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The coexistence of heterogeneous devices in wireless networks brings a new topic on cross-technology communication (CTC) to improve the coexistence efficiency and boost collaboration among these devices. Current advances on CTC mainly fall into two categories, physical-layer CTC and packet-level energy modulation (PLEM). The physical-layer CTC achieves a high CTC data rate, but with channel incompatible to commercial devices, making it hard to be deployed in current wireless networks. PLEM is channel and physical layer compatible, but with two main drawbacks of the low CTC data rate and MAC incompatibility, which will induce severe interference to the other devices’ normal data transmissions. In this paper, we propose symbol-level energy modulation (SLEM), the first CTC method that is fully compatible with current devices in both channel and the physical/MAC layer processes, having the ability to be deployed in commercial wireless networks smoothly. SLEM inserts extra bits to WiFi data bits to generate the transmitting bits, so as to adjust the energy levels of WiFi symbols to deliver CTC information. We make theoretical analysis to figure out the performance of both CTC and WiFi transmissions. We also conduct experiments to demonstrate the feasibility of SLEM and its performance under different network situations. [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]

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]

Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF’s future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones. [ABSTRACT FROM AUTHOR]

The next-generation of wireless network 6G is envisioned to achieve seamless global full coverage, and provide ultra-high-speed and extreme low delay network to connect the Internet of Everything (IoE) on the Earth. To this end, as the space-air-ground-underwater integrated network is a solid foundation of 6G, it is critical to develop reliable and robust cross-boundary communication techniques to connect the sky and the ocean. However, due to the harsh environment-induced channel complexity of different transmission media, penetrating through the air/water interface seamlessly faces significant challenges. In this paper, we present a comprehensive review of recent progress of air/water cross-boundary communications, and group the existing works into three categories, such as 1) optical direct communication, 2) relay-based communication, and 3) non-optical direct communication, and then summarize them respectively. The three types of cross-boundary approaches have their own pros and cons which are suitable for different application scenarios. Among them, the optical direct communication has made tremendous progress recently. However, the volatile wavy surface and depth-temperature-dependent parameters make it a great challenge to build robust communication links. Moreover, the misalignment caused by the dynamics of the channel severely degrades the performance and limits their practical applications. To tackle these challenges, we summarize the complex channel characteristics, analyze the causes of the misalignment and provide potential solutions. In the end of this paper, we also discuss future research directions and hope to inspire expanding research on the air/water cross-boundary communications to further propel towards the space-air-ground-ocean integrated network in 6G. [ABSTRACT FROM AUTHOR]

Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city. Recent advances primarily rely on deep reinforcement learning (DRL) to directly learn the optimal dispatching policy. These works, however, are still not sufficiently efficient because they overlook several pieces of valuable context information. As a result, they may generate quite a few improper actions and introduce unnecessary coordination costs. To improve existing works, we present COX – a context-aware taxi dispatching approach that incorporates rich contexts into DRL modeling for more efficient taxi reallocations. Specifically, rather than simply dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network graph into zones for practical taxi dispatching. In addition, COX comprehensively analyzes zone-level taxi demands and supplies through accurate taxi demand prediction and timely updates of taxi statuses. COX improves the DRL modeling by integrating these derived contexts, e.g., state representation with complete demand/supply data and sequential action generation with full coordination among idle taxis. In particular, we implement an environment simulator to train and evaluate COX using a large real-world taxi dataset. Extensive experiments show that COX outperforms state-of-the-art approaches on various performance metrics, e.g., on average improving the total order values by 6.74%, while reducing the number of unserved taxi orders and passengers’ waiting time by 4.92% and 44.84%, respectively. [ABSTRACT FROM AUTHOR]

Facial expressions are an essential form of human nonverbal communication. Recognition of this nonverbal sign may enable developers to understand the feedbacks on smart device functionality and advertising. Existing approaches for facial expression recognition are mainly based on cameras or on-body sensors, which are either sensitive to lighting conditions or cumbersome for users to wear devices on their faces. In this paper, we propose a new facial expression recognition system based on Wi-Fi signals, named WiFace. Our fundamental intuition is that facial muscle movements in different expressions will induce distinctive waveform patterns in the time-series of channel state information (CSI) in Wi-Fi signals. We develop a series of algorithms to process the CSI signals and extract the most representative waveform patterns for facial expression classification. We build a fully-functional prototype of WiFace using commercial off-the-shelf devices, which can recognize six typical facial expressions. We conduct extensive experiments to evaluate the performance of WiFace, and the experimental results show that the average recognition accuracy is 94.80 percent. [ABSTRACT FROM AUTHOR]

