Allocation Approach Research Articles

Priority-Aware Access Strategy for GF-NOMA System in IIoT: The Device-Specific Allocation Approach

Priority-Aware Access Strategy for GF-NOMA System in IIoT: The Device-Specific Allocation Approach

Routing and dynamic core allocation with fragmentation optimization in EON-SDM

The increasing need for rapid data transmission in optical networks has made the effective allocation of spectrum resources a crucial necessity. This study presents a new spectrum allocation approach that incorporates path selection and careful consideration of cross-talk (XT) limitations. We propose a novel fragmentation coefficient and dynamic core-changing-based routing, spectrum, and core allocation (FC-DCC-RSCA) technique. The algorithm begins by searching for the shortest paths using the Dijkstra algorithm. It then proceeds to evaluate fragmentation coefficients (FC) using the Continuous Aligned Slot Ratio (CASR) model. Subsequently, the FC undergoes a filtration process wherein only those falling inside predetermined fragmentation ranges (min and maxFC) are retained. Further spectrum allocation has been done based on XT constraints. The algorithm has better proficiency in dynamic flexibility, enabling it to effectively handle varying traffic loads. By conducting a series of rigorous simulations, our proposed approach exhibits a notable decrease in bandwidth blocking probability (BBP) while also opening the door for considerable improvements in spectrum utilization. The algorithm’s proficiency in adapting to changing network circumstances signifies its importance as a helpful thing in enhancing the allocation of resources within optical networks. It is well-positioned to address the developing requirements of contemporary communication systems. The spectrum utilization of the FC-DCC-RSCA increases by 12.5% and 6.25%, exhibits a reduction in its FR by 13.5% and 2.7%, and has a BBP of 0.051 for the traffic load of 450 Erlang, compared to SBP-PMC and FMDE-RSCA. The algorithm that has been provided provides opportunities for more investigation and enhancement. Potential areas for future research include the examination of strategies to improve energy efficiency, the development of scalable solutions for bigger network infrastructures, and the incorporation of machine learning methodologies to facilitate dynamic decision-making processes. Furthermore, it is worth investigating the potential of the method to be applied in developing optical communication technologies, such as space-division multiplexing. These endeavors hold the potential to enhance the current level of knowledge and practices in the field of optical network management and optimization.

Improved PVTOL Test Bench for the Study of Over-Actuated Tilt-Rotor Propulsion Systems

In recent years, applications exploiting the advantages of tilt-rotors and other vectored thrust propulsion systems have become widespread, particularly in many novel Vertical Takeoff and Landing (VTOL) configurations. These propulsion systems can provide additional control authority, enabling more complex flight modes, but the resulting control systems can be challenging to design due to the mismatch between the vehicle degrees of freedom and physical input variables. These propulsion systems present both advantages and difficulties because they can exert the same overall forces and moments in many different propulsive configurations. This leads to the traditional non-uniqueness problem when using the inverse dynamics control allocation approach, which is the basis of many popular VTOL control algorithms. In this article, a modified Planar VTOL (PVTOL) test bench configuration, which considers an arbitrary number of co-linear tilting rotors, is introduced as a benchmark for the study of the control allocation problem. The resulting propulsion system is then modeled and linearized in a closed and compact form. This allows a simple and systematic derivation of many of the currently used control allocation approaches. According to the proposed PVTOL configuration, a two-rotor test bench is implemented experimentally and a decoupling control allocation strategy based on Singular Value Decomposition (SVD) analysis is developed. The proposed approach is compared with a traditional input mixer algorithm based on physical intuition. The results show that the SVD-based solution achieves better cross-coupling reduction and preserves the main properties of the physically derived approach. Finally, it is shown that the proposed PVTOL configuration is effective for studying the control allocation problem experimentally in a controlled environment and could serve as a benchmark for comparing different approaches.

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

Allocation of national renewable expansion and sectoral demand reduction targets to municipal level

Despite the ubiquitous term “climate neutral cities”, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.

EV Charging Command Fast Allocation Approach Based on Deep Reinforcement Learning With Safety Modules

Efficient real-time management of electric vehicle (EV) charging in a charging station (CS) is vital to the integration of large-scale EVs in power grids. It faces critical challenges such as frequent changes in the grid’s dispatch commands, the complexity of EVs’ costs, and the uncertainties in the EVs’ charging/traveling behaviors and in the future dispatch commands. To tackle these challenges, this paper proposes a deep reinforcement learning (DRL)-based allocation approach that optimally and efficiently allocates the grid’s commands to the EVs and controls their charging in real time. It includes two stages. Stage 1 includes a data-driven EV cost quantification method, which efficiently quantifies the EVs’ flexibility contributions with long-term return consideration. Stage 2 proposes a high sample efficiency DRL-based allocation method, which optimizes the EVs’ charging and addresses the EV-and grid-related uncertainties. The proposed allocation has a fast computational speed. Finally, to address the security risk due to DRL’s stochastic exploratory actions, two safety modules are developed which ensure the EV charging security and the allocation accuracy. The effectiveness and efficiency of the proposed strategy are verified by comparing its performance against multiple benchmark approaches.

