This paper presents LDSP (i.e., low-duty-cycle synchronization protocol), a design that enables scalable clock synchronization in wireless networks with low-duty-cycle radio operations. LDSP prevents exponential error proliferation of many available solutions, if applied in the low-duty-cycle scenario, by introducing a new mechanism of parallel synchronization that is naturally immune to excessive message delays widely existing in such networks. The key novelty behind LDSP is its separation of clock drift rate estimation from the error polluted global reference time in a unique manner, which helps eliminate compound errors that would otherwise amplify aggressively over the message delay at each hop during time dissemination. With LDSP, the time error is bounded to low-order polynomial growth as O(h√h), where h is the hop distance to the reference node, according to theoretical analysis verified by numerical simulation. To evaluate, LDSP was implemented on two hardware platforms equipped with different driven clock sources and compared with representative synchronization protocols via experiments in both indoor and outdoor environments. Results show that the proposed design is practical, effective, and features significantly improved scalability under real-world conditions.

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

Low-duty-cycle radio operations have been proposed for wireless networks facing severe energy constraints. Despite energy savings, duty-cycling the radio creates transient-available wireless links, making communication rendezvous a challenging task under the practical issue of clock drift. To overcome limitations of prior work, this paper presents PSR, a practical design for synchronous rendezvous in low-duty-cycle wireless networks. The key idea behind PSR is to extract timing information naturally embedded in the pattern of radio duty-cycling, so that normal traffic in the network can be utilized as a “free” input for drift detection, which helps reduce (or even eliminate) the overhead of traditional time-stamp exchange with dedicated packets or bits. To prevent an overuse of such free information, leading to energy waste, an energy-driven adaptive mechanism is developed for clock calibration to balance between energy efficiency and rendezvous accuracy. PSR is evaluated with both test-bed experiments and extensive simulations, by augmenting and comparing with four different MAC protocols. Results show that PSR is practical and effective under different levels of traffic load, and can be fused with those MAC protocols to improve their energy efficiency without major change of the original designs.

Sensor node localization is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Convex Optimization-based Method for Event-Driven Localization, i.e., COMEDL, in large-scale wireless sensor networks. The key idea behind COMEDL is to estimate the two-dimensional location coordinates of sensor nodes by processing multiple one-dimensional node sequences that can be easily obtained via loosely guided localization event distributions. The proposed design is evaluated through theoretical analysis, extensive simulations, and physical experiments (an indoor test-bed with 44 MICAz sensor nodes). Evaluation results demonstrate that COMEDL can effectively localize nodes with improved accuracy and flexibility.

Although existing flooding protocols can provide efficient and reliable communication in wireless sensor networks on some level, further performance improvement has been hampered by the assumption of link independence, which requires costly acknowledgments (ACKs) from every receiver. In this paper, we present collective flooding (CF), which exploits the link correlation to achieve flooding reliability using the concept of collective ACKs. CF requires only 1-hop information at each node, making the design highly distributed and scalable with low complexity. We evaluate CF extensively in real-world settings, using three different types of testbeds: a single-hop network with 20 MICAz nodes, a multihop network with 37 nodes, and a linear outdoor network with 48 nodes along a 326-m-long bridge. System evaluation and extensive simulation show that CF achieves the same reliability as state-of-the-art solutions while reducing the total number of packet transmission and the dissemination delay by 30%-50% and 35%-50%, respectively.

Traditional mechanisms for risk control and task scheduling may not be very suitable for security-critical embedded real-time systems under dynamic operation environments. In this paper, we are interested in design of an adaptive risk control and management mechanism of the uniprocessor system for real-time embedded applications. With the purpose of obtaining adaptive capability to reduce system risks without sacrificing soft real-time performance, a two-level feedback scheduling framework was deployed at runtime. The upper-level component automatically monitors the status of the waiting queue, then admits or rejects the input workloads and assigns a reasonable security level for ready tasks. The lower-level scheduling framework can dynamically adjust the security level of ready tasks based on the runtime situation. Simulation results show the superiority of the proposed mechanism, especially the good adaptivity under dynamic system workloads.

In wireless mobile networks, energy can be saved by using dynamic transmission scheduling with pre-knowledge about channel conditions. Such pre-knowledge can be obtained via profiling as proposed by several existing systems which assumed that the existence of spatial link correlation makes the measured channel status at one location reusable over a long period of time. Our empirical data, however, tells a different story: spatial link correlation only maintains well within a short duration (from seconds to tens of seconds) while decreases significantly afterwards, a phenomena we call ephemeral link correlation. By leveraging this observation, we design and implement a real-time transmission scheduling system, named PreSeer, on the railway platform for cargo transportation, where the transmission of a sink (to cellular towers) can be scheduled intelligently by utilizing future channel status measured by sinks located in front of it on the same train. We have implemented and evaluated the PreSeer system on the collected data extensively over 7,000-kilometer railway routes during a period of one and half years. Results reveal that PreSeer can help save as much as 40% energy, comparing with three base-line algorithms. More importantly, lessons learned from this major effort provide useful guidelines for transmission scheduling in highly-dynamic mobile environments, where (i) channel measurements cannot be perfectly aligned due to varying vehicle velocities, and (ii) the accuracy of channel measurements is subject to hardware discrepancy as well as environment irregularity.

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.

Event-driven localization has been proposed as a low-cost solution for node positioning in wireless sensor networks. In order to eliminate the costly requirement for accurate event control in existing methods, we present a practical design using uncontrolled events. The main idea is to estimate both event generation parameters and the location of sensor nodes simultaneously, by processing node sequences that can be easily obtained from event detections. Besides the basic design, we proposed two enhancements to further extract information embedded in node orderings for two scenarios: (i) node density is high; and (ii) abundant events are available. To demonstrate the generality of our design, both straight-line scan and circular wave propagation events are addressed in the article, and we evaluated the design with extensive simulation as well as a testbed implementation with 41 MICAz motes. Results show that with only randomly generated events, our design can effectively localize nodes with great flexibility while adding little extra cost at the resource constrained sensor node side. In addition, localization via uncontrolled events provides a potential option of achieving node positioning through long-term ambient events.

As an essential requirement for surveillance systems, target tracking has been studied extensively. Most of the tracking schemes are based on trilateration, which requires each point in the monitoring area to be covered by at least three anchors. However, due to the inadequate deployment of costly anchors and environment constraints, the target might not always be detected by three or more anchors simultaneously, resulting in intermittent localization failures and performance degradation. To address this issue, this paper proposes a tracking method called Bubble Trace (BT) for insufficient anchor coverage and asynchronous networks. By fully extracting the location information embedded in dual, single and zero anchor coverage, we develop a bidirectional bounding algorithm to offer the bubble-shaped regions that indicate the possible locations of the target. Moreover, instead of separately estimating each position point of the target, we construct the trace by finding a maximum-likelihood path in a graph. The design is evaluated through extensive simulation and a test-bed experiment with 20 MicaZ nodes. Results show that the proposed scheme improves the tracking accuracy without using additional hardware under insufficient anchor coverage.

Working in the duty cycling mode enables sensor nodes to utilize limited energy efficiently instead of unnecessary idle listening. In such networks, awareness of neighboring nodes' working schedules is essential, especially when each node sets up the schedule independently. Most traditional research assumes that a node can always share its working schedule with its neighbors once it joins the network. However, dynamic energy supply and varied requirements of system performance make adjusting duty cycles necessary. Consequently, sensor nodes have to regularly change their schedules and advertise the new schedule to their one-hop neighbors. In this work, instead of changing nodes' working schedule directly, we introduce SSD, a staged and smooth design for safeguarding schedule updates. Our design is aimed at bounding average packet loss rate with a minimum energy cost. We evaluate our design with large-scale simulation and show that under the comparable packet loss rate, our design saves up to 35% energy compared with the minimal packet loss solution.

Time synchronization remains as a challenging task in wireless sensor networks that face severe resource constraints. Unlike previous work's aiming at pure clock accuracy, this paper proposes On-Demand Synchronization (ODS), a design to achieve efficient clock synchronization with customized performance. By carefully modeling the error uncertainty of skew detection and its propagation over time, ODS develops a novel uncertainty-driven mechanism to adaptively adjust each clock calibration interval individually rather than traditional periodic synchronization, for minimum communication overhead while satisfying the desired accuracy. Besides, ODS provides a nice feature of predictable accuracy, allowing nodes to acquire the useful information about real-time qualities of their synchronization. We implemented ODS on the MICAz mote platform, and evaluated it through test-bed experiments with 33 nodes as well as simulations obeying real world conditions. Results show that ODS is practical, flexible, and quickly adapts to varying accuracy requirements and different traffic load in the network for improved system efficiency.

Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on radio connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to these limitations, this paper introduces a proximity metric called RSD to capture the distance relationships among 1-hop neighboring nodes in a range-free manner. With little overhead, RSD can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy. We implemented RSD with three well-known algorithms and evaluated using two outdoor test beds: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10,000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with a subhop resolution, and reduces localization errors by as much as 35 percent. In addition, simulations confirm its effectiveness for large-scale networks and reveal an interesting feature of robustness under unevenly distributed radio path loss.

