On Capacity and Optimal Scheduling for the Half-Duplex Multiple-Relay Channel

We study the half-duplex multiple-relay channel (HD-MRC) where every node can either transmit or listen but cannot do both at the same time. We obtain a capacity upper bound based on a max-flow min-cut argument and achievable transmission rates based on the decode-forward (DF) coding strategy, for both the discrete memoryless HD-MRC and the phase-fading HD-MRC. We discover that both the upper bound and the achievable rates are functions of the transmit/listen state (a description of which nodes transmit and which receive). More precisely, they are functions of the time fraction of the different states, which we term a schedule. We formulate the optimal scheduling problem to find an optimal schedule that maximizes the DF rate. The optimal scheduling problem turns out to be a maximin optimization, for which we propose an algorithmic solution. We demonstrate our approach on a four-node multiple-relay channel, obtaining closed-form solutions in certain scenarios. Furthermore, we show that for the received signal-to-noise ratio degraded phase-fading HD-MRC, the optimal scheduling problem can be simplified to a max optimization.Comment: Author's final version (to appear in IEEE Transactions on Information Theory

Ensuring Network Connectivity for Decentralized Planning in Dynamic Environments

This work addresses the issue of network connectivity for a team of heterogeneous agents operating in a dynamic environment. The Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework previously developed by the authors and their colleagues, is introduced as a methodology for complex mission planning, and extensions are proposed to address limited communication environments. In particular, CBBA with Relays leverages information available through already existing consensus phases to predict the network topology at select times and creates relay tasks to strengthen the connectivity of the network. By employing underutilized resources, the presented approach improves network connectivity without limiting the scope of the active agents, thus improving mission performance.United States. Air Force Office of Scientific Research (Grant FA9550-08-1-0086)United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (FA9550-08-1-0356

The Effect of Modern Web Content and Caching on The Tor Onion Router

This work evaluates Tor users\u27 risk of de-anonymization in the presence of a network-level adversary. We evaluate the likelihood that a Tor user, who is consuming modern web content, will be susceptible to a traffic analysis or watermarking attack. This work shows that the previously studied point-to-point model for Tor connections is not realistic and does not fully capture the risk of de-anonymization for Tor users. We show these results by measuring network paths along key parts of a Tor circuit. First, we measure the paths between the Tor exit relays and web resources requested when accessing the Alexa Top 1000 websites. Then, we use available and trusted traceroute data to approximate paths between Tor users and likely guard nodes. Then, the intersection of these paths at an autonomous system level is examined to determine if they share any elements. If the intersection of the paths is non-empty, then a Tor user making a request with those paths is susceptible to de-anonymization.Results from weighted selection of Tor exit and guard relays indicate that a Tor user visiting a random Alexa Top 1000 website is susceptible to de-anonymization with 20% probability for almost half of the Alexa Top 1000. Multiple resources account for significant additional de-anonymization risk over the point-to-point model, and shorter network paths to content distribution nodes do not effectively compensate. Moreover, examining the intersection of paths to resources in the top-level domains of a website does not full eliminate the risk of de-anonymization under the AS-Aware Tor problem

On secure system performance over SISO, MISO and MIMO-NOMA wireless networks equipped a multiple antenna based on TAS protocol

This study examined how to improve system performance by equipping multiple antennae at a base station (BS) and all terminal users/mobile devices instead of a single antenna as in previous studies. Experimental investigations based on three NOMA down-link models involved (1) a single-input-single-output (SISO) scenario in which a single antenna was equipped at a BS and for all users, (2) a multi-input-single-output (MISO) scenario in which multiple transmitter antennae were equipped at a BS and a single receiver antenna for all users and (3) a multi-input-multi-output (MIMO) scenario in which multiple transmitter antennae were equipped at a BS and multiple receiver antenna for all users. This study investigated and compared the outage probability (OP) and system throughput assuming all users were over Rayleigh fading channels. The individual scenarios also each had an eavesdropper. Secure system performance of the individual scenarios was therefore also investigated. In order to detect data from superimposed signals, successive interference cancellation (SIC) was deployed for users, taking into account perfect, imperfect and fully imperfect SICs. The results of analysis of users in these three scenarios were obtained in an approximate closed form by using the Gaussian-Chebyshev quadrature method. However, the clearly and accurately presented results obtained using Monte Carlo simulations prove and verify that the MIMO-NOMA scenario equipped with multiple antennae significantly improved system performance.Web of Science20201art. no. 1

Framework for Content Distribution over Wireless LANs

Wireless LAN (also called as Wi-Fi) is dominantly considered as the most pervasive technology for Intent access. Due to the low-cost of chipsets and support for high data rates, Wi-Fi has become a universal solution for ever-increasing application space which includes, video streaming, content delivery, emergency communication, vehicular communication and Internet-of-Things (IoT). Wireless LAN technology is defined by the IEEE 802.11 standard. The 802.11 standard has been amended several times over the last two decades, to incorporate the requirement of future applications. The 802.11 based Wi-Fi networks are infrastructure networks in which devices communicate through an access point. However, in 2010, Wi-Fi Alliance has released a specification to standardize direct communication in Wi-Fi networks. The technology is called Wi-Fi Direct. Wi-Fi Direct after 9 years of its release is still used for very basic services (connectivity, file transfer etc.), despite the potential to support a wide range of applications. The reason behind the limited inception of Wi-Fi Direct is some inherent shortcomings that limit its performance in dense networks. These include the issues related to topology design, such as non-optimal group formation, Group Owner selection problem, clustering in dense networks and coping with device mobility in dynamic networks. Furthermore, Wi-Fi networks also face challenges to meet the growing number of Wi Fi users. The next generation of Wi-Fi networks is characterized as ultra-dense networks where the topology changes frequently which directly affects the network performance. The dynamic nature of such networks challenges the operators to design and make optimum planifications. In this dissertation, we propose solutions to the aforementioned problems. We contributed to the existing Wi-Fi Direct technology by enhancing the group formation process. The proposed group formation scheme is backwards-compatible and incorporates role selection based on the device's capabilities to improve network performance. Optimum clustering scheme using mixed integer programming is proposed to design efficient topologies in fixed dense networks, which improves network throughput and reduces packet loss ratio. A novel architecture using Unmanned Aeriel Vehicles (UAVs) in Wi-Fi Direct networks is proposed for dynamic networks. In ultra-dense, highly dynamic topologies, we propose cognitive networks using machine-learning algorithms to predict the network changes ahead of time and self-configuring the network