Instantly Decodable Network Coding for Completion Time or Decoding Delay Reduction in Cooperative Data Exchange Systems

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

This paper investigates the delay minimization problem for instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D enabled systems, users cooperate to recover all their missing packets. The paper proposes a game theoretic framework as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. The session is modeled by self-interested players in a non-cooperative potential game. The utility functions are designed so as increasing individual payoff results in a collective behavior which achieves both a desirable system performance in a shared network environment and the Nash equilibrium. Three games are developed whose first reduces the completion time, the second the maximum decoding delay and the third the sum decoding delay. The paper, further, improves the formulations by including a punishment policy upon collision occurrence so as to achieve the Nash bargaining solution. Learning algorithms are proposed for systems with complete and incomplete information, and for the imperfect feedback scenario. Numerical results suggest that the proposed game-theoretical formulation provides appreciable performance gain against the conventional point-to-multipoint (PMP), especially for reliable user-to-user channels.

The traditional instantly decodable network coding (IDNC) schemes discard the non-instantly decodable packets (NIDPs), which results in the loss of useful information. In this letter, we consider a network coding scheme named as buffered instantly decodable network coding (B-IDNC) where each receiving node can cache the NIDPs containing two unreceived packets for subsequent network decoding, and give a decoding algorithm by utilizing the maximum decoding clique. The maximum decoding clique is defined as a subset of vertices in the IDNC graph, which can be decoded together. We approximate the average packet decoding delay minimization problem as the maximum weight encoding clique problem, and a heuristic algorithm named as maximum decoding clique based maximum weight vertex (MDC-MWV) searching algorithm is proposed to solve the approximated problem. Simulation results show that our proposed algorithm can reduce the average packet decoding delay compared with the conventional IDNC schemes.

We are concerned with designing feedback-based adaptive Instantly Decodable Network Coding (IDNC) schemes with the aim of minimizing retransmission times in wireless multicast and broadcast scenarios. In this paper, we first analyze the multiple factors influencing the transmission times and give the quantitative formula. Meantime, we introduce the concept of Cache-based Instantly Decodable Network Coding (C-IDNC) that the correctly received undecodable retransmission packets are waited in the cache for a future network decoding opportunity rather than delete. Then, we formulate the retransmission times minimization problem as an max-weight search problem and propose efficient algorithms for finding its optimal solution. Further, we propose a heuristic algorithms with worst case linear execution complexity based on sorting and iteration. Simulation results show that our proposed algorithms significantly reduce the IDNC retransmission times compared to previous works.

We consider a group of n wireless clients and a set of k messages. Each client initially holds a subset of messages and is interested in an arbitrary subset of messages. Each client cooperates with other clients to obtain the set of messages it wants by exchanging instantly decodable network coded (IDNC) packets. This problem setting is known as the cooperative index coding problem. Clients are assumed to be connected through an arbitrary topology. In the absence of any known algorithm to complete the exchange of packets for general network topologies, we propose a greedy algorithm to satisfy the demands of all the clients with the aim of reducing the mean completion time. Our algorithm, in a completely distributed fashion, decides which subset of clients should transmit at each round of transmission and which messages should be coded together by each transmitting client to generate an IDNC packet. The algorithm encourages transmissions which are decodable for a larger number of clients and attempts to avoid collisions. We evaluate the performance of our algorithm via numerical experiments.

In this paper, we are concerned with extending the graph representation of generalized instantly decodable network coding (GIDNC) to a more general opportunistic network coding (ONC) scenario, referred to as order-2 GIDNC (O2-GIDNC). In the O2-GIDNC schem

URLLC in UAV-enabled multicasting systems: A dual time and energy minimization problem using UAV speed, altitude and beamwidth

The most heavily investigated detector concept for medical photon-counting X-ray imaging, in particular photon-counting X-ray computed tomography (CT), is based on the semiconductors CdTe and CdZnTe (CZT). Alternatively, a detector design based on Si is under investigation. Both approaches have advantages and disadvantages, which leaves room for the development of other types of detector. In this chapter, we investigate the feasibility of scintillation detectors based on silicon photomultipliers (SiPMs) for this application. We first summarize the potential clinical advantages of energy-resolving photon-counting X-ray detectors, the associated challenges, and the working principles and drawbacks of detectors based on CdTe/CZT and Si. We then introduce the concept of SiPM-based scintillation detectors and discuss the scintillator and SiPM properties that determine two fundamental performance metrics, viz. the rate capability and the energy resolution. We have previously developed a pulse shape model that takes all of these properties into account. We use the model to estimate the expected detector performance as a function of pixel size, for LaBr3:Ce and YAP:Ce scintillators coupled to currently available SiPM technology and to a more advanced SiPM prototype. The model calculations show that the short decay time constant of the scintillators, combined with the reduced recharge time constant of the new SiPM prototype, results in pulses short enough that a pixel size of 400 µm (LaBr3:Ce) or 333 µm (YAP:Ce) yields comparable rate capability as a CdTe/CZT detector with a pixel size of 500 µm. The high light yield and nearly proportional response of the investigated scintillators results in good energy resolution, provided that the SiPM does not suffer from excessive saturation when the pixel size is reduced to such small dimensions. We show that the level of saturation remains limited with the new SiPM prototype and that energy resolutions of 12% (LaBr3:Ce) and 15% (YAP:Ce) at 60 keV may be achieved. Our work indicates that it may be feasible to develop SiPM-based scintillation detectors for medical photon-counting X-ray imaging with comparable rate capability and energy resolution as CdTe/CZT detectors. Remaining challenges include a dose-efficient optical isolation between pixels. However, the fact that the detector is based on light transport rather than charge transport means that issues typically associated with CdTe/CZT detectors, such as charge sharing and polarization, do not play a role.