Node localization is one of the major challenges that exist in underwater communication. Various techniques exist for terrestrial networks, while few of them are applicable in underwater networks due to the dynamic characteristic of the underwater channels, e.g., the lack of global positioning system (GPS) coverage under the water surface. Moreover, assorted environmental properties affect almost all employed communication techniques. In this letter, we propose an environmental-aware positioning system by considering the variations of the underwater speed of sound according to the dynamic changes in the physical properties of the seawater, such as temperature, salinity, and pressure, besides the internal waves’ effects. The proposed system employs the received signal strength (RSS) technique in estimating the distances between the network nodes. Moreover, we examine the application of various dynamic responses neural networks (NNs) in predicting the underwater node position, such as the feedforward, recurrent, time delay, and distributed delay NNs. The results show that the NN-based prediction models enhance the performance of the positioning system and could achieve small prediction errors in the range of 0.002 for both training and testing patterns. [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]

Mobile video chatting has emerged as an important Internet multimedia application that greatly enriches interpersonal communications. Mobile power efficiency is crucial to the service quality and time of video chatting on battery-limited smartphones. However, the power characteristics of the video coding and data communication are highly complex due to the time-varying network conditions and dynamic mobile energy features. This incurs crucial challenges to maintaining the low power dissipation of mobile chatting application while streaming satisfactory-quality videos. To address these challenges, this paper presents a joinT cOding-tranSmission Optimization (TOSO) protocol at application layer that performs machine learning based adaptation of the video bit rate and FEC (Forward Error Correction) coding parameters. By taking advantage of analytical and empirical models characterizing the quality-power relationship, TOSO is able to maximize video quality subject to a specified upper bound of power consumption in mobile chat application. This distinguishing feature prevents the video chat from draining battery too quickly. Moreover, it allows the smartphone operating system or the mobile user to define a desired video chat duration given the remaining battery, avoiding unpleasant conversation disruption due to battery depletion. Extensive experiments based on the Linphone platform and Exata network emulator show that TOSO outperforms baseline approaches by 29.3 percent in power conservation while achieving the same video quality level. [ABSTRACT FROM AUTHOR]

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness. [ABSTRACT FROM AUTHOR]

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances. [ABSTRACT FROM AUTHOR]

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts. [ABSTRACT FROM AUTHOR]

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV’s mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV’s sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV’s transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks. [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]

Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month. [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]

Underwater is a harsh and dynamic environment. Its physical properties are unsteady in the space-time domain and that complicates the communication process inside the water. Moreover, underwater transmissions suffer from many attenuations and scattering sources due to several environmental noises such as shipping, turbulence, wave dynamics, and the background life of the marine organisms. Therefore, opportunistic and cooperative communications are necessary to compensate for the prompt changes in underwater environments. In this paper, we propose a new opportunistic cooperative transmission scheme for underwater networks that is adaptable for various environmental conditions. The proposed scheme employs the key physical variables of the seawater, e.g. temperature, salinity, density, and waves speed, to indicate the proper relaying scheme jointly with the appropriate modulation method and the transmitting power level that suit the current transmission situations. The paper also proposes a study that demonstrates the effects of these variables on the underwater speed of sound which is the most commonly used carrier for underwater communication and hence affect the transmitted signal intensity. The results show that the proposed opportunistic cooperative transmission scheme provides an efficient compromise between the overall network performance and the energy efficiency at the nodes. [ABSTRACT FROM AUTHOR]

Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem—how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused. [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]

Multi-hop communication has attracted a lot of attention recently due to its ability to extend the coverage range and to overcome blockage. In this letter, we propose a machine learning-based selection approach that adaptively chooses the best forwarding scheme in hybrid multi-hop dense networks. The proposed transmission scheme employs a multi-layer selection where each layer represents one possible relaying case, namely amplify-and-forward, half-detection, or full-detection of the transmitted symbol, or even no-relaying. Moreover, the proposed system dynamically learns the proper forwarding scheme out of these layers for each involved relay to minimize the transmission error rate based on the relay location, and its residual energy. A heuristic approach is proposed for the forwarding scheme selection and transmission power control where a minimum threshold value for the transmission power of each relay node is derived in order to satisfy a target QoS requirement. The results are used for training a decision trees-based prediction model that achieves a remarkable accuracy beyond the 99% for both training and testing patterns. [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]

