Towards Viable Large Scale Heterogeneous Wireless Networks

We explore radio resource allocation and management issues related to a large-scale heterogeneous (hetnet) wireless system made up of several Radio Access Technologies (RATs) that collectively provide a unified wireless network to a diverse set of users through co-ordination managed by a centralized Global Resource Controller (GRC). We incorporate 3G cellular technologies HSPA and EVDO, 4G cellular technologies WiMAX and LTE, and WLAN technology Wi-Fi as the RATs in our hetnet wireless system. We assume that the user devices are either multi-modal or have one or more reconfigurable radios which makes it possible for each device to use any available RAT at any given time subject to resource-sharing agreements. For such a hetnet system where resource allocation is coordinated at a global level, characterizing the network performance in terms of various conflicting network efficiency objectives that takes costs associated with a network re-association operation into account largely remains an open problem. Also, all the studies to-date that try to characterize the network performance of a hetnet system do not account for RAT-specific implementation details and the management overhead associated with setting up a centralized control. We study the radio resource allocation problem and the implementation/management overhead issues associated with a hetnet system in two research phases. In the first phase, we develop cost models associated with network re-association in terms of increased power consumption and communication downtime taking into account various user device assumptions. Using these cost models in our problem formulations, the first phase focuses on resource allocation strategies where we use a high-level system modeling approach to study the achievable performance in terms of conflicting network efficiency measures of spectral efficiency, overall power consumption, and instantaneous and long-term fairness for each user in the hetnet system. Our main result from this phase of study suggests that the gain in spectral efficiency due to multi-access network diversity results in a tremendous increase in overall power consumption due to frequent re-associations required by user devices. We then develop a utility function-based optimization algorithm to characterize and achieve a desired tradeoff in terms of all four network efficiency measures of spectral efficiency, overall power consumption and instantaneous and long-term fairness. We show an increase in a multi-attribute system utility measure of up to 56.7% for our algorithm compared to other widely studied resource allocation algorithms including max-sum rate, proportional fairness, max-min fairness and min power. The second phase of our research study focuses on practical implementation issues including the overhead required to implement a centralized GRC solution in a hetnet system. Through detailed protocol level simulations performed in ns-2, we show an increase in spectral efficiency of up to 99% and an increase in instantaneous fairness of up to 28.5% for two sort-based user device-to-Access Point (AP)/Base Station (BS) association algorithms implemented at the GRC that aim to maximize system spectral efficiency and instantaneous fairness performance metrics respectively compared to a distributed solution where each user makes his/her own association decision. The efficiency increase for each respective attribute again results in a tremendous increase in power consumption of up to 650% and 794% for each respective algorithm implemented at the GRC compared to a distributed solution because of frequent re-associations

An Integrated Routing and Distributed Scheduling Approach for Hybrid IEEE 802.16E Mesh Networks For Vehicular Broadband Communications

An integrated routing and distributed scheduling approach for fast deployable IEEE 802.16e networks is presented where distributed base stations with dual radios form a mesh backhaul and subscriber stations communicate through these base stations. The mesh backhaul is formed via an IEEE 802.16e mesh mode radio on each base station, while the subscriber stations communicate with base stations via PMP mode radios. The proposed routing scheme divides the deployed network into several routing zones. Each routing zone contains several base stations that form the mesh backhaul with one base station equipped with either a fiber, satellite or any other point-to-point backhaul link to reach a gateway on the core network (for example, Internet or Enterprise Network). Traffic from the subscriber stations is routed by the serving base station through the mesh to the gateway-connected base station using min-hop routing metric. Mobile IP scheme is used to assign a care-of address to a subscriber station that moves from one routing zone to the other, thereby avoiding a change in IP address for network layer applications. The scheduling approach consists of two phases. In the first phase, a centralized mesh scheduling algorithm is applied with collected information on network topology, radio parameters, and initial QoS provisioning requirements. At the same time, each base station derives a PMP schedule for actual demands from associated subscriber stations constrained by the initial mesh schedule. In the second phase, each base station monitors its carried PMP traffic load statistics; to accommodate traffic load changes in a distributed fashion, each base station lends or borrows time slots from neighboring base stations to adjust its mesh and PMP radio schedules. The distributed schedule adaptation method not only allows individual base stations to accommodate short-term increases in bandwidth demands, it also provides the means for optimizing the mesh and PMP schedules with respect to actual bandwidth demands. Several deployment strategies are considered and an analytical model is developed to identify the achievable increase in overall network throughput using the proposed scheduling approach. Simulations are run in network simulator ns-2 to verify results obtained using the analytical model

Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications

This paper develops an optimal data aggregation policy for learning-based traffic control systems based on imagery collected from Road Side Units (RSUs) under imperfect communications. Our focus is optimizing semantic information flow from RSUs to a nearby edge server or cloud-based processing units by maximizing data diversity based on the target machine learning application while taking into account heterogeneous channel conditions (e.g., delay, error rate) and constrained total transmission rate. As a proof-of-concept, we enforce fairness among class labels to increase data diversity for classification problems. The developed constrained optimization problem is non-convex. Hence it does not admit a closed-form solution, and the exhaustive search is NP-hard in the number of RSUs. To this end, we propose an approximate algorithm that applies a greedy interval-by-interval scheduling policy by selecting RSUs to transmit. We use coalition game formulation to maximize the overall added fairness by the selected RSUs in each transmission interval. Once, RSUs are selected, we employ a maximum uncertainty method to handpick data samples that contribute the most to the learning performance. Our method outperforms random selection, uniform selection, and pure network-based optimization methods (e.g., FedCS) in terms of the ultimate accuracy of the target learning application

RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples

In some practical learning tasks, such as traffic video analysis, the number of available training samples is restricted by different factors, such as limited communication bandwidth and computation power; therefore, it is imperative to select diverse data samples that contribute the most to the quality of the learning system. One popular approach to selecting diverse samples is Determinantal Point Process (DPP). However, it suffers from a few known drawbacks, such as restriction of the number of samples to the rank of the similarity matrix, and not being customizable for specific learning tasks (e.g., multi-level classification tasks). In this paper, we propose a new way of measuring task-oriented diversity based on the Rate-Distortion (RD) theory, appropriate for multi-level classification. To this end, we establish a fundamental relationship between DPP and RD theory, which led to designing RD-DPP, an RD-based value function to evaluate the diversity gain of data samples. We also observe that the upper bound of the diversity of data selected by DPP has a universal trend of phase transition that quickly approaches its maximum point, then slowly converges to its final limits, meaning that DPP is beneficial only at the beginning of sample accumulation. We use this fact to design a bi-modal approach for sequential data selection

Role of Ayurveda based non-invasive intervention in management of ischemic heart disease patient of diabetes

Background: The aim of the study was to determine the effectiveness of IRP therapy in patients of myocardial ischemia attending Madhavbaug clinics in Vidarbha region, Maharashtra.Methods: This was a retrospective study conducted from June 2019 to December 2019, wherein we identified the data of patients suffering from IHD (positive for inducible ischemia from stress test) of either gender or any age, and who had attended the Out-patient departments (OPDs) of Madhavbaug clinics across India. The data of patients who had been administered IRP with minimum 7 sittings over a span of 12 weeks were considered for the study.Results: In the present study, medical records of 50 patients of IHD were analyzed.  At the end of IRP therapy there was statistically significant reduction in weight, BMI, SBP, and DBP. VO2 peak was improved at the end of therapy i.e. 26.51±5.93 ml/kg/min as compared to baseline i.e.; 15.62±5.36 ml/kg/min and the difference was highly statistically significant (p<0.001). DTS improved from -2.93±5.88 at baseline to 3.21±6.03 at week 12 of IRP therapy and the difference was highly statistically significant (p<0.0001).Conclusions: Findings of present study suggest that IRP can serve as effective therapeutic option for the management of myocardial ischemia

Role of Aahar and Panchakarma on restoration of euglycemia in known type II diabetes mellitus

