Wireless sensor network [WSN] energy efficiency is the most critical task of researchers. Power regulation is a very effective technique for interference minimizing in WSN and energy consumption. WSN with several nodes linked to the network, including transmitting power to measure network output. The energy usage is connected to node size and weight. Aggregation data secure in WSN is a highly challenging activity. In this research paper suggested stable aggregation of data using fuzzy logic, based on clustering techniques. The clustering method is done in a network. The distance power consumed and faith value are measured for each cluster. The stable data aggregation using fuzzy logic techniques (FLT) is based on these parameters. The proposed research work would minimize energy usage to increase the lifespan of WSN.
Background: IoT networks are being used frequently for meeting the requirements of some specifications such as applications related to automobiles, aerospace, etc. The performance is always an issue as many intricacies exist built into the developed IoT networks such as handling heterogeneity, failures of communication paths, lack of bandwidth, non-availability of alternate paths for communication, etc. Many layers exist in an IoT network. Each layer built using specific technology and is faced with many performance bottlenecks addressed. The performance of the entire network is affected when there are many performance issues in any layer. The performance of an IoT network must be analyzed considering all the layers and the issues related to those layers. Objective: The main objective of this paper is to present the way the performance of an IoT network improved by using a specific networking topology used at the gateway level of the IoT network. Method: Receiving data in multiple low-speed channels using different communication systems, stacking the same and transmitting results using the splitters using dual high-speed channels to improve the time required for transmission and also to reduce the latency at the gateway level. Results: The splitter method introduced at the gateway level improved the performance of the IoT network from 1519 Microseconds to 1029 Microseconds for transmitting 100 data packets either way. Throughput as such improved from 0.31 packets / Microsecond to 0.19 packets / Microsecond. Conclusions: The performance of IoT networks suffers due to various reasons. The performance of an IoT network gets improved at gateway by using splitters that merge and bifurcate the communication traffic.
Abstract:In wireless sensor network one of the most security threats is the reactive jammer because of the mass destruction to the sensor communication and it is difficult to disclose. So we have to deactivate the reactive jammers by identifying all the trigger nodes, because the transmission invokes the jammer. Such a trigger identification procedure can work as an application-layer service and benefit many existing reactive jamming defending schemes. In this paper, on the one hand, we leverage several optimization problems to provide a complete trigger-identification service framework for unreliable wireless sensor networks. On the other hand, we provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios. Theoretical analysis and simulation results are included to validate the performance of this framework..
Cloud computing is an Internet-based computing model. This model enables access to resources and services on demand. Cloud computing users have applications with different Quality of Service requirements. On the other hand, there are different cloud service providers offering services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a significant research problem. Ranking of cloud service providers compares different services offered by different providers based on quality of service, in order to select the most suitable cloud service provider.QoS parameters provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, realworld invocations on the service candidates are usually required. This project proposes a QoS ranking prediction framework for cloud services that eliminates delay and expenses involved in real-world service invocations. It makes use of the past service usage experiences of other users. This framework does not require any additional invocations of cloud services while making QoS ranking prediction.The algorithm is implemented by considering both cost and benefit parameters such as Response time and throughput respectively using a database containing response time and throughput values of 300 users for 10 different cloud providers. Also, Sensitivity analysis is done by varying weights of individual QoS parameters to verify the correctness of the algorithm. It is observed from the results that the proposed cloud service selection algorithm is able to appropriately choose the best cloud service provider depending on the weights of the respective QoS parameters. General TermsAlgorithms
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