This PDF file contains the front matter associated with SPIE Proceedings Volume 6562, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and the Conference Committee listing.

Methods of human detection utilizing low-frequency seismic signals (typically below a few hundred Hertz) from footsteps are well known in the literature and in a practice. This frequency band is used for seismic detectors. Different walking styles (regular, soft, and stealthy) result in different vibration signatures in the low-frequency range that limit the maximum ranges for this method of footstep detection. For example, the stealthy walking style was undetectable even a few meters from a seismic detector. Human footsteps generate broadband frequency vibrations in the ground/floor and sound in the air from a few Hertz up to ultrasonic frequencies. The dynamic forces from footsteps that are normal to the ground/floor are the primary cause of the low-frequency component in these signals. Striking and sliding contacts between a foot and the ground/floor produce the high-frequency responses. The physical mechanisms involved in the generation of high frequency signals and the possibility of their application for human footstep detection were investigated by the authors [A. Ekimov, and J. M. Sabatier "Vibration and sound signatures of human footsteps in buildings," J. Acoust. Soc. Am., 120, 762-768 (2006)]. The present paper introduces an approach for human footstep detection using a passive ultrasonic method. The passive method employs an ultrasonic sensor that is sensitive to the sound from sliding contacts. Test results for the detection of a walking person indoors and outdoors are presented and discussed.

The ability to automatically detect humans in infrared images has value in military and civilian applications. Robots and unattended ground stations equipped with real-time human ATR capability can operate as scouts, perform reconnaissance for military units, and serve to locate humans in remote or hazardous sites. With the algorithm proposed in this study, human targets can be detected in infrared images based on the structure and radiance of the human head. The algorithm works in a three step process. First, the infrared image is segmented primarily based on edges and secondarily based on intensity of pixels. Once the regions of interest have been determined, the segments undergo feature extraction, in which they are characterized based on circularity and smoothness. The final step of the algorithm uses a k-Nearest Neighbor classifier to match the segment's features to a database, determining whether the segment is a head or not. This algorithm operates best in environments in which contrast between the human and the background is high, such as nighttime or indoors. Tests show that 82% accuracy in identification of human heads is possible for a single still image. After analyzing two uncorrelated frames viewing the same scene, the likelihood of correctly classifying a human head that appears in both frames is 97%.

In this paper we present a multi-modal multi-sensor fusion algorithm for the detection of personnel. The unattended ground sensors employed consist of acoustic, seismic, passive infrared (PIR) and video camera. The individual sensor data is processed and the probabilities of detection of a person are estimated. Then, the individual probability estimates are fused to estimate the overall probability of detection of a person. The confidence levels of each sensor modality are estimated based on a large set of data. The performance of the algorithm is tested on data collected in an unoccupied basement of a building with single and multiple people present.

We address fusion of vector magnetometer and acoustic data for the purpose of classifying civilian vehicles such as cars, SUVs, and trucks. We use an Anderson function model to estimate the source speed and reduce the vector-magnetic data to 9 parameters. The joint statistics of magnetic-acoustic data are learned using nonparametric probability density estimation, and the magnetic-acoustic data is fused by extracting features for classification that maximize an information-theoretic criterion. We apply the approach with measured magnetic-acoustic data from civilian vehicles and demonstrate the ability to discriminate between cars and SUVs. Discrimination is improved when the features and classifier are designed with additional information about the vehicle's track, specifically, the speed and direction of motion (left-to-right or right-to-left along a road).

As part of a research initiative within the US Army Research Laboratory, we investigate acoustic sensing capability from UAVs for remote Intelligence Surveillance and Reconnaissance (ISR) applications. Acoustic sensing from UAVs offers several advantages over acoustic sensing on the ground including: (i) longer detection ranges due to upward refraction and (ii) a single elevated acoustic array (versus multiple acoustic arrays on the ground) can provide a pointing vector to the acoustic source on the ground. However, there are technical challenges with having acoustic sensors on UAVs which involve self generated noise from the platform and the air flow. In this paper, we describe our current work with acoustic sensors on small UAV platforms and present preliminary processing results from a recent field experiment.

