1,477 research outputs found

    Routing Algorithm with Uneven Clustering for Energy Heterogeneous Wireless Sensor Networks

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    Aiming at the “hotspots” problem in energy heterogeneous wireless sensor networks, a routing algorithm of heterogeneous sensor network with multilevel energies based on uneven clustering is proposed. In this algorithm, the energy heterogeneity of the nodes is fully reflected in the mechanism of cluster-heads’ election. It optimizes the competition radius of the cluster-heads according to the residual energy of the nodes. This kind of uneven clustering prolongs the lifetime of the cluster-heads with lower residual energies or near the sink nodes. In data transmission stage, the hybrid multihop transmission mode is adopted, and the next-hop routing election fully takes account of the factors of residual energies and the distances among the nodes. The simulation results show that the introduction of an uneven clustering mechanism and the optimization of competition radius of the cluster-heads significantly prolonged the lifetime of the network and improved the efficiency of data transmission

    Routing Design Issues in Heterogeneous Wireless Sensor Network

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    WSN has important applications such as habitat monitoring, structural health monitoring, target tracking in military and many more. This has evolved due to availability of sensors that are cheaper and intelligent but these are having battery support. So, one of the major issues in WSN is maximization of network life. Heterogeneous WSNs have the potential to improve network lifetime and also provide higher quality networking and system services than the homogeneous WSN. Routing is the main concern of energy consumption in WSN. Previous research shows that performance of the network can be improve significantly using protocol of hierarchical HWSN. However, the appropriateness of a particular routing protocol mainly depends on the capabilities of the nodes and on the application requirements. This study presents different aspects of Heterogeneous Wireless Sensor network and design issues for routing in heterogeneous environment. Different perspectives from different authors regarding energy efficiency based on resource heterogeneity for heterogeneous wireless sensor networks have been presented

    A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

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    Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes\u27 optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets

    A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

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    This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented

    Energy Efficient Protocol for Lifetime Prediction of Wireless Sensor Network using Multivariate Polynomial Regression Model

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    The sensor network performs gathering, monitoring, and tracking of objects in the given area. The sensor nodes are normally distributed randomly in the network area for collecting the information. The major issues in Wireless Sensor Networks (WSN) are coverage, energy, and limited resources. Sensor Nodes’ (SN) performance depends on so many parameters but normally depends on Residual Energy (RE) and Distance from the base station. The Cluster Head (CH) cooperatively communicates with Base Station (BS) via routing protocols. The proposed Energy Efficient Multilevel Region Based (EEMRB) protocol performs the task by partitioning the entire network area into multiple levels and sub-levels. The sub-levels are partitioned to perform clusters to communicate the sensor via CH (s) using single/multi-hop communication to BS. The proposed protocol is compared with the Stable Election Protocol and shows improvement in network lifetime. Based on the proposed protocol data set, a Multivariate Polynomial Regression (MPR) Model is proposed to predict network lifetime. The model uses packet size and node density as network design parameters. The simulation results show that the size of the packet and network area play a major role in network lifetime. Therefore, the lifetime of the predicted model and EEMRB protocol are close to each other. This prediction model is suitable for the prediction of any network area's lifetime

    HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

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    Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments

    A Survey on Energy Efficient Routing Protocols in Wireless Sensor Networks

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    Energy efficiency is one of the critical issues in the Wireless Sensor Networks (WSNs), since sensor devices are tiny and integrated with a limited capacity battery. In most of the advanced applications, WSNs operate in very harsh areas and not under supervision of human controls. Routing protocols play a significant role in energy balancing by incorporating the techniques that can reduce control overhead, proper data aggregation method and feasible path selection. It demands a unique requirement due to its frequent topology changes and distributive nature. One of the major concerns in the design of routing protocol in WSNs is efficient energy usage and prolonging Network lifetime. This paper mainly discusses different issues related to energy efficiency in routing protocols of all categories. It incorporates most recent routing protocols which improves the energy efficiency in various application environments. This paper also provides comprehensive details of each protocol which emphasize their principles and explore their advantages and limitations. These protocols belong to different classifications based on Network Structures, communication model, topology and QoS parameters. It also includes more relevant and prominent comparisons with all recent State-of-Art works
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