This paper presents StrLight, the first practical VLC system that leverages widely-deployed string lights to transmit data. The data transmission is imperceptible to human eyes. Users can decode the data by mobile devices (e.g., smartphones) equipped with cameras. StrLight primarily differs from existing VLC systems in using string lights which are composed of a large number of small LEDs and thus the unique design to address practical issues including a special data modulation/encoding scheme, a data representation with unstructured/unknown topologies of LEDs in the string light, and a fault tolerance against broken and blocked LEDs. To the best of our knowledge, StrLight is the first practical VLC system of its kind. We build several prototypes of string light transmitters and test with different smartphone models and a customized mobile device as receivers. The experiment results show that StrLight provides an efficient and robust data broadcasting. A string light of 100 LEDs working in $450 \;\mathrm{Hz}$450 Hz and a camera with a capture rate of $30 \;\mathrm{Hz}$30 Hz and an image resolution of as low as $320 \times 240$320×240 pixels, delivers data rate of $\sim\!1\;\mathrm{kbps}$∼1 kbps , without observable light flickers. [ABSTRACT FROM AUTHOR]

Recently, deep learning has been used to tackle mobile sensing problems, and the inference phase of deep learning is preferred to be run on mobile devices for speedy responses. However, mobile devices are resource-constrained platforms for both computation and power. Moreover, an inference task with deep learning involves tens of billions of mathematical operations and tens of millions of parameter reads. Thus, it is a critical issue to reduce the energy consumption of deep learning inference algorithms. In this article, we survey various energy reduction approaches, and classify them into three categories: the compressing neural network model, minimizing the data transfer required in computation, and offloading workloads. Moreover, we simulate and compare three techniques of model compression, by applying them to an object recognition problem. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have tremendous potential to improve wireless network capacity, but are challenging to operate in ultra-dense networks, mainly due to the strong interference received from the dominated lineof- sight channels of the UAVs. By forming multiple highly directional beams, millimeter-wave (mmWave) communication technology allows concurrent user transmissions via beam-division multiple access (BDMA), and thus has emerged as a promising solution to mitigate interference for the fifth generation (5G) UAV communication. However, due to the limited number of beams generated in practical mmWave communication systems, conventional BDMA cannot meet the ever increasing capacity requirement. A new multiple access technique is intensely desired. In this article, we integrate mmWave communication with UAV-aided 5G ultra-dense networks, and design a novel link-adaptive constellation- division multiple access (CoDMA) technique. We discuss key challenges in efficient multiple access technique design, and then investigate design principles on new multiplexing methods and beamwidth optimization for interference management in UAV-aided dynamic networks. We further apply flexible constellation division in rateless codes, and put forward the system-level design of CoMDA with beamwidth adaptation to unleash the multiplexing gain. Finally, we show that our design can successfully enable multiple-user access within a single beam without causing any intra-beam interference while efficiently mitigating interference from adjacent beams. We also demonstrate that the proposed design is adaptive to the UAV network dynamics, and can greatly improve the system throughput. [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]

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]

The mmWave frequency band has been widely accepted as an ideal carrier for the next-generation ultra-dense small cell communications to achieve the ambitious objective of 1000-fold increase in network capacity, but such a novel architecture also invokes tremendous anxiety for operators regarding the planning, configuration, maintenance, optimization, and troubleshooting. SON, introduced about 10 years ago, aiming to affiliate network management through addressing complexity and decreasing cost in an automatic way, nowadays has been extensively considered the only viable way to enable small cell deployments; however, as a technology traditionally designed and standardized for LTE-based systems, the conventional paradigm will no longer be suitable for mmWavebased cellular networks, which contain some peculiar characteristics, such as directivity of signal transmission and sensitivity to blockage and mobility, differing from their low-frequency counterparts and thereby bringing numerous unprecedented challenges. To smoothly migrate toward the 5G era, the legacy SON campaign eagerly demands a thorough revolution to think over the new features and cope with the new problems by defining new functionalities and proposing new algorithms. It is important, but not easy. In this article, we share some preliminary insights about the mmWave-oriented proactive SON paradigm, hopefully spurring further research in this area and accelerating its practical application in 5G. [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]

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared with other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, the information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared with the downlink ones due to the individual users’ power constraints and the discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios. [ABSTRACT FROM PUBLISHER]

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]

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose $Coco$ , a protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco$ addresses the key challenges in achieving collision tolerance, such as precise sender alignment and the control of transmission concurrency. We implement Coco$ in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco in general cases, compared with state-of-the-arts protocols. [ABSTRACT FROM AUTHOR]