Miltec Research & Technology along with the National Center for Physical Acoustics, Raspet Flight Research Laboratory, and Mississippi State University Aerospace Department have performed a detailed study of the benefits and issues associated with the implementation of an array of acoustic sensors on airborne platforms both moving and stationary. In order to facilitate this a platform has been developed for the testing of airborne acoustic arrays used in detection, tracking, and identification of objects of interest. The test bed has been selected such that it is optimized for this effort within reasonable limitations on complexity and costs. This approach allows for the determination of best case performance parameters for acoustic arrays on aerial platforms. Issues related to the design of the platform as well as measured results will be presented.

This paper presents an overview of Intelligent Video work currently under development at the GE Global Research Center and other research institutes. The image formation process is discussed in terms of illumination, methods for automatic camera calibration and lessons learned from machine vision. A variety of approaches for person detection are presented. Crowd segmentation methods enabling the tracking of individuals through dense environments such as retail and mass transit sites are discussed. It is shown how signature generation based on gross appearance can be used to reacquire targets as they leave and enter disjoint fields of view. Camera calibration information is used to further constrain the detection of people and to synthesize a top-view, which fuses all camera views into a composite representation. It is shown how site-wide tracking can be performed in this unified framework. Human faces are an important feature as both a biometric identifier and as a method for determining the focus of attention via head pose estimation. It is shown how automatic pan-tilt- zoom control; active shape/appearance models and super-resolution methods can be used to enhance the face capture and analysis problem. A discussion of additional features that can be used for inferring intent is given. These include body-part motion cues and physiological phenomena such as thermal images of the face.

Both the military and consumer sectors are pursuing distributed networked systems and sensors. A major stumbling block to deployment of these sensors will be the radio frequency (RF) propagation environment within a few wavelengths of the earth. Increasing transmit power (battery consumption) is not a practical solution to the problem. This paper will discuss some of the physical phenomena related to the near earth propagation (NEP) problem. When radiating near the earth the communications link is subjected to a list of physical impairments. On the list are the expected Fresnel region encroachment and multipath reflections. Additionally, radiation pattern changes and near earth boundary layer perturbations exist. A significant amount of data has been collected on NEP. Disturbances in the NEP atmosphere can have a time varying attenuation related to the time of day and these discoveries will be discussed. Solutions, or workarounds, to the near earth propagation problem hinge on dynamic adaptive RF elements. Adaptive RF elements will allow the distributed sensor to direct energy, beam form, impedance correct, increase communication efficiency, and decrease battery consumption. Small electrically controllable elements are under development to enable antenna impedance matching in a dynamic environment. Additionally, small dynamic beam forming arrays are under development to focus RF energy in the direction of need. With an increased understanding of the near earth propagation problem, distributed autonomous networked sensors can become a reality within a few centimeters of the earth.

Fusing acoustic and visual data has the potential to significantly improve target tracking performance by exploiting the complementary and redundant information. However, it is not clear how to efficiently fuse these two modalities and how to reduce the high energy consumption of video sensors to improve network lifespan. High energy requirement of video sensors is mainly due to the high sample acquisition cost and high computational complexity of target detection algorithms. We address the computation energy by performing target detection in a region-ofinterest and studying target detection algorithms' computational complexity and data fusion architecture. Some strategies are proposed to help video sensors in performing target detection and reducing the power consumption by incorporating the acoustic detection result. We study two widely used target detection algorithms, background subtraction and snake, for their use in joint acoustic-video tracking, and analyze their computational complexities and memory requirements. We hope that the analysis can effectively guide the designer to select the most suitable algorithm and architecture for a given scenario or application.

We report recent progress in the development of low modulus, highly electrically conducting thin film sheet and fabric materials and devices formed by molecular-level self-assembly processing methods and their use in flexible circuits.

