Software defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowdsourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address these needs, we design and develop a compact, portable, untethered, and inexpensive SDR we callSitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. We present results from a user study involving more than 100 participants to evaluate Sitara in a hypothetical large-scale crowdsourced spectrum monitoring application. We also present a comparative analysis of Sitara to related crowdsensing systems with a particular emphasis on the role of incentives and user participation.

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time , versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ∼99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.

We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods.

With the tremendous increase in the use of wireless devices, understanding the surrounding wireless/RF environment is becoming essential for many application areas. In this work, we develop the technical building blocks needed for a spectrum monitoring system that can incrementally learn about the signals present in a deployed environment. We achieve incremental learning (IL) by identifying and grouping the new/unknown signals and, automatically building new machine learning (ML) models for detecting them. A thorough evaluation of our approach demonstrates its adaptability and high accuracy with signal data from several over-the-air scenarios.

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.

Many networked users through their devices are interested in participating in distributed sensing and data collection for the purpose of betterment of human society or for earning rewards. Preservation of their location privacy is an important requirement for users participating and contributing to the data collection. We develop a novel privacy preserving approach for collecting noise-free data from vehicular users. Collection of noise-free, "pure" data, enhances its utility in the applications that use it. Location privacy must be preserved from the entity that we call a central controller, that collects all the vehicular data, and is assumed to be adversarial. We collect the data in a noise-free form by introducing temporal and spatial variations using Random Delays and Indirections. We run simulations using network and vehicle simulators driven by a real-world traffic scenario from the city of Luxembourg to evaluate our approach. Our simulation results show that the adversary cannot localize the uploaders within the thresholds of the number of streets and the length of the region of interest chosen by them.

The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

Fast and effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Electrically powered systems in industrial settings require monitoring of emitted electromagnetic fields to determine the status of the equipment and ensure their safe operation. In situations such as these, wireless sensor nodes (WSNs) at fixed predetermined locations provide monitoring to ensure safety. A challenging algorithmic problem is determining the locations to place these WSNs while meeting several criteria: (1) to provide complete coverage of the domain; (2) to create a topology with problem-dependent node densities; and (3) to minimize the number of WSNs. This paper presents a novel approach, advancing front mesh generation with constrained Delaunay triangulation and smoothing (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine WSN locations for areas of high interest (hospitals, schools, and high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm provides significant reduction in the number of nodes, in some cases over 40%, compared with an advancing front mesh generation algorithm; maintains and improves optimal spacing between nodes; and produces simulation run times suitable for real-time applications.

This is the first of a two-part investigation of centralized and decentralized approaches for determining the optimal configuration of a sensor network. In this first part, we present a centralized approach for the generation of mesh (wireless sensor) network configurations that provide complete sensing coverage and communication connectivity of a domain. A challenging problem in deploying wireless sensor networks is maximizing coverage in irregular shaped polygonal areas while maintaining a high degree of node connectivity. The novelties presented in this paper are: 1) a centralized mesh simplification technique, the Iterative Node Removal with Constrained Delaunay Triangulation and Smoothing (INRCDTS) algorithm, and 2) a centralized mesh generation approach with INRCDTS that may be used for any nonintersecting closed polygonal area. Additionally, we provide a comparison of two centralized mesh generation techniques. The INRCDTS was built and tested as an enhancement of two traditional mesh generation techniques: advancing front technique and Matlab partial differential equation toolbox. The INCRCDTS introduces the ability to tune the generated mesh configuration to the number of nodes and nodal spacing. The INRCDTS enhancement has proven to increase the uniformity of the mesh in an irregular shaped polygonal area relative to advancing front and MATLAB partial differential equation algorithms by 23% and 41%, respectively.

This is the second of a two-part investigation of the generation of wireless sensor network (WSN) configurations that: 1) maximize coverage of irregular shaped polygonal areas and 2) maintain a high degree of node connectivity. The first-part of the investigation presented centralized algorithms for the generation of mesh (wireless sensor) network configurations that maximize coverage and connectivity. In this second part, we present a decentralized and distributed approach using an Extended Virtual Spring Mesh (EVSM)-Adaptive Coverage Algorithm and Protocol (ACAP) algorithm. The EVSM-ACAP algorithm represents an extension of EVSM algorithm with the newly developed ACAP. ACAP provides adaptive coverage and configuration of the mesh network by dynamically adjusting the sensing range of sensor nodes. EVSM-ACAP is compared to centralized mesh generation algorithms (described in the part one of the investigation), as well as other decentralized algorithms from artificial physics, for the control of large numbers of physical agents in sensor networks. EVSM-ACAP is shown to produce a sensor network deployment with an average sensor spacing within 1.6% of the desired spacing, versus 5.75% for the best centralized algorithmic approach. To the best of our knowledge, this is the first time that these centralized mesh network configuration algorithms have been contrasted with the scalable, robust, decentralized algorithms of artificial physics and EVSM.

A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. Pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A wireless object in proximity to some area for a duration of time may pose a risk hazard to the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that a wireless object may present to the environment. The presented Matlab based application for cluster extraction is verified on a diagonal vehicle movement example.

Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neuro-fuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.