This paper develops an energy-aware ultrasonic sensor network architecture using a Pulse Switching approach for lightweight,
through-substrate operation in Structural Health Monitoring applications. Pulse Switching protocols employ
single pulses instead of multi-bit packets for information delivery with maximal lightness in event monitoring with
binary sensing requirements i.e. where event information transmitted is only a single bit (YES / NO) based on evaluation
of structural characteristics. The paper presents a simulation study of the Energy-Aware Through-Substrate Pulse
Switching protocol performance for structural monitoring when operated using energy harvested from intermittent
vibrations in the structure itself. The paper incorporates an energy harvesting model for simulating memory-less
vibration patterns using exponentially distributed random processes at different networked nodes. These nodes are
placed inside a rectangular plate structure and the corresponding harvested energy profiles are simulated. The vibration
profiles are a function of the position of the node on the plate as well as time. Such spatio-temporal variation leads to
interesting dynamics in the energy-aware protocol operation which have been explored in the current paper setting.
Through the simulations, it is shown that the proposed Energy-Aware Pulse Switching protocol mechanisms can offer a
robust through-substrate network that can be reliably used for Structural Health Monitoring using vibration-harvested
This paper proposes a novel signal analysis based node localization strategy for sensor networks used in structural health
monitoring (SHM) applications. The key idea is to analyze location-dependent multipath signal patterns in inter-node
ultrasonic signals, and use machine-learning mechanisms to detect such patterns for accurate node localization on metal
substrates on target structures. Majority of the traditional mechanisms rely on radio based Time Delay of Arrival
(TDOA), coupled with multilateration, and multiple reference nodes. The proposed mechanism attempts to solve the
localization problem in an ultrasonic sensor network (USN), avoiding the use of multiple reference beacon nodes.
Instead, it relies on signal analysis and multipath signature classification from a single reference node that periodically
transmits ultrasonic localization beacons. The approach relies on a key observation that the ultrasonic signal received at
any point on the structure from the reference node, is a superposition of the signals received on the direct path and
through all possible multi-paths. It is hypothesized that if the location of the reference node and the substrate properties
are known a-priori, it should be possible to train a receiver (source node), to identify its own location by observing the
exact signature of the received signal. To validate this hypothesis, steps were taken to develop a TI MSP-430 based
module for implementing a run-time system from a proposed architecture. Through extensive experimentation within an
USN on the 2024 Aluminum substrate, it was demonstrated that localization accuracies up to 92% were achieved in the
presence of varying spatial resolutions.
This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.
This paper presents the design, system structure and performance for a wireless and wearable diet monitoring system. Food and drink intake can be detected by the way of detecting a person’s swallow events. The system works based on the key observation that a person’s otherwise continuous breathing process is interrupted by a short apnea when she or he swallows as a part of solid or liquid intake process. We detect the swallows through the difference between normal breathing cycle and breathing cycle with swallows using a wearable chest-belt. Three popular machine learning algorithms have been applied on both time and frequency domain features. Discrimination power of features is then analyzed for applications where only small number of features is allowed. It is shown that high detection performance can be achieved with only few features.
Wireless sensor network used in military applications may be deployed in hostile environments, where privacy and security is
of primary concern. This can lead to the formation of a trust-based sub-network among mutually-trusting nodes. However,
designing a TDMA MAC protocol is very challenging in situations where such multiple sub-networks coexist, since TDMA
protocols require node identity information for slot assignments. This paper introduces a novel distributed TDMA MAC
protocol, ZEA-TDMA (Zero Exposure Anonymous TDMA), for anonymous wireless networks. ZEA-TDMA achieves slot
allocation with strict anonymity constraints, i.e. without nodes having to exchange any identity revealing information. By using
just the relative time of arrival of packets and a novel technique of wireless collision-detection and resolution for fixed packetsizes,
ZEA-TDMA is able to achieve MAC slot-allocation which is described as follows. Initially, a newly joined node listens to
its one-hop neighborhood channel usage and creates a slot allocation table based on its own relative time, and finally, selects a
slot that is collision free within its one-hop neighborhood. The selected slot can however cause hidden collisions with a two-hop
neighbor of the node. These collisions are resolved by a common neighbor of the colliding nodes, which first detects the
collision, and then resolve them using an interrupt packet. ZEA-TDMA provides the following features: a) it is a TDMA
protocol ideally suited for highly secure or strictly anonymous environments b) it can be used in heterogeneous environments
where devices use different packet structures c) it does not require network time-synchronization, and d) it is insensitive to
channel errors. We have implemented ZEA-TDMA on the MICA2 hardware platform running TinyOS and evaluated the
protocol functionality and performance on a MICA2 test-bed.
This paper presents a novel wireless sensor networking technique using ultrasonic signal as the carrier wave for binary
data exchange. Using the properties of lamb wave propagation through metal substrates, the proposed network structure
can be used for runtime transport of structural fault information to ultrasound access points. Primary applications of the
proposed sensor networking technique will include conveying fault information on an aircraft wing or on a bridge to an
ultrasonic access point using ultrasonic wave through the structure itself (i.e. wing or bridge). Once a fault event has
been detected, a mechanical pulse is forwarded to the access node using shortest path multi-hop ultrasonic pulse routing.
The advantages of mechanical waves over traditional radio transmission using pulses are the following: First, unlike
radio frequency, surface acoustic waves are not detectable outside the medium, which increases the inherent security for
sensitive environments in respect to tapping. Second, event detection can be represented by the injection of a single
mechanical pulse at a specific temporal position, whereas radio messages usually take several bits. The contributions of
this paper are: 1) Development of a transceiver for transmitting/receiving ultrasound pulses with a pulse loss rate below
2·10-5 and false positive rate with an upper bound of 2·10-4. 2) A novel one-hop distance estimation based on the properties of lamb wave propagation with an accuracy of above 80%. 3) Implementation of a wireless sensor network
using mechanical wave propagation for event detection on a 2024 aluminum alloy commonly used for aircraft skin
This paper presents a novel energy-efficient distributed self-organized pulse switching architecture with a cell based
event localization for wireless sensor and actuator network applications. The key idea of this pulse switching architecture
is to abstract a single pulse, as opposed to multi-bit packets, as the information exchange mechanism. Unlike multi-bit
packet communication, the proposed pulse switching architecture is based on pulse communications where a node either
transmits a pulse or keeps silent at every time unit. Specifically, an event can be coded as a single pulse in a specific time
unit with respect to the global clock. Then the pulse is transported multi-hop while preserving the event’s localization
information in the form of temporal pulse position representing its originating cell, destination cell and next-hop cell.
The proposed distributed pulse switching is shown to be energy-efficient compared to traditional packet switching
especially for binary event sensing and actuation applications. Binary event sensing and actuation with conventional
packet transport can be prohibitively energy-inefficient due to the communication, processing, and buffering overheads
of the large number of bits within a packet’s data, header, and preambles. This paper presents a joint MAC and Routing
architecture for self-organized distributed pulse switching. Through simulation experiments, it is shown that pulse
switching can be an effective distributed means for event based networking in wireless sensor and actuator networks,
which can potentially replace the packet transport when the information to be transported is binary in nature.