Wildlife poaching is a critical driver of biodiversity loss and population decline. Poaching is a particular threat to high value, large-bodied species, such as elephants, that are slow to reproduce. Increasingly, GPS tracking collars serve as a key tool for studying the behavior and monitoring wildlife globally, including application to anti-poaching efforts. However, collars provide indirect information on poaching, such as immobility, that is often not available in real time. In parallel to collar development, acoustic gunshot detection systems have proliferated in the military and law enforcement. Static systems in wildlife areas have been deployed for detecting poaching, but such systems do not scale geographically. This paper explores the idea of fusing GPS tracking collars with acoustic shockwave detectors to create an animal-borne anti-poaching sensor. A real-time alert of gunshots near elephant groups would enable rangers to respond immediately to such events. The two main technical challenges to such a system are battery life and detection accuracy. The paper presents a prototype designed for elephants that has great promise in addressing these significant technical challenges.

Poaching is one of the primary drivers of wildlife decline [1]. Animalborne sensors, particularly GPS-equipped collars, are used to enhance real-time wildlife protection. Innovations that can be integrated into these systems can immediately scale, offering broad application. GPS tracking data streams have been valuable to resolve a number of conservation challenges, but these systems have not been particularly effective in identifying poaching in real-time. Detecting poaching events is a critical need to provide actionable information for law enforcement.

The paper presents RoboScape, a collaborative, networked robotics environment that makes key ideas in computer science accessible to groups of learners in informal learning spaces and K-12 classrooms. RoboScape is built on top of NetsBlox, an open-source, networked, visual programming environment based on Snap! that is specifically designed to introduce students to distributed computation and computer networking. RoboScape provides a twist on the state of the art of robotics learning platforms. First, a user's program controlling the robot runs in the browser and not on the robot. There is no need to download the program to the robot and hence, development and debugging become much easier. Second, the wireless communication between a student's program and the robot can be overheard by the programs of the other students. This makes cybersecurity an immediate need that students realize and can work to address. We have designed and delivered a cybersecurity summer camp to 24 students in grades between 7 and 12. The paper summarizes the technology behind RoboScape, the hands-on curriculum of the camp and the lessons learned.

This paper introduces NetsBlox, a visual programming environment for learning distributed programming principles. Extending both the visual formalism and open source code base of Snap!, NetsBlox provides two accessible distributed programming abstractions to simplify the process of creating networked applications: message passing and Remote Procedure Calls (RPC). Messaging passing allows NetsBlox applications to send data to other connected NetsBlox clients. Remote Procedure Calls enable seamless integration of third party services, such as Google Maps, weather, traffic and other public domain data sources, into NetsBlox applications. Other RPCs help coordinating distributed clients which may be difficult for novice programmers allowing the user to more quickly create captivating and sophisticated applications. These abstractions empower users to develop networked programs, including multi-player games and client-server applications. By providing networking support, NetsBlox not only allows users to learn distribute programming concepts but also makes programming more engaging by incorporating diverse services available on the web.

For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This paper presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Networked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical basis for our technique, a working prototype is developed, enabling experimental evaluation of several real-world test scenarios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magnitude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions.

One of the primary limitations to widescale adoption of many of today's high-precision location-aware applications is the cost associated with achieving the levels of accuracy necessary for safe and reliable operation. We have developed a relative localization solution enabling centimeter-scale positioning of a network of mobile nodes. The success of this research in the relative sense can be coupled with the known locations of a set of stationary nodes as well as the computational power of the cloud to open the door for an absolute localization service, whereby base stations at well-known locations can be used to transform relative coordinates into absolute locations. We will demonstrate the current progress of our work via live walking and driving experiments in which observers in an exhibition hall can issue commands to walkers and drivers in the field and see the results of these commands as determined by our positioning system in real-time.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This video demonstration presents an approach that uses GPS to derive relative location information for multiple receivers. Cooperating nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. This is achieved via creation of a new error model that incorporates GPS localization errors specific to the multiple-receiver case, development of a new, highly accurate observation model that allows for the change in range between a single satellite and two receivers to be mapped through time, and the synthesis of these two models into a novel 3D pairwise tracking algorithm that uses the local GPS node as its own reference and requires only an approximate estimate of its own absolute position to achieve centimeter-scale relative tracking precision.

Abstract We prove that it is decidable of a finite algebra whether it has a near-unanimity term operation, which settles a ten-year-old problem. As a consequence, it is decidable of a finite algebra in a congruence distributive variety whether it admits a natural duality.

