Modified UWB Spatio-Temporal Channel Simulation Including Pulse Distortion and Frequency Dependence

A modified simulation of ultra-wideband (UWB) multipath channels, combined with cluster classification and physics based pulse distortion mechanisms, is proposed in this letter. Spatiotemporal characteristics of multipath clusters are specifically generated based on 3 x 3 planar array systems with regard to scenario types and are simulated over ten frequency subbands (2–11 GHz). Thus, frequency-dependent characteristics of the propagation channels are also investigated and compared between each scenario. Finally, the probability of the bit-error rate is determined to quantify distortion effects on UWB multipath channels for all frequency subbands.</p

Mitigating the Effect of Free-Riders in BitTorrent using Trusted Agents

Even though Peer-to-Peer (P2P) systems present a cost-effective and scalable solution to content distribution, most entertainment, media and software, content providers continue to rely on expensive, centralized solutions such as Content Delivery Networks. One of the main reasons is that the current P2P systems cannot guarantee reasonable performance as they depend on the willingness of users to contribute bandwidth. Moreover, even systems like BitTorrent, which employ a tit-for-tat protocol to encourage fair bandwidth exchange between users, are prone to free-riding (i.e. peers that do not upload). Our experiments on PlanetLab extend previous research (e.g. LargeViewExploit, BitTyrant) demonstrating that such selfish behavior can seriously degrade the performance of regular users in many more scenarios beyond simple free-riding: we observed an overhead of up to 430% for 80% of free-riding identities easily generated by a small set of selfish users. To mitigate the effects of selfish users, we propose a new P2P architecture that classifies peers with the help of a small number of {\em trusted nodes} that we call Trusted Auditors (TAs). TAs participate in P2P download like regular clients and detect free-riding identities by observing their neighbors' behavior. Using TAs, we can separate compliant users into a separate service pool resulting in better performance. Furthermore, we show that TAs are more effective ensuring the performance of the system than a mere increase in bandwidth capacity: for 80\% of free-riding identities a single-TA system has a 6\% download time overhead while without the TA and three times the bandwidth capacity we measure a 100\% overhead

SDN-Sim: Integrating System Level Simulator with Software Defined Network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. With the introduction of diverse technology paradigms in next-generation cellular and vehicular networks, design and structural complexity are skyrocketing. The beyond- 5G use cases such as mobile broadband, 5G-V2X and UAV communications require support for ultra-low latency and high throughput and reliability with limited operational complexity and cost. These use cases are being explored in 3GPP Release 16 and 17. To facilitate end-to-end performance evaluation for these applications, we propose SDN-Sim - an integration of a System Level Simulator (SLS) with a Software Defined Network (SDN) infrastructure. While the SLS models the communication channel and evaluates system performance on the physical and data link layers, the SDN performs network and application tasks such as routing, load balancing, etc. The proposed architecture replicates the SLS-defined topology into an SDN emulator for offloading control operations. It uses link and node information calculated by the SLS to compute routes in SDN and feeds the results back to the SLS. Along with the architecture, data modeling and processing, replication and route calculation frameworks are proposed

Positioning as Service for 5G IoT Networks

Big Data and Artificial Intelligence are new tech- nologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms

A Case for P2P Delivery of Paid Content

P2P file sharing provides a powerful content distribution model by leveraging users' computing and bandwidth resources. However, companies have been reluctant to rely on P2P systems for paid content distribution due to their inability to limit the exploitation of these systems for free file sharing. We present TP2, a system that combines the more cost-effective and scalable distribution capabilities of P2P systems with a level of trust and control over content distribution similar to direct download content delivery networks. TP2 uses two key mechanisms that can be layered on top of existing P2P systems. First, it provides strong authentication to prevent free file sharing in the system. Second, it introduces a new notion of trusted auditors to detect and limit malicious attempts to gain information about participants in the system to facilitate additional out-of-band free file sharing. We analyze TP2 by modeling it as a novel game between malicious users who try to form free file sharing clusters and trusted auditors who curb the growth of such clusters. Our analysis shows that a small fraction of trusted auditors is sufficient to protect the P2P system against unauthorized file sharing. Using a simple economic model, we further show that TP2 provides a more cost-effective content distribution solution, resulting in higher profits for a content provider even in the presence of a large percentage of malicious users. Finally, we implemented TP2 on top of BitTorrent and use PlanetLab to show that our system can provide trusted P2P file sharing with negligible performance overhead

Can P2P Replace Direct Download for Content Distribution

While peer-to-peer (P2P) file-sharing is a powerful and cost-effective content distribution model, most paid-for digital-content providers (CPs) rely on direct download to deliver their content. CPs such as Apple iTunes that command a large base of paying users are hesitant to use a P2P model that could easily degrade their user base into yet another free file-sharing community. We present TP2, a system that makes P2P file sharing a viable delivery mechanism for paid digital content by providing the same security properties as the currently used direct-download model.} introduces the novel notion of trusted auditors (TAs) -- P2P peers that are controlled by the system operator. TAs monitor the behavior of other peers and help detect and prevent formation of illegal file-sharing clusters among the CP's user base. TAs both complement and exploit the strong authentication and authorization mechanisms that are used in TP2 to control access to content. It is important to note that TP2 does not attempt to solve the out-of-band file-sharing or DRM problems, which also exist in the direct-download systems currently in use. We analyze TP2 by modeling it as a novel game between misbehaving users who try to form unauthorized file-sharing clusters and TAs who curb the growth of such clusters. Our analysis shows that a small fraction of TAs is sufficient to protect the P2P system against unauthorized file sharing. In a system with as many as 60\% of misbehaving users, even a small fraction of TAs can detect 99\% of unauthorized cluster formation. We developed a simple economic model to show that even with such a large fraction of malicious nodes, TP2 can improve CP's profits (which could translate to user savings) by 62 to 122\%, even while assuming conservative estimates of content and bandwidth costs. We implemented TP2 as a layer on top of BitTorrent and demonstrated experimentally using PlanetLab that our system provides trusted P2P file sharing with negligible performance overhead

Localising social network users and profiling their movement

© 2018 Elsevier Ltd Open-source intelligence (OSINT) is intelligence collected from publicly available sources to meet specific intelligence requirements. This paper proposes a new method to localise and profile the movement of social network users through OSINT and machine learning techniques. Analysis of obtained OSINT social networks posts data from targeted users, suggests that it is possible to extract information such as their approximate location, leading also to the profiling of their movement, without using any supported Global Navigation Satellite System functionality which may be passed to the social network through a capable smart device. The ability to profile a target's movement activity could allow anyone to track a social network user or predict his or her future location. Moreover, in this work, we also demonstrate that information from social networks can be extracted relatively in real time, thus targeted users are prone to lose any sense of physical privacy