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Estimating the population of defects is a key reference for completion evaluation in crowdsourced testing. Current studies commonly use the biological capture–recapture model (CRC model) to estimate the population of defects in crowdsourced tests with good results. However, the lack of consideration of false negatives for defects in existing studies leads to imprecise estimation of completion in crowdsourced testing, which in turn influences decision support during crowdsourced testing. This study analyzes the basic assumptions of the CRC model in biology and maps them to the field of crowdsourced testing, improves the Lincoln–Peterson estimator and sample coverage estimator utilizing false negative assumptions, and compares them with original estimators in 29 real crowdsourced testing projects. The experimental results show that our improved estimators can effectively reduce the estimation relative error of the population of defects caused by false negatives. [ABSTRACT FROM AUTHOR]

Resources allocation (RA) is a challenging task in many fields and applications including communications and computer networks. The conventional solutions of such problems usually come with a time and memory cost, especially for massive networks such as Internet-of-Things (IoT) networks. In this paper, two RA deep network models are proposed for enabling a clustered underlay IoT deployment, where a group of IoT nodes are uploading information to a centralized gateway in their vicinity by reusing the communication channels of conventional cellular users. The RA problem is formulated as a two-dimensional matching problem, which can be expressed as a traditional linear sum assignment problem (LSAP). The two proposed models are based on the recurrent neural network (RNN). Specifically, we investigate the performance of two long short-term memory (LSTM) based architectures. The results show that the proposed techniques could be used as replacement of the well-known Hungarian algorithm for solving LSAPs due to its ability to find the solution for the problems with different sizes, high accuracy, and very fast execution time. Additionally, the results show that the obtained accuracy outperforms the state-of-the-art deep network techniques. [ABSTRACT FROM AUTHOR]

Future Internet is expected to meet explosive traffic growth and extremely complex architecture, which tend to make the traditional NTC strategies inefficient and even ineffective. Inspired by the latest breakthroughs of AI and its power to address large-scale and complex difficulties, the network community has begun to consider shifting the NTC paradigm from legacy rule-based to novel AI-based. As an applied inter-discipline, design and implementation are important. Although there have been some preliminary explorations along this frontier, they are either limited by only envisioning the prospects, or too scattered to provide high-level insight into a general methodology. To this end, we start with the domain knowledge relationships of AI and NTC, summarizing a baseline workflow toward deep reinforcement learning, which will be the dominant method for the AI-NTC paradigm. On top of that, we argue that AI-NTC training and running must be carried out in online environments in closed-loop fashion for the purpose of putting ti into practice. A series of challenges and opportunities are discussed from a realistic viewpoint, and a set of new architecture and mechanism to enable the online and closed-loop AI-NTC paradigm are proposed. Hopefully, this work can help the AI community to better understand NTC and the NTC community to better live with AI. [ABSTRACT FROM AUTHOR]

Ocean and water basically cover the major parts of our planet. To obtain the best utilization of the underlying resources on these parts of the Earth, people have made some research advancements. Specifically, the research on underwater wireless acoustic sensor networks (UWA-SNs) has made great progress. However, wide deployment of UWA-SNs is far from a reality due to several reasons. One important reason is that offshore deployment and field-level experiments of ocean-centric applications are both expensive and labor intensive. Other alternatives to attain this objective are to conduct simulation or experimentation that can reduce cost and accelerate the research activities and their outcomes. However, designing efficient and reliable simulation and experimentation platforms have proven to be more challenging beyond the expectation. In this article, we explore the main techniques (including their pros and cons) and components to develop simulation and experimentation platforms and provide a comprehensive survey report in this area. We classify simulation and experimentation platforms based on some typical criteria and then provide useful guidelines for researchers on choosing suitable platforms in accordance with their requirements. Finally, we address some open and un-resolved issues in this context and provide some suggestions on future research. [ABSTRACT FROM AUTHOR]

