Fair Resource Allocation Research Articles

FAIR WEALTH DISTRIBUTION THROUGH INSTRUMENTS IN ISLAMIC ECONOMICS

Distribution plays a crucial role in ensuring fair allocation of resources and commodities to individuals in a community, with the objective of achieving maslahat (benefit). In Islamic economics, distribution has a broader significance compared to other economic systems, encompassing the fair distribution of resources, goods, and services through market mechanisms, enabling individuals to experience the benefits of produced commodities. This research aims to examine the importance of fair wealth distribution in Islam using principles such as waqaf, qard al-Hasan, musharakah, mudharabah, zakat, and infaq. Employing a qualitative methodology, data was collected from papers, research journals, books, and written sources, which were reviewed, interpreted, and analyzed through content analysis. The findings indicate that distribution instruments in Islamic economics serve as effective means to achieve fair and equitable distribution within society. By implementing waqaf, qard al-Hasan, musharakah, mudharabah, zakat, and infaq, communities can attain social justice and reduce economic disparities. However, public awareness, adequate infrastructure, and a supportive regulatory framework are crucial factors for the successful implementation of these distribution instruments. Through appropriate efforts, these instruments can become powerful tools in fostering a more inclusive, sustainable, and just economy.

Energy-efficient power allocation algorithm based on the BFGS algorithm and the armijo criterion

In high-speed railway (HSR), the increasing demand for wireless communication and high-speed broadband leads to higher energy consumption by the system. To improve energy efficiency (EE) and fair allocation of resources in multi-user communication, an algorithm combining the BFGS algorithm and the Armijo criterion is used. Firstly, an EE optimisation model of the system is constructed by considering the constraints, such as the average transmitting power of the evolved Node B (eNB), the minimum transmission rate and fairness among users. Secondly, the objective function is transformed into a convex optimisation problem through equivalent transformation. Finally, an algorithm combining the BFGS algorithm and the Armijo criterion is used to solve the objective function and then determine the power allocation scheme for each moment of the system. The simulation results show that the proposed scheme shows a better performance in terms of EE than the traditional algorithm in the same scenario over time. Under different user numbers and proportional rate constraint conditions, the adopted scheme can optimise system EE while satisfying the user communication fairness threshold. In addition, compared with the traditional power allocation scheme, the proposed algorithm is significantly improved in terms of eNB transmit power, fairness and EE performance.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks

The application of device-to-device (D2D) communication and multi-carrier (MC) non-orthogonal multiple access (NOMA) have been considered as promising technologies to enhance energy efficiency, network connectivity, and spectrum efficiency in the next generation of communication systems. This paper investigates a joint optimization problem of spectrum-efficient mode selection, fair sub-channel (SC) allocation, and power allocation of user pairs for a D2D-enabled MC-NOMA network. In the proposed network, user pairs are enabled to transmit and receive data in either underlaid D2D mode or cellular mode, and the NOMA technique is adopted in both downlink and uplink transmission. To maximize the throughput of the system and to manage the co-channel interference, the joint optimization problem is formulated under the constraint of guaranteeing all users with a certain amount of resources while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements of users. As an NP-hard problem, the optimization problem is first transformed into two sub-problems namely 1- the joint SC allocation and mode selection and 2- the power allocation. Then, to jointly solve the SC allocation and mode selection sub-problem, an enhanced three-side many-to-one matching algorithm is proposed and to obtain the solution to the power allocation problem, the known sequential convex programming method is used. Moreover, an iterative algorithm is used to enhance the performance of the system in terms of sum-rate. Finally, a mathematical expression is introduced to check the quality of service (QoS) of the proposed fair resource allocation algorithm and the results of the simulation are stated to indicate improved system performance.

Multi-Service Network Slicing 5G NR Orchestration via Tailored HARQ Scheme Design and Hierarchical Resource Scheduling

In the radio access network domain, novel applications' requirements and network dynamics claim scalable processing capabilities, decentralized architecture, and on-demand resource allocation. Due to the intrinsic heterogeneous nature of the network resources, a fully context-aware fronthaul management is extremely complex to design. Operators have a high level-of-abstraction of the infrastructure's resources, which limits their decision-making capabilities. In this paper, a novel radio access network orchestrator is proposed, which jointly combines network function placement and resource allocation techniques in a single optimization framework. Given the infrastructure and users' delay constraints, we formulate a constrained linear maximization problem to determine the optimal timing parameters of the downlink hybrid automatic repeat scheme for each user. At inter-slice layer, our proposed resource allocation method is modelled as a Markov decision process, where the resources' sharing policy is tailored according to the real-time slice traffic load. At the intra-slice level, under mild conditions the radio resources are strategically prioritized, following a game theoretic model. Simulation results show that our method performs the hybrid automatic repeat request response 37%-88% compared to the classical LTE schemes. The dynamic allocation method at the inter-slice level improves the resource's utilization up to 50% compared to static network slicing. Moreover, at the intra-slice, the under and over resource provisioning has been decreased by 21.35% and 60.08%, respectively, compared to the classical fair resource allocation schemes.

