Natural language processing (NLP) applications face challenges in understanding and processing region or industry specific language. In real-world scenarios, conversational language used in blogs and social media platforms often contains slang and non-standard words (SNSW). These unconventional terms are not typically present in curated datasets used to train NLP models. As a consequence, the performance of these models is negatively affected when they encounter such real-world linguistic variations. In this paper, we introduce SNSW-DB, an automatically curated, regularly updated, large-scale lexical database from crowdsourced dictionaries. The database contains SNSW terms, example sentences, and their standard English synonyms generated by our synonym generator module. We also propose a transformer-based translation system that converts articles with SNSW to standard English. Translated documents enhance readability and utility for human readers, and can improve the performance of downstream NLP tasks.

Unmanned aerial vehicles (UAVs) are an emerging technology that can be effectively utilized to perform data collection tasks in the Internet of Things (IoT) networks. However, both the UAV and the sensors in these networks are energy-limited devices, necessitating an energy-efficient data collection procedure to ensure network lifetime. In this paper, we consider a UAV-assisted network, where a UAV flies to the ground sensors according to a predetermined schedule and controls the sensor’s transmit power when hovering above the sensor. Our goal is to minimize the total energy consumption of the UAV and the sensors, which is needed to accomplish the data collection mission. We formulate this problem into two sub-problems of UAV navigation and sensor power control and model each part as a finite-horizon Markov Decision Process (MDP). We deploy the deep deterministic policy gradient (DDPG) method to generate the best trajectory for the UAV in an obstacle-constrained environment and to control the sensor’s transmit power during data collection. Our simulations show that the UAV can find a safe and energy-efficient path for each trip. In addition, continuous sensor power control achieves better performance against the fixed-power and fixed-rate approaches in terms of the total energy consumption during data collection.

Self-sovereign identity is the next evolution of identity management models. This survey takes a journey through the origin of identity, defining digital identity and progressive iterations of digital identity models leading up to self-sovereign identity. It then states the relevant research initiatives, platforms, projects, and regulatory frameworks, as well as the building blocks including decentralized identifiers, verifiable credentials, distributed ledger, and various privacy engineering protocols. Finally, the survey provides an overview of the key challenges and research opportunities around self-sovereign identity.

We present the findings of our discovery of a novel Trojan malware on Facebook named Magnet. The new malware exploited the photo tagging feature of Facebook to reach a wider pool of victims, enabling it to spread faster than any existing Trojan malware. We provide in-depth technical details of the malware, its spreading mechanism and simulation results showing that Magnet can spread faster than traditional Trojan malware.

Data centralization and the growing rate of security breaches and identity fraud have led us to seek privacy-preserving and decentralized identity management solutions. The success of decentralized models, such as Self-Sovereign Identity, hinges on the positive-sum combination of usability and security. To support this goal, we propose a decentralized system capable of performing key management operations including key generation, key backup, and key recovery. In addition we propose our preliminary solution for a decentralized identity verification protocol. To this end, we design a digital wallet that relies on Shamir's Secret sharing scheme and blockchain technology, and we present a number of the security parameters affecting our model.

The short messaging service (SMS) is one of the most popular and also most affordable telecommunication services. The popularity and affordability of SMS, however, have made it an ideal target for spamming. Spam is a major nuisance to mobile subscribers, but can also lead to security breaches or criminal activities. In this paper, we propose a novel lightweight deep neural model called Lightweight Gated Recurrent Unit (LGRU) for SMS spam detection. In addition, we incorporate enhancing semantics retrieved from external knowledge (WordNet) to augment the understanding of SMS text inputs for better classification. We compare the performance of our proposed model with that of more than 30 SMS spam classifiers that use various conventional machine learning and deep learning techniques. Experimental results show that our model outperforms these existing classifiers in terms of precision, recall and accuracy. In addition, our model requires fewer training parameters and incurs significantly less training time than state-of-the-art deep learning based classifiers.