This project focuses on developing electro-optic algorithms which rank images by their likelihood of containing vehicles and people. These algorithms have been applied to images obtained from Textron's Terrain Commander 2 (TC2) Unattended Ground Sensor system. The TC2 is a multi-sensor surveillance system used in military applications. It combines infrared, acoustic, seismic, magnetic, and electro-optic sensors to detect nearby targets. When targets are detected by the seismic and acoustic sensors, the system is triggered and images are taken in the visible and infrared spectrum. The original Terrain Commander system occasionally captured and transmitted an excessive number of images, sometimes triggered by undesirable targets such as swaying trees. This wasted communications bandwidth, increased power consumption, and resulted in a large amount of end-user time being spent evaluating unimportant images. The algorithms discussed here help alleviate these problems. These algorithms are currently optimized for infra-red images, which give the best visibility in a wide range of environments, but could be adapted to visible imagery as well. It is important that the algorithms be robust, with minimal dependency on user input. They should be effective when tracking varying numbers of targets of different sizes and orientations, despite the low resolutions of the images used. Most importantly, the algorithms must be appropriate for implementation on a low-power processor in real time. This would enable us to maintain frame rates of 2 Hz for effective surveillance operations. Throughout our project we have implemented several algorithms, and used an appropriate methodology to quantitatively compare their performance. They are discussed in this paper.

This paper presents the advantages of using distributed arrays with the beamspace implementation of a wideband Capon algorithm. Distributed arrays have recently demonstrated a decreased sensitivity to environmental coherence loss and array mismatches. In an attempt to build upon this robustness, beamspace preprocessing is applied in this paper. Beamspace allows sector-focused beamforming and reduced computational complexity. A unique property of this implementation of beamspace, namely being indifferent to losses of entire channels of data, has been demonstrated. This implementation of beamspace to Capon beamforming does not require beam orthogonalization, thus saving computational time, especially in the whitening process.

In this paper, four underwater source location algorithms based on energy measurement using a randomly distributed sensor array are proposed. First, closed form of least squares (LS), total least squares (TLS) and constraint least squares (CLS) formulation for single source location are derived from the definition of energy ratio between different sensor readings. Then, the maximum likelihood estimation for multiple sources is presented and the alternating projection-multiresolution (APMR) algorithm is proposed to locate multiple sources. Simulation results show that our localization algorithms achieve good performance with high computational efficiency.

Effective management of sensor collaboration is crucial to the success of any distributed unattended ground sensor (UGS) system. A successful management scheme must allow nodes to share enough information to form and maintain tracks while minimizing unnecessary or excessive collaboration. Systems developed with the traditional unidirectional or request/response models are typically susceptible to excessive collaboration in the presence of a persistent loud sound source. The work presented in this paper addresses the challenge of suppressing excessive sensor collaboration in the presence of loud targets. The Loud Target Suppression (LTS) algorithm utilizes Voronoi tessellation as a means to allow sensor nodes to autonomously determine alert regions that support track formation with neighboring nodes. By replying only with sensor measurements that fall within the alert regions, the LTS algorithm is able to significantly reduce message quantities without impacting track accuracy. This paper will demonstrate that an alert-based sensor collaboration scheme, employed by Distributed Cluster Management (DCM), greatly reduces sensor collaboration in the presence of loud targets which results in a more scalable system.

Implementation of an intelligent, automated target acquisition and tracking systems alleviates the need for operators to monitor video continuously. This system could identify situations that fatigued operators could easily miss. If an automated acquisition and tracking system plans motions to maximize a coverage metric, how does the performance of that system change when the user intervenes and manually moves the camera? How can the operator give input to the system about what is important and understand how that relates to the overall task balance between surveillance and coverage? In this paper, we address these issues by introducing a new formulation of the average linear uncovered length (ALUL) metric, specially designed for use in surveilling urban environments. This metric coordinates the often competing goals of acquiring new targets and tracking existing targets. In addition, it provides current system performance feedback to system users in terms of the system's theoretical maximum and minimum performance. We show the successful integration of the algorithm via simulation.

