilaterally improve its own performance by changing its strategy. Game theory can capture the distributed nature of wireless networks and provide insights into the behavior of users in resource allocation scenarios Additionally, heuristics and approximation algorithms are often employed in wireless resource allocation due to the complexity of the optimization problems involved. In traditional cellular systems, each user is allocated a fixed time slot for transmission, regardless of their channel conditions. However, in opportunistic scheduling. Alternative parameters for “Data rate Ž kbps, Geographic coverage , Service requirements , cost ” Evaluation parameter for “Circuit-switched cell, CDPD, WLAN, Paging, Satellite.” “the first ranking training is obtained with the lowest quality of compensation.”

HH-IPG: Leveraging Inter-Packet Gap Metrics in P4 Hardware for Heavy Hitter Detection

The research community has recently proposed several solutions based on modern programmable switches to detect entirely in the data plane the flows exceeding pre-determined thra eshold in a time window, i.e., Heavy Hitters (HH). This is commonly achieved by dividing the network stream into fixed time slots and identifying each separately without considering the traffic trends from previous intervals. In this work, we show that using specified time windows can lead to high inaccuracies. We make a case for rethinking how switches analyze the incoming packets and propose to leverage per-flow Inter Packet Gap (IPG) analytics instead of using flow counters for HH detection. We propose an algorithm and present a P4 pipeline design using this new metric in mind. We implement our solution on P4 hardware and experimentally evaluate it against real traffic traces. We show that our results are more accurate than related work by up to 20% while reducing the control channel overhead by up to two orders of magnitude. Finally, we showcase a QoS-oriented application of the proposed dataplane-only IPG-based HH detection in a mobile network scenario.

Patient safety 4.0: "Failure of the Week" It's all about role modelling!

The rate of mistakes and near misses in clinical medicine remains staggering. The tendency to cover up mistakes is rampant in "name-blame-shame" cultures. The need for safe forums where mistakes can be openly discussed in the interest of patient safety is evident. Following a comprehensive review of the literature, a semi-structured weekly conference, named "mistake of the week" (MOTW), was introduced, enabling physicians to voluntarily discuss their mistakes and near-misses. The MOTW is intended to encourage cultural change in how physicians approach, process, accept and learn from their own and their peers' mistakes. This study seeks to assess if physicians appreciate, benefit from and are motivated to participate in MOTW. Physicians and medical students of the I. and II. Medizinische Klinik at the Academic Teaching Hospital Klinikum Konstanz (Germany) were eligible to participate voluntarily. Four groups of physicians (n=3-6) and one group of medical students (n=5) volunteered to participate in focus group interviews, which were videotaped, transcribed and analyzed. The following success factors are crucial for dealing with and voluntarily disclosing mistakes and near-misses: 1. Exemplification ("follow the boss's lead"), 2. Fixed time slots and a clear forum, 3. Reporting mistakes without fear of penalty or punishment, 4. A trusting working atmosphere. The key effects of the MOTW approach are: 1. People report their mistakes more, 2. Relief, 3. Psychological safety, 4. Lessons learned/errors (potentially) reduced. The MOTW conference models an ideal forum to mitigate hierarchy and promote a sustainable organizational dynamic in which mistakes and near misses can be discussed in an environment free from "name-blame-shame", with the ultimate goal of potentially improving patient care and safety.

Resource Allocation for OFDM-Based Status Update Systems: A Timeliness Perspective

Increasingly more time-sensitive applications emerge in Internet of things (IoT), requiring fresh status updates. Efficient resource allocation is critical for improving the timeliness of information. This paper investigates how to allocate bandwidth and configure the transmission in an orthogonal frequency division multiplexing (OFDM)-based IoT systems for timely status updating. Specifically, a general resource allocation problem is formulated using age of information (AoI) as the timeliness metric. We first consider a static channel case where we propose a fixed time slot length policy, a fixed blocklength policy, an iterative optimization algorithm, and an approximate optimal policy to address the optimal resource allocation and transmission configuration. Then, we extend the study to quasi-static fading channel cases. In particular, sticking to the transmission rate and spectral efficiency, the four policies designated for static channel case are tailored for the quasi-static fading channel case. We show that the iterative algorithms can improve the timeliness of the system significantly. Besides, the approximate optimal policy achieves an AoI performance competitive with the iterative algorithm, while reduces the computation complexity in both channel cases.

