While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. In this paper, we classify DML into basic-DML and semi-DML. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. Index Terms-Communication systems traffic control, diff-serv networks, quality of service, smart grids, subscriber loops, WiMAX.ĭistributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. In general, the proposed traffic model simplifies the design of benchmarks for the comparison of candidate access technologies. From previous results obtained for a purpose-built WiMAX SGLM network, the intuition that a leased broadband access yields lower latencies is verified. This permits consideration of the potential starvation of domestic traffic, which is avoided by applying well-known traffic management techniques. It is then applied to the study of an access architecture based on leased lines from local broadband access providers. From functional descriptions of SG, a traffic model is developed. SGLM networks serve customers' Energy Services Interfaces. This paper addresses the modeling of specific Smart Grid (SG) communication requirements from a data networking research perspective, as a general approach to the study of different access technologies suitable for the last mile (LM). Case studies are also conducted to validate and evaluate the proposed platform. Based on these studies, a data-centered Fog platform has been developed to support smart living. And then the data flow analysis has been investigated to disclose a variety of data flow characteristics.
In this chapter, smart living as one of the primary elements of smart cities has been conceptualized to EHOPES, namely smart Energy, smart Health, smart Office, smart Protection, smart Entertainment and smart Surroundings. In particular, the local processing capability of Fog computing significantly scales down the data volume towards the Cloud, and it in turn has great impacts on the entire Internet. The key idea is to bring the computing power from the remote Cloud closer to the users, which further enables real-time interaction and location-based services. To address this concern, a new Fog computing paradigm has been recently proposed by the industry. The Cloud is typically centralized but smart objects are ubiquitously distributed thus, data transmission latency (i.e., end-to-end delay or response time) between Cloud and smart objects is a critical issue especially to the applications that have strict delay requirements. Nowadays, smart environments (e.g., smart home, smart city) are built heavily relying on Cloud computing for the coordination and collaboration among smart objects. Illustrative results indicate that the joint design is able to intelligently allocate radio bandwidth based on QoS demands in resource-constrained home M2M networks. This proposed strategy is aware of the QoS requirements and resilience of multimedia services. Cross-layer joint admission and rate control design is reported for QoS-aware multimedia sharing. Three standards for multimedia sharing and their QoS architectures are outlined.
Finally, we focus on QoS management in home M2M networks, considering the increasing number of multimedia devices and growing visual requirements in a home area. Then we present the architecture of home M2M networks decomposed into three subareas depending on the radio service ranges and potential applications. In this article, we first identify the fundamental challenges in home M2M networks. It is envisioned that home networks will shift from current machine-to-human communications to the machine-to-machine paradigm with the rapid penetration of embedded devices in home surroundings.
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