Publications

We address the problem of localizing an (illegal) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner. Localization of illegal transmitters is an important problem which arises in many important applications. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors’ energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations.

Arani Bhattacharya, Caitao Zhan, Himanshu Gupta, Samir R. Das, and Petar M. Djuric

Infocom 2020

We address the problem of localizing multiple intruders (unauthorized transmitters) using a distributed set of sensors in the context of a shared spectrum system. While the single transmitter localization problem has been well studied during the past two decades, multiple transmitter localization (\mtl) problem caught interest only in recent years. In shared spectrum systems, it is important to be able to localize simultaneously present multiple intruders to effectively protect a shared spectrum from malware-based, jamming, or other multi-device unauthorized usage attacks. The key challenge in solving the MTL problem comes from the need to ``separate’’ an aggregated signal received by a sensor from multiple intruders present, into separate signals from individual intruders. Furthermore, in a shared spectrum paradigm, primary transmitters, secondary transmitters, and intruders (unauthorized spectrum users) all exist together which makes the multiple transmitter localization more challenging. In this paper, we propose an efficient multiple transmitter localization algorithm based on the optimal hypothesis-based Bayesian approach. We show our method provides native support to the shared spectrum paradigm. To minimize the incurred training cost, we also design a new interpolation method to significantly reduce the training cost of our proposed approach. We evaluate our techniques on both large-scale simulations based on a terrain-based propagation model, as well on real-time indoor and outdoor testbeds. Our experiments demonstrate that our approach outperforms the best known approach by up to 100\% in large-scale simulation.

Caitao Zhan, Himanshu Gupta, Arani Bhattacharya, and Mohammad Ghaderibaneh

ACM/IEEE IPSN 2020

The RF spectrum is a natural resource in great demand. The research community has addressed this unabated increase in demand via development of shared spectrum paradigms, wherein the spectrum is made available to unlicensed users (secondaries) as long as they do not interfere with the transmission of licensed incumbents (primaries). In this paradigm, typically a centralized entity (spectrum manager) is responsible for allocation of spectrum bands to a requesting SU, based on known parameters of the primaries and channel state. Optimal power allocation is important for efficient management of spectrum. In this work, we consider a crowdsourced architecture of the shared spectrum paradigm, wherein the spectrum is monitored by a large number of inexpensive spectrum sensors deployed in the area of interest. In this context, we propose a spectrum allocation algorithm that is based on estimation of path loss from the sensing reports of these spectrum sensors. Such an architecture obviates the need to assume a propagation model, but requires design of accurate estimation techniques. We consider various possible estimation schemes involving splitting of aggregate power received at each sensor and multiple interpolation schemes. We evaluate the performance of our proposed schemes with respect to the optimal allocation, and observe that allocation using the best of our methods is close to the optimal.

Himanshu Gupta, Md. Shaifur Rahman, and Max Curran

IEEE DySPAN 2019

Radio spectrum is a limited natural resource under a significant demand and thus, must be effectively monitored and protected from unauthorized access. Recently, there has been a significant interest in the use of inexpensive commodity-grade spectrum sensors for large-scale RF spectrum monitoring. These sensors being inexpensive can be deployed at much higher density, and thus, can provide much more accurate spectrum occupancy maps or intruder detection schemes. However, these sensors being inexpensive also have limited computing resources, and being independent and distributed can suffer from clock skew (i.e., their clocks may not be sufficiently synchronized). In this paper, we are interested in the problem of detection and localization of multiple intruders present simultaneously, in the above context of distributed sensors with limited resources and clock skew. The key challenge in addressing the intruder localization problem using sensors with clock skew is that it is very difficult to even derive an observation vector over sensors, for any (absolute) instant. In this work, we propose Group-Based Algorithm, a skew-aware multiple intruders localization method that essentially works by extracting observations across sensors for certain small sets of transmitters. Our results show that Group-Based Algorithm yields significant improvement of accuracy over relatively simpler approaches.

Mohammad Ghaderibaneh, Mallesham Dasari, and Himanshu Gupta

IEEE DySPAN 2019

We use a crowdsourcing approach for RF spectrum patrolling, where heterogeneous, low-cost spectrum sensors are deployed widely and are tasked with detecting unauthorized transmissions in a collaborative fashion while consuming only a limited amount of resources. We pose this as a collaborative signal detection problem where the individual sensor’s detection performance may vary widely based on their respective hardware or software configurations, but are hard to model using traditional approaches. Still an optimal subset of sensors and their configurations must be chosen to maximize the overall detection performance subject to given resource (cost) limitations. We present the challenges of this problem in crowdsourced settings and present a set of methods to address them. The proposed methods use data-driven approaches to model individual sensors and develops mechanisms for sensor selection and fusion while accounting for their correlated nature. We present performance results using examples of commodity-based spectrum sensors and show significant improvements relative to baseline approaches.

Arani Bhattacharya, Ayon Chakraborty, Samir R. Das, Himanshu Gupta, Petar M. Djuric

IEEE Infocom 2018, expanded version in IEEE Transactions on Cognitive Communications and Networking 2019

There is a recent interest in large-scale RF spectrum monitoring using low-cost crowdsourced spectrum sensors. A major challenge here is improving the latency and energy usage of signal processing algorithms on the sensor. This improves operational cost and effectiveness and also makes the sensors more responsive to the monitoring task. We specifically consider the case of signal detection using such sensors. Typical crowdsourced implementation using a low-cost software radio connected to a Raspberry Pi or a smartphone as host is not energy-efficient and incurs significant latencies. We propose use of field-programmable gate array (FPGA) to improve both metrics for the signal detection task. Our benchmarking shows significant improvements with FPGA platforms relative to using a Raspberry Pi or smartphone, up to a factor of 73 in terms of latency and a factor of 29 in terms of energy usage.

Arani Bhattacharya, Han Chen, Peter Milder, and Samir R Das

IEEE DySPAN 2018

Spectrum sensing on mobile clients will be both necessary and feasible to manage the white space spectrum optimally in indoor spaces. We demonstrate the necessity with a set of empirical measurements showing the need for fine grained sensing. We demonstrate the feasibility by building a spectrum sensing infrastructure that collects measurements from sensing devices to analyze and better use spectrum resources. The infrastructure consists of mobile spectrum sensors that are built using DTV receiver dongles interfaced with Android-based mobile devices and a cloud-based central server to manage such sensing devices. We also show results about resource consumption (energy, network overhead) involved in operating such sensors. The vision is ultimately creating a system where mobile devices perform part-time spectrum sensing in a coordinated fashion under the control of a central spectrum manager. We lay out the research challenges based on our initial prototyping and benchmarking experience.

Ayon Chakraborty, and Samir R. Das

IEEE DCOSS 2016