## 6 Conclusions  
In DiffServ networks, data have varying degrees of tolerance for delay. According to this feature, we propose a DDAR scheme to enhance the performance in DiffServ networks. DDAR scheme first sets service tag and configures \(N_{t}\) and \(T_{t}\) for all aggregators. Then the sensors select the aggregators that send their packets to according to the service requirements of the data packets. Meanwhile, aggregators select the aggregators in the upper layer with the same service tag as their next hop. During the data transmission, the aggregators periodically rotate the provided service to balance the energy consumption. In this paper, DDAR is compared with CDAR, and the performances of DDAR regarding service guarantee rate, network lifetime, and energy efficiency are studied. In the networks, the probability of generating a data pakcet, aggregation ratio, and the proportion of aggregators are the environment parameters that mainly affect the performances, extensive simlations are conducted by changing the values of these parameters. On the whole, DDAR scheme achieves a remarkable optimization effect on DiffServ networks. In the simulations, the service guarantee rate increases by 25.01% on average. The optimization is determined by the average difference between the service requirements. The optimization is improved with the increase of the difference. In terms of network environment parameters, there is little difference when \(\eta\) changes. The good optimization effect appears when \(P_{\alpha}\) is large or \(\lambda\) is small or large. Network lifetime increases by 55.45% and energy efficiency increases by 83.99%. These two indicators do not change with changes in service requirements. They both increase with the increase of \(\eta\) or \(\lambda\). With the increase of \(P_{\alpha}\), the lifetime decreases slightly and the energy efficiency decreases. On the basis of DDAR, the improved DDAR is proposed for the problem of low energy utilization for the nodes far away from the sink. In the scheme, the value of \(N_{t}\) and \(T_{t}\) are narrowed to increase the aggregation frequency. The results indicate that the improved DDAR improved the service guarantee rate and energy efficiency by 10.11% and 33.97% respectively without degrading the lifetime.  
DDAR starts with the specific delay requirements and determines the relevant parameters during the transmission according to the nature of the network, thereby ensuring the data validity of various services. It is believed that DDAR has an optimization effect in the DiffServ networks with same logical structure(e.g., cluster networks). We hope that more inspirations can be stimulated by our idea of the combination of the specific requirements and the nature of the networks and our research can contribute to the future works.  
Figure 22: The ratio of energy efficiency vs. \(\lambda\).  
**Author Contributions:** [PERSON] designed the algorithms and wrote part of the manuscript. [PERSON] conceived of the work, designed the algorithms, and wrote part of the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] comment the paper.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (61772554, 61572528, 6157256), the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT1800391), the National Basic Research Program of China (973 Program) (2014 CB046305), and the Natural Science Foundation of Zhejiang Province (LZ16F020001, LY17F020032).  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.