Dynamic multi-frame multi-spreading factor scheduling algorithm for LoRaWAN

Long-Range Wide Area Network (LoRaWAN), as Low Power Wide Area Network that connects battery-powered devices, has gained much attention lately. The LoRaWAN technology uses ALOHA as the medium access control where the End Devices (EDs) transmit data randomly and re-transmit up to eight times if collisions occur, which is not feasible in large networks. Several techniques, including synchronization and scheduling schemes, to address the challenge have been reported. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different Spreading Factors (SFs). This phenomenon can result in collisions, idle slots, and inefficient energy use, hence limiting the LoRaWAN network scalability. To alleviate the aforementioned problems, this work proposes a dynamic Multi-Frame Multi-Spreading Factor (MFMSF) scheduling algorithm with slotted synchronization approach. In the proposed algorithm, the time slots are assigned dynamically to EDs on first to synchronize first to be assigned basis. It was revealed that the proposed dynamic MFMSF TDMA algorithm is more energy-efficient than the existing algorithm in a denser network with devices arranged in a circular disk with different radii. The packet delivery rate for the dynamic MFMSF TDMA is about 99% signifying reduced collisions during data transmission. This implies that the use of the proposed dynamic MFMSF scheduling algorithm in LoRaWAN allows large number of EDs to communicate with reduced collisions and enhanced energy efficiency of the battery-powered devices.

Adaptive Time Slot Resource Allocation in SWIPT IoT Networks

The rapid advancement of Internet of Things (IoT) technology has brought convenience to people’s lives; however further development of IoT faces serious challenges, such as limited energy and shortage of network spectrum resources. To address the above challenges, this study proposes a simultaneous wireless information and power transfer IoT adaptive time slot resource allocation (SIATS) algorithm. First, an adaptive time slot consisting of periods for sensing, information transmission, and energy harvesting is designed to ensure that the minimum energy harvesting requirement is met while the maximum uplink and downlink throughputs are obtained. Second, the optimal transmit power and channel assignment of the system are obtained using the Lagrangian dual and gradient descent methods, and the optimal time slot assignment is determined for each IoT device such that the sum of the throughput of all devices is maximized. Simulation results show that the SIATS algorithm performs satisfactorily and provides an increase in the throughput by up to 14.4% compared with that of the fixed time slot allocation (FTS) algorithm. In the case of a large noise variance, the SIATS algorithm has good noise immunity, and the total throughput of the IoT devices obtained using the SIATS algorithm can be improved by up to 34.7% compared with that obtained using the FTS algorithm.

Financial and Economic Sequence Forecasting Based on Time Slot Allocation Algorithm

Financial and economic series prediction was very important for the study of economic time series. Because the traditional time series analysis method was mainly suitable for stable data and cannot be applied to unstable economic variables, this paper proposed a knowledge model of financial and economic series prediction time slot algorithm, which provided the basis for solving unstable financial data. Firstly, based on the analysis of relevant financial and economic series prediction methods at home and abroad, this paper applied the time slot allocation algorithm to economic series analysis and prediction and designed a path level time slot allocation algorithm, which introduced a fixed time slot to shorten the time required to complete the path level time slot allocation. Secondly, this paper abstracted the calculation of slot allocation as a convex optimization problem and proposed a round slot allocation algorithm based on constraints and optimal solution. The algorithm can improve the end-to-end throughput and the effective utilization of slots. Finally, the model proposed in this paper was used to predict the indicators of relevant industries, and the simulation results were compared with the standard data. The experimental results showed that the financial and economic series prediction system based on time slot allocation algorithm proposed in this paper can effectively improve the accuracy of financial and economic series prediction, which verified the effectiveness and feasibility of this model.

Real-Time Traffic Management System

Abstract: Monitoring traffic efficiently has been one of the biggest modern day problems. As population has exponentially increased in the last century, the same effect has been observed on vehicles. This has put tremendous pressure on the existent traffic systems. Even today, several traffic systems work with the concept of fixed time slots attached to each traffic light while overlooking the density of traffic at that junction. This causes millions of people to waste their precious time in traffic while concurrently affecting the nature through air and noise pollution. Another consequence of this waiting is the toll on the mental health of the commuters. To overcome these problems, we propose a system that manages traffic by taking the primary factor of real time traffic density into consideration. This system utilizes cameras along with image processing to achieve its goal. It ensures that the lanes switch based on the number of vehicles, consequently cutting down waiting periods while reducing pollution substantially. Keywords: Traffic management, Traffic control, Real-time surveillance, Vehicle density, Smart system.

