Fairness Index Research Articles

Performance Analysis of QoS-Oriented OFDMA Protocol Based on IEEE 802.11ax for Cognitive Radio Network

To improve the quality of service (QoS) on the internet of medical things, a cognitive radio (CR) protocol based on orthogonal frequency division multiple access (OFDMA) is proposed, named CR-OFDMA. In this protocol, we divide a complete channel into multiple orthogonal subchannels and enhance the subchannel assignment scheme, which achieves QoS improvement under consideration of priority and fairness. Furthermore, we improve spectrum resource utilization by fully utilizing the remaining subchannels, feedback slots, and backoff slots. Then, a two-dimensional Markov model is established to describe the dynamic characteristics of the protocol operation, where the backoff stage and the backoff counter value constitute the system state. By establishing the traffic conservation equations for the system operation, the transmission probability and collision probability are calculated, and expressions of system throughput, channel utilization, and fairness index are derived. Finally, numerical results validate the advantages of CR-OFDMA.

Traffic grooming for massive light-path blockages in D2D-enabled hybrid LiFi and WiFi networks

Hybrid light-fidelity (LiFi) and wireless-fidelity (WiFi) networks (HLWNets) provide a promising solution for the future indoor wireless communications. This network structure faces the challenge of traffic congestion since LiFi links are prone to be blocked due to angular misalignment and path obstruction while WiFi has limited capacity. In this paper, a novel network structure that enables device-to-device (D2D) technology in HLWNets is considered. Then, traffic grooming (TG) for D2D-enabled HLWNets with massive light-path blockages is researched. By jointly handling mode selection, user pairing, and resource allocation, TG is formulated as a joint optimization problem. This can efficiently groom low-speed connections from WiFi onto high-capacity LiFi when massive light-path blockages occur, thus increasing network throughput. Next, a three-stage heuristic TG algorithm is developed to reduce the computational complexity required to solve the optimization problem. Finally, by simulation, the effectiveness of the proposed algorithm has been demonstrated. The simulation results indicate that the network throughput can be increased by up to 20% with the proposed algorithm. Besides, the proposed algorithm also has significant advantages in terms of Jain's fairness index and user satisfaction.

Low‐complexity user scheduling for LEO satellite communications

AbstractWith the increasing number of user terminals (UTs), the interference among UTs might significantly decrease the throughput of the low earth orbit satellite communication system. In this paper, the user scheduling method is investigated to suppress user interference. Specifically, leveraging the strong spatial directivity of satellite channels, a low‐complexity angle‐based orthogonal user selection (AOUS) algorithm is proposed, which selects UTs with nearly orthogonal channels via angle information of UTs. A rate‐based proportionally fair (PF)‐AOUS algorithm is further proposed to ensure fairness among UTs, which combines the AOUS with the PF criterion. To reduce complexity, an improved angle‐based PF‐AOUS algorithm that schedules UTs according to their pitch angles rather than their rates is proposed. In addition, efficient precoding schemes for orthogonal UTs are designed by combining the steering vector and power allocation matrix, and it is shown that precoding can be converted into power allocation problems that further balance fairness and throughput. The numerical results indicate that the AOUS achieves a near‐optimal sum rate performance, and the angle‐based PF‐AOUS has the similar performance to the rate‐based PF‐AOUS, which achieves a high fairness index with the proposed precoding scheme.

Fairness in maximal covering location problems

This paper provides a mathematical optimization framework to incorporate fairness measures from the facilities’ perspective to discrete and continuous maximal covering location problems. The main ingredients to construct a function measuring fairness in this problem are the use of (1) ordered weighted averaging operators, a popular family of aggregation criteria for solving multiobjective combinatorial optimization problems; and (2) α-fairness operators which allow generalizing most of the equity measures. A general mathematical optimization model is derived which captures the notion of fairness in maximal covering location problems. The models are first formulated as mixed integer non-linear optimization problems for both the discrete and the continuous location spaces. Suitable mixed integer second order cone optimization reformulations are derived using geometric properties of the problem. Finally, the paper concludes with the results obtained from an extensive battery of computational experiments on real datasets. The obtained results support the convenience of the proposed approach.

Quad-Interleaving Routing Algorithm for Performance Enhancement of Oil and Gas Pipeline Network Monitoring

The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).