This paper summarizes the results of experiments in developing a method for extracting 3D information and indices of refraction from a scene by means of a pair of polarimetric passive imaging sensors. Each sensor provides Stokes vector at each sensor pixel location, from which, degree and angle of linear polarization are computed. Angle of linear polarization provides the azimuth angle of the surface normal vector. The depression angle of this surface normal vector is obtained in terms of the emitting object's index of refraction. By relating the corresponding surface normal orientation angles at each pixel from the images of the two sensors index of refraction at that pixel can be estimated.

In a sparse sensor network, the sensor detection regions are often not overlapped. The traditional instantaneous detection scheme is less effective due to the fact that targets may not be detected by any sensors at certain sampling instances. To detect the moving targets in a sparse sensor network, we have developed a new system suitable for multiple targets detection and tracking. An optimization based random field estimation method has been developed to characterize spatially distributed sensor reports without making any assumptions of their underlying statistical distributions. FBMM (Forward & Backward Mapping Mitigation) technology is developed to reduce the false detections resulted from the random field estimation. To further reduce the false detections, the refined random field is clustered using gap statistics. STLD (Spatial & Temporal Layering Discrimination) method is developed to individual clusters and true sensor detections are determined based on both spatial and temporal patterns. Simulation results have shown that our system can effectively detect multiple target tracks in a large surveillance region.

Challenges in border security may be resolved through a team of autonomous mobile robots configured as a flexible sensor array. The robots will have a prearranged formation along a section of a border, and each robot will attempt to maintain a uniform distance with its nearest neighbors. The robots will carry sensor packages which can detect a signature that is representative of a human (for instance, a thermal signature). When a robot detects an intruder, it will move away such that it attempts to maintain a constant distance from the intruder and move away from the border (i.e. into its home territory). As the robot moves away from the border, its neighbors will move away from the border to maintain a uniform distance with the moving robot and with their fixed neighbors. The pattern of motion in the team of robots can be identified, either algorithmically by a computer or by a human monitor of a display. Unique patterns are indicative of animal movement, human movement, and mass human movement. To realize such a scheme, a new control architecture must be developed. This architecture must be fault tolerant to sensor and manipulator failures, scalable in number of agents, and adaptable to different robotic base platforms (for instance, a UGV may be appropriate at the southern border and a UAV may be appropriate at the northern border). The Central Arkansas Robotics Consortium has developed an architecture, called Layered Mode Selection Logic (LMSL), which addresses all of these concerns. The overall LMSL scheme as applied to a multi-agent flexible sensor array is described in this paper.

Performance, optimal employment, and interpretation of data from acoustic and seismic sensors depend strongly and in complex ways on the environment in which they operate. Software tools for guiding non-expert users of acoustic and seismic sensors are therefore much needed. However, such tools require that many individual components be constructed and correctly connected together. These components include the source signature and directionality, representation of the atmospheric and terrain environment, calculation of the signal propagation, characterization of the sensor response, and mimicking of the data processing at the sensor. Selection of an appropriate signal propagation model is particularly important, as there are significant trade-offs between output fidelity and computation speed. Attenuation of signal energy, random fading, and (for array systems) variations in wavefront angle-of-arrival should all be considered. Characterization of the complex operational environment is often the weak link in sensor modeling: important issues for acoustic and seismic modeling activities include the temporal/spatial resolution of the atmospheric data, knowledge of the surface and subsurface terrain properties, and representation of ambient background noise and vibrations. Design of software tools that address these challenges is illustrated with two examples: a detailed target-to-sensor calculation application called the Sensor Performance Evaluator for Battlefield Environments (SPEBE) and a GIS-embedded approach called Battlefield Terrain Reasoning and Awareness (BTRA).

