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2ASenNet: A multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization for WSN

机译:2ASenNet:基于群体智能优化的WSN的多个QoS度量层次路由协议

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The increasing demand for real-time application in wireless sensor network (WSN) has made the Quality of Service (QoS) based communication protocols a hot research topic. Most routing protocols consider energy saving as the main objective and assume data traffic with unconstrained delivery requirements to be a given. However, with the arrival of wireless multimedia sensor networks, the transmission of imaging and video information requires both energy efficiency and QoS assurance (e.g. bandwidth, packet loss and delay constraints). Swarm intelligence algorithms are the relatively effective strategy for distributed optimization and show the desirable properties of being adaptive, scalable, and robust in particular of routing in WSN. In this paper, In order to achieve the balancing of energy-efficiency with QoS improvements for applications specific, a multiple QoS metrics hierarchical routing protocol based on swarm intelligence optimization (combination of improving ACO and AFSO) for WSN is proposed, namely 2ASenNet. The 2ASenNet protocol built a hierarchical structure on the network before choosing suitable paths to meet various QoS requirements. Then, the 2ASenNet protocol adopted hybrid ant behavior to produce diverse original paths, adding AFSO to ACO's per iterative process, and the optimization path was explored according to multiple QoS constrained. Simulation results have shown the efficiency of this novel approach, while simultaneously reducing the consumption of constrained resources as much as possible.
机译:在无线传感器网络(WSN)中对实时应用的不断增长的需求已使基于服务质量(QoS)的通信协议成为一个热门研究主题。大多数路由协议都将节能作为主要目标,并假定具有不受限制的交付要求的数据流量。然而,随着无线多媒体传感器网络的到来,成像和视频信息的传输需要能量效率和QoS保证(例如,带宽,分组丢失和延迟约束)。群智能算法是用于分布式优化的相对有效的策略,并且显示出具有自适应性,可伸缩性和鲁棒性的理想属性,尤其是WSN中的路由。为了实现针对特定应用的能源效率与QoS改进之间的平衡,提出了一种基于群体智能优化(改进ACO和AFSO的组合)的WSN多重QoS度量层次路由协议,即2ASenNet。在选择合适的路径以满足各种QoS要求之前,2ASenNet协议在网络上建立了分层结构。然后,2ASenNet协议采用混合蚂蚁行为产生不同的原始路径,将AFSO添加到ACO的每个迭代过程中,并根据多个QoS约束探索了优化路径。仿真结果表明了这种新颖方法的效率,同时尽可能减少了受限资源的消耗。

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