Time Slot Research Articles

Review of QoS-aware resource allocation schemes for 5g networks

Resource management in wireless networks refers to the processes and techniques used to allocate, control, and optimize the various resources in a wireless communication system to ensure efficient and reliable operation. These resources typically include spectrum (frequency bands), power, time slots, and network infrastructure (e.g., base stations, access points). Effective resource management is crucial for maximizing network performance, ensuring Quality of Service (QoS), and providing a good user experience, especially in environments with limited resources and high demand. Quality of Service (QoS) refers to the ability of the network to provide different levels of service quality to various types of traffic, applications, and users. QoS in 5G is essential due to the diverse range of services the network is designed to support, each with its own specific requirements for performance metrics such as latency, reliability, bandwidth, and availability. In this paper, we study how the QoS-aware resource allocation procedure is handled in wireless networks. Different resource management techniques were studied and also the paper looked at some of the existing QoS-aware resources allocation schemes by highlighting the operation, strength and weakness and the QoS parameters considered by each of the schemes.

The Impact of Personality on Productivity and Job Performance

Abstract: There is a growing body of research on the impact of personality on job performance. Additional research is required to examine the influence of individual variations and perceptions on workplace conduct and productivity. Productivity is a critical quantitative element in determining the success of any individual to excel in a competitive environment and must effectively utilise limited resources. Job performance is a qualitative indicator of an individuals level of success within the workspace. For this study, the research method used is a survey to analyse the impact of different personalities on job performance and productivity. The results illustrate introverts are more productive despite displaying lower job performance than extroverts. In general, the personality questionnaire's design may have a limited representation of introverts due to their quiet nature. Ambiverts may be more challenging to distinguish during analysis. The study indicates the necessity of a more comprehensive comprehension of personality traits, with a specific emphasis on demographics and the working environment. Providing peaceful environments and designated time slots can enhance introverted performance in the workplace. Extraverts can excel at extroverted tasks but may struggle to adapt to introverted settings. Ambiverts possess both qualities, making influential leaders possible. Furthermore, this paper advises including supplementary control factors to establish a causal relationship between personality and workplace capability.

Simulation of 1 MWe hybrid solar power plant by the use of nano-fluid with eccentric backup system.

In past years, concentrated solar power (CSP) with an energy backup system has been a unique renewable energy utilization system among intermittent renewable energy systems. It could allow a CSP plant to operate as a base load system in the future. This paper simulates a solar power plant for 1 MWe. Parabolic trough collector (PTC) array and linear Fresnel reflector (LFR) field attached consecutively to produce superheated steam at 40MPa. The Rankine cycle has been used to run the steam turbine and an electricity generator is attached to a steam turbine to convert mechanical energy into electrical energy. Maximum temperature attained at the turbine inlet is 418.13 ˚C in 12:00-13:00 time slot in the month of January. Results show that solar power plant is feasible to produce 1 MWe. The minimum value of the power produced by the generator is 1.01 MWe in November in the 10:00-11:00 time slot whereas the maximum value of generated power is 1.57 MWe in December in the 11:00-12:00 time slot. The overall efficiency of power generated by the Rankine cycle is 21.25% in January in the 10:00-11:00 time slot. An energy storage system is attached to the system to work at night hours or in cloudy weather conditions.

A Fair MAC Protocol Based on Dual Bandwidth Allocation Iterations in Underwater Acoustic Sensor Networks

