Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical codesign approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates 1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and 2) an energy-efficient, multilevel computing architecture specifically designed to leverage the multiresolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a simulated truss structure and a real full-scale truss structure demonstrate the system's efficacy in damage localization and energy efficiency.

Low Power Listening (LPL) is a common MAC-layer technique for reducing energy consumption in wireless sensor networks, where nodes periodically wakeup to sample the wireless channel to detect activity. However, LPL is highly susceptible to false wakeups caused by environmental noise being detected as activity on the channel, causing nodes to spuriously wakeup in order to receive nonexistent transmissions. In empirical studies in residential environments, we observe that the false wakeup problem can significantly increase a node's duty cycle, compromising the benefit of LPL. We also find that the energy-level threshold used by the Clear Channel Assessment (CCA) mechanism to detect channel activity has a significant impact on the false wakeup rate. We then design AEDP, an adaptive energy detection protocol for LPL, which dynamically adjusts a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. Empirical experiments in both controlled tests and real-world environments showed AEDP can effectively mitigate the impact of noise on radio duty cycles, while maintaining satisfactory link reliability.

The integration of wireless sensors with mobile phones is gaining momentum as an enabling platform for numerous emerging applications. These mobile systems face dynamic environments where both application requirements and ambient wireless conditions change frequently. Despite the existence of many MAC protocols, none can provide optimal characteristics along multiple dimensions, especially when the conditions are frequently changing. Instead of pursuing a one-MAC-fit-all approach we present the Self-Adapting MAC Layer (SAML) that dynamically selects and switches MAC protocols to gain the desired characteristics in response to changes in ambient conditions and application requirements. SAML comprises (1) a Reconfigurable MAC Architecture (RMA) that can switch to different MAC protocols at run time and (2) a learning-based MAC Selection Engine that selects the protocol most suitable for the current condition and requirements. To the application SAML appears as a traditional MAC layer and realizes its benefits through a simple API for the mobile applications. We have implemented SAML in TinyOS 2.x and built three prototypes containing up to five MACs. We evaluate the system in controlled tests and real-world environments using a new gateway device that integrates a 802.15.4 radio with Android phones. Our experimental results show that SAML can effectively adapt MAC layer behavior to meet varying application requirements in dynamic environments through judicious selection and efficient switching of MAC protocols.

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy. These applications impose unique network management constraints, requiring low data rates but high network reliability in the face of unpredictable wireless environments. This paper presents two in-depth empirical studies on wireless channels in real homes, providing key design guidelines for meeting the network management constraints of HAN applications. The spectrum study analyzes spectrum usage in the 2.4 GHz band where HANs based on the IEEE 802.15.4 standard must coexist with existing wireless devices. We characterize the ambient wireless environment in six apartments through passive spectrum analysis across the entire 2.4 GHz band over seven days in each apartment. We find that the wireless conditions in these residential environments are much more complex and varied than in a typical office environment. Moreover, while 802.11 signals play a significant role in spectrum usage, there also exists non-negligible noise from non-802.11 devices. The multi-channel link study measures the reliability of different 802.15.4 channels through active probing with motes in ten apartments. We find that there is not always a persistently reliable channel over 24 hours, and that link reliability does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through infrequent channel hopping, suggesting dynamic channel hopping as a key tool for meeting the network management requirements of HAN applications. Our empirical studies provide important guidelines and insights in designing HANs for residential environments.

Transmission power control (TPC) has the potential to reduce power consumption in Wireless Sensor Networks (WSNs). However, despite a multitude of existing protocols, they still face significant challenges in real-world deployments. A practical TPC protocol must be robust against complex and dynamic wireless properties, and efficient for resource-constrained sensors. This paper presents P-TPC, a practical TPC protocol designed on control-theoretic techniques. P-TPC features a highly efficient controller designed on a dynamic model that combines a theoretical link model with online parameter estimation. P-TPC's robustness and energy savings are demonstrated through trace-driven simulations and real-world experiments in a campus building and residential environments.

