IoT Cloud Computing Middleware for Crowd Monitoring and Evacuation

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19.

Optimal Common Job Block Table (CJBT) to improve the Performance in Hadoop framework

<p>By rapid transformation of technology, huge amount of data (structured data and Un Structured data) is generated every day. With the aid of 5G technology and IoT the data generated and processed every day is very large. If we dig deeper the data generated approximately 2.5 quintillion bytes.<br> This data (Big Data) is stored and processed with the help of Hadoop framework. Hadoop framework has two phases for storing and retrieve the data in the network.</p> <ul> <li>Hadoop Distributed file System (HDFS)</li> <li>Map Reduce algorithm</li> </ul> <p>In the native Hadoop framework, there are some limitations for Map Reduce algorithm. If the same job is repeated again then we have to wait for the results to carry out all the steps in the native Hadoop. This led to wastage of time, resources. If we improve the capabilities of Name node i.e., maintain Common Job Block Table (CJBT) at Name node will improve the performance. By employing Common Job Block Table will improve the performance by compromising the cost to maintain Common Job Block Table.<br> Common Job Block Table contains the meta data of files which are repeated again. This will avoid re computations, a smaller number of computations, resource saving and faster processing. The size of Common Job Block Table will keep on increasing, there should be some limit on the size of the table by employing algorithm to keep track of the jobs. The optimal Common Job Block table is derived by employing optimal algorithm at Name node.</p>

An Approach in Big Data Analytics to improve the velocity of unstructured data Using Map Reduce

Big Data Analytics is an innovative approach for extracting the data from a huge volume of data warehouse systems. It reveals the method to compress the high volume of data into clusters by MapReduce and HDFS. However, the data processing has taken more time for extract and store in Hadoop clusters. The proposed system deals with the challenges of time delay in shuffle phase of map-reduce due to scheduling and sequencing. For improving the speed of big data, this proposed work using the Compressed Elastic Search Index (CESI) and MapReduce-Based Next Generation Sequencing Approach (MRBNGSA). This approach helps to increase the speed of data retrieval from HDFS clusters because of the way it is stored in that. this method is stored only the metadata in HDFS which takes less memory during runtime compare to big data due to the volume of data stored in HDFS. This approach is reduces the CPU utilization and memory allocation of the resource manager in Hadoop Framework and imroves data processing speed, such a way that time delay has to be reduced with minimum latency.