Emerging smart services, such as indoor smart parking or patient monitoring and tracking in hospitals, incur a significant technical roadblock stemming primarily from a lack of cost-effective and easily deployable localization framework that impedes their widespread deployment. To address this concern, in this paper we present a low-cost, indoor localization and navigation system, which performs continuous and real-time processing of Bluetooth Low Energy (BLE) and IEEE 802.15.4a compliant Ultra-wideband (UWB) sensor data to localize and navigate the concerned entity to its desired location. Our approach depends upon fusing the two feature sets, using the UWB to calibrate the BLE localization mechanism.

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.

In this paper we propose a distributed, low-power, self-organizing MAC scheme for low-power wireless sensor and control applications. In such applications, nearly periodic traffic needs to be handled so that the communication delays are minimized and the transmission is as predictable as possible. We propose Asynchronous Random Schedules with Collision Forecast (ARS/CF) for this purpose, together with a multichannel extension based on an improved modulation scheme to improve capacity scaling of the basic ARS/CF scheme. We analyze and simulate the basic single-hop ARS/CF implementation with regard to throughput and delay when used both in single- and multi-channel settings. Finally, we consider the implementation of ARS/CF in a multi-hop setting.

The paper presents a smartphone-based shooter localization system. As muzzle blasts are difficult to detect at longer distances and consequently present higher false detection rates, the system relies on shockwaves only. Each sensor uses four microphones to detect the Angle of Arrival and the length of the shockwave. This information, along with the sensor's own GPS coordinates, are shared among nearby smartphones. Assuming a known weapon type, it then proceeds to estimate the two possible projectile trajectory candidates for each sensor that are consistent with the observations in the horizontal plane of the sensors. A simple clustering algorithm identifies the correct projectile trajectory relying on as few as two sensors. The trajectory is then used to estimate the bearing to the shooter relative to each sensor. The paper presents the overall system architecture, the design of the sensor node that interfaces with the smartphone, the trajectory and bearing estimation algorithms, and the evaluation of the system based on a field experiment.

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.

Molecular dynamics simulators are indispensable tools in the arsenal of chemical engineers and material scientists. However, they are often difficult to use and require programming skills as well as deep knowledge of both the given scientific domain and the simulation software itself. In this paper, we describe a metaprogramming approach where simulator experts can create a library of simulation components and templates of frequently used simulations. Domain experts, in turn, can build and customize their own simulations and the required input for the various supported simulators is automatically synthesized. The web-based environment also supports setting up a suite of simulation jobs, for example, to carry out automated parameter optimization, via a visual programming environment. The entire simulation setup – including the various parameters, the version of tools utilized and the results – is stored in a database to support searching and browsing of existing simulation outputs and facilitating the reproducibility of scientific results.

Over the past decade, wireless sensor networks have advanced in terms of hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been growing interest in mobile wireless sensor networks, and several small-profile sensing devices that are able to control their own movement have already been developed. Unfortunately, resource constraints inhibit the use of traditional navigation methods, because these typically require bulky, expensive, and sophisticated sensors, substantial memory and processor allocation, and a generous power supply. Therefore, alternative navigation techniques are required. In this paper we present TripNav, a localization and navigation system that is implemented entirely on resource-constrained wireless sensor nodes. Localization is realized using radio interferometric angle of arrival estimation, in which bearings to a mobile node from a small number of infrastructure nodes are estimated based on the observed phase differences of an RF interference signal. The position of the mobile node is then determined using triangulation. A digital compass is also employed to keep the mobile node from deviating from the desired trajectory. We demonstrate using a real-world implementation that a resource-constrained mobile sensor node can accurately perform waypoint navigation with an average position error of 0.95 m.

Acoustic shooter localization systems are being rapidly deployed in the field. However, these are standalone systems---either wearable or vehicle-mounted---that do not have networking capability even though the advantages of widely distributed sensing for locating shooters have been demonstrated before. The reason for this is that certain disadvantages of wireless network-based prototypes made them impractical for the military. The system that utilized stationary single-channel sensors required many sensor nodes, while the multi-channel wearable version needed to track the absolute self-orientation of the nodes continuously, a notoriously hard task. This paper presents an approach that overcomes the shortcomings of past approaches. Specifically, the technique requires as few as five single-channel wireless sensors to provide accurate shooter localization and projectile trajectory estimation. Caliber estimation and weapon classification are also supported. In addition, a single node alone can provide reliable miss distance and range estimates based on a single shot as long as a reasonable assumption holds. The main contribution of the work and the focus of this paper is the novel sensor fusion technique that works well with a limited number of observations. The technique is thoroughly evaluated using an extensive shot library.

Mobile wireless sensors require position updates for tracking and navigation. We present a localization technique that uses the Doppler shift in radio transmission frequency observed by stationary sensors. We consider two scenarios. In the first, the mobile node is carried by a person. In the second, the mobile node controls a robot. In both approaches the mobile node transmits an RF signal, and infrastructure nodes measure the Doppler-shifted frequency. Such measurements enable us to calculate the position and velocity of the mobile transmitter. Our experimental results demonstrate that this technique is viable and accurate for resource-constrained mobile sensor tracking and navigation.

Location-awareness is an important requirement for many mobile wireless applications today. When GPS is not applicable because of the required precision and/or the resource constraints on the hardware platform, radio interferometric ranging may offer an alternative. In this paper, we present a technique that enables the precise tracking of multiple wireless nodes simultaneously. It relies on multiple infrastructure nodes deployed at known locations measuring the position of tracked mobile nodes using radio interferometry. In addition to location information, the approach also provides node velocity estimates by measuring the Doppler shift of the interference signal. The performance of the technique is evaluated using a prototype implementation on mote-class wireless sensor nodes. Finally, a possible application scenario of dirty bomb detection in a football stadium is briefly described.

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

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.

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.