Fair Trade-off Research Articles

Hexagon Based Compressed Diamond Algorithm for motion estimation and its dedicated VLSI system for HD videos

Motion estimation (ME) is crucial to the performance of a video codec as it recognizes and mitigates temporal redundancies between consecutive frames of a video sequence. It is one of the most computationally intensive blocks in the video encoder. Block matching algorithm (BMA) is considered as one of the best approaches for performing ME due to its ease of implementation and efficiency. Several ME algorithms are based on fixed search patterns while the others are adaptive in nature and exploit the correlation present between neighboring macroblocks of a video frame. This paper introduces an efficient ME algorithm named Hexagon Based Compressed Diamond Algorithm (HCDA) that tries to reduce the complexity of ME by lowering the number of search points in the process. The algorithm uses an early termination technique and an adaptive search pattern that can uniformly deal with slow and fast motion content in a video. The proposed HCDA uses dual threshold technique based on the available spatial information. This further helps in terms of clock cycles or hardware resources during VLSI implementation. Moreover, VLSI design for HCDA can explore the adaptive nature of the algorithm and the adopted search methodology is handled by interleaved memory organization that enhances the operational speed. Supportive data re-use scheme with effective block for SAD computation contributes to a fair trade-off between speed and on-chip area. The designs can work at a maximum frequency of 262 MHz to process 76 HD (1280 × 720) frames per second. The corresponding gate count comes out to be 35.8 K gate equivalent. The encouraging experimental results obtained under various defined metrics indicate suitability of the proposed hardware architecture for its inclusions in portable battery powered consumer video devices.

Energy and Delay Aware Task Assignment Mechanism for UAV-Based IoT Platform

Unmanned aerial vehicles (UAVs) are gaining much momentum due to the vast number of their applications. In addition to their original missions, UAVs can be used simultaneously for offering value added Internet of Things services (VAIoTS) from the sky. VAIoTS can be achieved by equipping UAVs with suitable Internet of Things (IoT) payloads and organizing UAVs’ flights using a central system orchestrator (SO). SO holds the complete information about UAVs, such as their current positions, their amount of energy, their intended use-cases or flight missions, and their onboard IoT device(s). To ensure efficient VAIoTSs, there is a need for developing a smart mechanism that would be executed at the SO in order to take into account two major factors: 1) the UAVs’ energy consumption and 2) the UAVs’ operation time. To effectively implement this mechanism, this paper presents three complementary solutions, named energy aware UAV selection (EAUS), delay aware UAV selection (DAUS), and fair tradeoff UAV selection (FTUS), respectively. These solutions use linear integer problem (LIP) optimizations. While the EAUS solution aims to reduce the energy consumption of UAVs, the DAUS solution aims to reduce the operational time of UAVs. Meanwhile, FTUS uses a bargaining game to ensure a fair tradeoff between the energy consumption and the operation time. The results obtained from the performance evaluations demonstrate the efficiency and the robustness of the proposed schemes. Each solution demonstrates its efficiency at achieving its planned goals.

Corporate power purchase agreement: Formulation of the related levelized cost of energy and its application to a real life case study

For corporate and industries the reducing of its environmental footprint is becoming more and more important, in order to improve the ‘green and social brand’ of their products and services. Many instruments may be used to this purpose but in recent years Corporate Power Purchase Agreement (CPPA) is the form of contract that is used more and more frequently. CPPA is a contract between an energy producer, from renewable sources and an end-user. Using CPPAs, the ‘green brand’ for a corporation and its product and services could be achieved. The main concern is related to the costs: still today, renewables are more expensive with respect to fossil fuels, therefore this results in higher prices for products and services, so a fair trade off between CPPA price and the gain of ‘green brand’ has to be determined. In this paper, the authors propose a method based on an opportune Levelized cost of energy formula able to support a stakeholder to define CPPA price and contractual length. A case study with significant results for a PV plant in the Italian real life application is provided. From the performed simulations emerges realistic ranges CPPA’s contractual length and price convenient for involved stakeholder. In particular the length should be not less than 7 years and not more than 10 years while the price should be in the range between 75 €/MWh and 100 €/MWh.

