Jithin Jagannath

A hybrid MAC protocol with channel-dependent optimized scheduling for clustered underwater acoustic sensor networks

We propose a novel optimal time slot allocation scheme for clustered underwater acoustic sensor networks that leverages physical (PHY) layer information to minimize the energy consumption due to unnecessary retransmissions thereby improving network lifetime and throughput. To reduce the overhead and the computational complexity, we employ a two-phase approach where: (i) each member node takes a selfish decision on the number of time slots it needs during the next intra-cluster cycle by solving a Markov decision process (MDP), and (ii) the cluster head optimizes the scheduling decision based on the channel quality and an urgency factor. To conserve energy, we use a hybrid medium access scheme, i.e., time division multiple access (TDMA) for the intra-cluster communication phase and carrier sense multiple access with collision avoidance (CSMA/CA) for the cluster head-sink communication phase. The proposed MAC protocol is implemented and tested on a real underwater acoustic testbed using SM-75 acoustic modems by Teledyne Benthos. Simulations illustrate an improvement in network lifetime. Additionally, simulations demonstrate that the proposed scheduling scheme with urgency factor achieves a throughput increase of 28% and improves the reliability by up to 25% as compared to the scheduling scheme that neither use MDP nor optimization. Furthermore, testbed experiments show an improvement in throughput by up to 10% along with an improvement in reliability.

Multisensor Modulation Classification (MMC): Implementation Considerations -- USRP Case Study

This paper provides hardware implementation considerations for previously developed algorithms designed to improve the classification of the modulation of weak radio signals utilizing multiple sensors. The case study presented focuses on a likelihood-based approach in a centralized data fusion framework. Data sets from multiple sensors are fused to obtain a more accurate modulation classification as previously demonstrated in simulations. The algorithms are implemented on a hardware test bed that consists of the laboratory grade software defined radio platforms. The performance is examined in realistic environments and compared with results obtained via simulations. The test bed results indicate that the predicted performance improvements are difficult to achieve in practice and the algorithms need to be tailored to account for hardware features and signal propagation effects. Differences between results obtained in simulations and in hardware implementation are discussed and adjustments are made to achieve consistent improvement necessary for refinement of the solution toward military applications.

Distributed asynchronous modulation classification based on hybrid maximum likelihood approach

In this paper, we consider the problem of automatic modulation classification (AMC) with multiple sensors. A distributed hybrid maximum likelihood (HML) based algorithm in the presence of unknown time offset, phase offset and channel gain is presented. The proposed distributed algorithm that employs the generalized expectation maximization (GEM) algorithm is robust to initialization of unknown parameters, computationally efficient and require much less communication overhead compared to performing GEM in a centralized setting. Simulation and experimental results depict the efficacy of the proposed algorithm.

Framework for automatic signal classification techniques (FACT) for software defined radios

The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) for software defined radios (SDR) capable of classifying multiple signals simultaneously. The focus of this work is to create a modular classification framework to facilitate the testing and implementation of new classification methods. The framework is divided into three parts: (i) Sensor resource manager (SRM), which performs the initial signal detection, preprocessing and the delegation of secondary receivers to the corresponding signals of interest (SOIs); (ii) Modulation classifier block (MCB), which takes the received signal from SRM and performs the required modulation classification and (iv) Data library and statistical block contains all the templates required to perform classification, thresholds for signal detection and also known parameters of expected signals. To prove the feasibility of the framework, FACT is implemented and tested on a Universal Software Radio Peripheral (USRP) test bed using GNU radio signal processing toolkit. We evaluate the performance of signal detection based on the probability of detection (Pd) in varying signal-to-noise ratios (SNR). Additionally, the USRP based experiments demonstrate FACT operating as a single unit, preforming both blind detection and classification of multiple SOIs using different classification methods.

LANET: Visible-light ad hoc networks

Abstract Visible light communication (VLC) is a wireless technology complementary to well-understood radio frequency (RF) communication that is promising to help alleviate the spectrum crunch problem in overcrowded RF spectrum bands. While there has been significant advancement in recent years in understanding physical layer techniques for visible light point-to-point links, the core problem of developing efficient networking technology specialized for visible-light networks is substantially unaddressed. This article discusses the current existing techniques as well as the main challenges for the design of visible-light ad hoc networks - referred to as LANETs. The paper discusses typical architectures and application scenarios for LANETs and highlights the major differences between LANETs and traditional mobile ad hoc networks (MANETs). Enabling technologies and design principles of LANETs are analyzed and existing work is surveyed following a layered approach. Open research issues in LANET design are also discussed, including long-range visible light communication, full-duplex LANET MAC, blockage-resistant routing, VLC-friendly TCP and software-defined prototyping, among others.

COmBAT: Cross-layer Based testbed with Analysis Tool implemented using software defined radios

In this paper, we discuss the implementation of a CrOss-layer Based testbed with Analysis Tool (COmBAT). COmBAT is developed to enable the design and development process of next-generation cross-layer based wireless communication technologies for tactical ad-hoc networks. The COmBAT architecture comprises of two major components; (i) Adaptive cross-layer (AXL) framework implemented on each node in the testbed and (ii) NEtwork Analyzing Tool (NEAT) that provides a graphical interface for users to track and analyze network metrics as well as provide a single seat control over the network parameters on-the-fly. In this paper, we discuss the design and implementation of these components in detail and demonstrate its feasibility by implementing three cross-layer based algorithms on COmBAT using a nine node ad-hoc network. The results validate the modularity and adaptability of COmBAT and demonstrate how COmBAT can be used to develop as well as refine current and future cross-layer algorithms providing a feasibility study that lends itself to the transition of theoretical network research from testbed to relevant military hardware.

DRS: Distributed Deadline-Based Joint Routing and Spectrum Allocation for Tactical Ad-Hoc Networks

In this paper, we propose a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks. The proposed algorithm will enable nodes to adapt to various deadline requirements unique to each traffic classes. A tactical ad-hoc network needs to handle a variety of data flowing through the network including voice, surveillance video, threat alert among others. Each of these traffic classes may have different quality of service (QoS) based deadline requirements. It is critical to receive these packets before the deadline expires to make crucial decisions in the battlefield. Therefore, the network should be able to adapt to these requirements and maximize the effective throughput. Accordingly, a distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources to ensure delivery of packets within the deadline constraints. The simulations show up to 35 % improvement in effective throughput and 26 % improvement in reliability as compared to the routing and spectrum allocation algorithm (ROSA).

Design and evaluation of hierarchical hybrid automatic modulation classifier using software defined radios

Automatic modulation classification (AMC) is a key component of intelligent communication systems used in various military and cognitive radio applications. In AMC, it is desired to increase the number of different modulation formats that can be classified, reduce the computational complexity of classification, and improve the robustness and accuracy of the classifier. Generally, AMC techniques are classified into feature based (FB) and likelihood based (LB) classifiers. In this paper, we propose a novel hierarchical hybrid automatic modulation classifier (HH-AMC) that employs both feature based and likelihood based classifiers to improve performance and reduce complexity. As another major contribution of this paper, we implement and evaluate the performance of HH-AMC over-the-air (OTA) using software defined radios (SDRs) to demonstrate the feasibility of the proposed scheme in practice. Experimental evaluation shows high probability of correct classification (Pcc) for both linear and non-linear modulation formats including BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, CPFSK, GFSK and GMSK under lab conditions.