Instrument of Augmented Reality Technology Based Biology Practicum: Psychometrics Analysis

Digital literation skills are student’s need in nowadays modern era to refine the information accurately as an education media. Supporting the digital literation is by using the proper application by applying augmented reality (AR) technology. The purpose of the research is to analyze an implementation of AR technology as an effort to improve student’s digital literation skill in Surabaya senior high schools. The method of the research is quantitative with around 500 respondents that consist of students form three non-government senior high schools in Surabaya, Indonesia. The instruments validation to measure student’s acceptance towards AR technology as a teaching media is unexplored yet. This study is to evaluate the questionnaire about AR technology towards digital literation skill. The questionnaire consists of 10 items by google form and sent by online to 500 students in Surabaya. The data is processed by statistics with data analysis using Exploratory and Confirmatory Factor analysis. The whole average scores of questionnaires point that analyzed descriptively around, 60 and 2.63 with deviation standard 0.52-0.77. The scores of Pearson correlation by the whole items is around 0.08 and 0.35. The measurement of sample fairness using Kaiser-Meyer-Olkin (KMO) analysis generate 0.796 (Primely). This study identified three dimensions of total variation explains 51.28% from 10 items. The whole items generate factor loading score 0.5. However there are 2 items that eliminated that is item 1 and item 2.

Backscatter-enabled CR-NOMA based cooperative V2X communication with imperfect CSI

Non orthogonal multiple access (NOMA) and backscatter communications are considered to be promising technology due to their applications in large scale communications and spectral efficiency. Cognitive radio (CR) also has distinct advantage in terms of allowing secondary users to share spectrum with the primary users. Thus CR and NOMA provides a new way to enable vehicle to everything (V2X) communication through cooperation, in terms of the achievable rate (throughput), which is further enhanced with use of backscatter devices. Moreover, in V2X it is critical to inspect imperfect channel state information (CSI) because, mobility of vehicles leads to more channel estimation uncertainties. Therefore, CR based approach which is supported by NOMA with imperfect CSI, called as B-CR-NOMA is proposed in which several vehicles connect with the base stations (BSs) through different road side units (RSUs) and backscatter devices. To optimize the performance of the proposed approach, a problem of optimal (maximization) of throughput has been formulated. Moreover, the non-convexity associated with the signal to interference plus noise ratio (SINR) is removed by the use of Lagrangian dual transformation by introducing auxiliary variables, which is further solved under Karush–Kuhn–Tucker (KKT) conditions along with using sub-gradient techniques. The exceptional attainment of the throughput for the proposed approach in comparison to the contemporary algorithms in terms of: power as well as number of RSU, number of vehicle users and channel estimation error, accentuate the capability of the proposed approach. The fortitude of B-CR-NOMA is further substantiated in terms of fairness (fairness index), which is measured and compared based on the channel CSI: perfect and imperfect. Also, the effect of the auxiliary variables and iterations on the throughput for the proposed approach is also analyzed. Along with this, two key metrics: average outage probability and average fairness index, which measures the cooperative V2X communication, are also considered for in depth analysis of the proposed approach.

Contents of a Fair Personnel Assessment

The article discusses the requirements for an effective and fair personnel evaluation system and measures to improve such a system. The five factors of effectiveness cover the emotional and cognitive reactions of the employee and include both human and impersonal aspects of the assessment, as well as the usability of the instruments.

Fair travel distances in tournament schedules: A cooperative game theory approach

The most studied problem in sports scheduling, so-called traveling tournament problem (TTP), aims at finding schedules minimizing the total distance traveled by the teams. While minimizing all the traveling between games is efficient from the overall perspective, it overlooks the distribution of the travel among the teams. Consequently, some teams may end up better than others with respect to their individual goals, an imbalance which may affect teams’ often-limited resources or preparedness for the games. This article adopts a cooperative game theory framework to obtain tournament schedules where the distances traveled by the teams are allocated according to fairness criteria. The approach consists of three steps. First, the scheduling problem is reformulated as a transferable utility game. Second, by means of well-established allocation methods, an ideal distance distribution among the teams is determined. Third, we introduce fairness measures to produce a schedule which approximately resembles the ideal distribution. We also discuss the case of not pursuing fairness, but rather a compromise between fairness and minimum total distance. We illustrate the approach by a numerical example in one of the classic TTP data instances.