This paper characterizes the meta distribution of the downlink signal-to-interference ratio (SIR) attained at a typical Internet-of-Things (IoT) device in a dual-hop IoT network. The IoT device associates with either a serving macro base station (MBS) for direct transmissions or associates with a decode and forward (DF) relay for dual-hop transmissions, depending on the biased received signal power criterion. In contrast to the conventional success probability, the meta distribution is the distribution of the conditional success probability (CSP), which is conditioned on the locations of the wireless transmitters. The meta distribution is a fine-grained performance metric that captures important network performance metrics such as the coverage probability and the mean local delay as its special cases. Specifically, we derive the moments of the CSP in order to calculate analytic expressions for the meta distribution. Further, we derive mathematical expressions for special cases such as the mean local delay, variance of the CSP, and success probability of a typical IoT device and typical relay with different offloading biases. We take in consideration in our analysis the association probabilities of IoT devices. Finally, we investigate the impact of increasing the relay density on the mean local delay using numerical results.

We study the fundamental problem of spectrum allocation and device association in uplink two-hop Internet of Things (IoT) networks under two spectrum allocation schemes: 1) orthogonal spectrum partition (OSP) and 2) full spectrum reuse (FSR). We propose a novel analytical model to estimate the uplink data rate utility function, which takes into account power control fractional and spatial density of aggregators. We then compute the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme) using constraint gradient ascent optimization. Using the above obtained optimal values and the proposed model, we compare the performance of the optimized OSP and FSR schemes with the benchmark maximum-SIR-based association scheme and the minimum-distance association scheme in terms of the cumulative distribution function of device uplink data rate. By optimizing key network parameters, namely the spectrum partition ratio and aggregator association bias, we mitigate interference and enhance the mean uplink per-device data rate for both FSR and OSP. To the best of our knowledge, this paper is the first that proposes an analytical model to estimate the log utility of the uplink data rate of two-hop IoT networks.

Abstract Recent advances of the Internet of Things (IoT) technologies have enhanced the use of radio‐frequency identification‐based tracking system to be widely deployed in supply chain management covering every step involved in the flow of merchandise from the supplier to the customer to ensure a trustworthy delivery environment. Such authentication system (also known as path authentication) not only guarantees the merchandise to be available in the right destination with no discrepancies and errors but also ensures the route of the merchandise progress to be valid. This paper outlines the current state‐of‐the‐art cryptographic solutions for path authentication, highlights their properties and weakness, and proposes a novel, privacy‐preserving, and efficient solution. Compared with the existing elliptic curve ElGamal re‐encryption–based solution, our homomorphic message authentication code on arithmetic circuit–based solution offers less memory storage (with limited scalability) and no computational requirement on the reader. Moreover, we allow computational ability inside the tag that articulates a new privacy direction to the state‐of‐the‐art path privacy. This privacy notion helps support the confidentiality of the tag movement in the context of IoT‐enabled cross‐organizational tracking environment where the stakeholders can be from different organizations associated together with the merchandise being delivered. As a potential extension to the path authentication protocol, we further propose a polynomial‐based mutual authentication as a security extension and batch initialization as an efficiency extension. Besides our brief security and privacy analysis, our evaluation shows that the proposed solution can significantly reduce memory requirements on tags with marginal computational overhead to ensure transmission path confidentiality. We observe that SupAUTH requires maximum 513‐bit tag memory and 57.3 ms of processing time during evaluation, which is not only practical but also suitable for any suitable low‐cost radio‐frequency identification deployment in IoT.