Deploying wireless sensor networks in the ocean poses many challenges due to the harsh conditions of the ocean and the nonnegligible node mobility. In this paper, we propose hybrid ocean sensor networks called drifting restricted floating ocean sensor networks (DR-OSNs) for long-term maritime surveillance monitoring tasks, which combines both the advantages of wireless sensor networks and underwater wireless acoustic sensor networks. We present a localization scheme termed localization for double-head maritime sensor networks (LDSN) for DR-OSNs, which leverages the unique characteristics of DR-OSNs to establish the whole localization system after the network is deployed from a plane or a ship, and it does not need the presence of designated anchor nodes deployed underwater. The whole localization process consists of three steps with algorithms self-moored node localization (SML), underwater sensor localization (USD), and floating-node localization algorithm (FLA). The first step is for the super group nodes to localize their underwater moored nodes via an SML algorithm by leveraging the free-drifting movement of their surface nodes. Once the moored nodes in the super group nodes have localized themselves, they turn into anchor nodes underwater. Thus, in the second step, with the help of these new anchor nodes, the unlocalized underwater moored nodes use the USD algorithm to localize their positions. In the last step, when the free-drifting floating nodes without a Global Positioning System (GPS) module need to know their instant position, they apply the FLA to figure out their position. We conduct extensive simulations to evaluate the scheme, with the results indicating that LDSN achieves high localization accuracy and is an effective localization scheme for DR-OSNs. [ABSTRACT FROM PUBLISHER]

Improving channel utilization is a well-known issue in wireless networks. In traditional point-to-point wireless communication, significant efforts had been made by the existing studies on enhancing the utilization of the channel access time. However, in the emerging wireless network using MU-MIMO, considering only the time domain in channel utilization is not sufficient. As multiple transmitters are allowed to transmit packets simultaneously to the same AP, allowing more antennas at AP would lead to higher channel utilization. Thus, the channel utilization in MU-MIMO should consider both time and spatial domains, i.e., the channel access time and the antenna usage, which have not been considered in the existing methods. In this paper, we point out that the fundamental problem is lacking of the antenna information of contention nodes in channel contention. To address this issue, we propose a new MAC-PHY architecture design, CUTS, to allow distributed nodes effectively contend for the channel and utilize the channel in both domains. Particularly, CUTS adopts interference nulling to attach the antenna information in channel contention. Meanwhile, techniques such as channel contention in frequency domain and ACK in frequency domain using self-jamming are adopted. Through the software defined radio-based real experiments and extensive simulations, we demonstrate the feasibility of our design and illustrate that CUTS provides better channel utilization with the gain over IEEE 802.11 reaching up to 470 percent. [ABSTRACT FROM AUTHOR]

Leveraging concurrent transmission is a promising way to improve throughput in wireless networks. Existing media access control (MAC) protocols like carrier sense multiple access always try to minimize the number of concurrent transmissions to avoid collision, although collisions at sender sides are harmless to the overall performance. The reason for such conservative strategy is that those protocols cannot obtain accurate channel status (who is transmitting and receiving) with low cost. They can only avoid potential collisions through rough channel status (idle or busy). To obtain additional information in a cost-efficient way, we propose a novel coding scheme, Attachment Coding, to allow control information to be “attached” on data packet. Nodes then transmit two kinds of signals simultaneously, without degrading the effective throughput of the original data traffic. Based on Attachment Coding, we propose an Attached-RTS MAC (AR-MAC) to exploit exposed terminals for concurrent transmissions. The attached control information provides accurate channel status for nodes in real time. Therefore, nodes can identify exposed terminals and utilize them for concurrent transmission. We theoretically analyze the feasibility of Attachment Coding, and implement it on the GNU Radio testbed to further verify it. We also conduct extensive simulations to evaluate the performance of Attached-RTS. The experimental results show that by leveraging Attachment Coding, AR-MAC achieves up to 180 percent in dense deployed ad hoc networks. [ABSTRACT FROM AUTHOR]

