Outdoor Sensor Networks Research Articles

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure.

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 μg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

Modeling Communication over Terrain for Realistic Simulation of Outdoor Sensor Network Deployments

Popular wireless network simulators have few available propagation models for outdoor Internet of Things applications. Of the available models, only a handful use real terrain data, yet an inaccurate propagation model can skew the results of simulations. In this article, we present TerrainLOS, a low-overhead propagation model for outdoor Internet of Things applications that uses real terrain data to determine whether two nodes can communicate. To the best of our knowledge, TerrainLOS is the first terrain-aware propagation model that specifically targets outdoor IoT deployments and that uses height maps to represent terrain. In addition, we present a new terrain classification method based on terrain “roughness,” which allows us to select a variety of terrain samples to demonstrate how TerrainLOS can capture the effects of terrain on communication. We also propose a technique to generate synthetic terrain samples based on “roughness.” Furthermore, we implemented TerrainLOS in the COOJA-Contiki network simulation/emulation platform, which targets IoT deployments and uses TerrainLOS to evaluate how often a network is fully connected based on the roughness of terrain, as well as how two popular power-aware routing protocols, RPL and ORPL, perform when terrain is considered.

An autonomous bioluminescent bacterial biosensor module for outdoor sensor networks, and its application for the detection of buried explosives

We describe a miniaturized field-deployable biosensor module, designed to function as an element in a sensor network for standoff monitoring and mapping of environmental hazards. The module harbors live bacterial sensor cells, genetically engineered to emit a bioluminescent signal in the presence of preselected target materials, which act as its core sensing elements. The module, which detects and processes the biological signal, composes a digital record that describes its findings, and can be transmitted to a remote receiver. The module is an autonomous self-contained unit that can function either as a standalone sensor, or as a node in a sensor network. The biosensor module can potentially be used for detecting any target material to which the sensor cells were engineered to respond. The module described herein was constructed to detect the presence of buried landmines underneath its footprint. The demonstrated detection sensitivity was 0.25 mg 2,4-dinitrotoluene per Kg soil.

Characterization of Low-Antenna Ultrawideband Propagation in a Forest Environment

Impulse ultrawideband (UWB) communication promises a number of benefits for use in wireless sensor networks, particularly in forest environments, where it has the potential to provide robust operation, along with the ability to combine communications with precision position location. In this paper, we present measurement results and empirical models for UWB signal propagation in a forest environment. Path-loss measurements were performed using a 620-ps duration UWB pulse with a frequency range of 830-4200 MHz. More than 22 000 measurements were recorded in 165 locations in four diverse forest environments in Virginia and Maryland, USA. Transmitters and receivers were separated by distances that range from 4 m to 50 m. Large-scale path loss was most closely modeled by log-distance propagation, with path-loss exponents ranging from 2.5 to 3.8. Small-scale fading analysis indicated that UWB signals experience Rician fading, with K-factors in the range of 10-16 dB. K-factors were found to depend on forest type, with the medium-density forest providing the greatest number of multipath components and highest overall K-factor. Multipath component analysis demonstrated that a forest is a fairly rich multipath environment; however, in most cases, approximately 90% of the available energy was contained within the strongest 3-15 multipath components. Postprocessing analysis divided the UWB pulse into three frequency subbands and demonstrated that model parameters also had frequency dependence. These measurements and models should aid in the development of future UWB outdoor sensor networks.

Resilient localization for sensor networks in outdoor environments

The process of determining the physical locations of nodes in a wireless sensor network is known as localization . Self-localization is critical for large-scale sensor networks, because manual or assisted localization is often impractical due to time requirements, economic constraints, or inherent limitations of the deployment scenarios. We propose scalable solutions for reliably localizing wireless sensor networks in environments conducive to several types of ranging errors. We follow a hybrid hardware-software approach for acoustic ranging or radio interferometry to acquire internode distance measurements, and a resilient self-localization algorithm to compute the node location estimates. The acoustic ranging method improves on previous work, extending the practical measurement range up to 35 m in grassy outdoor environments, achieving a distance-invariant median measurement error of about 1% (33 cm). The localization algorithm is based on least-squares scaling with soft constraints. Empirical evaluation using ranging results obtained from sensor network field experiments and simulations confirms that our approach is more resilient than multidimensional scaling (MDS) algorithms against large-magnitude ranging errors and sparse range measurements: conditions that are common in large-scale outdoor sensor network deployments.

Achieving long-term surveillance in VigilNet

Energy efficiency is a fundamental issue for outdoor sensor network systems. This article presents the design and implementation of multidimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. A novel tripwire service is integrated with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. The tripwire service partitions a network into distinct, nonoverlapping sections and allows each section to be scheduled independently. Sentry scheduling selects a subset of nodes, the sentries, which are turned on while the remaining nodes save energy. Duty cycle scheduling allows the active sentries themselves to be turned on and off, further lowering the average power draw. The multidimensional power management strategies proposed in this article were fully implemented within a real sensor network system using the XSM platform. We evaluate key system parameters using a network of 200 XSM nodes in an outdoor environment, and an analytical probabilistic model. We evaluate network lifetime using a simulation of a 10,000-node network that uses measured XSM power values. These evaluations demonstrate the effectiveness of our integrated approach and identify a set of lessons and guidelines, useful for the future development of energy-efficient sensor systems. One of the key results indicates that the combination of the three presented power management techniques is able to increase the lifetime of a realistic network from 4 days to 200 days.

A Novel Distributed Sensor Positioning System Using the Dual of Target Tracking

As one of the fundamental issues in wireless sensor networks (WSNs), the sensor localization problem has recently received extensive attention. In this work, we investigate this problem from a novel perspective by treating it as a functional dual of target tracking. In traditional tracking problems, static location-aware sensors track and predict the position and/or velocity of a moving target. As a dual, we utilize a moving location assistant (LA) (with a global positioning system (GPS) or a predefined moving path) to help location-unaware sensors to accurately discover their positions. We call our proposed system Landscape. In Landscape, an LA (an aircraft, for example) periodically broadcasts its current location (we call it a beacon) while it moves around or through a sensor field. Each sensor collects the location beacons, measures the distance between itself and the LA based on the received signal strength (RSS), and individually calculates their locations via an Unscented Kalman Filter (UKF)-based algorithm. Landscape has several features that are favorable to WSNs, such as high scalability, no intersensor communication overhead, moderate computation cost, robustness to range errors and network connectivity, etc. Extensive simulations demonstrate that Landscape is an efficient sensor positioning scheme for outdoor sensor networks.

Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, named LochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed for LochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control system LochNess is evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deployment