Joint Shortest Chain and Fair Transmission Design for Energy-Balanced PEGASIS in WSNs

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

Differential Fairness: An Intersectional Framework for Fair AI.

We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens from the legal, social science, and humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability. We show that our criteria behave sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. Our theoretical results show that our criteria meaningfully operationalize AI fairness in terms of real-world harms, making the measurements interpretable in a manner analogous to differential privacy. We provide a simple learning algorithm using deterministic gradient methods, which respects our intersectional fairness criteria. The measurement of fairness becomes statistically challenging in the minibatch setting due to data sparsity, which increases rapidly in the number of protected attributes and in the values per protected attribute. To address this, we further develop a practical learning algorithm using stochastic gradient methods which incorporates stochastic estimation of the intersectional fairness criteria on minibatches to scale up to big data. Case studies on census data, the COMPAS criminal recidivism dataset, the HHP hospitalization data, and a loan application dataset from HMDA demonstrate the utility of our methods.

A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks

With the increasing attention to front-edge vehicular communication applications, distributed resource allocation is beneficial to the direct communications between vehicle nodes. However, in highly dynamic distributed vehicular networks, quality of service (QoS) of the systems would degrade dramatically because of serious packet collisions in the absence of sufficient link knowledge. Focusing on the fairness optimization, a Q-learning-based collision avoidance (QCA) scheme, which is characterized by an ingenious bidirectional backoff reward model R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QCA</inf> corresponding to arbitrary backoff stage transitions, has been proposed in an intelligent distributed media access control protocol. In QCA, an intelligent bidirectional backoff agent based on the Markov decision process model can actively motivate each vehicle agent to update itself toward an optimal backoff sub-intervel BSI <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> through either positive or negative bidirectional transition individually, resulting in the distinct fair communication with a proper balance of the resource allocation. According to the reinforcement learning theory, the problem of goodness evaluation on the backoff stage self-selection policy is equal to the problem of maximizing Q function of the vehicle in the current environment. The final decision on BSI <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf> related to an optimal contention window range was solved through maximizing the Q value or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q_{\max}$</tex> . The ε-qreedy algorithm was used to keep a reasonable convergence of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q_{\max}$</tex> solution. For the fairness evaluation of QCA, four kinds of dynamic impacts on the vehicular networks were investigated: mobility, density, payload size, and data rate with a network simulator NS2. Consequently, QCA can achieve fair communication efficiently and robustly, with advantages of superior Jain's fairness index, relatively high packet delivery ratio, and low time delay.

New approach for electric vehicles charging management in parking lots considering fairness rules

Electric Vehicles are replacing conventional vehicles and imposing new challenges in the power systems management comprising all voltage levels. The methodologies proposed in the literature have as their main goal the minimization of operation costs neglecting fairness rules. Additionally, most of the methods were developed considering information regarding travel needs, which is far from reality. In the present paper, it is proposed an energy management system to be used in parking lots considering the optimization of a fairness index, low installed capacity, and low level of information exchanged between the Electric Vehicle (EV) and the parking energy management system as well as the use of charging stations with multiple outlets (charge more than one EV in the same charging station). The proposed approach is modelled as a mixed-integer linear programming problem with the main goal to improve fairness in the charging process considering different types of contracts (normal use, privileged contract and long-duration parking contracts). A case study is presented which considers 100 electric vehicles in a residential parking lot. Several options for charging stations are compared, and the proposed fairness methodology is compared with the First-In First-Served approach. The obtained results show the adequacy and fairness of the proposed methods. The fairness index increased from 0.289 to 0.748 on a scale of 0 to 1, where 1 is the perfect solution when all the EVs have 100% of State-of-Charge (SOC) at departure time. The proposed methodology can be adopted in real parking lots with different characteristics.

