Automated WSN based System for Emission level and Air pollution Detection in Vehicles

Air pollution is now a serious problem for those living in big, congested, industrialized cities with heavy vehicular traffic. The real wellspring of air contamination in enormous urban communities and modernly propelled nations is the autos and engine vehicles. Each vehicle will have outflow however the issue happens when it is past the institutionalized qualities. Air contamination from autos is part into essential and auxiliary contamination. Essential contamination is discharged straightforwardly into the environment; optional contamination comes about because of concoction responses between toxins in the climate. In both cases poisons are made due to burning procedure. This discharge from vehicles can't be totally maintained but, it certainly can be controlled. Since with the advancement of technology semiconductor sensor can detect various gases like carbon monoxide, nitrogen oxide and hydrocarbons, this paper goes for utilizing those sensors at the discharge outlets of vehicles which distinguishes the level of contamination. At the point when the contamination or discharge level shoots past the officially set edge level, the signal in the vehicle will be activated to demonstrate that the farthest point has been ruptured and the driver of the vehicle may stop after a specific time frame, driver is given the decision to either kill the bell and reset the drove light. Amid this day and age, the GPS begins finding the closest administration stations. After the clock runs out, the fuel supplied to the motor will be cut-off and the vehicle must be towed to the repairman or to the closest administration station. The synchronization and execution of the whole procedure is checked and controlled by a microcontroller. Predominantly for pragmatic utilization this paper depends on AVR based microcontroller. This paper, when utilized as a continuous venture, will profit the general public and help in diminishing the air contamination

An Analytical Incorporation of Power Priority Model with Replication and Expedition based Routing Protocol

Delay tolerant network (DTN) is a class of wireless ad-hoc network. It works when end to end direct path does not exist between source and destination by using the Store and Forwarding routing mechanism. DTN has several features such as long delay, limited resources, high error rate, reliable transmission etc. Its application fields are in wildlife behavior monitoring, military battle field, post disaster communication, under water communication and many more. The purpose of this paper is to compare between two different strategic (Replication and Expedition based) routing protocols with the Power Priority Model, which is proposed in recently. The evaluated result of this performance analysis was obtained from Opportunistic Network Environment (ONE) simulator on various performance metrics such as, Delivery Probability, Overhead ratio, Average latency and Hop count

Severe COVID-19 infection in a 32 weeks pregnant woman successfully treated with convalescent plasma therapy

From the beginning of corona virus disease 19(COVID-19) pandemic, there has been concern how to protect vulnerable group like pregnant women from severe acute respiratory syndrome corona virus 2(SARS-CoV-2). Historically, pregnant women experiences increased mortality during any pandemic situation. Pregnant women show almost the similar clinical features as that of non-pregnant adults with COVID-19 infection. Different systematic reviews have begun to focus light on pregnancy outcomes in COVID-19 patients, but knowledge is very limited and still the basis is case series and individual experiences. Apart from the scientifically proven therapeutic options used in COVID-19 such as steroid, low molecular weight heparin, the role of convalescent plasma therapy (CPT) has never been evaluated. We present a case of a pregnant woman of 32 weeks of conception, treated with CPT with favourable outcome in a private hospital of Dhaka, Bangladesh. BSMMU J 2021; 14 (COVID -19 Supplement): 64-6

Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach

This paper proposes a novel energy-efficient multimedia delivery system called EStreamer. First, we study the relationship between buffer size at the client, burst-shaped TCP-based multimedia traffic, and energy consumption of wireless network interfaces in smartphones. Based on the study, we design and implement EStreamer for constant bit rate and rate-adaptive streaming. EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP), November 201

Feasibility Analysis of the Algorithms: Secured and Efficient Routing Path Update in Software Defined Networking (SDN)

Software-defined networking is the talk of the town in today’s networking industry. Because of the limitations of traditional networking, SDN is getting more popular every year. Lots of researches are taking place to improve the efficiency and overcome the challenges of SDN though it has many advantages. Hence one key problem of SDN is the network update. If the route update does not perform well, it causes congestion and inconsistencies in the network system whereas bandwidth utilization and security is our main concern. We have compared two pre-built algorithms especially for routing path update and proposed a new algorithm with maximum security and loop-free network

Full Charge Capacity and Charging Diagnosis of Smartphone Batteries

Full charge capacity (FCC) refers to the amount of charge a battery can hold. It is the fundamental property of smartphone batteries that diminishes as the battery ages and is charged/discharged. We investigate the behavior of smartphone batteries while charging and demonstrate that battery voltage and charging rate information can together characterize the FCC of a battery. We propose a new method for accurately estimating FCC without exposing low-level system details or introducing new hardware or system modules. We further propose and implement a collaborative FCC estimation technique that builds on crowd-sourced battery data. The method finds the reference voltage curve and charging rate of a particular smartphone model from the data and then compares with those of an individual device. After analyzing a large data set towards a crowd-sourced rate versus FCC model, we report that 55 percent of all devices and at least one device in 330 out of 357 unique device models lost some of their FCC. For some old device models, the median capacity loss exceeded 20 percent. The models further enable debugging the performance of smartphone charging. We propose an algorithm, called BatterySense, which utilizes crowd-sourced rate to detect abnormal charging performance, estimate FCC of the device battery, and detect battery changes.Peer reviewe

Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.Peer reviewe

d-Simplexed : Adaptive Delaunay Triangulation or Performance Modeling and Prediction on Big Data Analytics

Big Data processing systems (e.g., Spark) have a number of resource configuration parameters, such as memory size, CPU allocation, and the number of running nodes. Regular users and even expert administrators struggle to understand the mutual relation between different parameter configurations and the overall performance of the system. In this paper, we address this challenge by proposing a performance prediction framework, called $d$-Simplexed, to build performance models with varied configurable parameters on Spark. We take inspiration from the field of Computational Geometry to construct a d-dimensional mesh using Delaunay Triangulation over a selected set of features. From this mesh, we predict execution time for various feature configurations. To minimize the time and resources in building a bootstrap model with a large number of configuration values, we propose an adaptive sampling technique to allow us to collect as few training points as required. Our evaluation on a cluster of computers using WordCount, PageRank, Kmeans, and Join workloads in HiBench benchmarking suites shows that we can achieve less than 5% error rate for estimation accuracy by sampling less than 1% of data.Peer reviewe

Multiple Set Matching with Bloom Matrix and Bloom Vector

Bloom Filter is a space-efficient probabilistic data structure for checking the membership of elements in a set. Given multiple sets, a standard Bloom Filter is not sufficient when looking for the items to which an element or a set of input elements belong. An example case is searching for documents with keywords in a large text corpus, which is essentially a multiple set matching problem where the input is single or multiple keywords, and the result is a set of possible candidate documents. This article solves the multiple set matching problem by proposing two efficient Bloom Multifilters called Bloom Matrix and Bloom Vector, which generalize the standard Bloom Filter. Both structures are space-efficient and answer queries with a set of identifiers for multiple set matching problems. The space efficiency can be optimized according to the distribution of labels among multiple sets: Uniform and Zipf. Bloom Vector efficiently exploits the Zipf distribution of data for further space reduction. Indeed, both structures are much more space-efficient compared with the state-of-the-art, Bloofi. The results also highlight that a Lookup operation on Bloom Matrix is significantly faster than on Bloom Vector and Bloofi.Peer reviewe