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy. These applications impose unique QoS constraints, requiring low data rates but high network reliability in the face of unpredictable wireless environments. This paper presents two in-depth empirical studies on wireless channels in real homes, providing key design guidelines for meeting the QoS constraints of HAN applications. The spectrum study analyzes spectrum usage in the 2.4 GHz band where HANs based on the IEEE 802.15.4 standard must coexist with existing wireless devices. We characterize the ambient wireless environment in six apartments through passive spectrum analysis across the entire 2.4 GHz band over seven days in each apartment. We find that the wireless conditions in these residential environments are much more complex and varied than in a typical office environment. Moreover, while 802.11 signals play a significant role in spectrum usage, there also exists non-negligible noise from non-802.11 devices. The multichannel link study measures the reliability of different 802.15.4 channels through active probing with motes in ten apartments. We find that there is not always a persistently reliable channel over 24 hours, and that link reliability does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through infrequent channel hopping, suggesting dynamic channel hopping as a key tool for meeting the QoS requirements of HAN applications. Our empirical studies provide important guidelines and insights in designing HANs for residential environments.

Home area networks (HANs) promise to enable sophisticated home automation applications such as smart energy usage and assisted living. However, recent empirical study of HAN reliability in real-world residential environments revealed significant challenges to achieving reliable performance in the face of significant and variable interference from a multitude of coexisting wireless devices. We propose the Adaptive and Robust Channel Hopping (ARCH) protocol: a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. ARCH has several key features. First, ARCH is an adaptive protocol that channel-hops based on changes in channel quality observed in real time. Second, ARCH is a distributed protocol that selects channels on a per-link basis, due to the large link-to-link variations in channel quality observed under empirical study. Third, ARCH is designed to be robust and lightweight. ARCH uses a practical handshaking approach to handle channel desynchronization and an efficient sliding-window scheme that does not involve expensive calculations or modeling, and can be reasonably implemented on memory-constrained wireless sensor platforms. Fourth, ARCH introduces minimal communication overhead for applications where packet acknowledgements are already enabled. We evaluate our approach through real deployment in real-life apartments with residents' daily activity. Our results demonstrate that ARCH can reduce packet retransmissions by a median of 42.3% compared to using a single, fixed wireless channel, and can enable up to a 2.2X improvement in delivery rate on the most unreliable links in our experiment. Under a multi-hop routing scenario, ARCH reduced radio usage by 31.6% on average, by reducing the ETX of each link by up to 83.6%. Due to ARCH's lightweight reactive design, most links achieve this improvement in reliability with 10 or fewer channel hops per day.

The robust operation of many sensor network applications depends on deploying relays to ensure wireless coverage. Radio mapping aims to predict network coverage based on a small number of link measurements. This problem is particularly challenging in complex indoor environments where walls significantly affect radio signal propagation. Nevertheless, we show that it is feasible to accurately predict coverage through a two-step process: a propagation model is used to predict signal strength at a recipient node, which is then mapped to a coverage prediction. Through an in-depth empirical study, we show that complex models do not necessarily produce accurate estimates of signal strength: there is an important tradeoff between model accuracy and the number of parameters that must be estimated from limited training data. We find that the best performance is achieved by a family of models which classify walls based on their attenuation into a small number of classes and develop an algorithm to perform this classification automatically. Based on these insights, we build a novel Radio Mapping Tool (RMT) for predicting radio converge in indoor environments. Experimental results demonstrate RMT's effectiveness in two buildings: RMT reduces the number of locations where coverage is erroneously predicted to exist by as much as 39% and 54% compared to the classic log-normal radio propagation model.