ASMAC: An Adaptive Slot Access MAC Protocol in Distributed VANET

The media access control (MAC) protocol is responsible for channel resource sharing and conflict management in the communication process in VANET, which is of great significance for ensuring efficient and reliable communication. The existing MAC protocol with fixed time slot allocation cannot solve the problem of time slot resource exhaustion or waste when the traffic density is unbalanced. In this paper, we propose an adaptive time slot access MAC protocol in distributed VANET (ASMAC). ASMAC adapts the access time slot according to the driving direction of the vehicle and the traffic density ratio to improve the time slot access efficiency. In the ASMAC protocol, the frame is divided into several time slots based on TDMA, and the time slots in a frame are divided into two time slot sets according to the driving direction. The vehicle selects a time slot access corresponding to the time slot set. When the vehicle finds that there is no accessible time slot in the time slot set, it accesses the available time slot in the reverse time slot set to improve the efficiency of time slot access. In this paper, the analysis and simulation results of highway and four-way intersection scenarios are given and the validity of the ASMAC protocol is verified by comparing with the VeMAC and SAMD protocols.

A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

This paper proposes an adaptive data-driven novel technique for generating physician’s schedules exploring the trade-off between physician waiting time and patient experience. Generally, in hospitals fix time slots are assigned to the patients, without differentiating patients into different categories. Some patients are referral patients who need less time, others are first-time visitors who need more time than referral patients. Some are serious patients who take even more time, so a fixed time slot can increase the waiting time both at the doctor and the patient level. So, there is a need to categorize patients according to their treatment which could help in improving the waiting time of doctors and as well of the patients. This technique trains the patient’s data according to their specific category based on the time doctors usually spend on them. This can help in generating a schedule that will minimize the waiting of patients and at the same time improve the hospital performance by scheduling maximum patients ensuring that the same patient’s get the best experience. To achieve this a data-driven scheduling algorithm (DDSA) is proposed which uses probability distribution, clustering and doctor’s scheduling algorithm to classify patients into different categories. The data is collected through the RFID machines. The patients are given cluster head time for the treatment. Different patient categories are given different treatment times. Then patient and doctor commutative waiting times are calculated. The system applies different set-up times to allow hospital management to trade-off between doctor and patient waiting times. The average waiting time for each patient using DDSA comes out to be seven minutes. Hence, this technique can help hospitals across the globe in improving their performance and patient experience.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.

University Course Timetabling Problem with Professor Assignment

One of the decision problems in many organizations and institutions is to decide how to schedule different tasks, in particular, in higher education institutions. One of the main problems is the university course timetabling problem (UCTP): this problem consists of the allocation of events (courses, professors, and students) to a number of fixed time slots and rooms, this at the beginning of each academic period of the universities. The existent formulations include particular requirements from different educational levels and institutions, as in our case. In this paper, we focus on the university course timetabling problem with the assignment of professor-course-time slot for an institution in Mexico. Timetabling is constructed for the disciplinary courses that are offered by one of the academic departments. The main characteristics are as follows: (1) there are full-time and part-time professors; (2) a mandatory fixed number of courses has to be assigned to each full-time professor according to their academic profile; (3) there is a maximum number of courses assigned to part-time professors; (4) a professor-course matrix that specifies the valid assignation is defined; and (5) mandatory time periods for courses in different semesters are established and other traditional constraints. We present the integer linear programming model proposed to solve the case studied. The optimal solution was obtained with low computational effort through the classical branch-and-bound algorithm. We describe the complete timetable to show the model effectiveness.

Cortical glutamate and gamma-aminobutyric acid over the course of a provoked migraine attack, a 7 Tesla magnetic resonance spectroscopy study

Enhanced activity of the glutamatergic system has been linked to migraine pathophysiology. The present study aimed to assess the involvement of the glutamatergic system in the onset of attacks. We provoked attacks by infusion of glyceryl trinitrate (GTN; 0.5 µg/kg/min over 20 min) in 24 female episodic migraineurs without aura and 13 female age-matched healthy controls. Over the course of a single day participants were scanned three times at fixed time slots (baseline before GTN infusion, 90 min and 270 min after start of GTN infusion). Single-volume proton magnetic resonance spectra (1H–MRS) were acquired at 7 Tesla from a volume of interest (VOI, 2x2x3 cm) in the visual cortex. We assessed the concentrations of glutamate, its major precursor glutamine, and its product gamma-aminobutyric acid (GABA) over the course of a provoked attack. The preictal state was defined as the period after GTN infusion until the migraine-like headache started, independent of possible experienced premonitory symptoms, and the ictal state was defined as the period with provoked migraine-like headache. Data were analyzed using a linear mixed-effect model for repeated measures. Glutamate and glutamine levels did not change from interictal to the preictal and ictal state. GABA levels increased from interictal towards the preictal state for migraine patients compared with healthy controls. We conclude that high resolution 7T MRS is able to show changes in the glutamatergic system towards a triggered migraine attack, by revealing an increased GABA concentration associated with the onset of a migraine attack.