Achievable rate optimization for space–time block code-aided cooperative NOMA with energy harvesting

Cooperative non-orthogonal multiple access (C-NOMA) is one of the promising techniques that support the massive connectivity of users and improve the fair allocation of network resources. However, the performance of C-NOMA networks is restricted due to the complexity introduced by multiple user-assisted relays coupled with their energy consumption bottleneck. In this paper, we develop a low-complexity network framework with the main goal of achieving a fair and optimal achievable rate. For the network model, we adopt a Space–time block code transmission scheme to increase the spatial diversity of the network with a minimum number of relays. Further to this, we consider a radio frequency-based energy harvesting technique known as simultaneous wireless information and power transfer in order to compensate for energy expended by the relays over the cooperating transmission links. Based on the proposed network model, we solve the achievable rate maximization problem from the perspective of joint optimal power allocation factor and power splitting ratio. Due to the non-convex nature of the problem and to also ensure fair resource allocation, we propose a low-complexity solution based on the concept of Nash bargaining solution. Further to this, we evaluate the achievable rate and energy efficiency of the system. Simulation results show that our proposed solution outperforms the baseline approaches.

Contention-Based Proportional Fairness (CBPF) Transmission Scheme for Time Slotted Channel Hopping Networks

Time slotted channel hopping (TSCH) is a mediumaccess control (MAC) protocol introduced in IEEE802.15.4e standard, addressing low-power requirements of the Internet of Things and low-power and lossy networks. The 6TiSCH operation (6top) sublayer of IEEE802.15.4e defines the schedule that includes sleep, transmit, and receive routines of the nodes. However, the standard does not specify the design of the schedule. In this article, we propose a contention-based proportional fairness transmission scheme for TSCH networks to maximize the system throughput addressing the fair allocation of resources to the nodes. We propose a convex programming-based method to achieve the fairness and throughput objectives. We model TSCH MAC as a multichannel slotted ALOHA and analyze it for a schedule given by the 6top layer. Performance metrics, such as throughput, delay, and energy spent per successful transmission, are derived and validated through simulations and real-time testbed implementations.

Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation

Consider the problem of allocating indivisible goods among agents with additive valuations, where monetary payments are not allowed. When randomization is allowed, it is possible to achieve compelling notions of fairness such as EV, which states that no agent should prefer any other agent's allocation to their own. When allocations must be deterministic, achieving exact fairness is impossible but approximate notions such as EV up to one good can be guaranteed. In “Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation,” H. Aziz, R. Freeman, N. Shah, and R. Vaish ask whether it is possible to achieve both types of guarantees simultaneously. More specifically, they ask whether there exists a probability distribution over deterministic allocations such that every deterministic allocation is envy-free up to one good and the distribution is exactly envy-free in expectation. The main result of the paper answers this question in the affirmative, showing that ex ante and ex post fairness need not be in conflict.

Ethical implications for children’s exclusion in the initial COVID-19 vaccination in Ghana

ABSTRACTBioethics provides various models of fair allocation of scarce health resources like COVID-19 vaccines. Even though these models are grounded in some ethical principles like justice and beneficence, there were severe inequalities in global access to COVID-19 vaccines. In Ghana, about 21.5 million COVID-19-doses have been administered but comprise mainly members of the adult population. As a result, ethical issues related to vaccinating children have been largely ignored in the country. This paper explores some of the ethical implications related to children’s exclusion in the initial COVID-19 vaccination programs in Ghana. It provides a general overview of the COVID-19 pandemic in Ghana and how it related to children and discusses the risks to which Ghanaian children were exposed by delaying their COVID-19 vaccination. A guide to facilitating the full rollout of COVID-19 vaccination in Ghana for children has been proposed that indicates that a fair vaccine distribution for children should prioritize children on admission at health facilities, those diagnosed with severe underlying health conditions, and children who could play an instrumental role in promoting vaccine uptake. It concludes that children must not be placed at the peripheries of the COVID-19 vaccination program in Ghana.

Burnout symptoms and workplace satisfaction among veterinary emergency care providers.