Abstract Many wireless sensor networks must sustain long lifetimes on limited energy resources. Two major approaches, transmission power control and sleep scheduling, have been proposed to reduce the radio power consumption in the transmission state and the idle state, respectively. In this paper, we first review existing transmission power control and sleep scheduling approaches and then describe a Unified Radio Power Management framework for the design and implementation of holistic radio power management solutions in wireless sensor networks. It has two key components: (1) a novel optimization approach called Minimum Power Configuration that minimizes the aggregate radio power consumption of all ratio states and (2) a Unified Power Management Architecture (UPMA) that aims to support the flexible cross‐layer integration of different power management strategies. A novel feature of UPMA is that it enables cross‐layer coordination and joint optimization of different power management strategies that exist at multiple network layers. Copyright © 2008 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) have become an increasingly compelling platform for Structural Health Monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the Damage Localization Assurance Criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by an order of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system's ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 64.8% and energy consumption by 69.5% compared to a typical centralized solution, achieving a projected lifetime of 191 days using three standard AAA batteries. Our work demonstrates the advantages of a holistic approach to cyber-physical systems that closely integrates the design of computing systems and physical engineering techniques.

Topology control can reduce power consumption and channel contention in wireless sensor networks by adjusting the transmission power. However, topology control for wireless sensor networks faces significant challenges, especially in indoor environments where wireless characteristics are extremely complex and dynamic. We first provide insights on the design of robust topology control schemes based on an empirical study in an office building. For example, our analysis shows that Received Signal Strength Indicator and Link Quality Indicator are not always robust indicators of Packet Reception Rate in indoor environments due to significant multi-path effects. We then present Adaptive and Robust Topology control (ART), a novel and practical topology control algorithm with several salient features: (1) ART is robust in indoor environments as it does not rely on simplifying assumptions about the wireless properties; (2) ART can adapt to variations in both link quality and contention; (3) ART introduces zero communication overhead for applications which already use acknowledgements. We have implemented ART as a topology layer in TinyOS 2.x. Our topology layer only adds 12 bytes of RAM per neighbor and 1.5 kilobytes of ROM, and requires minimal changes to upper-layer routing protocols. The advantages of ART have been demonstrated through empirical results on a 28-node indoor testbed.

The diverse requirements of wireless sensor network applications necessitate the development of multiple media access control (MAC) protocols to meet their varying throughput, latency, and network lifetime needs. Building new MAC protocols has proven to be extremely difficult, however, given the monolithic nature of existing protocol implementations as well as their dependence on a particular radio or processor platform. To address these issues, we propose the MAC Layer Architecture (MLA), a componentbased architecture for power-efficient MAC protocol development in wireless sensor networks. MLA consists of optimized, reusable components that implement a common set of features shared by existing MAC protocols, as well as abstractions that encapsulate the intricacies of the hardware platforms they run on. Through an instantiation of MLA in TinyOS 2.0.1, we have implemented five representative MAC protocols. Empirical results show that MLA results in significant code reuse among different protocols, while achieving comparative performance and memory footprints to monolithic implementations of the same protocols.

The decoupled nature of computing in mobile ad hoc networks (MANETs) can result in disconnections at inopportune times during an interaction between a pair of hosts. We introduce the notion of a priori selection of partner hosts to reduce the likelihood of disconnection during such interactions. An application may specify the times when and the physical locations where it requires interactions with peer applications on partner hosts. A knowledge base of the physical motion profiles of various hosts maintained on each mobile host is used to select partner hosts that are co-located with the reference host at the required time and are least likely to disconnect. In this paper, we present a formal model for such knowledge management, along with an algorithm used to determine suitable partner hosts. We also provide details of our implementation of partner selection, which has been used in the context of a service-oriented computing middleware for MANETs, developed previously by our group. Finally, we present simulation results of our approach.

No abstract available.

Service oriented computing, with its aim of unhindered interoperability, is an appropriate paradigm for ad hoc networks, which are characterized by physical mobility of heterogenous hosts and by the absence of standardized application level protocols. The decoupled nature of computing in ad hoc networks can result in disconnections at inopportune times during the client-service interaction process. We introduce the notion of a priori selection of services to reduce the likelihood of disconnection during service usage. A client may specify the times when it requires certain services. A knowledge base of the physical motion profiles of various service providers is used to select instances of a service that are co-located with the client at the required time and least likely to disconnect. A system for constructing the knowledge base is presented in this paper, along with the implementation details and the algorithm used to determine the service usage pattern.