An Innovation Approach for Optimal Resource Allocation in Emergency Management

In metropolitan regions, emergency events with different severity levels usually require multiple resources that have appropriate functionalities, money expenditure, moving velocities, etc. These resources could distribute over an extensive area with different ownerships. Solving the resource allocation problem for such an event involves complicated collaboration of multiple emergency departments under strict time constraints. Traditional resource allocation approaches usually have difficulties to efficiently find out the best resource assignment within the time limits by considering the large number of possibilities, which result in a considerable increase in fatalities. In this paper, a multiagent-based decentralised resource allocation approach using the domain transportation theory is proposed to handle a multi-task emergency event. The proposed approach is designed to effectively select appropriate resources without the global information and to concurrently generate the resource deployment plans for multiple tasks by considering the severity level of an emergency event. In the experiments, the proposed approach is tested along with other related approaches, and the experimental results indicate that the proposed approach can efficiently generate the optimal solution in terms of resource allocation time and money expenditure.

Cost of Quality and Financial Performance of Small and Medium Enterprises

This study delves into the complex interplay between the cost of quality elements—specifically, prevention, appraisal, and failure costs—and the financial performance of Small and Medium Enterprises (SMEs). By employing empirical analysis based on 246 completed questionnaires from various sectors within Ghana's SME landscape, the research endeavors to illuminate the critical associations between these cost factors and financial outcomes. The Partial Least Structural Equation Model (PLS-SEM) technique was utilized to scrutinize these relationships. The findings reveal a significant positive correlation between prevention and appraisal costs with improved financial performance, while failure costs exhibit a negative association. These outcomes substantiate the vital role of resource allocation and strategic management of quality costs in shaping the financial health of SMEs. The implications extend to both academia and practical business strategies, highlighting the need for a balanced approach in resource allocation and the significance of preventive measures in fostering enhanced financial performance for SMEs. The study underscores the importance of proactive quality management and strategic decision-making in driving the financial success and sustainability of SMEs in today's competitive business environment.

A Two-Stage Investment Behavior-Based Approach for Efficient Allocation of Electrical Energy With Shared Energy Storage Station

A Two-Stage Investment Behavior-Based Approach for Efficient Allocation of Electrical Energy With Shared Energy Storage Station

A Dynamic Hierarchical Game Approach for User Association and Resource Allocation in HetNets With Wireless Backhaul

A Dynamic Hierarchical Game Approach for User Association and Resource Allocation in HetNets With Wireless Backhaul

Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

Graph Neural Networks Approach for Joint Wireless Power Control and Spectrum Allocation

Modelling recycling for the life cycle assessment of perovskite/silicon tandem modules

With the massive growth of the global capacity of photovoltaics (PV) over the last decade, the PV waste is expected to increase dramatically in the near future. Having potential to reduce the use of raw materials and preserve natural resources, PV recycling is attracting more and more attention. This being said, the environmental impacts over the life cycle of PV technologies, including the end-of-life (EoL) stage, should be evaluated carefully. Life cycle assessment (LCA) is currently the most common methodology to assess the potential environmental impacts of a product throughout its entire life cycle. However, the modelling of recycling in LCA has always been a challenge and no consensus has yet been reached, since the treatment of recycling does not only involve an EoL management of waste, but also the production of recycled material. Perovskite on silicon tandem is a widely investigated emerging PV technology having the potential to overcome the power conversion efficiency (PCE) limit of the single-junction crystalline silicon technology. The EoL modelling seems more challenging in the case of emerging technologies for which the EoL is more uncertain than for established technologies. In this article, six common and important approaches of EoL modelling in LCA were applied to future perovskite/silicon tandem modules to analyze the effect of the different EoL modelling approaches on the LCA results. The aim was to identify the most suitable methodological approaches to account for recycling, when modelling the life cycle of PV modules. The environmental performance of perovskite/silicon tandem modules was assessed over their life cycle and expressed in terms of impacts per m2 of module. After testing the six EoL modelling approaches and comparing the LCA results, the EoL modelling choice was found to lead to non-negligible differences. For example, in terms of climate change, the impact of the tandem modules ranges from 45 to 59 kg CO2-eq/m2. Among the six EoL modelling options, the approaches of simple cut-off and cut-off with economic allocation are more oriented towards the promotion of high rates of recycled material integrated as an input to the assessed product among industrial actors, while the approach of closed-loop allocation provides incentives to maximize the ratio of recycling at the EoL, regardless the initial ratio of recycled content within the product. Some approaches such as the circular footprint formula (CFF) tend to provide both incentives to increase the content of recycled input material in the manufacturing of the product and the recycling ratio at the EoL of such product. After applying the different alternatives, a set of recommendations to select the relevant EoL modelling approaches are provided: 1) the CFF is recommended as a representative approach due to its wide applicability, tending to provide an intermediate result and reflecting the characteristics of materials; 2) sensitivity analysis should be applied to check the robustness of the results, 3) the cut-off approach and the closed-loop allocation should be used at least for the sensitivity analysis.