ABSTRACTMost existing passive allocation‐based media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) overlook the fairness issue in channel bandwidth allocation, resulting in certain nodes occupying channel resources for extended periods, thereby leading to low channel utilization. To address this issue, this paper proposes a fair MAC protocol based on dual bandwidth allocation iterations (FBA‐MAC). Firstly, by limiting the maximum number of packets and the maximum number of available time slots that the host node can receive in each communication cycle, the protocol reduces collisions and collisions more effectively, thus controlling the communication delay. Secondly, in the process of channel bandwidth allocation, a dual bandwidth allocation iterative method was used to allocate appropriate channel bandwidth for each member node according to its communication requirements and the generation time of its generated data packets. Finally, in order to minimize the time interval of each packet arriving at the host node to reduce the communication delay, the transmission scheduling scheme was adaptively adjusted by combining the results of channel bandwidth allocation to maximize the channel utilization. Simulation results indicate that compared to the other four MAC protocols, FBA‐MAC demonstrates significant advantages in terms of fairness (10% higher), achieving higher network throughput (10% higher) and shorter end‐to‐end delay. Therefore, FBA‐MAC exhibits superior adaptability and efficiency in dynamic network environments.

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

Impact of COVID-19 lockdown on water quality of major rivers in Sri Lanka

ABSTRACT The COVID-19 lockdown resulted in a reduction in human activities, lowering pollution levels in these rivers. Nevertheless, limited research can be found on the impact of COVID-19 on river water quality in Sri Lankan rivers. Therefore, this research was carried out to investigate the water quality index (WQI) of four major rivers (Mahaweli, Nilwala, Kelani, and Kalu) in Sri Lanka in three consecutive time slots spanning from 2019 to 2022 (before, during, and after the lockdown). In-depth WQI analysis has unveiled nuanced dynamics in river health. While the lockdown period contributed to positive alterations in key parameters, subsequent periods demonstrated declining water quality post-lockdown. This decline underscores the direct correlation between human activity and river water quality deterioration. Several parameters showed positive changes during the lockdown, indicating an improvement in river water quality. When comparing the periods before, during, and after the lockdown, pH, colour, turbidity, total iron, sulphate, chloride, total coliform, and E. coli all showed a significant positive impact. However, some parameters, such as fluoride and electrical conductivity (EC), had a neutral effect. This study sheds light on the positive effects of the COVID-19 lockdown on reducing river pollution in Sri Lanka.

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Business process simulation: Probabilistic modeling of intermittent resource availability and multitasking behavior

In business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday–Friday, 9:00–18:00. Resources are assumed to be always available during each time slot in their availability calendar. This assumption often becomes invalid due to interruptions, breaks, or time-sharing across processes. In other words, existing approaches fail to capture intermittent availability. Another limitation of existing approaches is that they either do not consider multitasking behavior, or if they do, they assume that resources always multitask (up to a maximum capacity) whenever available. However, studies have shown that the multitasking patterns vary across days. This paper introduces a probabilistic approach to model resource availability and multitasking behavior for business process simulation. In this approach, each time slot in a resource calendar has an associated availability probability and a multitasking probability per multitasking level. For example, a resource may be available on Fridays between 14:00–15:00 with 90% probability, and given that they are performing one task during this slot, they may take on a second concurrent task with 60% probability. We propose algorithms to discover probabilistic calendars and probabilistic multitasking capacities from event logs. An evaluation shows that, with these enhancements, simulation models discovered from event logs better replicate the distribution of activities and cycle times, relative to approaches with crisp calendars and monotasking assumptions.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

A novel differential chaos shift keying system based on joint time slot and code index of carrier phase modulation

This paper proposes a novel differential chaos shift keying (DCSK) system that utilizes carrier phase modulation in combination with time slot and code index modulation, referred to as CP-TCIM-DCSK system, to achieve high data rate transmission. In the proposed CP-TCIM-DCSK system, the carrier modulated by the carrier phase is used to transmit the information-bearing signal. In addition to physically transmitted information bits, extra information bits are conveyed through time slot and Walsh code modulation, as well as carrier phase modulation, making full use of the benefits of multidimensional modulation. To successfully estimate carrier phase bits and code index bits, an effective joint detection algorithm for carrier phase and code index tailored for the CP-TCIM-DCSK system is introduced. The theoretical Bit Error Rate (BER) expressions for the CP-TCIM-DCSK scheme are derived for both Additive White Gaussian Noise (AWGN) and multipath Rayleigh fading channels. Furthermore, a comparison of data rates, energy efficiency, and complexity between the CP-TCIM-DCSK system and the most advanced systems in the same category at present is conducted. The results validate the accuracy of theoretical analysis through simulation and demonstrate that the proposed scheme outperforms its competitive systems in terms of BER performance.