Energy‐efficient and reliable in‐memory classifier for machine‐learning applications

Large-scale machine-learning (ML) algorithms require extensive memory interactions. Managing or reducing data movement can significantly increase the speed and efficiency of many ML tasks. Towards this end, the authors devise an energy efficient in-memory computing (IMC) kernel for linear classification and design an initial prototype. The authors achieve a power savings of over 6.4 times than a conventional discrete system while improving reliability by 54.67%. The authors employ a split-data-aware technique to manage process, voltage, and temperature variations and to achieve fair trade-offs between energy efficiency, area requirements, and accuracy. The authors utilise a trimodal architecture with a hierarchical tree structure to further decrease power consumption. The authors also explore alternatives to the hierarchical tree structure with a significantly reduced number of linear regression blocks, while maintaining a competitive classification accuracy. Overall, the scheme provides a fast, energy efficient, and competitively accurate binary classification kernel.

A Novel Framework for Resource Discovery and Self-Configuration in Software Defined Wireless Mesh Networks

A novel framework is proposed for software defined wireless mesh networks involving mobile switches and controllers operating under in-band control in order to self-configure depending on the network dynamics. Besides the addition/removal of nodes (including switches and controllers) to/from the network in real-time, the problem becomes more challenging for switches to select an appropriate controller from multiple physical controllers. Therefore, first we design a resource discovery scheme to address the dynamic addition and removal of nodes with a software defined optimized link state routing (SD-OLSR) protocol. Apart from resource discovery, SD-OLSR captures the network dynamism and provides a global network view to the controller. Further, two controller handoff schemes, controller-initiated handoff (CIH) and switch-initiated handoff (SIH), are designed for switches to efficiently handover to a suitable controller. The framework and handoff schemes are tested using a software defined wireless mesh network testbed involving mobile switches and controllers operating under in-band control. The results show that SIH performs better in terms of SDN control overhead and PacketIn-FlowMod delay, while CIH outperforms SIH in terms of the number of handoffs and controller handoff time. A fair trade-off between the number of handoffs and the desired performance metric can be achieved by tuning the handoff threshold in both CIH and SIH schemes. As an added benefit, our framework provides better load sharing among the controllers in terms of flow-rule requests.

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child’s wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported by clinical assessment and written tests. A definitive diagnosis is usually made based on the DSM-V criteria. There is a lot of ongoing research on ADHD, in order to determine the neurophysiological basis of the disorder and to reach a more objective diagnosis. The advent of Machine Learning (ML) opens up promising prospects for the development of systems able to predict a diagnosis from phenotypic and neuroimaging data. This was the reason why the ADHD-200 contest was launched a few years ago. Based on the publicly available ADHD-200 collection, participants were challenged to predict ADHD with the best possible predictive accuracy. In the present work, we propose instead a ML methodology which primarily places importance on the explanatory power of a model. Such an approach is intended to achieve a fair trade-off between the needs of performance and interpretability expected from medical diagnosis aid systems. We applied our methodology on a data sample extracted from the ADHD-200 collection, through the development of decision trees which are valued for their readability. Our analysis indicates the relevance of the limbic system for the diagnosis of the disorder. Moreover, while providing explanations that make sense, the resulting decision tree performs favorably given the recent results reported in the literature.

User scheduling for capacity-Jain’s fairness tradeoff in millimeter-wave MIMO systems

Abstract This paper investigates user scheduling in millimeter-wave multiple-input multiple-output systems that can achieve a tradeoff between capacity and Jain’s fairness index. By decomposing the original problem into digital beamforming design, duration allocation, and analog beamforming design, each subproblem is solvable. It is proved that the duration allocation problem possesses a property such that the convex optimization algorithm can be employed for the near optimal result. Moreover, the analog beamforming vectors and the user grouping are designed to avoid interference caused by the reuse of the same subspace. Finally, the computational complexity of the proposed approach is analyzed and is compared with that of other approaches. The simulations verify that the proposed approach can achieve the highest fairness and a higher capacity than approaches of close fairness.

