140 research outputs found

    An Innovative Multiple Attribute Based Distributed Clustering with Sleep/Wake Scheduling Mechanism for WSN

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    Wireless sensor network is a dynamic field of networking and communication because of its increasing demand in critical Industrial and Robotics applications. Clustering is the technique mainly used in the WSN to deal with large load density for efficient energy conservation. Formation of number of duplicate clusters in the clustering algorithm decreases the throughput and network lifetime of WSN. To deal with this problem, advance distributive energy-efficient adaptive clustering protocol with sleep/wake scheduling algorithm (DEACP-S/W) for the selection of optimal cluster head is presented in this paper. The presented sleep/wake cluster head scheduling along with distributive adaptive clustering protocol helps in reducing the transmission delay by properly balancing of load among nodes. The performance of algorithm is evaluated on the basis of network lifetime, throughput, average residual energy, packet delivered to the base station (BS) and CH of nodes. The results are compared with standard LEACH and DEACP protocols and it is observed that the proposed protocol performs better than existing algorithms. Throughput is improved by 8.1% over LEACH and by 2.7% over DEACP. Average residual energy is increased by 6.4% over LEACH and by 4% over DEACP. Also, the network is operable for nearly 33% more rounds compared to these reference algorithms which ultimately results in increasing lifetime of the Wireless Sensor Network

    An Innovative Multiple Attribute Based Distributed Clustering with Sleep/Wake Scheduling Mechanism for WSN

    Get PDF
    Wireless sensor network is a dynamic field of networking and communication because of its increasing demand in critical Industrial and Robotics applications. Clustering is the technique mainly used in the WSN to deal with large load density for efficient energy conservation. Formation of number of duplicate clusters in the clustering algorithm decreases the throughput and network lifetime of WSN. To deal with this problem, advance distributive energy-efficient adaptive clustering protocol with sleep/wake scheduling algorithm (DEACP-S/W) for the selection of optimal cluster head is presented in this paper. The presented sleep/wake cluster head scheduling along with distributive adaptive clustering protocol helps in reducing the transmission delay by properly balancing of load among nodes. The performance of algorithm is evaluated on the basis of network lifetime, throughput, average residual energy, packet delivered to the base station (BS) and CH of nodes. The results are compared with standard LEACH and DEACP protocols and it is observed that the proposed protocol performs better than existing algorithms. Throughput is improved by 8.1% over LEACH and by 2.7% over DEACP. Average residual energy is increased by 6.4% over LEACH and by 4% over DEACP. Also, the network is operable for nearly 33% more rounds compared to these reference algorithms which ultimately results in increasing lifetime of the Wireless Sensor Network

    Performance optimization in IoT-based next-generation wireless sensor networks

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    AbstractIn this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is the data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.Abstract In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is the data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results

    A Cluster Head Assisted Routing (CHAR) Scheme For Improved Energy Load Balancing In Wireless Sensor Networks

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    This study presents a Cluster Head Assisted Routing (CHAR) scheme aimed at reducing the burden on Cluster Heads (CHs) while maintaining the hierarchical structure and its advantages. The Assisting Cluster Head (ACH) collates data within the cluster while the cluster head (CH) is left with the task of transmitting collated data towards the base station (BS). In this manner, the huge energy burden hitherto on the CH is reduced. An energy cost function is developed; which considers the residual energy of the nodes and the energy burden imposed on the cluster should any member is elected as a head. The node that best satisfies the two criteria is favored to be elected as the head. Simulations were carried out in MATLAB and in a test bed using ESP8266, conditioned with power supply unit, a voltage measuring circuit and a firmware, as the wireless sensor nodes. The results revealed an improvement in the energy consumption profile of the CHAR over non cluster head assisted routing. The CHAR scheme recorded a 12.9% improvement in round-count before First Node Death (FND). Other parameters compared are Last Node Death (LND), Residual energy per round and energy left unused at the end of the experiment

    Design and Implementation of a Self Adaptive Architecture for QoS (SAAQ) in IoT based Wireless Networks

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    The rapid growth of Internet of Things (IoT) applications has made ensuring quality of service (QoS) in wireless networks essential. This paper presents the design and implementation of a Self-Adaptive Architecture for QoS (SAAQ) in IoT-based wireless networks, using the NS-2 simulation tool as a foundation for analysis and evaluation. The SAAQ framework is carefully tailored to meet the dynamic demands of IoT applications, enabling real-time adjustment of QoS parameters such as packet delivery ratio, throughput, end-to-end delay, packet loss ratio, energy consumption and routing overhead. By integrating with NS-2, a simulation tool in network research, we conduct extensive simulations and experiments to evaluate the SAAQ's effectiveness in diverse IoT scenarios. This paper explores the adaptability and scalability of the SAAQ architecture and results of experiments reveal the practical benefits of the SAAQ in enhancing QoS in a simulated IoT application over other methods such as AODV, AOMDV, and LEACH

    Air Force Institute of Technology Research Report 2009

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    This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

    Air Force Institute of Technology Research Report 2007

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    This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics
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