دانلود رایگان مقاله انگلیسی الگوریتم خوشه بندی مبتنی بر گرید با تحلیل بار برای اینترنت اشیا صنعتی - IEEE 2018

ABSTRACT

In the industrial internet of things, wireless sensor network technology makes devices communicate with each other. The information integrated from multiple data sources will be transformed into productivity. However, the clusters close to the base station take a considerable load over multi-hop transmission, in this case, the lifetime of the industrial wireless sensor network is restricted. To solve this problem, a grid-based clustering algorithm via load analysis for industrial internet of things is presented in this study. First, the network load is quantitatively analyzed and then a load model is constructed. Furthermore, a set of expressions is deduced to indicate the network load distribution. It is concluded that the number of delivered packets in each level is related to the grid length at that level. The optimal grid length is obtained by solving polynomials to achieve the uniform energy consumption of nodes at each level. Finally, the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster. Results of the experiments show that compared with ACT, ER-HEED and RUHEED, our algorithm balances energy depletion effectively and extends the whole network lifetime.

VII. CONCLUSION

IIoT is seen as the next industrial revolution that will improve the industrial productivity greatly. In this study, we focus on the problem of energy usage imbalance that occurs in cluster-based routing protocols in IIoT and present a new solution to this problem. Numerous works based on optimizing the number of CHs, fuzzy sets, or completive radii have been proposed to achieve balancing energy consumption. However, load distribution fundamentally affects energy consumption. We present a precise load analysis model to guide cluster size adjustment in this study and prove that the cluster near the BS should be smaller than that far from the BS. We also analyze the total energy consumption of a network. The comparison among GCA, ACT, ER-HEED and RUHEED is shown in Section 6. The death time of the first sensor node is delayed effectively in GCA. The performance of GCA is superior to the other three algorithms when the node density increases and the size of the monitored area extends. When it comes to large scale network, there is still much room for improvement in GCA. Our strategy is not only suitable for IIoT but also for numerous applications that require sensor nodes to collect data periodically, such as in environmental monitoring. Our future work will focus on the type of event trigger for IIoT.