The Command, Control, Communication, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) On-The- Move (OTM) demonstration is an annual showcase of how innovative technologies can help modern troops increase their situational awareness (SA) in battlefield environments. To evaluate the effectiveness these new technologies have on the soldiers' abilities to gather situational information, the demonstration involves United States Army National Guard troops in realistic war game scenarios at an Army Reserve training ground. The Army Research Laboratory (ARL) was invited to participate in the event, with the objective demonstrating system-level integration of disparate technologies developed for gathering SA information in small unit combat operations. ARL provided expertise in Unattended Ground Sensing (UGS) technology, Unmanned Ground Vehicle (UGV) technology, information processing and wireless mobile ad hoc communication. The ARL C4ISR system included a system of multimodal sensors (MMS), a trip wire imager, a man-portable robotic vehicle (PackBot), and low power sensor radios for communication between an ARL system and a hosting platoon vehicle. This paper will focus on the integration effort of bringing the multiple families of sensor assets together into a working system.

This paper describes a novel algorithm for collaborative target engagement by unmanned systems (UMS) resulting in emergent behavior. We demonstrate UMS collaborative engagement using a simulation testbed model of a road, convoy vehicles traveling along the road, a squadron of unmanned aerial vehicles (UAVs), and multiple unmanned ground vehicles (UGVs) which are set to detonate when within close proximity to a convoy vehicle. No explicit artificial intelligence or swarming algorithms were used. Collision avoidance was an intrinsic phenomena. All entities acted independently throughout the simulation, but were given similar local instructions for possible courses of action (COAs) depending on current situations. Our algorithm and results are summarized in this paper.

Ad-hoc sensor networks need to create their own network after deployment. Various schemes have been suggested for sensors to create a better coverage pattern than if they are randomly deployed. A better coverage pattern translates into a geometry of having disks cover an area completely and even redundantly. In this paper, we present two coverage arrangements which turn out to be equivalent to grid lattice arrangements and analyze their efficacy.

Distributed wireless sensor networks consisting of several single sensors are becoming very popular in many important applications. The widespread proliferation of low-cost sensor nodes is plagued by several technical challenges namely resource (e.g. bandwidth and battery power) constraints, reliability and health of sensors, and more importantly computational limitations of the nodes. The computational capabilities of low-cost distributed sensor nodes can be enhanced by inexpensive sensor boards that give the nodes the ability to preprocess the captured signals for sensor-level detection, feature extraction and direction of arrival (DOA) estimation. This paper presents a custom designed sensor board that can be interfaced with typical zigbee-based motes for multichannel sensor-level data processing. The design is around FPGA supporting real-time data processing using five acoustic channels that can be used for numerous applications such as vehicle detection and tracking and acoustic transient (e.g. gunshots) localization. In this paper, the results on acoustic transient detection using our custom designed board sources will be presented.

McQ has developed a family of state of the art miniaturized low cost unattended ground sensors (UGS). The iScout^TM sensors are designed for indoor and outdoor intrusion detection and battle damage assessment. McQ has developed an enhanced version of the iScout^TM sensor that is a very flexible platform capable of performing in a variety of applications. Sensors are equipped with mesh radios and integrated seismic, acoustic, infrared, and magnetic transducers. Typical sensor sizes are similar to that of a deck of playing cards. Intended for high volume production, these are tactically useful sensors that can be manufactured in high volumes at a low cost. This paper will provide an overview of the iScout^TM sensor systems, features, and performance.