A Proposed Method for Assessing the Spatio-Temporal Distribution of Carcharhinus melanopterus (Quoy and Gaimard, 1824) in Shallow Waters Using a UAV: A Study Conducted in Koh Tao, Thailand

In this study, we propose a method for assessing the temporal and spatial distribution of Carcharhinus melanopterus in shallow waters using unmanned aerial vehicles (UAVs). Aerial surveys were conducted in Tien Og Bay (Koh Tao, Thailand) thrice daily (morning, afternoon, evening) along a 360 m transect at a 30 m altitude. Environmental factors, including cloudiness, sea conditions, wind, tide, and anthropogenic disturbance, were recorded for each time slot. We developed a Python/AppleScript application to facilitate individual counting, correlating sightings with GPS data and measuring pixel-based length. Abundance varied significantly across time slots (p < 0.001), with a strong morning preference, and was influenced by tide (p = 0.040), favoring low tide. Additionally, abundance related to anthropogenic disturbance (p = 0.048), being higher when anthropogenic activity was absent. Spatial distribution analysis indicated time-related, sector-based abundance differences (p < 0.001). Pixel-based length was converted to Total Length, identifying juveniles. They exhibited a strong sector preference (p < 0.001) irrespective of the time of day. Juvenile abundance remained relatively stable throughout the day, constituting 94.1% of afternoon observations. Between 2020 and 2022, an underwater video survey was conducted to determine the sex ratio of the individuals. Only females and juveniles were sighted in the bay.

Process improvements related to new patient scheduling.

26 Background: With over 530 physicians across 280 locations, the new patient time to first appointment varied across the state, with results ranging from 1 to 33 average days to schedule. Beginning in January 2021, a new patient scheduling model was developed to improve access to care and the patient experience. A new patient scheduling model was developed. The program was structured to support both on site and remote scheduling. Methods: Provider scheduling cards detailing physician preferences for scheduling, required records and prioritization for the type of diseases seen was implemented. Physicians were required to designate new patient time slots in their clinics tied to new patient volume trends. New patient scheduling workflows and required record guidelines were standardized and revised. Reporting and tracking metrics were created – average days to schedule, average days to appointment, referral fulfillment rate, referral to appointment ratio, true cancellation rate. Physicians were eligible to participate in a yearly bonus if time to first appointment goals were met. Utilized Virtual and in-person APPs provided benign hematology consults to address capacity constraints. Results: New patient true cancellation rates decreased from 9.84% to 5.22% resulting in capture of over 800 patients a month. There was a 56% increase in patient retention with a 138% increase in referral volume. The Practice realized a 69% reduction in average days to schedule from 9 to 3 days. The average time to first appointments improved from 23 to 14 days; a 38% decrease. Conclusions: With increasing numbers of new cancer patients, increasing numbers of cancer survivors and shortages of medical oncologists, it is essential to maintain access to timely cancer care. New models helped improve patient experience and practice retention of new cancer patients, by focusing on administrative staff to implement these programs. APP benign hematology clinics freed up physician time for new cancer patient consults. Physician support was obtained with education, frequent outreach, and financial incentives. 2021 Q1 2021 Q2 2021 Q3 2021 Q4 2022 Q1 2022 Q2 2022 Q3 2022 Q4 2023 Q1 2023 Q2 2023 Q3 2023 Q4 2024 Q1 Referral Volume 44,305 54,379 57,282 57,475 67,890 75,111 79,281 75,843 85,419 91,811 94,286 88,396 100,115 Avg. Days to Scheduled 9.1 6.9 5.4 6.2 5.8 4.9 3.9 4.0 3.7 4.0 3.8 3.6 3.3 Avg. Days to Appointment 22.4 24.3 23.3 22.9 20.9 21.8 20.0 19.2 17.0 17.5 17.1 16.7 14.9