Stackelberg Game for Secure CR-NOMA Networks Against Internal Eavesdropper

The combination of CR-NOMA has enormous potential interest to improve spectral efficiency and increase system capacity in future wireless communication networks. Hence, according to the broadcast nature of the CR-NOMA wireless channel, handling the confidentiality of information is one of the most important requirements of the network. In this paper, PLS against internal Eve is investigated by the CR-NOMA under the limitations of joint delay and confidentiality rate. First, initial power allocation for SU based on the PU interference threshold is constructed. Then, a multi-objective problem based on different SUs demands is formulated based on Stackelberg game modeling. After that, the SE is studied and explored. At the final stage, an overall algorithm has been proposed whereby SE strategies are frequently updated to find the best parameters for the proposed system model. The simulation results show the superiority of the proposed scheme in achieving a higher average utility function, spectral efficiency, fairness index, up to 44%, 90%, and 54.7%, respectively, and reliability compared to the conventional scheme.

Modeling and simulation of extended ant colony labor division for benefit distribution of the all-for-one tourism supply chain with front and back decoupling.

This paper takes the supply chain alliance under the decoupling of the front and back of the all-for-one tourism as the research object. Considering the three behavior stimuli of self-benefit, altruism, and invariance, this article resets the attributes such as environmental stimuli and response threshold of ants based on the characteristics of the all-for-one tourism supply chain with shared services as the core under the decoupling of the front and back. Moreover, it introduces dual intervention factors to coordinate the benefit distribution process of different member companies, takes fairness as the main goal of benefit distribution, introduces relative deprivation as the measure index of fairness, and establishes a dynamic all-for-one tourism supply chain alliance benefit distribution model. The experimental results show that the extended model has good flexibility of benefit distribution and realizes the fair distribution of supply chain benefits.

QoS-aware secure transmission design for VLC with semi-grant-free NOMA scheme.

This work aims to enhance the physical layer security (PLS) of non-orthogonal multiple access (NOMA) aided indoor visible light communication (VLC) system with semi-grant-free (SGF) transmission scheme, in which a grant-free (GF) user shares the same resource block with a grant-based (GB) user whose quality of service (QoS) must be strictly guaranteed. Besides, the GF user is also provided with an acceptable QoS experience, which is closely aligned with the practical application. Both active and passive eavesdropping attacks are discussed in this work, where users' random distributions are taken into account. Specifically, to maximize the secrecy rate of the GB user in the presence of an active eavesdropper, the optimal power allocation policy is obtained in exact closed-form and the user fairness is then assessed by Jain's fairness index. Moreover, the secrecy outage performance of the GB user is analyzed in the presence of the passive eavesdropping attack. Both exact and asymptotic theoretical expressions for the secrecy outage probability (SOP) of the GB user are derived, respectively. Furthermore, the effective secrecy throughput (EST) is investigated on the basis of the derived SOP expression. Through simulations, it is found that the PLS of this VLC system can be significantly improved by the proposed optimal power allocation scheme. The radius of the protected zone, the outage target rate for the GF user, and the secrecy target rate for the GB user would have pronounced impacts on the PLS and user fairness performance of this SGF-NOMA assisted indoor VLC system. The maximum EST will increase with the increasing transmit power and it is hardly influenced by the target rate for the GF user. This work will benefit the design of indoor VLC system.

Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis.

The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure algorithmic bias, but their application to real-world tasks is limited. This study aims to evaluate the algorithmic bias associated with the application of common 30-day hospital readmission models and assess the usefulness and interpretability of selected fairness metrics. We used 10.6 million adult inpatient discharges from Maryland and Florida from 2016 to 2019 in this retrospective study. Models predicting 30-day hospital readmissions were evaluated: LACE Index, modified HOSPITAL score, and modified Centers for Medicare & Medicaid Services (CMS) readmission measure, which were applied as-is (using existing coefficients) and retrained (recalibrated with 50% of the data). Predictive performances and bias measures were evaluated for all, between Black and White populations, and between low- and other-income groups. Bias measures included the parity of false negative rate (FNR), false positive rate (FPR), 0-1 loss, and generalized entropy index. Racial bias represented by FNR and FPR differences was stratified to explore shifts in algorithmic bias in different populations. The retrained CMS model demonstrated the best predictive performance (area under the curve: 0.74 in Maryland and 0.68-0.70 in Florida), and the modified HOSPITAL score demonstrated the best calibration (Brier score: 0.16-0.19 in Maryland and 0.19-0.21 in Florida). Calibration was better in White (compared to Black) populations and other-income (compared to low-income) groups, and the area under the curve was higher or similar in the Black (compared to White) populations. The retrained CMS and modified HOSPITAL score had the lowest racial and income bias in Maryland. In Florida, both of these models overall had the lowest income bias and the modified HOSPITAL score showed the lowest racial bias. In both states, the White and higher-income populations showed a higher FNR, while the Black and low-income populations resulted in a higher FPR and a higher 0-1 loss. When stratified by hospital and population composition, these models demonstrated heterogeneous algorithmic bias in different contexts and populations. Caution must be taken when interpreting fairness measures' face value. A higher FNR or FPR could potentially reflect missed opportunities or wasted resources, but these measures could also reflect health care use patterns and gaps in care. Simply relying on the statistical notions of bias could obscure or underplay the causes of health disparity. The imperfect health data, analytic frameworks, and the underlying health systems must be carefully considered. Fairness measures can serve as a useful routine assessment to detect disparate model performances but are insufficient to inform mechanisms or policy changes. However, such an assessment is an important first step toward data-driven improvement to address existing health disparities.

