Engineering adaptive communication protocols for upcoming WSN applications

随着物联网（IoT）的出现，越来越多的设备开始交换信息。虽然这可以实现新的有趣应用，但它也对底层通信基础设施提出了很高的要求，特别是无线传感器网络（WSNs），它促进了物联网设备与环境之间的交互。这些网络必须能够实现可靠、节能和及时的通信，而随着此类网络的动态性和异质性的增加，通信变得越来越复杂。在这种情况下，自适应通信协议似乎是一个有趣的解决方案，因为这些协议可以自动重新配置自己，以满足不断变化的条件下指定的性能目标。不幸的是，现有的解决方案仍然具有限制其性能和适用性的主要缺点。例如，它们仅支持单个性能目标（例如，可靠性或能量，但不是两者），并且不考虑其他的异质性。在这项研究工作中，为了克服这个问题，提出了一种新的自适应通信协议。与文献中现有的解决方案相比，所提出的方法是基于一个专门的MAC（媒体访问控制）协议，本质上允许自适应性，这是扩展的ODA（观察，决定，行动）控制和自动化循环。在这种情况下，将研究不同的自适应技术，例如控制理论方法和机器学习（例如，Q-Learning）。目的是评估它们的有效性以及它们对嵌入式节点的内存/计算性能的影响。在这种情况下的有效性是衡量如何良好的不同的适应技术是在找到一个相对的性能目标之间的妥协。例如，可靠性和能耗是两个矛盾的要求，即，增加一个等于另一个。因此，找到合适的折衷方案至关重要。此外，考虑到无线传感器网络是分布式的，具有异构（即，最后，将在仿真和真实的硬件上评估所提出的自适应通信协议，例如，T-Mote Sky节点上。这提供了有价值的见解，有助于纠正当今解决方案的缺点，从而提高其适用性和性能-目前的自适应协议已经实现了比经典的非自适应协议高出80%以上的性能，但这可以进一步提高。总之，本研究项目，从而有助于设计和开发的自适应无线传感器网络，能够科普现代和即将到来的应用日益复杂，动态和异构性。

With the advent of the Internet of Things (IoT), a growing number of devices start exchanging information. While this enables new interesting applications, it also places high demands on the underlying communications infrastructure, in particular, wireless sensor networks (WSNs), which facilitate interaction between IoT devices and the environment. These must enable reliable, energy-efficient and timely communication, which becomes increasingly complex as dynamics and heterogeneity of such networks increase. In this context, adaptive communication protocols seem to be an interesting solution, since these can automatically reconfigure themselves to meet specified performance goals under changing conditions. Unfortunately, existing solutions still have major drawbacks limiting their performance and applicability. For example, they only support single performance goals (e.g., reliability or energy, but not both) and do not account for heterogeneity among others. In this research work, to overcome this problem, a novel adaptive communication protocol is proposed. In contrast to existing solutions from the literature, the proposed approach is based on a specialized MAC (medium access control) protocol that intrinsically allows for adaptivity, which is extended by an ODA (observe, decide, act) loop for control and automation. In this context, different adaption techniques will be investigated such as control-theoretic approaches and machine learning (e.g., Q-Learning). The purpose is to assess their effectiveness as well as their impact on memory/computational performance of the embedded nodes. Effectiveness in this context is measured with respect to how good different adaption techniques are in finding a compromise between opposing performance goals. For example, reliability and energy consumption are two contradictory requirements, i.e., increasing one worsens the other. As a result, it is paramount to find suitable tradeoffs. In addition, considering that WSNs are distributed, embedded systems with heterogeneous (i.e., node-specific) limitations, this turns to be a very challenging task.Finally, the proposed adaptive communication protocol will be evaluated in simulation and on real hardware, e.g., on T-Mote Sky nodes. This provides valuable insights that help correct shortcomings by today's solutions and thus increase their applicability and performance – current adaptive protocols already achieve >80% higher performance than classic, non-adaptive ones, but this can be further increased. In summary, this research project thus contributes to the design and development of adaptive WSNs able to cope with the increasing complexity, dynamics and heterogeneity of modern and upcoming applications.