The unrelenting increase in the population of mobile users and their traffic demands drive cellular network operators to densify their network infrastructure. Network densification shrinks the footprint of base stations (BSs) and reduces the number of users associated with each BS, leading to an improved spatial frequency reuse and spectral efficiency, and thus, higher network capacity. However, the densification gain comes at the expense of higher handover rates and network control overhead. Hence, user's mobility can diminish or even nullifies the foreseen densification gain. In this context, splitting the control plane (C-plane) and user plane (U-plane) is proposed as a potential solution to harvest densification gain with reduced cost in terms of handover rate and network control overhead. In this paper, we use stochastic geometry to develop a tractable mobility-aware model for a two-tier downlink cellular network with ultradense small cells and C-plane/U-plane split architecture. The developed model is then used to quantify the effect of mobility on the foreseen densification gain with and without C-plane/ U-plane split. To this end, we shed light on the handover problem in dense cellular environments, show scenarios where the network fails to support certain mobility profiles, and obtain network design insights.

The development of Web bots capable of exhibiting human-like browsing behavior has long been the goal of practitioners on both side of security spectrum - malicious hackers as well as security defenders. For malicious hackers such bots are an effective vehicle for bypassing various layers of system/network protection or for obstructing the operation of Intrusion Detection Systems (IDSs). For security defenders, the use of human-like behaving bots is shown to be of great importance in the process of system/network provisioning and testing. In the past, there have been many attempts at developing accurate models of human-like browsing behavior. However, most of these attempts/models suffer from one of following drawbacks: they either require that some previous history of actual human browsing on the target web-site be available (which often is not the case), or, they assume that 'think times' and 'page popularities' follow the well-known Poisson and Zipf distribution (an old hypothesis that does not hold well in the modern-day WWW). To our knowledge, our work is the first attempt at developing a model of human-like browsing behavior that requires no prior knowledge or assumption about human behavior on the target site. The model is founded on a more general theory that defines human behavior as an 'interest-driven' process. The preliminary simulation results are very encouraging - web bots built using our model are capable of mimicking real human browsing behavior 1000-fold better compared to bots that deploy random crawling strategy.

The unrelenting increase in the mobile users' populations and traffic demand drive cellular network operators to densify their infrastructure. Network densification increases the spatial frequency reuse efficiency while maintaining the signal-to-interference-plus-noise-ratio (SINR) performance, hence, increases the spatial spectral efficiency and improves the overall network performance. However, control signaling in such dense networks consumes considerable bandwidth and limits the densification gain. Radio access network (RAN) virtualization via control plane (C-plane) and user plane (U-plane) splitting has been recently proposed to lighten the control signaling burden and improve the network throughput. In this paper, we present a tractable analytical model for virtualized downlink cellular networks, using tools from stochastic geometry. We then apply the developed modeling framework to obtain design insights for virtualized RANs and quantify associated performance improvement.

With ever-rising popularity of social media, some sort of on-the-fly experiences are being learned and obtained by daily users. Despite a wide spectrum of research development efforts, how to facilitate knowledge discovery from an open environment and provide corresponding services remains an open challenge. In this special issue, we aim to foster the dissemination of high-quality research in methods, theories and techniques concerning the knowledge discovery from the next generation social media. Original and research articles include all aspects of theoretical studies, practical applications and experimental prototypes.

We present analytical models and simulation results that characterize the impacts of the following factors on the propagation of cross-site scripting (XSS) worms in online social networks (OSNs): 1) user behaviors, namely, the probability of visiting a friend's profile versus a stranger's; 2) the highly clustered structure of communities; and 3) community sizes. Our analyses and simulation results show that the clustered structure of a community and users' tendency to visit their friends more often than strangers help slow down the propagation of XSS worms in OSNs. We then present a study of selective monitoring schemes that are more resource efficient than the exhaustive checking approach used by the Facebook detection system which monitors every possible read and write operation of every user in the network. The studied selective monitoring schemes take advantage of the characteristics of OSNs such as the highly clustered structure and short average distance to select only a subset of strategically placed users to monitor, thus minimizing resource usage while maximizing the monitoring coverage. We present simulation results to show the effectiveness of the studied selective monitoring schemes for XSS worm detection.