The presented paper focuses on the possibilities of technical methods designed to detect a trespasser under the ground, and in general on the possibilities of detection a trespasser behind an obstruction. The paper analyses method of detection of a trespasser that were practically verified by the author of the paper. The first part of the paper discusses the characteristics and use of piezoelectric films that could be used as a replacement for the traditional geophone for detection of underground mining operation. It also provides a block connection diagram of the measuring chain and photos of the practical implementation of the sensor. The consequent part of the paper then discusses the possibilities of detecting a trespasser based on electromagnetic waves emission by humans in the ELF - Extremely Low Frequency band. The paper is supplemented with illustrative photos and results of numeric processing of signals in the form of graphs and courses. The history of excavating and using tunnels spans long into the past. Tunnels were used not only as storage for food and war material but mainly as effective means of protection against attackers. A significant motivating factor for constructing tunnels lies in the hidden possibility of movement of people and transfer of material under the ground of a protected perimeter. At present some tunnels are used as roads for smuggling drugs, weapons, ammunition or illegal passages of people. There are even cases, not exceptional, when tunnels were excavated with the aim to rob a bank safe etc. The fact that construction of tunnels, often quite primitive ones, is not sporadic, can be continually documented not only by historical sources but often also by the daily news summary. The concurrent lack of proper technological means results in the renaissance of using tunnels for illegal purposes even at present. The presented paper focuses on the above mentioned area and points to little used physical principles of detection underground activities of trespassers.

The concept of sensor networks that can detect intrusions by hostile personnel and provide live, real time video of the intrusions to a central location has been circulated for over three decades. While there have been permanent installations of continuous surveillance monitors along small sections of the US border and such systems are routinely installed around high value facilities, these systems are not practical over large regions. The ideal sensor network would be covert, have self-contained power, be resistant to false alarms, be low cost, enable wireless data transfer, rapidly deployed and easily maintained, and require minimal personnel to operate/monitor. Unfortunately, the technical capability to produce such a sensor network has heretofore not existed. The advent of Ultra-Wideband (UWB) radiofrequency technology, digital cameras and night/day imaging technology developed during the telecom boom has changed this. By combining General Atomics' UWB communications and radar technology with commercially available micro-CCD or CMOS cameras, night illuminators, and lithium-ion batteries, an unattended sensor network capable of monitoring large (10 - 2000 km) class perimeters has been developed.

The presence of snipers in modern conflicts leads to high insecurity for the soldiers. In order to improve the soldier's protection against this threat, the French German Institute of Saint-Louis (ISL) and Rheinmetall Defence Electronics GmbH (RDE) work together under the hospice of the German MOD to develop a helmet integrated acoustic array for the detection and localization of snipers. This paper summarizes the results obtained during the collaboration between RDE and the ISL concerning the detection and the localization of the Mach and muzzle waves generated by rifle shots. It summarizes the technical choices that have been made and explains the algorithms that have been used in October 2006 in Lehnin (proving ground of the German MOD), where some measurements in an urban environment have been made. The estimation of the distance between the shooter and the arrays is made with one head equipment alone. In the first tests that have been made with the algorithms developed in ISL, more than 2000 shots have been detected and localized successfully in real-time in non-urban environment. No false alarms have been observed. This paper will present the first results that have been obtained in urban environment.

This paper compares three methods that estimate the location of an acoustic event based on measurements of its time-of-arrival (TOA) and direction-of-arrival (DOA) at a set of microphone arrays. We propose first a Least-Square (LS) estimator for source location for this combined DOA-TOA measurement model. We then look at the Maximum Likelihood (ML) estimator, comparing both estimators to the Cramer-Rao lower bound (CRB). Our third estimator is based on the Maximum A Posteriori (MAP) formulation and is designed to handle the association problem, where detections at different arrays must be matched if they correspond to a single event. Simulations show that the LS estimator performs slightly better than the ML estimator when the observation noise is not the expected one. Both methods exhibit a bias in the range estimate, which accounts for most of the square error. The MAP estimator, applied to live fire data, was accurate and successfully resolved multiple targets from outlier and multipath noise.

In this paper, we present a set of attributes that are being proposed to characterize quality of information (QoI) for sensor-enabled applications in a domain-agnostic manner. We then focus on two important of these attributes, timeliness and data reliability, which capture the quality of detection processes with respect to how fast and how accurately a detection is made. With special emphasis on transient phenomena, i.e., phenomena of limited duration, using traditional Bayesian-based hypothesis testing techniques, we investigate the detection of these phenomena and we analytically derive relationships that capture the QoI of a phenomenon detector as a function of the duration of the observed phenomena and the rate with which observations of the phenomena are collected.