A Novel Approach of Efficient Resource Allocation in Smart Consumer Electronics Device Network

A Novel Approach of Efficient Resource Allocation in Smart Consumer Electronics Device Network

A Dual-Objective Approach for Allocation of Virtual Machine with improved Job Scheduling in Cloud Computing

In Cloud Computing (CC) environment, requests of user are maintained via workloads that are allocated to Virtual Machines (VMs) using scheduling techniques which primarily focus on reducing the time for processing by generating efficient schedules of smaller lengths. The efficient processing of requests also needs larger usage of resources that incurs higher overhead in the form of utilization of energy and optimization of cost utilized by Physical Machines (PMs). Assignment of VMs optimally in the environment of CC for jobs submitted by users is a challenge. In order to obtain better solution involving scheduling of jobs to VMs, considering two parameters utilization of energy and cost, we present a dual-objective approach for VM allocation with improved scheduling of jobs in CC environment. The proposed work aimed to build a dual-objective scheduling model for improved job scheduling, focusing on minimization of cost and utilization of energy at a time. For evaluating performance of dual-objective approach, we utilized two types of benchmark datasets and compared with existing approaches such as Whale, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Metaheuristic Dynamic VM Allocation (MDVMA) techniques. The results obtained from simulation demonstrated that dual-objective approach performs better in the form of minimization of utilization of energy and cost

Optimizing Scalable Online Services: A Dynamic Resource Allocation Approach Utilizing Artificial Intelligence

Optimizing Scalable Online Services: A Dynamic Resource Allocation Approach Utilizing Artificial Intelligence

Vehicular Fog Resource Allocation Approach for VANETs Based on Deep Adaptive Reinforcement Learning Combined With Heuristic Information

Vehicular Fog Resource Allocation Approach for VANETs Based on Deep Adaptive Reinforcement Learning Combined With Heuristic Information

QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming

QoE-Driven Cross-Layer Bitrate Allocation Approach for MEC-Supported Adaptive Video Streaming

A Stochastic Bi-level Optimal Allocation Approach of Intelligent Buildings Considering Energy Storage Sharing Services

A Stochastic Bi-level Optimal Allocation Approach of Intelligent Buildings Considering Energy Storage Sharing Services

A Distributionally Robust Optimization Approach to Two-Sided Chance-Constrained Stochastic Model Predictive Control With Unknown Noise Distribution

In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of multiple two-sided chance constrained discrete-time linear systems corrupted by additive noise. The prevalent mechanism to cope with two-sided chance constraints is the so-called risk allocation approach, which conservatively approximates the two-sided chance constraints with two single chance constraints by applying Bool's inequality. In this proposed DR-SMPC framework, an exact second-order cone approach is adopted to abstract the multiple two-sided chance constraints by considering the first and second moments of the noise. With the proposed DR-SMPC algorithm, the worst-case probability of violating safety constraints is guaranteed to be within a pre-specified maximum value. By flexibly adjusting this pre-specified maximum probability, the feasible region of the initial state can be increased for the SMPC problem. The recursive feasibility and convergence of the proposed DR-SMPC are rigorously established by introducing a binary initialization strategy for the nominal state. A simulation study of a single spring and double mass system was conducted to demonstrate the effectiveness of the proposed DR-SMPC algorithm.

Algorithm for ranking applications in QS with priorities in the agro-industrial sector

The study delves into the queuing process for warranty or post- warranty service requests in companies specializing in repairing electronic devices within the agro-industrial sector. The research also investigates the environmental implications of such operations and the environmental challenges related to servicing electronic devices in agricultural production. To streamline maintenance operations and cut costs, a logistic allocation approach for requests is proposed based on queuing systems theory principles. By analyzing workshop operations, a repair work performance scheme is developed, aligning with the specifics of agricultural production and environmental considerations in device maintenance. This has helped outline the primary requirements for the information system, creating diagrams that detail the sequence of processes for order addition, diagnostics, repair, spare parts ordering, and waste disposal. Using terminology from mass service systems, the workshop process is described, and an algorithm for preliminary time parameter calculations has been formulated to predict service completion dates. This methodology not only enables timely updates for customers regarding service completion times and the optimization of order routing through various channels, but it also contributes to addressing the adverse environmental impacts, a critical aspect in the organization of activities within the agricultural sector.