Dynamic Multi-Scale Spatial-Temporal Graph Convolutional Network for Traffic Flow Prediction

This paper proposes a dynamic multi-scale spatial-temporal graph convolutional network (DS-STGCN) for traffic flow prediction. The network aims to comprehensively extract global and local dependencies in dynamic spatial-temporal data by inputting traffic network flow data to construct node feature graphs, topology graphs, and time slot feature graphs, capturing the complexity and dynamics of traffic flow. DS-STGCN interprets feature information of the traffic network from both spatial and temporal dimensions through dynamic multi-scale graph convolutional blocks. In the spatial dimension, these blocks use constraints at different levels to balance fine-grained local features and extensive global features, revealing the intrinsic structure of traffic flow data. In the temporal dimension, these blocks jointly learn with temporal convolutional blocks to capture multi-frequency time patterns and handle long sequence data, effectively extracting potential dependencies of time series. Furthermore, DS-STGCN effectively models the changing spatial-temporal relationships in road network flow by constructing dynamically adaptive updated adjacency tensors, generating dynamic graph structures to address the challenge of changing spatial-temporal relationships in the transportation system. Experimental results show that our method significantly outperforms other competing methods on five real traffic datasets (PEMS03, PEMS04, PEMS07, PEMS08 and METR-LA).

IoT Integrated Smart Parking Zone and Website with Stand-alone System and Wireless Technology

The research project works to digitalize the full parking management system, transform the manual parking methods, solve traffic and road congestion problem and show live updates of the designated parking slots. It is ecofriendly and eliminates human interaction. It also offers personal and corporate business earnings. In the project, anyone can book time slots for parking following their convenient schedule, pay bills via the online payment system, control the entrance while entering and exit gate in the parking area with the vehicle using the website system. Users do not have to use any extra identity instrument, like- RFID Cards, QR Codes etc. The system will verify them by online credentials. Moreover, people can see the live slot updates for better security. The project collaborated with 3 major technology parts which are practical hardware, software and database system. NodeMCU, Ultrasonic Sensors, Servo motors, and other technical components are used in the hardware part. PHP, HTML, JavaScript, CSS etc. codes are implemented for the website development. Firebase Realtime Database and MySQL are integrated for the cloud and local database management with the hardware and software parts for building the IoT technology environment and infrastructure.

Empowering Nontraditional College Students: A Self-Transformative Intervention for Promoting Positive Identity Development

ABSTRACT Identity development has been consistently linked to a variety of positive adjustment indicators and plays a critical role during times of uncertainty. Despite this, only a handful of interventions have been developed to promote positive identity development. However, these interventions have not only focused on adolescent and emerging adults, but apart from a handful of social identity interventions, have also not targeted specific domains of identity development. In the current study, we outline the development of a domain-specific identity intervention, the Making Career and Educational Choices workshop (MCEC), and evaluate pre to posttest outcomes in a sample of 58 college students (M age = 32.33, S.D.age = 10.95; 48% White; 82% female). Students were assigned to either MCEC (n = 32) or a control (n = 26) group by random assignment of selected time slots. Two-wave latent change modeling indicated intervention gains in identity commitment making, identification with commitment, and identity exploration in breadth and in depth. Although preliminary, the relatively large estimates of intervention-related change provide support for the capacity of career-focused identity interventions to support students in choosing and committing to a future life direction. Implications, challenges, and future directions are discussed to guide future research.

