1,071 research outputs found

    Strengths and Weaknesses of Prominent Data Dissemination Techniques in Wireless Sensor Networks

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    Data dissemination is the most significant task in a Wireless Sensor Network (WSN). From the bootstrapping stage to the full functioning stage, a WSN must disseminate data in various patterns like from the sink to node, from node to sink, from node to node, or the like. This is what a WSN is deployed for. Hence, this issue comes with various data routing models and often there are different types of network settings that influence the way of data collection and/or distribution. Considering the importance of this issue, in this paper, we present a survey on various prominent data dissemination techniques in such network. Our classification of the existing works is based on two main parameters: the number of sink (single or multiple) and the nature of its movement (static or mobile). Under these categories, we have analyzed various previous works for their relative strengths and weaknesses. A comparison is also made based on the operational methods of various data dissemination schemes

    Data Collection Protocols For Wireless Sensor Networks

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    Data collection in wireless sensor networks (WSNs) has a significant impact on the network’s performance and lifetime. Recently, several data collection techniques that use mobile elements (MEs) have been recommended, especially techniques that focus on maximising data delivery. However, energy consumption and the time required for data collection are significant for many WSN applications, particularly real-time systems. In this paper, a review of data collection techniques is presented, providing a comparison between the maximum amount shortest path (MASP) and zone-based energy-aware (ZEAL) data collection protocols implemented in the NS-3 simulator. Finally, the study provides a suitable data collection strategy that satisfies the requirements of WSN applications in terms of data delivery, energy consumption, and the time required for data collection

    A Review on Proposed Implementation of VGDRA and its Comparative Analysis.

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    Recently, a virtual Grid-based dynamic routes adjustment scheme for mobile sink-based wireless sensor networks is introduced. This paper presents the proposed implementation of VGDRA and its comparative analysis, in which we are discussing the approach of efficient data delivery using communication of distance priority i.e. avoiding straight line communication which was used in previous VGDRA scheme. While maintaining nearly optimal routes to mobile sink’s latest location, our scheme aims to minimize the routes reconstruction cost of sensor nodes. In this approach energy model for reducing energy consumption of nodes is used, which will improves lifetime and also reduce cost consumption. DOI: 10.17762/ijritcc2321-8169.150614

    Clustering objectives in wireless sensor networks: A survey and research direction analysis

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    Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

    A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

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    Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

    Routing Protocols for Underwater Acoustic Sensor Networks: A Survey from an Application Perspective

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    Underwater acoustic communications are different from terrestrial radio communications; acoustic channel is asymmetric and has large and variable end‐to‐end propagation delays, distance‐dependent limited bandwidth, high bit error rates, and multi‐path fading. Besides, nodes’ mobility and limited battery power also cause problems for networking protocol design. Among them, routing in underwater acoustic networks is a challenging task, and many protocols have been proposed. In this chapter, we first classify the routing protocols according to application scenarios, which are classified according to the number of sinks that an underwater acoustic sensor network (UASN) may use, namely single‐sink, multi‐sink, and no‐sink. We review some typical routing strategies proposed for these application scenarios, such as cross‐layer and reinforcement learning as well as opportunistic routing. Finally, some remaining key issues are highlighted

    Sink mobility aware energy-efficient network integrated super heterogeneous protocol for WSNs

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    In this paper, we propose Balanced Energy-Efficient Network Integrated Super Heterogeneous (BEENISH), improved BEENISH (iBEENISH), Mobile BEENISH (MBEENISH), and improved Mobile BEENISH (iMBEENISH) protocols for heterogeneous wireless sensor networks (WSNs). BEENISH considers four energy levels of nodes and selects cluster heads (CHs) on the base of residual energy levels of nodes and average energy level of the network, whereas iBEENISH dynamically varies the CHs selection probability in an efficient manner leading to increased network lifetime. We also present a mathematical sink mobility model and validate this model by implementing it in BEENISH (resulting in MBEENISH) and iBEENISH (resulting in iMBEENISH). Finally, simulation results show that BEENISH, MBEENISH, iBEENISH, and iMBEENISH protocols outperform contemporary protocols in terms of stability period, network lifetime, and throughput. © 2016, Akbar et al
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