To measure symptoms of burnout among veterinary emergency care providers (VECPs), determine how burnout symptoms among VECPs compare to human emergency care providers, and identify what demographic and workplace variables are associated with these symptoms. Cross-sectional study using an online survey and convenience sampling. A total of 1204 VECPs including mostly veterinarians and technicians. An online questionnaire using the Maslach Burnout Inventory and Areas of Worklife Survey was distributed between May and July 2018 via an email list and social media. VECPs working less than part-time (<20h/week) and incomplete survey responses were excluded. VECPs had higher total emotional exhaustion and depersonalization scores and lower total personal accomplishment scores compared to emergency department human healthcare professionals (P<0.001). Subsets of VECPs with the highest burnout symptom scores included females, residents, those working in private or corporate referral hospitals, and those with off-shift duties. Workplace variables positively associated with burnout symptom scores among these groups typically included perceptions of an unmanageable workload, lack of control over work, little reward (recognition) for work, or an unfair allocation of resources at work. Conversely, VECPs working >20years and those married or with children at home had lower burnout symptom scores. Workplace variables negatively associated with burnout symptom scores among respondents included perceptions of having a manageable workload, control over work, reward for work, or a fair allocation of resources at work. Multivariable analysis revealed that the variable most positively associated with emotional exhaustion and depersonalization was workload, whereas reward was most positively associated with personal accomplishment (P<0.001). Burnout symptom scores are higher among VECPs compared to human emergency department teams. Targeting workplace variables such as workload and reward is warranted in future studies to determine strategies for reducing burnout among VECPs.

All for One and One (Space) for All!

Global equity is the common pursuit of most people all over the world. Although fairness is not absolute, we should try our best to achieve global fairness in resource allocation and opportunity supply. But it’s hard to make the distribution of the resources and development opportunities fair enough with the limited resources on the earth. With the depletion of the earth's resources, more and more people realize that the earth's resources are not enough for people to use permanently, so scientists have created a great idea of asteroid mining. With the limitation of technology and the economy, there is still a long way to go. After solving the problems above, it’s still hard to achieve global equity while allocating mineral resources. Aiming at rationalizing the allocation of resources mined on the asteroids, we try to establish a global equity model. At the same time, we will also study the feasibility and vision of asteroid mining. The impact of variability and volatility factors on the model will be analyzed.

Performance Analysis of Intelligent Reflecting Surface Assisted Opportunistic Communications

Intelligent reflecting surfaces (IRSs) are a promising technology for enhancing coverage and spectral efficiency, both in the sub-6 GHz and the millimeter wave (mmWave) bands. Existing approaches to leverage the benefits of IRS involve the use of a resource-intensive channel estimation step followed by a computationally expensive algorithm to optimize the reflection coefficients at the IRS. In this work, focusing on the sub-6 GHz band of communications, we present and analyze several alternative schemes, where the phase configuration of the IRS is randomized and multi-user diversity is exploited to opportunistically select the best user at each point in time for data transmission. We show that the throughput of an IRS assisted opportunistic communication (OC) system asymptotically converges to the optimal beamforming-based throughput under fair allocation of resources, as the number of users gets large. We also introduce schemes that enhance the rate of convergence of the OC rate to the beamforming rate with the number of users. For all the proposed schemes, we derive the scaling law of the throughput in terms of the system parameters, as the number of users gets large. Following this, we extend the setup to wideband channels via an orthogonal frequency division multiplexing (OFDM) system and discuss two OC schemes in an IRS assisted setting that clearly elucidate the superior performance that IRS aided OC systems can offer over conventional systems, at very low implementation cost and complexity.

Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0

The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities. Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in heterogeneous networks with enhanced data transfer, two steps solution is implemented. The Long Short-Term Memory (LSTM) technique is used in the initial step of traffic prediction to assess the minimal time of prior traffic conditions before being applied to estimate dynamic traffic. In the second step, effective link scheduling and selection are made for each wireless technology, taking into account predicted load, gateway distance, link capacity, and estimated time. More data is transmitted at maximum capacity as a result of improved data transfer fairness for all gateways and then the data is protected by Digital Twin technology. Simulated results show that our suggested strategy performs better than other approaches by obtaining maximum network throughput in Industry 4.0 to provide protective solutions using Digital Twin technology.Index Terms – Internet of Things (IoT), Link Scheduling, Traffic Prediction, Machine Learning (ML), Industry 4.0.

Inverse Matrix Games With Unique Quantal Response Equilibrium

In an inverse game problem, one needs to infer the cost function of the players in a game such that a desired joint strategy is a Nash equilibrium. We study the inverse game problem for a class of multiplayer matrix games, where the cost perceived by each player is corrupted by random noise. We provide sufficient conditions for the players’ quantal response equilibrium—a generalization of the Nash equilibrium to games with perception noise—to be unique. We develop efficient optimization algorithms for inferring the cost matrix based on semidefinite programs and bilevel optimization. We demonstrate the application of these methods in encouraging collision avoidance and fair resource allocation.

