移动机会网络的位置感知混合微观路由方案

Q2 Decision Sciences
S. R. Bharamagoudar, S. Saboji
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引用次数: 0

摘要

移动机会网络(MON)已被用于提供可容忍延迟的应用程序。在MON中,由于拓扑结构的频繁变化、节点的可移动性、低密度和断断续续的连接,设备之间的通信没有确定的端到端路径。在MON中,设备电池在执行扫描、收发和其他计算过程等活动时消耗非常快,从而影响整体性能,因此设计节能路由是一项具有挑战性的任务。路由采用存储-携带-转发机制进行数据包通信,其中数据包由生存时间(TTL)组成,并保存在缓冲区中,直到机会出现。为了提高传递率,采用了消息复制;然而,引起高网络拥塞。本文提出了一种位置感知混合微观路由(LAHMR)方案,该方案通过限制网络中不必要的分组循环,提供了一种具有位置感知和高可靠性的有效分组传输方案。实验结果表明,LAHMR方案在更小的延迟下获得了更好的传输率,并且减少了传输数据包的转发器数量,有助于减少当前路由方法即社会感知可靠转发(围巾)技术的网络开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Location-aware hybrid microscopic routing scheme for mobile opportunistic network
Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.
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来源期刊
IAES International Journal of Artificial Intelligence
IAES International Journal of Artificial Intelligence Decision Sciences-Information Systems and Management
CiteScore
3.90
自引率
0.00%
发文量
170
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