Systems with multiple channels and multiple radios per node have been shown to enhance throughput of wireless mesh networks (WMNs). Recently, network coding has also been proved to be a promising technique for improving network throughput of WMNs. However, the performance of network coding in the context of multicast in multi-channel multi-radio (MCMR) WMNs is still unknown. In this paper, we present a comprehensive performance evaluation of network-coded multicast in MCMR WMNs using extensive simulations, realistic network settings and meaningful performance metrics such as throughput, file completion time, packet end-to-end delay, and packet delivery ratio under a wide range of scenarios.

Systems with multiple channels and multiple radios per node have been shown to enhance throughput of wireless mesh networks (WMNs). Recently, network coding has also been proved to be a promising technique for improving network throughput of WMNs. However, the performance of network coding in the context of multicast, a form of one-to-many communication, in multi-channel multi-radio (MCMR) WMNs is still unknown. In this paper, we present analytical models for estimating the average end-to-end delay, throughput and packet delivery ratio of a network-coded multicast session in 802.11-based MCMR WMNs. The proposed models are then validated using numerical analysis and simulations. To the best of our knowledge, our work is the first that studies the performance of network-coded multicast in MCMR WMNs.

Wireless sensor networks (WSNs) are being deployed widely thanks to recent advances in wireless communication technologies. Many WSNs may form in hostile environments, especially in military applications. Sensor nodes are thus prone to different types of attacks such as jamming, collision attacks, a

We propose new authentication protocols that support fast hand-off for real-time applications such as voice over IP and audio/video conferencing in wireless mesh networks (WMNs). A client and a mesh access point (MAP) mutually authenticate each other using one-hop communications. The central authentication server is not involved during the handover process. Fast authentication for roaming from one MAP to another is supported by using tickets. Our performance analysis, simulation results and security analysis show that our proposed authentication protocols are efficient and resilient to various kinds of attacks.

Traditional multicast routing algorithms such as shortest path tree (SPT) and Steiner tree (MST) do not consider the wireless broadcast advantage or the underlying channel assignments in a multi-channel multi-radio (MCMR) wireless mesh network (WMN). We propose multicast routing algorithms that take into account the above factors in order to minimize the amount of network bandwidth consumed by a routing tree. Experimental results show that routing trees constructed by the proposed algorithms outperform traditional trees such as SPTs, MSTs and minimum number of forwarders trees (MFTs) with respect to packet delivery ratio, throughput and end-to-end delay.

Traditional multicast routing algorithms such as shortest path tree (SPT) and minimum Steiner tree (MST) do not consider the wireless broadcast advantage or the underlying channel assignments in a multi-channel multi-radio (MCMR) wireless mesh network (WMN). We propose a multicast routing algorithm for MCMR WMNs that takes into account the above factors in order to minimize the amount of network bandwidth consumed by a routing tree. Experimental results show that routing trees constructed by the proposed algorithm outperform traditional trees such as SPTs, MSTs and minimum number of forwarders trees (MFTs) with respect to packet delivery ratio, throughput and end-to-end delay.

Abstract One of the most effective approaches to enhance the throughput capacity of wireless mesh networks (WMN) is to use systems with multiple channels and multiple radios per node. Multi‐channel multi‐radio (MCMR) networks require efficientchannel assignment(CA) algorithms to determine which channel a link should use for data transmission in order to maximize network throughput. The problem of CA has been studied extensively for unicast communications, but addressed only recently for multicast. We propose a CA algorithm named Minimum interference Multi‐channel Multi‐radio Multicast (M4) that minimizes interference among nodes in a multicast routing tree and uses both orthogonal and overlapping channels such as those in IEEE 802.11b/g systems. Simulation results show that M4 outperforms the Multi Channel Multicast algorithm proposed. in various scenarios with respect to average packet delivery ratio, throughput and end‐to‐end delay. Copyright © 2008 John Wiley & Sons, Ltd.

