Wireless sensor network applications, such as those for natural disaster warning, vehicular traffic monitoring, and surveillance, have stringent accuracy requirements for detecting or classifying events and demand long system lifetimes. Through quantitative study, we show that existing event detection approaches are challenged to explore the sensing capability of a deployed system and choose the right sensors to meet user-specified accuracy. Event detection systems are also challenged to provide a generic system that efficiently adapts to environmental dynamics and works easily with a range of applications, machine learning approaches, and sensor modalities. Consequently, we propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user-specified detection accuracy during runtime while significantly reducing energy consumption. Watchdog can use different machine learning techniques to learn the sensing capability of a heterogeneous sensor deployment and meet accuracy requirements. To address environmental dynamics and ensure energy savings, Watchdog wakes up and puts to sleep sensors as needed to meet user-specified accuracy. Through evaluation with real vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user-specified detection accuracy, energy savings, and environmental adaptability.

In many body sensor network (BSN) applications, such as activity recognition for assisted living residents or physical fitness assessment of a sports team, users spend a significant amount of time with one another while performing many of the same activities. We exploit this physical proximity with Remora, a smartphone-based Body Sensor Network activity recognition system which shares sensing resources among neighboring BSNs. Compared to other resource sharing approaches, Remora provides both increased accuracy and significant energy savings. To increase classification accuracy, Remora BSNs share sensors by overhearing neighbors' sensor data transmissions. When sharing, fewer on-body sensors are needed to achieve high accuracy, resulting in energy savings by turning off unneeded sensors. To save phone energy, neighboring BSNs share classifiers: only one classifier is active at a time classifying activities for all neighbors. Remora addresses three major challenges of sharing with physical neighbors: 1) Sharing only when the energy benefit outweighs the cost, 2) Finding and utilizing the shared sensors and classifiers which produce the best combination of accuracy improvement and energy savings, and 3) Providing a lightweight and collaborative classification approach, without the use of a backend server, which adapts to the dynamics of available neighbors. In a two week evaluation with 6 subjects, we show that Remora provides up to a 30% accuracy increase while extending phone battery lifetime by over 65%.

In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.

In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network.

Effective WiFi power management can strongly impact the energy consumption on Smartphones. Through controlled experiments, we find that WiFi power management on a wide variety of Smartphones is a largely autonomous process that is processed completely at the driver level. Driver level implementations suffer from the limitation that important power management decisions can be made only by observing packets at the MAC layer. This approach has the unfortunate side effect that each application has equal opportunity to impact WiFi power management to consume more energy, since distinguishing between applications is not feasible at the MAC layer. The power cost difference between WiFi power modes is high (a factor of 20 times when idle), therefore determining which applications are permitted to impact WiFi power management is an important and relevant problem. In this paper we propose SAPSM: Smart Adaptive Power Save Mode. SAPSM labels each application with a priority with the assistance of a machine learning classifier. Only high priority applications affect the client's behavior to switch to CAM or Active mode, while low priority traffic is optimized for energy efficiency. Our implementation on an Android Smartphone improves energy savings by up to 56% under typical usage patterns.

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting or classifying events while maximizing system lifetime. We define meeting such user accuracy requirements as confident sensing. To perform confident sensing and reduce energy, we must address sensing diversity: sensing capability differences among heterogeneous and homogeneous sensors in a specific deployment. We are among the first to explore the impact of sensing diversity on sensor collaboration, exploit diversity for sensing confidence, and apply diversity exploitation for confident sensing coverage. We show that our diversity-exploiting confident coverage problem is NP-hard for any specific deployment and present a practical solution, Wolfpack. Through a distributed and iterative sensor collaboration approach, Wolfpack maximizes a specific deployment's capability to meet user detection requirements and save energy by powering off unneeded nodes. Using real vehicle detection trace data, we demonstrate that Wolfpack provides confident event detection coverage for 30% more detection locations, using 20% less energy than a state of the art approach.

Body sensor networks (BSNs) have been developed for a set of performance-critical applications, including smart healthcare, assisted living, emergency response, athletic performance evaluation, and interactive controls. Many of these applications require stringent performance assurance in terms of communication throughput and bounded time delay. While solutions exist in literature for providing joint throughput and time delay assurance by proposing specific MAC protocols or extensions, we provide this joint assurance in a novel radio-agnostic manner. In our approach, the underlying MAC and PHY layers can be heterogeneous and their details do not need to be known to upper layers like the resource management. Such a radio-agnostic performance assurance is critical because a range of radio platforms are adopted for practical body sensor usage. Our approach is based on a group-polling scheme that is essential for radio-agnostic BSN design. Through theoretical analysis, we prove that with the group-polling scheme, achieving joint throughput and time delay assurance is an NP-hard problem. For practical system deployment, we propose the BodyT2 framework that assures throughput and time delay performance in a heterogeneous BSN. Through both TelosB mote lab tests and real body experiments in an Android phone-centric BSN, we demonstrate that BodyT2 achieves superior performance over existing solutions.

The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.

Many mission-critical applications such as military surveillance, human health monitoring, and obstacle detection in autonomous vehicles impose stringent requirements for event detection accuracy and demand long system lifetimes. Through quantitative study, we show that traditional approaches to event detection have difficulty meeting such requirements. Specifically, they cannot explore the detection capability of a deployed system and choose the right sensors, homogeneous or heterogeneous, to meet user specified detection accuracy. They also cannot dynamically adapt the detection capability to runtime observations to save energy. Therefore, we are motivated to propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user specified detection accuracy during runtime while significantly reducing energy consumption. Through evaluation with vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user specified detection accuracy and energy savings.

Smart wireless sensor devices are becoming increasingly ubiquitous and are expected to be embedded in everyday objects in the near future. When these devices are deployed in overlapping or adjacent geographic areas, the unlicensed 2.4 GHz ISM band will be crowded. To deal with the crowded spectrum, we propose SAS, a self-adaptive spectrum management middleware for wireless sensor networks. SAS enables single-frequency MAC protocols with multi-frequency capability, so that an existing MAC protocol, like B-MAC, can automatically adapt to the least congested physical channel at runtime. We implemented SAS in TinyOS 2.1 with nesC and evaluated its performance with TelosB motes. Our performance results demonstrate that SAS improves the performance of existing single-frequency MAC protocols, like B-MAC. The use of SAS results in higher packet reception ratio and system throughput, and a lower packet delay and energy consumption.

In-situ data collection for mobile wireless sensor network deployments has received little study, such as in the case of floating sensor networks for storm surge and innundation monitoring. We demonstrate through quantitative study that traditional approaches to routing in mobile environments do not work well due to volatile topology changes. Consequently, we propose Sidewinder, a predictive data forwarding protocol for mobile wireless sensor networks. Like a heat-seeking missile, data packets are guided towards a sink node with increasing accuracy as packets approach the sink. Different from conventional sensor network routing protocols, Sidewinder continuously predicts the current sink location based on distributed knowledge of sink mobility among nodes in a multi-hop routing process. Moreover, the continuous sink estimation is scaled and adjusted to perform with resource-constrained wireless sensors. Our design is implemented with nesC and evaluated in TOSSIM. The performance evaluation demonstrates that Sidewinder significantly outperforms state-of-the-art solutions in packet delivery ratio, time delay, and energy efficiency.