Detection, localization and classification of battlefield acoustic transient events are of great importance especially for military operations in urban terrain (MOUT). Generally, there can be a wide variety of battlefield acoustic transient events such as different caliber gunshots, artillery fires, and mortar fires. The discrimination of different types of transient sources is plagued by highly non-stationary nature of these signals, which makes the extraction of representative features a challenging task. This is compounded by the variations in the environmental and operating conditions and existence of a wide range of possible interference. This paper presents new approaches for transient signal estimation and feature extraction from acoustic signatures collected by several distributed sensor nodes. A maximum likelihood (ML)-based method is developed to remove noise/interference and fading effects and restore the acoustic transient signals. The estimated transient signals are then represented using wavelets. The multi-resolution property of the wavelets allows for capturing fine details in the transient signals that can be utilized to successfully classify them. Wavelet sub-band higher order moments and energy-based features are used to characterize the transient signals. The discrimination ability of the subband features for transient signal classification has been demonstrated on several different caliber gunshots. Important findings and observations on these results are also presented.

General Sensing Systems (GSS) has developed a new seismic, unattended small size module that detects and identifies not only human footsteps but also light and heavy vehicles with near zero false alarm rates. This module has extremely low power consumption and can operate for several months using standard commercial batteries. This paper describes the design of this module that can communicate with any radio transducer or computer. We also report on the preliminary lab and field testing that was implemented in various environment conditions. We show that the new unattended, small size detection module demonstrates the same reliable performance as our previous footstep detection systems and has the added capability of detecting and identifying light and heavy vehicles.

The objective of the DARPA Network Centric Radio System¹ (NCRS) Program was to design, develop, integrate, and demonstrate the enabling communication technologies and system capabilities required to enable network centric warfare. NCRS is a First Generation Mobile, Ad Hoc Network (MANET) designed to enable ground and airborne vehicle based on-the-move and on-the-halt network centric connectivity. It demonstrated a gateway architecture that offers interoperability among various current, future, coalition and first responder communications radios, via the network, not the radio. This capability illustrated a new dimension for military communications interoperability.

This paper presents the results of implementation of a novel protocol, Self-Healing Routing (SHR) for opportunistic multi-hop wireless communication, on MicaZ sensor motes. The protocol uses broadcast communication and a prioritized transmission back-off delay scheme to empower a receiving mote to use its hop distance from the destination to decide autonomously whether to forward a packet. When severed routes are encountered, the protocol dynamically and locally re-routes packets so they traverse the surviving shortest route. We have implemented this protocol on a set of MicaZ motes as well as in the SENSE sensor network simulator and conducted field testing with different mote and network configurations. We also tested scenarios with the motes turned off (modeling permanent failures) and in simulation also temporarily off line (modeling transient failures). We compared SHR with two traditional protocols: MintRoute and AODV. The results, as shown by experimental measurement and simulations reported in the paper, demonstrate that Self-Healing Routing is an efficient fault-tolerant protocol that performs well even with spontaneous network topology changes.

A simulation environment is very useful in analyzing sensor networks, but the development of a sensor simulation environment which can scale to a very large number of elements is hard to obtain using traditional simulation systems, or customized simulation environments. The desired level of scalability and high volumes are hard to achieve in customized simulation environments. One possible approach to obtain scalable simulation is by using commercially available messaging systems. Such messaging systems, e.g. IBM WebSphere MQ system or OSMQ, are designed to operate at a very high bandwidth of message transfers rate and number of interacting message queue end-points. However, the communications abstractions offered by message queue systems are very different from the communications abstractions required by sensor networks. In this paper, we describe an approach to map the communication abstractions of sensor network simulation systems to those of underlying message queue systems. We describe how issues related to message localization, propagation delays and error rates can be effectively handled, and a highly scalable infrastructure for message simulation be deployed.

One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended period of time. This requires collecting measurements from as many sensors as possible to have a better view of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3) single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look at the open research problems in this field.