Comprehensive survey of image steganography: Techniques, Evaluations, and trends in future research

Storing and communicating secret and/or private information has become part of our daily life whether it is for our employment or personal well-being. Therefore, secure storage and transmission of the secret information have received the undivided attention of many researchers. The techniques for hiding confidential data in inconspicuous digital media such as video, audio, and image are collectively termed as Steganography. Among various media types used, the popularity and availability of digital images are high and in this research work and hence, our focus is on implementing digital image steganography. The main challenge in designing a steganographic system is to maintain a fair trade-off between robustness, security, imperceptibility and higher bit embedding rate. This research article provides a thorough review of existing types of image steganography and the recent contributions in each category in multiple modalities. The article also provides a complete overview of image steganography including general operation, requirements, different aspects, different types and their performance evaluations. Different performance analysis measures for evaluating steganographic system are also discussed here. Moreover, we also discuss the strategy to select different cover media for different applications and a few state-of-the-art steganalysis systems.

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

Efficient HEVC intra‐frame prediction using curved angular modes

Blending curved and straight lines on angular modes are proposed for intra-frame prediction in high-efficiency video coding (HEVC). All 33 angular modes in HEVC have received an offset-based displacement calculation to each predicted sample, so that the resulting prediction block models image regions with curved textures. The proposal includes a negligible overhead in the syntax elements of the encoded bitstream (up to 2.56%) to transmit the curvature parameter along with the angular mode of a predicted block. Results demonstrate increased prediction accuracy and consequent reduction in residuals. There is a fair trade-off between prediction accuracy and computational complexity even considering few curvature parameters. Further evaluations show an average reduction in Bjontegaard delta rate of 3.19% for the HEVC test sequences.

Efficient Performance-Centric Bandwidth Allocation with Fairness Tradeoff

Fair bandwidth allocation in datacenter networks has received a substantial amount of research attention, as multiple tenants are hosted by virtual machines in a public cloud. In the context of private datacenters, link bandwidth is shared among applications running data parallel frameworks, such as MapReduce, instead. In this paper, we introduce the rigorous definition of performance-centric fairness , with the guiding principle that the performance that data parallel applications will enjoy should be proportional to their weights. We first investigate the problem of maximizing application performance while maintaining strict performance-centric fairness. We then present an inherent tradeoff between fairness and efficiency, which is interpreted from the perspectives of bandwidth utilization and social welfare, respectively. From the first perspective, we propose an algorithm to improve bandwidth utilization by introducing an extended version of fairness. From the second perspective, we formulate an optimization problem of bandwidth allocation that maximizes the social welfare across all the applications, allowing a tunable degree of relaxation on performance-centric fairness. A distributed algorithm is then presented to solve the problem, based on dual based decomposition. With extensive simulations, we demonstrate the effectiveness of our algorithms in improving efficiency and application performance (by up to $1.4X$ ), with flexible degree of relaxation on the performance-centric fairness.

Optimal Throughput Fairness Tradeoffs for Downlink Non-Orthogonal Multiple Access Over Fading Channels

