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Mobility‑aware service provisioning for delay tolerant applications in a mobile crowd computing environment

机译:移动人群计算环境中用于延迟容忍应用程序的移动感知服务配置

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Mobile crowd computing (MCC) has emerged as an ideal platform for accessing required computing services from public-owned mobile devices in the vicinity, not requiring to going to the Cloud. But it may happen that the service is not available within the network (of the consumer) always. In this case, a carrier is needed, which carries the request to the service provider (in another network), gets the service from it and handovers to the consumer. But the mobility of the service consumer, provider, and the carrier poses a great challenge in binding between them for service request and service exchange. Device mobility is considered as one of the performance metrics in MCC. Though it is not trivial to measure, the efficacy of MCC heavily depends on this metric. To mitigate this issue, in this paper, we propose a service provisioning model in MCC based on the mobility patterns of the above-mentioned three entities. We applied a mobility prediction algorithm on the UCSD dataset that comprises real-traces of 235 mobile device users for 78 days across 402 access points (APs). In this experiment, we focussed mainly to get the information: (a) average time gap after a user connects to an AP, (b) average duration he/she remains connected to an AP, and (c) a set of users who remains connected to a particular AP simultaneously. Knowing the mobility patterns of the service consumer, provider, and the carrier, in terms of the above-mentioned information, is helpful to bind them in particular time frames. This allows avoiding the liveness problem (consumer waits for the service indefinitely) and availability problem (carrier returns with the service but cannot find the consumer).
机译:移动人群计算(MCC)已经成为理想的平台,可以从附近的公有移动设备访问所需的计算服务,而无需访问云。但是可能会发生该服务在(消费者的)网络内始终不可用的情况。在这种情况下,需要一个承运人,它将请求传送给服务提供商(在另一个网络中),从中获取服务并将其移交给消费者。但是,服务消费者,提供者和运营商之间的移动性给它们之间绑定服务请求和服务交换提出了巨大的挑战。设备移动性被视为MCC中的性能指标之一。尽管衡量并非微不足道,但“我的客户中心”的效果在很大程度上取决于此指标。为了缓解这个问题,在本文中,我们基于上述三个实体的移动性模式提出了MCC中的服务供应模型。我们在UCSD数据集上应用了移动性预测算法,该算法包括402个接入点(AP)上235个移动设备用户历时78天的真实踪迹。在此实验中,我们主要集中于获取信息:(a)用户连接到AP后的平均时间间隔,(b)他/她保持连接到AP的平均持续时间,以及(c)剩下的一组用户同时连接到特定的AP。根据上述信息,了解服务使用者,提供商和运营商的移动性模式有助于将它们绑定到特定的时间范围。这样可以避免活动性问题(消费者无限期地等待服务)和可用性问题(运营商随服务一起返回,但找不到用户)。

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