Open Access Dissertation
Date of Award
Towards the end goal of providing predictable real-time wireless networking for sensing and control, we have developed a real-time routing protocol MTA that predictably delivers data by their deadlines, and a scheduling protocol PRKS to ensure a certain link reliability based on the Physical-ratio-K (PRK) model, which is both realistic and amenable for distributed implementation, and a greedy scheduling algorithm to deliver as many packets as possible to the sink by a deadline in lossy multi-hop wireless sensor networks.
Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path delays. The probabilistic nature of link/path delays makes the basic problem of computing the probabilistic distribution of path delays NP-hard, yet quantifying probabilistic path delays is a basic element of real-time routing and may well have to be executed by resource-constrained devices in a distributed manner; the highly-varying nature of link/path delays makes it necessary to adapt to in-situ delay conditions in real-time routing, but it has been observed that delay-based routing can lead to instability, estimation error, and low data delivery performance in general. To address these challenges, we propose the Multi-Timescale Estimation (MTE) method; by accurately estimating the mean and variance of per-packet transmission time and by adapting to fast-varying queueing in an accurate, agile manner, MTE enables accurate, agile, and efficient estimation of probabilistic path delay bounds in a distributed manner. Based on MTE, we propose the Multi-Timescale Adaptation (MTA) routing protocol; MTA integrates the stability of an ETX-based directed-acyclic-graph (DAG) with the agility of spatiotemporal data flow control within the DAG to ensure real-time data delivery in the presence of dynamics and uncertainties. We also address the challenges of implementing MTE and MTA in resource-constrained devices such as TelosB motes. We evaluate the performance of MTA using the NetEye and Indriya sensor network testbeds. We find that MTA significantly outperforms existing protocols, e.g., improving deadline success ratio by 89% and reducing transmission cost by a factor of 9.7.
Predictable wireless communication is another basic enabler for networked sensing and control in many cyber-physical systems, yet co-channel interference remains a major source of uncertainty in wireless communication. Integrating the protocol model's locality and the physical model's high fidelity, the physical-ratio-K (PRK) interference model bridges the gap between the suitability for distributed implementation and the enabled scheduling performance, and it is expected to serve as a foundation for distributed, predictable interference control. To realize the potential of the PRK model and to address the challenges of distributed PRK-based scheduling, we design protocol PRKS. PRKS uses a control-theoretic approach to instantiating the PRK model according to in-situ network and environmental conditions, and, through purely local coordination, the distributed controllers converge to a state where the desired link reliability is guaranteed. PRKS uses local signal maps to address the challenges of anisotropic, asymmetric wireless communication and large interference range, and PRKS leverages the different timescales of PRK model adaptation and data transmission to decouple protocol signaling from data transmission. Through sensor network testbed-based measurement study, we observe that, unlike existing scheduling protocols where link reliability is unpredictable and the reliability requirement satisfaction ratio can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) in different network and environmental conditions without a priori knowledge of these conditions, and, through local distributed coordination, PRKS achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. Ensuring the required link reliability in PRKS also reduces communication delay and improves network throughput.
We study the problem of scheduling packet transmissions to maximize the expected number of packets collected at the sink by a deadline in a multi-hop wireless sensor network with lossy links. Most existing work assumes error-free transmissions when interference constraints are complied, yet links can be unreliable due to external interference, shadow- ing, and fading in harsh environments in practice. We formulate the problem as a Markov decision process, yielding an optimal solution. However, the problem is computationally in- tractable due to the curse of dimensionality. Thus, we propose the efficient and greedy Best Link First Scheduling (BLF) protocol. We prove it is optimal for the single-hop case and provide an approach for distributed implementation. Extensive simulations show it greatly enhances real-time data delivery performance, increasing deadline catch ratio by up to 50%, compared with existing scheduling protocols in a wide range of network and traffic settings.
Liu, Xiaohui, "Predictable Real-Time Wireless Networking For Sensing And Control" (2015). Wayne State University Dissertations. 1149.