This paper examines the utilization of fuzzy logic in human resource management (HRM) to tackle the complex nature of performance assessment in organizations. Conventional performance appraisal techniques frequently neglect the uncertainties and ambiguities associated with evaluating qualitative metrics like leadership, teamwork, and communication. Conversely, fuzzy logic provides a methodical framework for addressing these ambiguities, so improving the equity, precision, and thoroughness of assessments. The study examines the application of fuzzy inference algorithms to evaluate six fundamental performance metrics: job knowledge, work quality, productivity, leadership, teamwork, and communication. The study uses verbal and numerical quantifiers to formulate probabilistic principles and categorizes performance results into five classifications: never, seldom, sometimes, often, and always. The dual-variable methodology facilitates comprehensive assessments at the individual, team, and organizational tiers. Research indicates that fuzzy logic markedly improves decision-making in performance management by incorporating expert information and mitigating subjective uncertainties. The fuzzy modelling outcomes correspond with real-world applications in the university setting, providing a solid foundation for measuring qualitative advantages such as employee engagement, organizational efficiency, and value. This study utilizes fuzzy techniques, such as Mamdani and Tsukamoto systems, to offer organizations practical insights for optimizing their appraisal systems in accordance with evolving HR requirements. Subsequent investigations may broaden these methodologies to more sectors, evaluating their enduring influence on organizational achievement. This study highlights the transformative capacity of fuzzy logic as a mechanism for fair and efficient performance assessments in intricate settings.

Molecular communication (MC) is one of the most promising technology to enable nanonetworks. Despite many aspects of MC have been investigated broadly, the physical design of the MC receiver has gained little interest. High-performance MC receivers based on bioFETs are proposed and extensively analyzed. However, they have some challenges such as limited detection with charged molecules, Debye screening, and the need for reference electrodes. To overcome these shortcomings, we propose a mechanical-based transducing scheme. In particular, we focus on a Flexure field-effect transistor (FET)-based MC receiver architecture, which provides exponentially high sensitivity by utilizing a nonlinear electromechanical coupling. In addition, the detection of neutral molecules with much simpler instrumentation is possible. In this paper, we analyze its fundamental performance metrics; sensitivity, noise power, signal-to-noise ratio, and the symbol error probability, from an MC theoretical perspective.

We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than 94.

The increasing popularity of the Web of Data is motivating the need to integrate semantic-web ontologies. Data exchange is one integration approach that aims to populate a target ontology using data that come from one or more source ontologies. Currently, there exist a variety of systems that are suitable to perform data exchange among these ontologies; unfortunately, they have uneven performance, which makes it appealing assessing and ranking them from an empirical point of view. In the bibliography, there exist a number of benchmarks, but they cannot be applied to this context because they are not suitable for testing semantic-web ontologies or they do not focus on data exchange problems. In this paper, we present MostoBM, a benchmark for testing data exchange systems in the context of such ontologies. It provides a catalogue of three real-world and seven synthetic data exchange patterns, which can be instantiated into a variety of scenarios using some parameters. These scenarios help to analyze how the performance of data exchange systems evolves as the exchanging ontologies are scaled in structured and/or data. Finally, we provide an evaluation methodology to compare data exchange systems side by side and to make informed and statistically sound decisions regarding: 1) which data exchange system performs better; and 2) how the performance of a system is influenced by the parameters of our benchmark.

P1.03-021 Initial Results from A Novel and Low Cost Method For Measuring CT Image Quality

Zinc-ion hybrid supercapacitor (ZIHSC) is one of the popular emerging battery hybrid supercapacitors which integrates Zinc-ion battery electrode with capacitive electrode, thereby, taking advantages of high energy and power densities in addition to cycle stability. Their easy availability, optimum stability, high volumetric capacity and safety efficacy make ZIHSC a favoured choice over other battery hybrid supercapacitor. The research progress on ZIHSC is still in the bud stage and mainly faces challenges in developing a high energy density cathode and dendrite-free anode which hinder its practical applicability. To tackle these issues there must a proper understanding of the Zn2+ ion’s charge storage mechanism for the synthesised electrode materials which would be helpful in developing a technologically efficient ZIHSC. This review provides an overview of the fundamental working and performance metrics of ZIHSC. It then discusses about the different cathode and anode materials in length. This review work also highlights some of the newly developed electrode materials’ effectiveness in enhancing the ZIHSC’s performance. The challenges and the solutions associated with cathode and anode were also addressed in this review.

The advent of meminductors as a basic passive circuit element has sparked widespread attention because of their potential uses in neuromorphic computing, memory storage, and programmable analog circuits. This study gives a detailed assessment of design techniques for meminductor emulators, emphasizing current-mode components. Current-mode circuits provide benefits such as fast operation, low power consumption, and a greater dynamic range, making them an excellent option for meminductor emulator design. The study categorizes and analyzes current designs based on their fundamental ideas, circuit design, and performance metrics. The challenges of precise emulation, robustness, and compatibility with contemporary integrated circuit technology are explored. The study also focuses on emerging trends, prospective applications, and prospects for research in this discipline. This study seeks to serve as a helpful resource for academics and practitioners, offering insights on state-of-the-art developments and influencing the growth of creative microcontroller emulation approaches.