Beyond fraudulent multiculturalism in higher education: moving forward

Abstract Recent demographic changes in the United States have shown that we live in a multicultural society. However, for some reason, colleges and universities are still floundering in mediocrity when it comes to multiculturalism. What we see in higher education is multiculturalism that is fraudulent and unaccountable and the fact that individuals from culturally and linguistically diverse (CLD) and vulnerable backgrounds consistently endure White supremacy, racism, discrimination, and xenophobia, to say the least. In addition, we consistently see that proactive and measurable efforts are not made in the recruitment, retention, graduation, continuation, tenure, and promotion of students, faculty, staff, and leaders from CLD, immigrant, and vulnerable backgrounds. While there are well-written and documented policies, mission and vision statements, and goals and objectives in institutions of higher learning, there appears to be deficits in applicability and accountability measures of equity, diversity, quality, goodness, and fairness. Most strategies seem to be feel-good, look-good, fake, bandaid, and fraudulent approaches that are divorced from realities. How do we reverse these moves while respecting the traditional roles of higher education? This article responds to this critical question.

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

Unmanned aerial vehicle-aided communication (UAB-BS) is a promising solution to establish rapid wireless connectivity in sudden/temporary crowded events because of its more flexibility and mobility features than conventional ground base station (GBS). Because of these benefits, UAV-BSs can easily be deployed at high altitudes to provide more line of sight (LoS) links than GBS. Therefore, users on the ground can obtain more reliable wireless channels. In practice, the mobile nature of the ground user can create uneven user density at different times and spaces. This phenomenon leads to unbalanced user associations among UAV-BSs and may cause frequent UAV-BS overload. We propose a three-dimensional adaptive and fair deployment approach to solve this problem. The proposed approach can jointly optimize the altitude and transmission power of UAV-BS to offload the traffic from overloaded UAV-BSs. The simulation results show that the network performance improves by 37.71% in total capacity, 37.48% in total energy efficiency and 16.12% in the Jain fairness index compared to the straightforward greedy approach.

On Data Collection in SIC-Capable Space–Air–Ground Integrated IoT Networks

This article considers routing and uplinks scheduling in space–air–ground integrated networks. Specifically, it considers a successive interference cancellation-capable unmanned aerial vehicle (UAV) that collects data from ground devices operating in an Internet of Things network. Its objective is to maximize the minimum flow rate to a terrestrial gateway over a fixed time horizon. It outlines a mixed integer linear program (MILP) to optimize in each time slot: the routing from the UAV to the gateway, the set of links from ground devices to the UAV, and the flow rate over links. It also proposes a novel algorithm, called iterative flow and path reservation (IFPR), for use by the UAV to select paths. Further, IFPR uses two methods to schedule communications between ground devices and the UAV. The first method, called simplified MILP (SMILP), aims to maximize the sum rate from ground devices. The second method, called less data schedule first, prioritizes ground devices that have uploaded the least amount of data to the gateway. Numerical results show satellite links help the UAV collects 61% more data from ground devices. Moreover, as compared to the MILP, IFPR collects 23% less data. Further, for both MILP and IFPR, their Jain’s fairness index reaches around one when the number of time slots is sufficiently large. Finally, when IFPR uses SMILP, the gateway collects more data but at the expense of fairness.