Underwater sensor networks (UWSNs) have been proposed for many location-dependent applications such as oceanographic data collection, pollution monitoring, mine reconnaissance, etc. Accurate node localization plays an important role in realizing the potential gains of these applications. Although many localization algorithms have been proposed for terrestrial sensor networks in recent years, it is not feasible to directly use these algorithms in UWSNs since UWSNs lack a fast and reliable communication channel. Further, due to their slow convergence speeds and high communication overhead, distributed localization algorithms designed for small-scale UWSNs are not practical for large-scale underwater sensor systems. To achieve accurate and energy efficient node localization in large-scale UWSNs, an asymmetrical round trip based localization (ARTL) algorithm is proposed in this paper. This algorithm has low computational complexity and excellent scalability. Without time synchronization, this algorithm can achieve highly accurate ranging in large-scale UWSNs. Simulation results demonstrate the effectiveness of our design in terms of both localization accuracy and energy consumption.

Tracking mobile targets using sensor networks is a challenging task because of the impacts of in-the-flled factors such as environment noise, sensing irregularity and etc. This paper proposes a robust tracking framework using node sequences, an ordered list extracted from unreliable sensor readings. Instead of estimating each position point separately in a movement trace, we convert the original tracking problem to the problem of finding the shortest path in a graph, which is equivalent to optimal matching of a series of node sequences. In addition to the basic design, multidimensional smoothing is developed to enhance tracking accuracy. Practical system deployment related issues are discussed in the paper, and the design is evaluated with both simulation and a system implementation using Pioneer III Robot and MICAz sensor nodes. In fact, tracking with node sequences provides a useful layer of abstraction, making the design framework generic and compatible with different physical sensing modalities.

To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakage-prone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settings - indoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functionnal faults, this paper proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

The ability to accurately profile energy consumption is of great importance for energy management in low-power devices. This work presents a novel energy profiling architecture by combining the high-speed CPLD bus signaling capability of mPlatform with the smart TwinStar power board. By running mPlatform under different modes, we are able to utilize an MMSE estimator to analyze per-component energy consumption rates with sufficient accuracy. In our experiment, we use the profiling results to determine the working condition of individual components, as well as the hardware configuration of the sensor node.

Wireless sensor networks have been proposed for many location-dependent applications. In such applications, the requirement of low system cost prohibitsmany range-based methods for sensor node localization; on the other hand, range-free localization depending only on connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to the above limitations, this paper presents a range-free approach to capturing a relative distancebetween1-hopneighboringnodesfromtheirneighborhood orderings that serve as unique high-dimensional location signatures for nodes in the network. With little overhead, the proposed design can be conveniently applied as a transparent supporting layer for many state-of-the-art connectivity-based localization solutions to achieve better positioningaccuracy. Weimplementedourdesignwiththree well-knownlocalizationalgorithmsandtesteditintwotypes ofoutdoortest-bedexperiments: an850-foot-longlinearnetwork with 54 MICAz motes, and a regular2D networkcovering an area of 10000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with sub-hop resolution, and reduces localization errors by as much as 35%. In addition, extensive simulations reveal an interestingfeature of robustnessfor our design underunevenly distributed radio propagation path loss, and confirm itseffectivenessforlarge-scalenetworks. Categories andSubject Descriptors

Many event-driven localization methods have been proposed as low cost, energy efficient solutions for wireless senor networks. In order to eliminate the requirement of accurately controlled events in existing approaches, we present a practical design using totally uncontrolled events for stationary sensor node positioning. The novel idea of this design is to estimate both the event generation parameters and the location of each sensor node by processing node sequences easily obtained from uncontrolled event distribution. To demonstrate the generality of our design, both straight-line scan and circular wave propagation events are addressed in this paper, and we evaluated our approach through theoretical analysis, extensive simulation and a physical test bed implementation with 41 MICAz motes. The evaluation results illustrate that with only randomly generated events, our solution can effectively localize sensor nodes with excellent flexibility while adding no extra cost at the resource constrained sensor node side. In addition, localization using uncontrolled events provides a nice potential option of achieving node positioning through natural ambient events.

Starting from the features and impact of energy leakage in ultra-capacitor powered systems, this demonstration highlights the design of a capacitor-only Twin-Star node, and a leakage-aware energy synchronization methodology.

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations, which work together to increase the localization accuracy. We address several interesting issues, such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). This evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. It also provides a nice tradeoff between physical cost (anchors) and soft cost (events), while maintaining localization accuracy.