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach. [ABSTRACT FROM AUTHOR]

Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods' accuracy and quality of building contours. [ABSTRACT FROM AUTHOR]

• Under pronunciation principles, this paper presents a smartphone-based pronunciation training system for practicing monophthong regarding vowel articulation. • Speech and ultrasonic signals are captured and analyzed simultaneously to determine the tongue position and shape of the lips respecting uttering vowels. • The proposed system implementing a commercial smartphone is robust with high accuracy for practical scenarios. • The evaluation for its impact on user experience shows that the proposed system has raised learning engagement and enjoyment. Learning a foreign language pronunciation is the most challenging task for non-native speakers. Improving pronunciation based on feedback on pronunciation error scores is also not easy for learners. Our goal is to develop an alternative approach to pronunciation training that can point out articulation errors at the phoneme level to users through a smartphone-based system. Using self-correction as a means of ensuring learners' engagement, we focus on self-correction of pronunciation by adjusting articulation. In order to identify articulation concerning pronunciation, the system evaluates both audible and inaudible acoustic signals to examine fine-grained frequency-shifting direction of mouth movements and tongue position simultaneously. The result shows that the system can reach an average accuracy of 99.09% and is robust in different scenarios and genders. Additionally, the evaluation of the user study reveals that the proposed system provides positive user experiences and allows learners to improve their pronunciation more efficiently. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) deployment underlaying cellular communication have been drawing increasing attention in recent years. In this work, we consider Device-to-Device (D2D) communication technology for enabling uplink transmission in multiple IoT groups/clusters at which the resource block (RB) of each cellular user is reused by multiple IoT devices (IoTDs). Joint resources and power allocation optimization problem is formulated for maximizing both the IoTDs connectivity and cell throughput under an interference constraint. A heuristic algorithm, namely greedy iterative matching (GIM), is proposed as a sub-optimal solution for the resource allocation problem while a fixed-power margin technique is used for the cellular users' power allocation. Simulations results show that the proposed GIM algorithm provides enhanced cell throughput gain and accessibility to IoTDs. The proposed reuse model is applicable in a network at which the number of IoTDs deployed within multiple groups is larger than the available cellular reuse partners. [ABSTRACT FROM AUTHOR]

Vehicular communications in intelligent transportation systems promise to improve traffic efficiency, road utilization, and safety. Achieving secure vehicular communications is vital for the deployment of vehicle-to-everything (V2X) applications. This article provides a comprehensive overview of physical-layer security (PLS) strategies employed for V2X. By exploiting the randomness and the physical characteristics of wireless channels, PLS offers reliable solutions against eavesdropper attacks as complementary approaches to cryptographic techniques. We give a brief introduction to the architecture and characteristics of V2X networks. Then the fundamental principles of PLS technology are proposed, followed by security threats in V2X. Additionally, we discuss some open issues and challenges for future research. Finally, we investigate a case study where different challenges and technologies coexist in one V2X scenario. [ABSTRACT FROM AUTHOR]

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale. [ABSTRACT FROM AUTHOR]

Wi-Fi, which enables convenient wireless access to Internet services, has become integral to our modern lives. With widely-deployed Wi-Fi infrastructure, modern people can enjoy a variety of online services such as web browsing, online shopping, social interaction, and e-commerce almost at any time and any place. Traditionally, the most significant functionality of Wi-Fi is to enable high-throughput data communication between terminal devices and the Internet. However, beyond that, we observe that a novel type of system based on commodity Wi-Fi is increasingly attracting intense academic interest. Without hardware modification and redeployment, researchers are exploiting channel state information output by commodity Wi-Fi and transforming existing Wi-Fi systems into radar-like ones that can recognize human behavior along with data communication. This fancy functionality is tremendously expanding the boundaries of Wi-Fi to a new realm and triggering revolutionary applications in the context of the Internet of Things. In this article, we provide a guide to and introduce the impressive landscape of this new realm. [ABSTRACT FROM AUTHOR]

Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user's identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings. [ABSTRACT FROM AUTHOR]

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity. [ABSTRACT FROM AUTHOR]

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40 percent tag read rate gain compared with traditional RFID protocol. [ABSTRACT FROM PUBLISHER]

Handwritten signatures are widely used for identity authorization. However, verifying handwritten signatures is cumbersome in practice due to the dependency on extra drawing tools such as a digitizer, and because the false acceptance of a forged signature can cause damage to property. Therefore, exploring a way to balance the security and user experiment of handwritten signatures is critical. In this paper, we propose a handheld signature verification scheme called SilentSign, which leverages acoustic sensors (i.e., microphone and speaker) in mobile devices. Compared to the previous online signature verification system, it provides handy and safe paper-based signature verification services. The prime notion is to utilize the acoustic signals that are bounced back via a pen tip to depict a user's signing pattern. We designed the signal modulation stratagem carefully to guarantee high performance, developed a distance measurement algorithm based on phase shift, and trained a verification model. In comparison with the traditional signature verification scheme, SilentSign allows users to sign more conveniently as well as invisibly. To evaluate SilentSign in various settings, we conducted comprehensive experiments with 35 participants. Our results reveal that SilentSign can attain 98.2 % AUC and 1.25 % EER. We note that a shorter conference version of this paper was presented in Percom (2019). Our initial conference paper did not finish the complete experiment. This manuscript has been revised and provided additional experiments to the conference proceedings; for example, by including System Robustness, Computational Overhead, etc. [ABSTRACT FROM AUTHOR]

Vehicular social networking is expected to become a reality soon, where secure and privacy-preserving identity management is a prerequisite. Various organizations and studies have recommended using Vehicular Public-key Infrastructure (VPKI) to securely and privately manage vehicles' identities. However, the existing VPKI schemes still do not deal with identity management in vehicular social networking. In this work, we propose a Privacy-Preserving Identity Management (PPTIM) scheme for vehicular social networking to securely and privately manage vehicles' social identities. PPTIM builds on the transparency log (distributed ledger) proposals for web PKI, but it addresses challenges specific to vehicular social networking. All pseudo-social identities, pseudonym certificates issued against pseudo-social identities, revocation process, and revocation status validation are conducted on a transparency log. With an efficient data structure named the Accumulation Tree (AT), we extend the conventional transparency log to offer trustworthy pseudonym certificate validation without relying on the Certificate Revocation Lists (CRLs). In addition, PPTIM strengthens the security of vehicular social networking by protecting vehicles to a greater extent from being duped by malicious pseudonym certificates through logging and constant monitoring. Finally, performance and security analysis show the cost-effectiveness and security of PPTIM. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods. [ABSTRACT FROM AUTHOR]

Since inaccuracies commonly exist in many applications, data uncertainty has become an important problem in database systems. To deal with data uncertainty, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with confidence. However, the results from a query or mining process may not be reliable when the uncertainty propagates in the systems. In this paper, we leverage the power of crowdsourcing by designing a set of Human Intelligence Tasks, or HITs in short, to ask a crowd to improve the quality of uncertain data. In particular, we consider crowds consists of workers with diverse accuracy rates when answering the HITs. We design solutions to maximize the data quality with minimal number of HITs. There are two obstacles for this non-trivial optimization, which lead to very high computational cost for selecting the optimal set of HITs. First, members of a crowd may return incorrect answers with different probabilities. Second, the HITs decomposed from uncertain data are often correlated. We have addressed these challenges in this paper by designing an effective approximation algorithm and an efficient heuristic solution, especially for crowds with diverse individual accuracy rates. To further improve the efficiency, we derive tight lower and upper bounds for effective filtering and estimation. Extensive experiments on both a simulated crowd and a real crowdsourcing platform are conducted to evaluate our solutions. [ABSTRACT FROM AUTHOR]