Geohash coding-powered deep learning network for vessel trajectory prediction using clustered AIS data in maritime Internet of Things industries

The increasing traffic flow and frequent navigational activities in maritime transportation raise accidents, such as collision and grounding. To mitigate such safety issues, high precision and stable vessel trajectory prediction play an essential role in developing accident prevention techniques in maritime Internet of Things (IoT) industries. However, vessel trajectory prediction is a challenge because conventional deep learning technologies struggle to capture complex changes in vessel trajectories. To address this challenge, this study designs a novel prediction method (namely DBSCAN-GeoCLSTM) comprising two steps. The first step employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to perform clustering analysis on trajectories. The second step feds trajectory data from different categories into a Long Short-Term Memory (LSTM) optimization network with Geohash Coding (called GeoCLSTM netework) for training. This GeoCLSTM network encodes the trajectory points using Geohash Coding to construct one-hot vectors, which captures the positional correlation information among trajectory points. The GeoCLSTM model further introduces a dimensionality reduction module by converting the one-hot vectors into feature vectors. Subsequently, the GeoCLSTM model employs LSTM to use these feature vectors from multiple consecutive time slots to accurately predict trajectory data. Results of comparative experiments demonstrate that the proposed DBSCAN-GeoCLSTM method achieved accurate and stable predictions, and outperformed eight state-of-the-art methods in the vessel trajectory prediction task.

First clinical implementation of a highly efficient daily online adapted proton therapy (DAPT) workflow.

This study presents the first clinical implementation of an efficient online daily adaptive proton therapy workflow (DAPT).
Approach: The DAPT workflow includes a pre-treatment phase, where a template and a fallback plan are optimized on the planning CT. In the online phase, the adapted plan is re-optimized on daily images from an in-room CT. Daily structures are rigidly propagated from the planning CT. Automated quality assurance (QA) involves geometric, sanity checks and an independent dose calculation from the machine files. 
Differences from the template plan are analyzed field-by-field, and clinical plan is assessed by reviewing the achieved clinical goals using a traffic light protocol. If the daily adapted plan fails any QA or clinical goals, the fallback plan is used. In the offline phase the delivered dose is recalculated from log-files onto the daily CT, and a gamma analysis is performed (3%/3mm). The DAPT workflow has been applied to selected adult patients treated in rigid anatomy for the last serie of the treatment between October 2023 and April 2024.

Main Results: DAPT treatment sessions averaged around 23 minutes [range: 15-30 min] and did not exceed the typical 30-minute time slot. Treatment adaptation, including QA and clinical plan assessment, averaged just under 7 minutes [range: 3:30-16 min] per fraction. All plans passed the online QAs steps. In the offline phase a good agreement with the log-files reconstructed dose was achieved (minimum gamma pass rate of 97.5 %). The online adapted plan was delivered for > 85% of the fractions. In 92% of total fractions, adapted plans exhibited improved individual dose metrics to the targets and/or organs at risk.
Significance: This study demonstrates the successful implementation of an online daily DAPT workflow. Notably, the duration of a DAPT session did not exceed the time slot typically allocated for non-DAPT treatment. As far as we are aware, this is a first clinical implementation of daily online adaptive proton therapy.&#xD.

Flight Schedule Optimization Considering Fine-Grained Configuration of Slot Coordination Parameters

In response to the rapid growth of air passenger and cargo transportation services and the sharp increase in congestion at various airports, it is necessary to optimize the allocation of flight schedules. On the basis of reducing the total airport delay time and ensuring the total deviation of flight schedules applied by airlines, it is necessary to consider finely configuring flight schedules with slot coordination parameters, introducing a 5 min slot coordination parameter, and optimizing airport flight schedules in different time periods. This article considers factors such as flight schedule uniqueness, corridor flow restrictions, and time adjustment range limitations to establish a three-objective flight-schedule refinement configuration model, which is solved using the NSGA-II algorithm based on the entropy weight method. Taking Beijing Capital International Airport as an example, the optimized results show that the total flight delay was reduced from 4130 min to 1142 min, and the original delay of 389 flights was reduced to 283 flights. Therefore, flight schedule optimization considering the fine-grained configuration of slot coordination parameters can effectively reduce airport delays, fully utilize time resources, and reduce waste of time slot resources.

Delay sensitive and energy adaptive clustering hierarchy protocol using enhanced agglomerative hierarchy algorithm with time‐controlled jellyfish optimization

AbstractWireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time‐controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy‐aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering‐based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.