Background: Diabetes mellitus, in particular, has emerged as a significant health concern, affecting millions of individuals and placing a considerable strain on the healthcare system. Promoting remission of diabetes, wherein patients achieve a state of sustained blood sugar control without the need for ongoing medication or with a reduced reliance on medication, can yield remarkable benefits. This study sought to understand the role of Aahar and Panchakarma on restoration of euglycemia in known type 2 diabetes patients. Methods: A retrospective, observational, cohort study was conducted at Madhavbaug Cardiac Care Clinic between April 2021 and April 2022 in Maharashtra, India. Patients aged 18 years and older with a diagnosis of type 2 diabetes mellitus with glycated haemoglobin level (HbA1c) &gt;7% and had participated in the Comprehensive Diabetes Care (CDC) program were included in this study. Parameters such as HbA1c, body weight, body mass index (BMI), and dependence on conventional allopathic medication were assessed at the end of the CDC program. Follow-up was conducted at 90 days. Day 1 and day 90 data were compared. Results: Of the 45 patients, 17 (40.5%) patients had a negative glucose tolerance and 14 (33.3%) patients had impaired glucose tolerance. HbA1c, body weight, and BMI improved at the end of CDC program. Dependency on conventional allopathic medications was also reduced. Conclusions: Restoration of euglycemia in patients with type 2 diabetes mellitus is possible, however, further studies to understand the affecting factors are warranted

Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality

We focus on addressing the challenges in responsible beauty product recommendation, particularly when it involves comparing the product's color with a person's skin tone, such as for foundation and concealer products. To make accurate recommendations, it is crucial to infer both the product attributes and the product specific facial features such as skin conditions or tone. However, while many product photos are taken under good light conditions, face photos are taken from a wide range of conditions. The features extracted using the photos from ill-illuminated environment can be highly misleading or even be incompatible to be compared with the product attributes. Hence bad illumination condition can severely degrade quality of the recommendation. We introduce a machine learning framework for illumination assessment which classifies images into having either good or bad illumination condition. We then build an automatic user guidance tool which informs a user holding their camera if their illumination condition is good or bad. This way, the user is provided with rapid feedback and can interactively control how the photo is taken for their recommendation. Only a few studies are dedicated to this problem, mostly due to the lack of dataset that is large, labeled, and diverse both in terms of skin tones and light patterns. Lack of such dataset leads to neglecting skin tone diversity. Therefore, We begin by constructing a diverse synthetic dataset that simulates various skin tones and light patterns in addition to an existing facial image dataset. Next, we train a Convolutional Neural Network (CNN) for illumination assessment that outperforms the existing solutions using the synthetic dataset. Finally, we analyze how the our work improves the shade recommendation for various foundation products.Comment: 7 pages, 5 figures. Presented in FAccTRec202

Nano-Resolution Visual Identifiers Enable Secure Monitoring in Next-Generation Cyber-Physical Systems

Today's supply chains heavily rely on cyber-physical systems such as intelligent transportation, online shopping, and E-commerce. It is advantageous to track goods in real-time by web-based registration and authentication of products after any substantial change or relocation. Despite recent advantages in technology-based tracking systems, most supply chains still rely on plainly printed tags such as barcodes and Quick Response (QR) codes for tracking purposes. Although affordable and efficient, these tags convey no security against counterfeit and cloning attacks, raising privacy concerns. It is a critical matter since a few security breaches in merchandise databases in recent years has caused crucial social and economic impacts such as identity loss, social panic, and loss of trust in the community. This paper considers an end-to-end system using dendrites as nano-resolution visual identifiers to secure supply chains. Dendrites are formed by generating fractal metallic patterns on transparent substrates through an electrochemical process, which can be used as secure identifiers due to their natural randomness, high entropy, and unclonable features. The proposed framework compromises the back-end program for identification and authentication, a web-based application for mobile devices, and a cloud database. We review architectural design, dendrite operational phases (personalization, registration, inspection), a lightweight identification method based on 2D graph-matching, and a deep 3D image authentication method based on Digital Holography (DH). A two-step search is proposed to make the system scalable by limiting the search space to samples with high similarity scores in a lower-dimensional space. We conclude by presenting our solution to make dendrites secure against adversarial attacks