This paper presents an enhanced version of a novel radio interferometric positioning technique for node localization in wireless sensor networks that provides both high accuracy and long range simultaneously. The ranging method utilizes two transmitters emitting radio signals at almost the same frequencies. The relative location is estimated by measuring the relative phase offset of the generated interference signal at two receivers. Here, we analyze how the selection of carrier frequencies affects the precision and maximum range. Furthermore, we describe how the interplay of RF multipath and ground reflections degrades the ranging accuracy. To address these problems, we introduce a technique that continuously refines the range estimates as it converges to the localization solution. Finally, we present the results of a field experiment where our prototype achieved 4~cm average localization accuracy for a quasi-random deployment of 16 COTS motes covering the area of two football fields. The maximum range measured was 170~m, four times the observed communication range. Consequently, node deployment density is no longer constrained by the localization technique, but rather by the communication range.

Experiences developing a sensor network-based acoustic shooter localization system are presented. The system is able to localize the position of a shooter and the trajectory of the projectile using observed acoustic events, such as the muzzle blast and the ballistic shockwave. The network consists of a large number of cheap sensors communicating through an ad-hoc wireless network, which enables the system to resolve multiple simultaneous acoustic sources, eliminate multipath effects, tolerate multiple sensor failures while providing good coverage and high accuracy, even in such challenging environment as urban terrain. The paper describes the hardware and software platform developed for this application and summarizes the lessons learned during the development of the system.

We present a mobile acoustic beacon based sensor node localization method. Our technique is passive in that the sensor nodes themselves do not need to generate an acoustic signal for ranging. This saves cost, power and provides stealthy operation. Furthermore, the beacon can generate much more acoustic energy than a severely resource constrained sensor node, thereby significantly increasing the range. The acoustic ranging method uses a linear frequency modulated signal that can be accurately detected by matched filtering. This provides longer range and higher accuracy than the current state-of-the-art. The localization algorithm was especially designed to work in such acoustically reverberant environment, as urban terrain. The algorithm presented handles non-Gaussian ranging errors caused by echoes. Node locations are computed centrally by solving a global non-linear optimization problem in an iterative and incremental fashion

We present a novel radio interference based sensor localization method for wireless sensor networks. The technique relies on a pair of nodes emitting radio waves simultaneously at slightly different frequencies. The carrier frequency of the composite signal is between the two frequencies, but has a very low frequency envelope. Neighboring nodes can measure the energy of the envelope signal as the signal strength. The relative phase offset of this signal measured at two receivers is a function of the distances between the four nodes involved and the carrier frequency. By making multiple measurements in an at least 8-node network, it is possible to reconstruct the relative location of the nodes in 3D. Our prototype implementation on the MICA2 platform yields an average localization error as small as 3 cm and a range of up to 160 meters. In addition to this high precision and long range, the other main advantage of the Radio Interferometric Positioning System (RIPS) is the fact that it does not require any sensors other than the radio used for wireless communication.

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems. However, the presented sensor network-based solution surpasses the traditional approach because it can mitigate acoustic multipath effects prevalent in urban areas and it can also resolve multiple simultaneous shots. These unique characteristics of the system are made possible by employing novel sensor fusion techniques that utilize the spatial and temporal diversity of multiple detections. In this article, in addition to the overall system architecture, the middleware services and the unique sensor fusion algorithms are described. An analysis of the experimental data gathered during field trials at US military facilities is also presented.

This paper presents an approach for dynamic software reconfiguration in sensor networks. Our approach utilizes explicit models of the design space of the embedded application. The design space is captured by formally modeling all the software components, their interfaces, and their composition. System requirements are expressed as formal constraints on QoS parameters that are measured at runtime. Reconfiguration is performed by transitioning from one point of the operation space to another based on the constraints. We demonstrate our approach using simulation results for a simple sensor network that performs one-dimensional tracking.

Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.

An ad-hoc wireless sensor network-based system is presented that detects and accurately locates shooters even in urban environments. The system consists of a large number of cheap sensors communicating through an ad-hoc wireless network, thus it is capable of tolerating multiple sensor failures, provides good coverage and high accuracy, and is capable of overcoming multipath effects. The performance of the proposed system is superior to that of centralized countersniper systems in such challenging environment as dense urban terrain. In this paper, in addition to the overall system architecture, the acoustic signal detection, the most important middleware services and the unique sensor fusion algorithm are also presented. The system performance is analyzed using real measurement data obtained at a US Army MOUT (Military Operations in Urban Terrain) facility.

Software production has become an industrial task usually involving teams of programmers working on complex problems to produce large, even huge software systems. Globally distributed teams are doing a growing share of all software development work. The management of software engineering teamwork, especially of a temporally and/or spatially distributed team, presents an enormous organizational challenge as well as an intricate technical problem, as such distributed teamwork requires tool support for coordination of cooperative activities, maintenance of project control, and sharing of information. Domain-specific Model Integrated Program Synthesis environments are created according to a modeling paradigm: a description of the class of models that can be created using the system. Just as model integrated computing applications are executable instances of domain models, domain models can be viewed as instances of metamodels. The representation of these models and the modeling paradigm is unique to the specific modeling environment. This poses a major problem for portability of models from one modeling environment to another. The purpose of the paper is to explore the possibility of a common standard for the storage of models, in what framework the standard should exist, and who should define the standard.