Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled networks.Also,the characteristic features,like constrained res...Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled networks.Also,the characteristic features,like constrained resources,heterogeneity,uncontrolled environment,and scalability requirement,make the security issues even more challenging.Hence,an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm(mod-EHTARA)is proposed to increase the energy efficiency and the lifespan of the nodes.The proposed mod-EHTARA is designed by adopting the Link Lifetime(LLT)model with the traditional EHTARA.The optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric,which considers the factors like delay,LLT,energy,and trust.The big data classification process is carried out at the Base Station(BS)using the MapReduce framework.Accordingly,the big data classification is progressed using a stacked autoencoder,which is trained by the Adaptive E-Bat algorithm.The Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm(BA)and Exponential Weighted Moving Average(EWMA).The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.展开更多
At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, man...At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System （HDFS）, Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.展开更多
文摘Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled networks.Also,the characteristic features,like constrained resources,heterogeneity,uncontrolled environment,and scalability requirement,make the security issues even more challenging.Hence,an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm(mod-EHTARA)is proposed to increase the energy efficiency and the lifespan of the nodes.The proposed mod-EHTARA is designed by adopting the Link Lifetime(LLT)model with the traditional EHTARA.The optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric,which considers the factors like delay,LLT,energy,and trust.The big data classification process is carried out at the Base Station(BS)using the MapReduce framework.Accordingly,the big data classification is progressed using a stacked autoencoder,which is trained by the Adaptive E-Bat algorithm.The Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm(BA)and Exponential Weighted Moving Average(EWMA).The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.
文摘At present, big data is very popular, because it has proved to be much successful in many fields such as social media, E-commerce transactions, etc. Big data describes the tools and technologies needed to capture, manage, store, distribute, and analyze petabyte or larger-sized datasets having different structures with high speed. Big data can be structured, unstructured, or semi structured. Hadoop is an open source framework that is used to process large amounts of data in an inexpensive and efficient way, and job scheduling is a key factor for achieving high performance in big data processing. This paper gives an overview of big data and highlights the problems and challenges in big data. It then highlights Hadoop Distributed File System （HDFS）, Hadoop MapReduce, and various parameters that affect the performance of job scheduling algorithms in big data such as Job Tracker, Task Tracker, Name Node, Data Node, etc. The primary purpose of this paper is to present a comparative study of job scheduling algorithms along with their experimental results in Hadoop environment. In addition, this paper describes the advantages, disadvantages, features, and drawbacks of various Hadoop job schedulers such as FIFO, Fair, capacity, Deadline Constraints, Delay, LATE, Resource Aware, etc, and provides a comparative study among these schedulers.
