Cooperative Access in Cognitive Radio Networks: Stable Throughput and Delay Tradeoffs

In this paper, we study and analyze fundamental throughput-delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. The proposed policy opens room for trading the PU delay for enhanced SU delay. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. Results reveal that cooperation expands the stable throughput region of the system and significantly reduces the delay at both users. Moreover, we quantify the gain obtained in terms of the SU delay under the proposed policy, over conventional relaying that gives strict priority to the relay queue.Comment: accepted for publication in IEEE 12th Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 201

Development of Low Energy Aeration System For Enhanced Biological Phosphorus Removal (EBPR)

In a world that is witnessing an everlasting growth and accelerating increase in its population, an increase in the amount of wastewater produced is inevitable. In order to recycle this wastewater back to the environment, all nutrients should be removed. Unfortunately, removing the nutrients from wastewater is expensive due to the oxygen and chemicals requirement. Phosphorus removal is an important part of wastewater treatment process; Enhanced Biological Phosphorus Removal (EBPR) is one of the main processes responsible for phosphorus removal in wastewater treatment plants. EBPR consist of two major phases: anaerobic phase and aerobic phase. Aeration costs in the aerobic phase are relatively high in EBPR system. Finding a new approach for decreasing the amount of aeration needed for EBPR systems recently has grown in importance. Most of the research done on EBPR process was focusing on continued aeration, the effect of intermittent aeration is not widely researched. Thus, this research aims to overcome the previously mentioned challenges towards achieving stable EBPR process through different optimization techniques. To achieve this goal, a new aeration strategy has been developed to stepwise decrease the dissolved oxygen (DO) to reach very low DO conditions for EBPR. The new strategy depends on using intermittent aeration as a method of providing DO to the system. The SBR was operated over the span of 140 days under very low DO concentrations ranged from 0.5-1.0 mg/L, and achieved stable nutrients removal with removal efficiencies of: phosphorus removal efficiency (99%), ammonia removal efficiency (99%), COD removal Efficiency (100%). In addition, the effect of acetate to propionate ratio as a carbon source for EBPR systems under low DO concentrations have been studied, to investigate the effect of carbon source on the competition between Glycogen Accumulating Organism (GAO) and Polyphosphate Accumulating Organism (PAO) in EBPR systems. Propionate was found to be the best carbon source for EBPR process, after different compositions of COD were used as a carbon source for the EBPR process. The combination of low DO concentrations and propionate as a carbon source has been found to be a successful approach in controlling the competition between GAO and PAO in EBPR systems

Modelling of Water Requirements for Some Vegetable Crops under Ismailia Conditions

The scarcity of water resources around the globe has generated a need for their optimum utilization. Further; an agriculture consume a lot of water to irrigate crops, trees and landscape with different processes relying on climate data or soil data...etc. thus, dealing with this  data help to maximizing on farm water management  and rationalize unit of water. Using smart devices for irrigation management can help in achieving optimum water-resource utilization. This paper presents an open-source technology (Arduino Board) based smart algorithm, to predict the irrigation requirements using the sensing of ground parameter like  Soil Moisture sensor, Real Time Clock (RTC) module, SD card module, Liquid Crystal Display. And, applied (Arduino design) at the field to irrigate a Bottle Gourd crop and comparing with CropWat model as a method for calculate the water requirement using climate data. Using Arduino device helps to rationalize a significant amount of water by 66.5mm comparing with CropWat model. Furthermore, Arduino design with algorithm model gain a good value for (IWUE) by (2.003 kg.m-3), (5.09 ton .fed-1) for Yield production. In addition; this design helping for monitoring the changing in soil water content with reducing total water applied for Bottle Gourd crop. Keywords: Arduino UNO, Bottle Gourd production, irrigation use efficiency, modeling, soil moisture, and water requirement. DOI: 10.7176/JNSR/11-20-04 Publication date:October 31st 202

Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User

In this paper, we study and analyze cognitive radio networks in which secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We design a random spectrum sensing and access protocol for the SU that exploits the primary link's feedback and requires less average sensing time. Unlike previous works proposed earlier in literature, we do not assume perfect feedback. Instead, we take into account the more practical possibilities of overhearing unreliable feedback signals and accommodate spectrum sensing errors. Moreover, we assume an interference-based channel model where the receivers are equipped with multi-packet reception (MPR) capability. Furthermore, we perform power allocation at the SU with the objective of maximizing the secondary throughput under constraints that maintain certain quality-of-service (QoS) measures for the primary user (PU)

Optimal Spectrum Access for a Rechargeable Cognitive Radio User Based on Energy Buffer State

This paper investigates the maximum throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources, e.g., solar, wind and acoustic noise. We propose a probabilistic access strategy by the SU based on the number of packets at its energy queue. We investigate the effect of the energy arrival rate, the amount of energy per energy packet, and the capacity of the energy queue on the SU throughput under fading channels. Results reveal that the proposed access strategy can enhance the performance of the SU.Comment: arXiv admin note: text overlap with arXiv:1407.726

Energy-aware cooperative wireless networks with multiple cognitive users

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings

Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning

Purpose: Recent disruptive events, such as COVID-19 and Russia-Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. Methods: This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. Results: The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue

Designing Neuromorphic Computing Systems with Memristor Devices

Deep Neural Networks (DNNs) have demonstrated fascinating performance in many real-world applications and achieved near human-level accuracy in computer vision, natural video prediction, and many different applications. However, DNNs consume a lot of processing power, especially if realized on General Purpose GPUs or CPUs, which make them unsuitable for low-power applications. On the other hand, neuromorphic computing systems are heavily investigated as a potential substitute for traditional von Neumann systems in high-speed low-power applications. One way to implement neuromorphic systems is to use memristor crossbar arrays because of their small size, low power consumption, synaptic like behavior, and scalability. However, these systems are in their early developing stages and still have many challenges to be solved before commercialization. In this dissertation, we will investigate designing of neuromorphic computing systems, targeting classification and generation applications. Specifically, we introduce three novel neuromorphic computing systems. The first system implements a multi-layer feed-forward neural network, where memristor crossbar arrays are utilized in realizing a novel hybrid spiking-based multi-layered self-learning system. This system is capable of on-chip training, whereas for most previously published systems training is done off-chip. The system performance is evaluated using three different datasets showing improved average failure error by 42% than previously published systems and great immunity against process variations. The second system implements an Echo State Network (ESN), as a special type of recurrent neural networks, by utilizing a novel memristor double crossbar architecture. The system has been trained for sample generation, using the Mackey-Glass dataset, and simulations show accurate sample generation within a 75% window size of the training dataset. Finally, we introduce a novel neuromorphic computing for real-time cardiac arrhythmia classification. Raw ECG data is directly fed to the system, without any feature extraction, and hence reducing classification time and power consumption. The proposed system achieves an overall accuracy of 96.17% and requires only 34 ms to test one ECG beat, which outperforms most of its counterparts. For future work, we introduce a preliminary neuromorphic system implementing a deep Generative Adversarial Network (GAN), based on ESNs. The system is called ESN-GAN and it targets natural video generation applications