面向泛在弱终端的感控网络多维度均衡化技术研究

Nowadays, the large-scale sensor network or the multi-mode Internet of Things, which is the main manifestation of the ubiquitous sensing&control network, has become an important source of information in the era of big data. However, with the deepening of the application, the number of sensing&control terminals is more and more large, but the structural features of the network are becoming more and more unobvious. In addition, the differences in the performance and role between the terminals make the imbalance of energy consumption and load more prominent. To solve this problem, we propose a multidimensional balance design for heterogeneous ubiquitous sensing&control network in terms of task scheduling and continuous collaborative working. First, the technology of controllable deployment and covering joint optimization for heterogeneous and weak terminals will be studied to achieve adaptive balanced deployment. Subsequently, we plan to design a type of data uploading strategy based on multi-target cooperation as well as a multi QoS based mobile data collection method, so as to enhance the balanced cooperative scheduling ability of multiple types of sensing&control terminals. Finally, a collaborative information capture method under high-efficient energy supply will be proposed to further enhance the ability of continuous working of the network. The results of this study will be verified by the combination of simulation experiment and hardware measurement. It is expected that a complete set of theories and methods about balanced scheduling of ubiquitous weak terminals will be formed, and it will has a positive effect on improving its application performance in heterogeneous networks.

当前，以大规模传感网、多模式物联网等为主要表现形式的泛在感控网络，已成为大数据时代的重要信息来源。然而随着应用的深入，感控终端的数量越发庞大，而网络的结构化特征却越来越不明显；此外，终端间自身性能和角色的差异，使得其能耗和负载的不均衡性进一步突出。为此，本课题拟针对多元感知模式下的异构泛在感控网在任务调度和持续性协同工作等方面的具体要求，开展多维度均衡化设计。首先，拟研究异构弱终端可控部署与覆盖联合优化技术，以实现自适应的均衡化部署；随后，拟设计基于多目标协同的异构弱终端数据上传和多QoS约束下的移动数据收集，以提升多类型感控终端的均衡化协同调度能力；最后，拟构建高效益能量补给下的协同信息捕获方法，以进一步增强网络的持续性工作能力。课题的研究结果拟通过仿真实验与实物辅助测试相结合的方式进行验证，预期将形成一套完整的泛在弱终端均衡调度理论和方法，并对其在异构网络中的应用性能提升起到积极作用。

本项目主要研究了多源感控网络的均衡化问题。首先，我们分析了感知终端在布署过程中的不确定性、以及布署和覆盖的联合优化设计未能完全被实现的问题，以降低其对终端间协同数据收集效率的影响。从终端在数据收集阶段的全面代价均衡性入手，我们分别为不同规模、不同布署密度的泛在感控网络，设计了行为可控的差异化布署方法，并在此基础上进一步实现了效益最大化的覆盖调度方案；随后，针对泛在感控网在真实物理空间的感知流和数据上传方式的不确定性的特点，我们进一步设计了其协同均衡化的数据采集的方法；接着，在负载和能耗协同优化的数据采集方法的研究成果基础上，并为了进一步提升信息处理效率，我们实现了网内信息聚合算法和分布式压缩传输技术。此外，我们还在异构泛在感控网在数据收集时效性、单/多MDC移动代价、终端吞吐量均衡性、能耗与状态切换开销等不同服务质量的约束基础上，实现了高适应性的移动数据收集方案；最后，为了给感知终端提供持续、稳定、高效的能量，我们通过部署在各区域的移动充电设备，实现了“静动结合”方案下的异构终端差异性消减。