Recently, non-orthogonal multiple access (NOMA) has attracted considerable interest as one of the 5G-enabling techniques. However, users with better channel conditions in downlink communications intrinsically benefits from NOMA thanks to successive decoding, judicious designs are required to guarantee user fairness. In this paper, a two-user downlink NOMA system over fading channels is considered. For delay-tolerant transmission, the average sum-rate is maximized subject to both average and peak power constraints as well as a minimum average user rate constraint. The optimal resource allocation is obtained using Lagrangian dual decomposition under full channel state information at the transmitter (CSIT), while an effective power allocation policy under partial CSIT is also developed based on analytical results. In parallel, for delay-limited transmission, the sum of delay-limited throughput (DLT) is maximized subject to a maximum allowable user outage constraint under full CSIT, and the analysis for the sum of DLT is also performed under partial CSIT. Furthermore, an optimal orthogonal multiple access (OMA) scheme is also studied as a benchmark to prove the superiority of NOMA over OMA under full CSIT. Finally, the theoretical analysis is verified by simulations via different trade-offs for the average sum-rate (sum-DLT) versus the minimum (maximum) average user rate (outage) requirement.

Integration of environmental aspects in modelling and optimisation of water supply chains

Climate change becomes increasingly more relevant in the context of water systems planning. Tools are necessary to provide the most economic investment option considering the reliability of the infrastructure from technical and environmental perspectives. Accordingly, in this work, an optimisation approach, formulated as a spatially-explicit multi-period Mixed Integer Linear Programming (MILP) model, is proposed for the design of water supply chains at regional and national scales. The optimisation framework encompasses decisions such as installation of new purification plants, capacity expansion, and raw water trading schemes. The objective is to minimise the total cost incurring from capital and operating expenditures. Assessment of available resources for withdrawal is performed based on hydrological balances, governmental rules and sustainable limits. In the light of the increasing importance of reliability of water supply, a second objective, seeking to maximise the reliability of the supply chains, is introduced. The epsilon-constraint method is used as a solution procedure for the multi-objective formulation. Nash bargaining approach is applied to investigate the fair trade-offs between the two objectives and find the Pareto optimality. The models' capability is addressed through a case study based on Australia. The impact of variability in key input parameters is tackled through the implementation of a rigorous global sensitivity analysis (GSA). The findings suggest that variations in water demand can be more disruptive for the water supply chain than scenarios in which rainfalls are reduced. The frameworks can facilitate governmental multi-aspect decision making processes for the adequate and strategic investments of regional water supply infrastructure.

Efficiency and fair access in Kindergarten allocation policy design

We examine Kindergarten allocation practices in an Estonian municipality, Harku. Based on our recommendations, the allocation mechanism in Harku was redesigned in 2016. The new mechanism produces a child-optimal stable matching, with priorities primarily based on siblings and distance. We evaluate seven policy designs based on 2016 admission data in order to understand efficiency and fairness trade-offs. In addition to the descriptive data analysis, we conduct a counter-factual policy comparison and sensitivity analysis using computational experiments with generated preferences. We fix the allocation mechanism to be the child-oriented Deferred-Acceptance algorithm, but we vary how the priorities are created by altering sibling and distance factors. Different lotteries are included for breaking ties. We find that different ways of considering the same priority factors can have a significant aggregate effect on the allocation. Additionally, we survey a dozen special features that can create significant challenges (both theoretical and practical) in redesigning the allocation mechanism in Estonian Kindergartens, and potentially elsewhere as well.

EFFICIENT TRACKING AREA MANAGEMENT FRAMEWORK FOR 5G NERWORK

To accommodate a various growing number of user equipment (UEs) on 5g networks. Managing with the huge signaling overhead expected from UEs is a main problem to tackle hence as to achieve this objective. In this thesis, they develop an efficient tracking area list management (ETAM) system for 5G cloud-based mobile networks. The proposed system contains of two parts. The first part is executed offline. The executed offline is responsible of assigning tracking areas (TAs) to TA lists (TALs). The second one is executed online. The executed online is responsible of the distribution of TALs on user equipment (UEs) during their movements across TA. For the first part, they propose three keys, which are: (a) F-PAGING favoring the paging overhead over tracking area update (TAU), (b) F- TAU favoring TAU over paging, and (c) FOTA (i.e., Fair and Optimal Assignment of TALs to TAs) for a solution that uses bargaining game to ensure a fair tradeoff between TAU and paging overhead. For the second part, two solutions are proposed to assign in real time, TALs to different UEs. The computation load is kept lightweight in both solutions not to reduce the network performance. Also, both solutions do not need any additional new messages when assigning TALs to UEs. The first solution takes into account only the priority between TALs. As for the second one, in addition to the priority between TALs, it takes into account the UEs activities (i.e., in terms of incoming communication frequency and mobility patterns) to improve further the network performance. The presentation of ETAM is estimated through investigation and imitations, and the achieved results validate its feasibility and ability in achieving its thesis goals, improving the network performance by minimizing the cost related with paging and TAU.