Preamble time-division multiple access fixed slot assignment protocol for secure mobile ad hoc networks

Mobile ad hoc networks are the “spontaneous networks” which create a temporary network in any place and any time without using any extra fixed radio device of a full infrastructure network. Each device in this network works as a router to develop end-to-end communication connections and move independently in any direction. Mostly, mobile ad hoc networks use the IEEE 802.11b protocol with carrier-sense multiple access with collision avoidance medium access control layer protocol for sharing a common medium among the nodes simultaneously. Due to this distributed medium, the routing and medium access control layer of the mobile ad hoc network are prone to attacks. Among several attackers, blackhole attacker is the dangerous one which causes the loss of all data packets of devices in the network. Efficient medium access control protocol designs in this respect play a key role in determining channel utilization, network delay, and, more importantly, network security. In the proposed work, preamble information is used with time-division multiple access medium access control. The preamble time-division multiple access uses time synchronization for each time slot and does not assign much time to the blackhole attacker due to a fixed time slot. As a result, blackhole is not stable in all communications and such an attack is effectively defended. Simulation results show that, in the presence of the blackhole attacker, carrier-sense multiple access with collision avoidance has a high packet loss ratio and low network throughput as compared to the proposed preamble time-division multiple access.

Estimating Traffic Congestion By Queuing theory

For economic progress and growth, commuting has become requisite in our life. We move across by various modes however, our common mode of commuting is via roads. Overcrowded roads is infuriating and annoying. The volume of traffic has been growing swiftly in some years. This change could be accredited to numerous reasons such as increase in population, deficient resources, everyone’s craving for their private vehicle, urbanization, etc. Due to unendingly growing of vehicular volume, there is awful traffic bottleneck at junctions. Some of the junctions suffer with long and irritating traffic jams occasionally. Traditional traffic lights try to fix the situation but generally fails since fixed time slots of green signals undermine the presence of huge traffic volume. These traffic lights are predefined and even ignore the presence of emergency vehicles. Through our paper we do modelling and estimate congestion on road in Rohini by using Queuing Theory. This method tries to figure various parameters invoking congestion and tries to work on smooth traffic flow.

Trajectory and Communication Design for UAV-Relayed Wireless Networks

In this letter, an unmanned aerial vehicle (UAV) is employed to relay data for multiple pairs of ground users in time-division manner. Our objective is to maximize the minimum average information rate of each communication pair by optimizing the time slot allocation, power allocation, and UAV trajectory. This is a non-convex optimization problem, which is challenging to address. First, we approximately solve the subproblem of time slot allocation with fixed power and trajectory, as well as the subproblem of power and trajectory optimization with fixed time slot allocation. Then we propose an efficient iterative algorithm to approximate the optimal solution by solving the two subproblems alternately. Numerical results show the effectiveness of the proposed algorithm.

MD-MAC: A Distributed TDMA Protocol Based on Desynchronization for Multi-Hop Topologies

Communication resource allocation and collision detection are important for the Ad Hoc network. Considering the existing TDMA-MAC protocol, the allocation way based on fixed time slot is mostly adapted, which cannot guarantee the performance and be not flexible about the business for different nodes in the distributed network. Desynchronization, as a biological term, can be utilized in the Ad Hoc network. It implies that sensor nodes interleave periodic events to occur in succession through negotiation and adjustment. In this paper, we design a MAC protocol(MD-MAC) in the multi-hop network based on the idea of Desynchronization to solve the problem caused by stale information and lay down the adjustment rule to allocate the communication resource. Also, we propose a scheme which the network can detect collision in a self-adapting way. Finally, we simulate the proposed protocol to evaluate the performance. The experimental results indicate that the proposed algorithm can accelerate the convergence speed of resource allocation, solve collision and improve the efficiency of the distributed network.