Democratic Wireless Channel Assignment: Fair Resource Allocation in Wi-Fi Networks

User experience is the ultimate quality of service criterion for modern WLAN networks. However, network configuration approaches are mainly network-centric. We envision a paradigm shift, empowering users in network management. We study how automated negotiation and collective intelligence can support the democratic configuration of a wireless network, leveraging client and provider interests. This new paradigm allows for flexible network configuration, which enables better exploitation of resources considering the clients’ real usage and needs, and a fair distribution of throughput among users.

Advancing remote consultation through the integration of blockchain and ant colony algorithm.

To guide the more reasonable and fair allocation of medical resources, to solve the problem of fee prices negotiated by various subjects in the medical and health system and patient payment, and to solve the problem of how to ensure the privacy, accuracy, consistency and traceability of data in the process of collecting patient information in each hospital, according to the operation process of a remote consultation service, a decentralized remote intelligent consultation blockchain model is proposed. The model uses the improved ant colony algorithm under a smart contract and studies the practicality of the improved ant colony algorithm on the multi-node remote consultation service simulation platform. According to the experimental analysis results, the improved ant colony algorithm can automatically execute and effectively match the target population under the smart contract.

On a Fair and Risk-averse Urban Air Mobility Resource Allocation Problem Under Demand and Capacity Uncertainties

On a Fair and Risk-averse Urban Air Mobility Resource Allocation Problem Under Demand and Capacity Uncertainties

How a child entangles empathy and computational thinking in reasoning about fairness

In this work, we argue for expanding the scope of K-12 computational thinking (CT) integration contexts to include everyday scenarios involving moral reasoning. Epistemic overlap between computational thinking practices and moral reasoning suggest that these contexts are potentially rich sites to see “seeds of CT” in children's reasoning and can provide rich educational pathways for children into CT. Taking a case-study approach, we examine the reasoning of a second-grader, Ollie, on a task involving fair allocation of resources to victims of a natural disaster. Our analysis finds that Ollie's reasoning was rich with seeds of CT (e.g., problem formulation, abstraction, complex-systems thinking) and that empathy served as an important supporting role to the CT. This work has implications for curricular design, suggesting that fairness and resource allocation scenarios with built-in opportunities for empathy might provide rich sandboxes for CT integration.

Design of Incidence Matrices With Limited Constellation Expansion in Massive Connectivity NOMA Systems

Non-orthogonal multiple-access (NOMA) is designed to transmit massive amounts of user communications. The incidence matrix manages the relationship between users and resources. This study focused on increasing user supportability and complexity reduction using larger incidence matrices. Our approach to optimizing the incidence matrix to improve system capacity and reduce complexity is based on two critical mathematical concepts: Parallel classes in hypergraph theory and combinatorial designs allow us to explore and extend incidence matrices. Frame theory is used to estimate matrix structures. Then, we investigate applications utilizing our incidence matrix designs–Simple Orthogonal Multi-Arrays (SOMA). The characteristics of SOMA reflect the unique Latin Square pattern, allowing us to produce a highly flexible and fair resource allocation matrix. SOMA designs let us support overload factors over 500%. The theoretical performance analysis equations of our NOMA system were established to support dynamic adaptability and optimization. We implemented and evaluated security methods for eavesdroppers. The prototype of the user hierarchy allows a higher-priority group to have a lower error rate without significantly affecting the system’s performance. Finally, the Monte Carlo simulation indicated that our NOMA systems allow higher degrees of freedom and lower complexity than other NOMA schemes while maintaining graceful error rates with a maximum 33% improvement.

Optimized Resource Allocation for IoT-D2D Communication Using WSN

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomial-time Proportional Fair Resource Allocation Method (PFRAM), which considers the MU’s rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices’ energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device’s Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT’s average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device’s NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence.

Weighted De-Synchronization Based Resource Allocation in Wireless Networks

Considering the exponential growth of wireless devices with data-starving applications fused with artificial intelligence, the significance of wireless network scalability using distributed behavior and fairness among users is a crucial feature in guaranteeing reliable service to numerous users in the network environment. The Kuramoto model is described as nonlinear self-sustained phase oscillators spinning at varying intrinsic frequencies connected through the sine of their phase differences and displays a phase transition at a specific coupling strength, in which a mutual behavior is accomplished. In this work, we apply the Kuramoto model to achieve a weighted fair resource allocation in a wireless network, where each user has different quality of service (QoS) requirements. Because the original Kuramoto model is the synchronization model, we propose a new weighting parameter for representing requirement of each node resource and modify the Kuramoto model to achieve weighted fair resource allocation for users with different QoS requirements. The proposed modified Kuramoto model allocates all users the resource based on their weight among contending nodes in a distributed manner. We analyze the convergence condition for the proposed model, and the results reveal that the proposed algorithm achieves a weighted fair resource allocation and with potentially high convergence speed compared to previous algorithm.