Repeated Overlapping Coalition Game Model for Mobile Crowd Sensing Mechanism

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

Energy consumption and quality of service in WBAN: A performance evaluation between cross-layer and IEEE802.15.4

Dentro de los esquemas de comunicación de redes inalámbricas de área corporal (WBAN), se encuentran los protocolos de capa cruzada, constituidos en una novedosa opción para alcanzar un balance efectivo entre consumo eficiente de energía y métricas de desempeño. En el presente trabajo, evaluamos el desempeño de una estrategia de capa cruzada al compararla contra los protocolos del estándar IEEE802.15.4 en una WBAN. Se evaluó el desempeño de ambas estrategias empleando una simulación de redes WBAN. Luego se ejecutó una comparación estadística y se encontró que la estrategia de capa cruzada ofrece un mejor desempeño con respecto a la compensación entre consumo eficiente de energía y algunas métricas de desempeño en nuestra WBAN. Observamos que en general, la estrategia de capa cruzada supera a ambos modos del estándar IEEE802.15.4 (ranurado y no-ranurado) con respecto a consumo eficiente de energía, retraso extremo a extremo, tasa de pérdida de paquetes y goodput.

Evaluation of Evaporation Estimation Methods: a Case Study of Karaj Dam Lake

Evaporation is one of the largest water losses from most of the dam lakes in Iran. Estimating the evaporation rate enables us to apply the proper evaporation mitigation technologies. In this study, the feasibility of different evaporation estimation methods was studied to find an optimum method with a fair tradeoff between cost and accuracy. The optimum method may vary depending on the climate. We found Penman, Montieth and Unsworth (PMU) method as the optimum estimation method applicable Karaj dam lake (located north west of Tehran, Iran). For validation, we used the filed measurements for 2005. The reason is that the PMU is highly sensitive to wind velocity and only for 2005 the meteorological data contained the wind velocity. For the sky clarity, we used the 22-year average sky clarity of Karaj dam lake in augusts (i.e. 80%). The PMU model is found to provide consistent results with filed measurements (less than 2% error). For example, from 2nd to 15th of August 2005, the PMU model predicts 7.98 ± 0.83 mm/day evaporation and field measurement for the same period was 8.13 ± 0.01 mm/day.

Optimal Performance Versus Fairness Tradeoff for Resource Allocation in Wireless Systems

Resource allocation is a challenging issue in multiuser wireless systems. Since users are not all in the same conditions and do not achieve the same performance given the same amount of resources, resource allocation must typically deal with the following two conflicting objectives: on the one hand, the overall performance should be maximized, tending to prioritize few users in better conditions, and on the other hand, fairness among users should be maximized, consequently reducing the overall performance. Identifying the compromise that is somehow optimum is all but simple and this aspect is normally worsened by the absence of a curve describing how the maximum performance varies changing the accepted level of fairness. To cover this gap, in this paper, we propose a unified and general mathematical formulation of the optimal performance versus fairness tradeoff in multiuser wireless communication systems. Differently from the existing literature, our approach is based on the generalized Lagrange method and adopts general definitions for both performance and fairness. Besides the exact solving equations describing how the maximum performance varies with fairness, we also derive a simpler lower bound with reduced computational cost. Example results are provided for two case studies, respectively, concerning linear and logarithmic dependence of performance on resources.

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.