Group key management (GKM) refers to the actions taken to up-date and distribute the group key upon members joining and leaving a multicast group. Although there exist several GKM schemes, they are not readily applicable to wireless mesh networks (WMNs) due to many differences between WMNs and wireline networks. We present a review of existing GKM protocols and identify their applicability to WMNs. Based on the review, we propose a framework for scalable and efficient GKM in WMNs, and a GKM protocol named CCoKA (Centralized COntributory Key Agreement) for use in a WMN. CCoKA is based on the scalable and efficient key tree approach and the well-known Diffie-Hellman cryptographic protocol. The proposed framework and CCoKA protocol take into account the characteristics of mesh network operations, wireless routers and mobile devices. We also suggest directions for future research on GKM in WMNs.

We propose a rate-adaptive multicast (RAM) routing protocol for mobile ad-hoc networks (MANETs) that is multirate-aware. During the process of path discovery, the quality of wireless links is estimated to suggest optimal transmission rates, which are then used to calculate the total transmission time incurred by the mobile nodes on a path. Among several considered paths from a source to a destination, RAM selects the path with the lowest total transmission time. Simulation results have shown that RAM outperforms single-rate multicast in terms of packet delivery ratio, packet end-to-end delay, and throughput of the multicast group. In this paper, we apply RAM to effectively support video multicast in MANETs

The paper presents a video multicasting architecture for mobile ad-hoc networks (MANETs). The proposed framework uses the scalable, noncausal predictive codec with vector quantization and conditional replenishment (SNP/VQR) for encoding video into multiple layers of data streams at bit rates between 10 Kbps to 500 Kbps. A rate adaptive multicast (RAM) routing algorithm is proposed, which dynamically adjusts the transmission rate based on the channel quality to obtain the optimum throughput. The scalability of SNP/VQR, coupled with RAM, enables strong resilience against bandwidth fluctuations as well as the capability to support a range of heterogeneous mobile receivers. Experimental results demonstrate that uninterrupted video of reasonable quality is multicasted with the proposed architecture even under heavy traffic conditions

We propose a real-time video transmission scheme, referred to as SNP/VQR (scalable non-causal predictive codec with vector quantization and conditional replenishment), which is capable of providing spatial and temporal scalabilities. SNP/VQR eliminates error propagation by combining bandwidth adaptability with error concealment. A software implementation of SNP/VQR is tested over a wireless network with 5% to 20% of simulated packet losses. SNP/VQR produces a relatively constant visual quality in our packet loss studies.

A current trend in wireless communications is to enable wireless devices to transmit at different rates. That multirate capability has been defined in many standards such as 802.11a, 802.11b, 802.11g, and HiperLAN2. We propose a rate-adaptive multicast (RAM) protocol that is multirate-aware. During the process of path discovery, the quality of wireless links is estimated to suggest optimal transmission rates, which are then used to calculate the total transmission time incurred by the mobile nodes on a path. Among several considered paths from a source to a destination, RAM selects the path with the lowest total transmission time. Our work is the first that proposes the use of the multirate capability in multicast. The proposed RAM protocol works with any multirate standards, and does not require any modifications to the standards. Our simulation results show that RAM outperforms single-rate multicast in terms of packet delivery ratio, packet end-to-end delay, and throughput of the multicast group.

In this paper, we first present a simulation-based evaluation of existing multipoint-to-point (mp-p) flow control schemes for ABR (available bit rate) services in ATM (asynchronous transfer mode) networks. The evaluation shows that these schemes suffer from one or more of the following problems: non-compliance with the selected bandwidth allocation definition (BAD), link under-utilization, and prolonged rate fluctuations. Moreover, most of them implement only one BAD, namely source-based allocation. We then propose an mpp flow control algorithm, the WSB (weighted-source-based) algorithm that is effective and supports a large set of BADs. Experimental results are presented to demonstrate the advantages of the WSB algorithm: correct rate allocations with fast convergence, maximum link utilization, and minimal rate oscillations.