The mixture of wireless and power line communications (PLC) is vital for implementing new applications in smart grid and vehicular communications. In this work, we investigate the performance of non-orthogonal multiple access (NOMA) based dual-hop hybrid communication systems with decode-and-forward relay. The wireless channel is characterized by Nakagami-m fading under an additive white Gaussian noise (AWGN), while the PLC channels are characterized by Log-normal distribution with Bernoulli Gaussian noise including both background and impulsive noise components. New closed-form expressions for the outage probability, the asymptotic outage probability and ergodic capacity are derived and verified via extensive representative simulations. For more insights on the outage performance, we analyze the diversity order. Additionally, we proposed a power allocation optimization technique to achieve an outage-optimal performance. The results show that the system outage probability improves as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise decrease, while their effect is negligible on the ergodic capacity. Finally, the performance of the proposed system is compared against a benchmark OMA-based system. [ABSTRACT FROM AUTHOR]

This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H‐AP), due to the short device‐to‐device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H‐AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non‐convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device‐to‐device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum‐throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non‐convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless‐powered communication system. [ABSTRACT FROM AUTHOR]

To accommodate the soaring mobile broadband traffic, the Federal Communications Commission (FCC) in the U.S. sets out to retrieve under-utilized spectrum (e.g., TV Whitespace) and lay the groundwork for spectrum redistribution. Auction is an efficient way to allocate resources to those who value them the most. The large pool of spectrums to be released, especially the ones in TV Whitespace, consist of wide-range frequencies. Apart from spatial reuse, spectrum heterogeneity imposes new challenges for spectrum auction design: 1) Wireless service providers with different targeted cell coverages have different spectrum frequency preferences; 2) interference relationship is frequency-dependent due to frequency-selective signal fading. Unfortunately, existing spectrum auction mechanisms either assume spectrum valuation is homogeneous or use homogeneous interference graph to group buyers who can reuse the same spectrum. In this paper, we propose TAMES, an auction framework for heterogeneous spectrum transaction. We consider a multi-seller-multi-buyer double auction, in which every buyer submits a bid, consisting of the spectrum demand and a bidding profile of prices for spectrums contributed by all sellers. A novel buyer grouping approach is proposed to tackle the problem of heterogeneous interference graph. TAMES is proved to be truthful as well as individually rational. The simulation results show that TAMES significantly improves spectrum utilization, sellers' revenue and buyers' utility by making smart use of spectrum heterogeneity, while keeping low running time comparable with existing auction mechanisms. Moreover, via simulation, we show how to help buyers obtain continuous spectrums which further improves buyers' satisfaction. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. Motivated by these, in this paper, we propose a novel approach, called Line-of-sight radio map matching, which only reserves the LOS signal among nodes. It leverages frequency diversity to eliminate the multipath behavior, making RSS more reliable than before. We implement our system MODLoc based on TelosB sensor nodes and commercial 802.11 NICs with Channel State Information (CSI) as well. Through extensive experiments, it shows that the accuracy does not decrease when localizing multiple targets in a dynamic environment. Our work outperforms the traditional methods by about 60 percent. More importantly, no calibration is required in such environment. Furthermore, our approach presents attractive flexibility, making it more appropriate for general RF-based localization studies than just the radio map based localization. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC) has emerged as a promising paradigm for enabling sustainable low-power operation of Internet of Things devices. This is due to its ability to enable sensing and communication through backscattering ambient wireless signals (e.g., WiFi and TV sig-nals). But a great impediment to AmBC-enabled networks is the difficulty in decoding the backscat-ter signals because the ambient signals are usually modulated and meant for other legacy receivers rather than AmBC devices. Drawing from the ability of machine learning (ML) to enhance the performance of wireless communication systems, some ML-aided techniques have been developed to assist signal detection in AmBC. Hence, this article aims to provide a comprehensive overview of the subject by describing the operation of the AmBC network, highlighting the major challenges to signal detection in AmBC, discussing and com-paring the performance of some existing ML-assisted solutions to AmBC signal detection, and highlighting some future research that could be carried out on the subject. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the uplink communication of a reconfigurable intelligent surface (RIS) assisted system, in which an user equipment (UE) with single radio frequency (RF) chain delivers information to an access point (AP) by adopting the spatial modulation (SM). Specifically, we first investigate the transmit SM (TSM) scheme and jointly optimize the UE’s power allocation matrix and the RIS reflection coefficients to enhance the system reliability. We formulate a non-convex optimization problem to reduce the system symbol-error-rate (SER) and propose a novel penalty-alternative optimizing algorithm to obtain a near-optimal solution. Following this, we show that with the assistant of RIS, receive SM (RSM) scheme can also be performed even if the UE has only one RF chain. Based on this observation, a novel RIS-assisted RSM scheme is proposed, which can provide a low cost and complexity solution for system realization. The reflection coefficients of the RIS are also optimized for the proposed RSM. Numerical results show that the RIS-assisted TSM can achieve a lower SER than the conventional communication scheme (CTS) without SM and the proposed RSM scheme has lower detection complexity than that of CTS. It is also shown that the performance of the TSM and RSM schemes is more sensitive to the quantization accuracy of the phase of the RIS coefficients than that of the amplitude. [ABSTRACT FROM AUTHOR]

• Cooperative NOMA Systems. • Physical-Layer Security. • Secure Communication Systems. • Passive Eavesdropping. A key feature of the non-orthogonal multiple access (NOMA) technique is that users with better channel conditions have prior knowledge about the information of other weak users. Given this prior knowledge, the idea that a strong user can serve as a relay node for other weak users in order to improve their performance, is known as cooperative NOMA. In this paper, we study the physical layer security of such a cooperative NOMA system. In order to reduce the complexity of the analytical process, the considered system in this paper has three users, in which the performance of the weaker users are enhanced by the stronger users. Given that there is an eavesdropper in the system that can hear all the transmissions, we study the secrecy performance of all the users. More specifically, we make an attempt to derive the ergodic secrecy capacity (ESC) and secrecy outage probability (SOP) of all the users. Due to the intractable nature of the exact analysis for the weak users, we provide the closed form expressions of the ESC and SOP for these users at the high SNR regime, while providing the exact analysis for the strongest user. Targeting on the optimality, we further reveal that better secrecy performance of the system is achievable through an appropriate power control mechanism. Finally, based on the analytical methodology of the three-user cooperative system, we provide insightful observations on the performance (in terms of ESC and SOP) of a multi-phase cooperative NOMA system with N users at the high SNR regime. Through rigorous numerical simulations, we verify the correctness of our analytical derivations under different practical scenarios while providing evidence of achieving optimal secrecy performance with the proposed power control scheme. [ABSTRACT FROM AUTHOR]

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (Ai) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an Ai-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of Ai techniques for 6G networks and elaborate how to employ the Ai techniques to efficiently and effectively optimize the network performance, including Ai-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. We highlight important future research directions and potential solutions for Ai-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management. [ABSTRACT FROM AUTHOR]