Injuries that are caused by falls have been regarded as one of the major health threats to the independent living for the elderly. Conventional fall detection systems have various limitations. In this work, we first look for the correlations between different radio signal variations and activities by analyzing radio propagation model. Based on our observation, we propose WiFall, a truly unobtrusive fall detection system. WiFall employs physical layer Channel State Information (CSI) as the indicator of activities. It can detect fall of the human without hardware modification, extra environmental setup, or any wearable device. We implement WiFall on desktops equipped with commodity 802.11n NIC, and evaluate the performance in three typical indoor scenarios with several layouts of transmitter-receiver (Tx-Rx) links. In our area of interest, WiFall can achieve fall detection for a single person with high accuracy. As demonstrated by the experimental results, WiFall yields 90 percent detection precision with a false alarm rate of 15 percent on average using a one-class SVM classifier in all testing scenarios. It can also achieve average 94 percent fall detection precisions with 13 percent false alarm using Random Forest algorithm. [ABSTRACT FROM PUBLISHER]

Gesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of $96.5$ and $92.8$ percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations. [ABSTRACT FROM AUTHOR]

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose Seer, a frame-level wideband spectrum adaptation solution which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the desired transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. Seer can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped Seer on the GNURadio/USRP platform to demonstrate its feasibility. Furthermore, using 1.6GHz channel measurements and trace-driven simulations, we have evaluated the merits of Seer over state-of-the-art approaches. [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]

Recent literature advances Wi-Fi signals to “see” people's motions and locations. This paper asks the following question: Can Wi-Fi “hear” our talks? We present WiHear, which enables Wi-Fi signals to “hear” our talks without deploying any devices. To achieve this, WiHear needs to detect and analyze fine-grained radio reflections from mouth movements. WiHear solves this micro-movement detection problem by introducing Mouth Motion Profile that leverages partial multipath effects and wavelet packet transformation. Since Wi-Fi signals do not require line-of-sight, WiHear can “hear” people talks within the radio range. Further, WiHear can simultaneously “hear” multiple people's talks leveraging MIMO technology. We implement WiHear on both USRP N210 platform and commercial Wi-Fi infrastructure. Results show that within our pre-defined vocabulary, WiHear can achieve detection accuracy of 91 percent on average for single individual speaking no more than six words and up to 74 percent for no more than three people talking simultaneously. Moreover, the detection accuracy can be further improved by deploying multiple receivers from different angles. [ABSTRACT FROM AUTHOR]

Seeing through walls and knowing clearly what exist inside just like a superman are not only fantastic wishes for humans, but also of much practical significance. For example, you would like to know whether there are pipes, or rebars inside a wall before drilling into it. Moreover, knowing how pipes are configured in a wall before attempting to fix defects would definitely prevent unnecessary damages. Existing methods that intend to address this issue are either costly due to the use of high-end technology, or restrictive for reasons of some strong assumptions. However, in this paper, we present a novel system, SmartScanner, which is based on off-the-shelf sensors embedded in a smartphone. SmartScanner makes full use of in-built sensors, namely, the accelerometer, gyroscope, and magnetometer to achieve this goal inexpensively and conveniently. Specifically, by combining these sensors, we are able to clearly distinguish certain objects inside a wall and map out the layout of an in-wall pipeline system. We implement SmartScanner on two smartphone platforms, namely iPhone 4 and Xiaomi Mi2S, and conduct extensive experiments to evaluate its performance. Experiments show that SmartScanner can achieve high accuracies in distinguishing objects in various scenarios. Meanwhile, as for layout mapping, 90 percent of length errors are limited to several centimeters for horizontal and vertical pipeline segments, respectively. Also, SmartScanner can achieve centimeter-level position errors of turning points in horizontal and vertical directions in the testbed. [ABSTRACT FROM AUTHOR]

Rate adaptation is an essential component in today’s wireless standards, which help approach the channel capacity and maximize the throughput. However, how to estimate the optimal data rate in a fluctuated channel remains of great concern. Previous wisdoms leverage PHY layer information for rate estimation, including confidence information like SoftPHY hints, and channel state information (CSI) measurements. However, when experiencing rapid time varying and frequency selective fading channel, the above metrics can be inaccurate. The reason roots from the fact that there are not enough cost-efficient pilots, which are pre-known symbols inserted in a packet for channel estimation. In this paper, we observe that by digging into both PHY layer decoder and upper layer protocol headers, more reliable data bits with high confidence level can be exploited. These data bits, termed smart pilot, can be used to calibrate the channel estimation measurements cost-efficiently. Based on the calibrated estimation, we further propose a novel greedy rate selection algorithm to track the optimal data rate, which successfully avoids the impact of deep fading subcarriers in both legacy 802.11a/g and 802.11n MIMO systems. Our experiments on GNU radio testbed show that SmartPilot quickly tracks the link variance, and improve the channel estimation accuracy by 87%. Furthermore, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards. [ABSTRACT FROM PUBLISHER]