QUALITY OF SERVICE IMPROVEMENTS IN IEEE 802.11AX WI-FI

IEEE 802.11ax is the latest high rate Wi-Fi technology (also known as high efficiency Wireless) introduces some new features like OFDMA, Uplink multi-user MIMO (UL MU-MIMO) to deliver benefits in reliability, capacity, and speed. 802.11ax is better suited to some new use cases, such as live video streaming and IoT than previous versions of Wi-Fi (802.11n/ac). In this article, we study the Quality of Service (QoS) mechanism to guarantee certain levels of service to various traffic flows with the help of weighted fair queuing (WFQ) hierarchical scheduler. The multi-user mode of enhanced distributed channel access (MU mode EDCA) illustrates the controlling aspects, issues, and possible solutions of EDCA for the 802.11ax standard to satisfy QoS requirements in dense deployment scenarios

DIRECTIONAL ANTENNA BASED EFFICIENT LOCATION AWARE ROUTING IN MOBILE ADHOC NETWORK

Mobile Adhoc Network (MANET) also called as wireless ad hoc network is a self-organizing, self-configuring infrastructure less network containing a group of mobile nodes communicating wirelessly. As the hosts move often resulting in dynamic topology of the network, routing seeks more attention. Therefore, routing protocol using node’s location information like LAR (location aided routing) has emerged as potential solution. Here, the route discovery is limited to a small region named as request zone in contrast to blind flooding over the entire network. Also it is noticeable that the shape and size of the request zone play a vital role in enhancing the protocol’s performance. After various analyses it was concluded that for higher node density, elliptical shaped request zone performs better than other possible shapes. Further, suitable route must be chosen based on current load status of the network so that successful delivery of packets is ensured. Generally, omni-directional antennas are used for communication between moving motes. The disadvantage of mobile ad hoc networks with omni-directional antenna lies in the limited capacity caused by high interference and low spatial reuse. This paper focuses on obtaining optimal size for request zone in accordance with varying node density. Further, optimal path between source and destination is selected using Dijkstra’s algorithm. Our simulation results show that directional antennas outshines the performance of omni-directional antennas in increasing transmission range of nodes, reducing the number of redundant nodes involving in data communication etc

A MULTI AWARE MULTI-LEVELS HETEROGENOUS ROUTING (MAMHR) PROTOCOL FOR WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSNs) are generally set out in a remote workplace, since the sensor nodes are tiny in size, cost-proficient, low-power gadgets, and have restricted battery supply. Due to the constrained availability of power sources, energy utilization has been considered as the most crucial factor for proposing network routing protocols. The fundamental concern is to improve the lifetime of the network based on the energy constraints. Several homogenous cluster-based routing protocols have been proposed in literature for lifetime improvement of the sensor network but many of them fail to function effectively in heterogeneous environment and moreover, these protocols have not considered any other awareness parameters than lifetime and energy consumption. In this work, a Multi Aware Multi-Levels Heterogeneous Routing (MAMHR) protocol, focusing on the principle of multi-level heterogeneity by considering multiple awareness parameters of the network such as Quality of Service (QoS), shortest path estimation and suitable localization technique, is proposed. Scoped Bellman Ford Routing (SBFR) algorithm is used for shortest path estimation, Reliability, Availability, and Serviceability (RAS) factors are considered for QoS awareness and Time Difference of Arrival (TDOA) technique is used for location estimation. Lifetime awareness parameters of the proposed scheme were compared with already existing prominent protocols LEACH, SEP and ZSEP and a significant improvement in lifetime of entire network was obtained. Simulation results corresponding to the respective multiple awareness parameters also shows that these parameters can be incorporated into the selected heterogeneous environment without affecting the energy consumption constraints of the proposed scheme

Proof of Concept of Scalable Integration of Internet of Things and Blockchain in Healthcare

The advent of Internet of Things (IoT) brought innovation along with unprecedented benefits of convenience and efficacy in many operations that were otherwise very cumbersome. This innovation explosion has surfaced a new dimension of vulnerability and physical threat to the data integrity of IoT networks. Implementing conventional cryptographic algorithms on IoT devices is not future-proof as these devices are constrained in terms of computational power, performance, and memory. In this paper, we are proposing a novel framework, a unique model that integrates IoT networks with a blockchain to address potential privacy and security threats for data integrity. Smart contracts are instrumental in this integration process and they are used to handle device authentication, authorization and access-control, and data management. We further share a new design model for interfaces to integrate both platforms while highlighting its performance results over the existing models. With the incorporation of off-chain data storage into the framework, overall scalability of the system can be increased. Finally, our research concludes how the proposed framework can be fused virtually into any existing IoT applications with minimal modifications

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients