دانلود رایگان ترجمه مقاله طرح تجمیعی داده های ترکیبی برای تامین کیفیت خدمات در اینترنت اشیا (نشریه IEEE 2016)

یک طرح تجمیعی داده های ترکیبی برای تامین کیفیت خدمات (QoS) در اینترنت اشیا (IoT)

A hybrid data aggregation scheme for provisioning Quality of Service (QoS) in Internet of Things (IoT)

Abstract

Internet of Things (IoT) is a new paradigm which is enormously gaining ground in today’s world. In order to maintain desirable service quality in the transmission of sensed data, data aggregation schemes are highly used. The main goal of data aggregation scheme is to collect and aggregate data packets in an efficient manner so as to reduce power consumption, traffic congestion, and to increase network lifetime, data accuracy, etc. In this paper, a hybrid Quality of service-Aware Data Aggregation (QADA) scheme is proposed. This scheme combines the features of the cluster and tree-based data aggregation schemes and addresses some of their important limitations. Simulation results show that QADA outperforms cluster and tree-based aggregation schemes in terms of power consumption, network lifetime and bearing higher traffic load.

1 Introduction

Internet of Things (loT) is the convergence of Internet, Sensors, RFID and other smart objects. Development of various applications with farsighted vision, loT is foreseen as an integral part of the future Internet. Things in loT provide access to real-world information. It allows interconnection of people and things anytime, anywhere, with anything or anyone using any path and any service. loT connects trillions of smart devices seamlessly covering a variety of applications, protocols and domains [1]. Such smart devices are uniquely identifiable and addressable. Wireless Sensor Network (WSN) is an essential part of loT which helps in collecting information from the surroundings. It has several applications in many areas such as health monitoring, industrial automation, environment, agriculture and building automation and military applications. WSN uses a large number of tiny low-power, low-cost, having low processing capability, low memory, and multi-functional sensor nodes which are randomly and highly distributed in the physical environment [2]. Due to the deployment of extremely large numbers of devices, a huge amount of relevant, correlated and redundant data are needed to be sent by the sensors to the sink node [3]. Data generated from neighboring sensor nodes are often correlated and highly redundant. These redundant data consumes network resources unnecessarily. To overcome this kind of imprudent data transmissions in such constraint network, a scheme for combining all the redundant and correlated data into valid high-quality information is needed at the intermediate nodes. This process can reduce the number of packets transmitted to the sink node ( [3] and [4]). In such situations, data aggregation scheme is a suitable solution. Various types of aggregation techniques are available in the literature. In cluster-based data aggregation, all the nodes of a cluster forward the sensed data to the Cluster Head (CH) node for aggregation. CHs aggregate data and directly forward them to the sink node for further processing. Here, energy consumption of the network increases along with the increasing distance between CHs and sink node. On the other hand, tree-based approaches reduce the distance between aggregator nodes (CH in the case of cluster-based) and sink node by constructing a logical tree among them thereby consuming lesser power than cluster-based ones. In this case, the responsibilities of aggregator node are not evenly distributed among the nodes which lessens network lifetime. To this end, we propose a hybrid data aggregation scheme which reduces network power consumption and increases network lifetime. The performance of the proposed scheme is compared with relevant ones such as LEACH, LEACH-C, and TREEPSI. The simulation results show that QADA significantly improves power consumption and network lifetime over the other protocols. The rest of the paper is organized into following sections. Section II discusses some related works on data aggregation. Details of the proposed scheme is discussed in Section III. Section IV presents the evaluated performance of the proposed scheme vis-a-vis other relevant protocols. Finally, Section V concludes the paper.