Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Formulated specifically for distributed predictable control of co-channel interference, the physical-ratio-K (PRK) interference model integrates the protocol model's locality and the physical model's high fidelity while addressing their weaknesses, and it transforms interference control in arbitrary networks to a problem involving coordination between close-by nodes only. To apply the PRK model in real-world settings, we design protocol PRKS that addresses the challenges of model instantiation and protocol signaling in PRK-based scheduling. In particular, PRKS uses a control-theoretic approach to instantiate the PRK model in dynamic uncertain networks, uses local signal maps to address the challenges of large interference range and anisotropic asymmetric wireless communication, and leverages the different timescales of PRK model adaptation and data transmission to decouple protocol signaling from data transmission. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local distributed coordination, PRKS also 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. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Integrating the protocol model's locality and the physical model's high fidelity, the physical-ratio-K (PRK) interference model has the potential to enable distributed, predictable control of co-channel interference and thus predictable link reliability. To realize the potential of the PRK model, we design protocol PRKS that addresses the challenge of instantiating the PRK model in the presence of network and environmental uncertainties. Formulating the PRK-model-instantiation problem as a minimum-variance regulation control problem, in particular, PRKS uses a control-theoretic approach to instantiating the PRK model on the fly. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local, distributed coordination, PRKS also 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. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Interference model is the basis of MAC protocol design in wireless networked sensing and control, and it directly affects the efficiency and predictability of wireless messaging. To exploit the strengths of both the physical and the protocol interference models, we analyze how network traffic, link length, and wireless signal attenuation affect the optimal instantiation of the protocol model. We also identify the inherent trade-off between reliability and throughput in the model instantiation. Our analysis sheds light on the open problem of efficiently optimizing the protocol model instantiation. Based on the analytical results, we propose the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model. Via analysis, simulation, and testbed-based measurement, we show that PRK-based scheduling achieves a network throughput very close to (e.g., 95%) what is enabled by physical-model-based scheduling while ensuring the required packet delivery reliability. The PRK model inherits both the high fidelity of the physical model and the locality of the protocol model, thus it is expected to be suitable for distributed protocol design. These findings shed new light on wireless interference models; they also suggest new approaches to MAC protocol design in the presence of uncertainties in network and environmental conditions as well as application QoS requirements.

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.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Toward understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and reaggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

To exploit the strengths of both the physical and the protocol interference models and to understand the varying observations on the relative goodness of scheduling based on the two models in literature, we analyze how network traffic, link length, and wireless signal attenuation affect the optimal instantiation of the protocol model. We also identify the inherent tradeoff between reliability and throughput in the model instantiation. Our analysis explains the seemingly inconsistent observations in literature and sheds light on the open problem of efficiently optimizing the protocol model instantiation. Based on the analytical results, we propose the physical-ratio-K (PRK) interference model as a reliability-oriented instantiation of the protocol model. Via analysis, simulation, and testbed-based measurement, we show that PRK-based scheduling achieves a network throughput very close to (e.g., 95%) what is enabled by physical-model-based scheduling while ensuring the required packet delivery reliability. The PRK model inherits both the high fidelity of the physical model and the locality of the protocol model, thus it is expected to be suitable for distributed protocol design. These findings shed new light on wireless interference models; they also suggest new approaches to MAC protocol design in the presence of uncertainties in traffic and application properties.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and re-aggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol \emph{tPack} that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.