In this correspondence, we investigate the transmit signal design for millimeter-wave (mmWave) communication systems with distributed antennas. To be specific, by taking advantage of the directional communication property of mmWave communication, we first propose a generalized space domain index modulation (GSDIM) scheme to improve the system reliability. In the proposed GSDIM scheme, the user equipment (UE) uses different beam direction combinations to carry additional information bits, i.e., using multi-domain modulations to optimize transmission. Then, we derive the symbol error rate (SER) of the proposed GSDIM, and design digital precoding matrices to minimize the SER. It is shown that the SER minimization problem is a non-convex problem, and we propose an iterative method to solve it. Numerical results show that the proposed GSDIM scheme can achieve a lower system SER as compared with the existing counterpart. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has emerged as a promising technology for 5G networks and beyond. In order to fully reap the benefits of NOMA, it is essential to characterize its performance for different channel conditions. In this paper, we carry out a performance analysis of downlink NOMA systems subject to $\kappa$ - $\mu$ shadowed fading channels. Specifically, ergodic capacity, outage probability, and average bit error rate expressions for a two-users NOMA scheme under $\kappa$ - $\mu$ shadowed fading links are derived. The accuracy of the analytical results has been validated by extensive simulations. [ABSTRACT FROM AUTHOR]

This paper represents the design, feasibility evaluation, and performance validation of ICMR, a novel cross layer protocol that can maximize concurrent transmissions and avoid data frame interference in wireless networks, achieving a higher throughput comparing with the 802.11 standard and other state-of-the-art protocols. Observations on the 802.11 standard reveal that nodes around both the transmitter and receiver of the ongoing link waste concurrent transmission opportunities, degrading the network throughput dramatically. A state-of-the-art protocol IRMA is proposed to improve the network throughput through exploiting concurrent transmissions at the transmitter side. In this paper, a new ICMR protocol focuses on the receiver side to further improve the network throughput, through exploiting discernible interference cancellation, a physical layer mechanism that can successfully detect data frames when collided by control frames. We analyze the concurrent transmission opportunities of one link from the transmitter's transmission opportunities and the receiver's reception opportunities, then formulate the opportunities and give theoretical analysis to indicate that ICMR will have a higher opportunity over other protocols. Feasibility of the discernible interference cancellation mechanism is demonstrated through experiment results based on USRP2, and the throughput improvement of ICMR comparing with the other protocols is confirmed through simulations based on ns-2. [ABSTRACT FROM AUTHOR]

UAV aided communication technology holds tremendous potential to upgrade outdoor link throughput and provide on-demand wireless services. The flexible deployment characteristic makes UAV-aided networks competent at emergency situations, including natural disasters and sudden traffic hotspots. In this backdrop, UAVs are required to be densely deployed to accommodate the huge volume of data traffic, where interference amid the neighboring cells turns out to be extremely challenging. To break this stalemate, this article systematically investigates spectrum sharing technology for ultra-dense UAV-aided networks from the architecture level down to the physical layer. We shed light on design principles and key challenges in utilizing overlapped spectrum for interference-enabled concurrent transmissions. With these principles in mind, we introduce SpecShare, which utilizes coding redundancy at the PHY layer for UAV spectrum sharing. We explore the optimal UAV placement strategy in the network layer to fully unleash the potential of such spectrum sharing capacity. We discuss the feasibility of SpecShare, and demonstrate its effectiveness in terms of network throughput. [ABSTRACT FROM AUTHOR]

RFID tag identification is a crucial problem in UHF RFID systems. Traditional tag identification algorithms can be classified into two categories, ALOHA-based and tree-based. Both of them are inefficient due to the incidental high coordination cost. In this paper, we bring CSMA into UHF RFID systems to enhance tag read rate by reducing coordination cost. However, it is not straightforward due to the simple hardware design of passive RFID tags, which is unable to sense the transmissions or collisions of other tags. To tackle this challenge, we propose receiver-based CSMA (RCSMA) in this paper. In RCSMA, the reader notifies the tags channel condition. According to different sensing results of reader's notifications, the tags take corresponding actions, e.g., random back off. RCSMA does not require special RFID tag hardware design. An absorbing Markov chain model is presented to analyze the performance of RCSMA and shown to be consistent with the simulation results. Compared with optimized ALOHA-based algorithms and optimized tree-based algorithms, RCSMA can enhance the tag read rate by 30-70 percent under different reader and tag data rates. [ABSTRACT FROM AUTHOR]

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users? perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure. [ABSTRACT FROM AUTHOR]