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Currently, increasing attention is devoted to AI-Enabled Software Defined Industrial Networks (AI-SDINs). Towards energy-efficient and secure data transmission in AI-SDINs, a metric called ECPUB, which means energy cost of transmitting per useful bit is presented to evaluate energy efficiency and a novel energy-efficiency based secure multi-path routing scheme is then put forward. Specifically, the ECPUB incorporates the utility and the law of diminishing marginal utility, for revealing the relationship among energy consumption, residual energy, and useful bits required. Moreover, an energy-efficiency based secure multi-path routing scheme (E2SMR) is proposed by adopting the ECPUB and (t, n) threshold secret sharing scheme, for enhancing the security under the premise of guaranteeing energy efficiency. Extensive simulation results show that ECPUB can evaluate the energy efficiency and facilitate the balance of network load, while E2SMR can prolong the lifetime of the network and simultaneously ensure the network functionality securely.

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requires a priori aiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-Aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

The authenticity and integrity of sensed data in the data collection stage is a very critical aspect for the smart city industrial environment. They impact the accuracy of data analysis and the objectivity of making decisions. However, how to identify attack behaviors from environmental interference and establish a secure route to transmit data for resource-constrained terminals are challenging problems. To address these problems, in this article, we propose a trust-based security system (TSS). In TSS, we first design a trust model using binomial distribution for calculating the node's trust value and a third-party recommendation scheme for improving the objectivity of trust value. Then, we propose a trust management scheme for preventing the on–off attack. After that, we design a secure routing protocol, which is used to balance the security, transmission performance, and energy efficiency. Finally, the analytical results of the TSS are evaluated with extensive simulation experiment.

In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms are unable to distinguish between signal components with similar frequencies. To address this problem, this study proposed an enhanced root-MUSIC algorithm based on matrix reconstruction. Firstly, based on the sparsity of a singular value vector, a convex optimization problem was formulated to identify a singular value vector. Two algorithms were proposed to solve the convex optimization problem according to whether the standard deviation of noise needed to be estimated, from which an optimized singular value vector was obtained. Then, a signal matrix was reconstructed using an optimized singular value vector, and the Hankel structure of the signal matrix was restored by utilizing the properties of the Hankel matrix. Finally, the conventional root-MUSIC algorithm was utilized to estimate the signal beat frequency. The simulation results showed that the proposed algorithm improved the frequency resolution of multi-frequency signals in a noisy environment, which is beneficial to improve the multi-target range accuracy and resolution capabilities of FMCW radar.

Based on fog computer, an industrial wireless sensor network (F-IWSN) is a novel wireless sensor network in the industry. It not only can more efficiently reduce information transmission latency, but also can more beneficially achieve the real-time control and the rapid resource scheduling. However, similar to other distributed networks, it also faces enormous security challenges, especially those internal attacks. The differences from those traditional security schemes are that, one is the trade-off between security, transmission performance and energy consumption to meet the requirements of information convergence and control, the other constructs a multi-dimensional selective forwarding scheme to achieve the real time transmission. In this paper, we propose a Gaussian distribution-based comprehensive trust management system (GDTMS) for F-IWSN. Furthermore, in its trust decision, the grey decision making is introduced to achieve the trade-off between security, transmission performance and energy consumption. The proposed trade-off can effectively select the secure and robust relay node, namely, a trust management-based secure routing scheme. In addition, the proposed schemes are also applicable to defending against bad mouthing attacks. Simulation results show that, the comprehensive performance of GDTMS is better than other similar algorithms. It can effectively prevent the appearance of network holes, and balance the network load, promote the survivability of the network.

Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects. Such effects are not only caused by massive metal surfaces but also moving operators and logistical vehicles, which will lead to temporal fading effects. A three-layer impulse response framework is proposed to characterize such effects, in which both the specular and scattered components vary with the spacial movement of nearby objects. In this context, a received signal strength indicator will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power. Consequently, an augmented Kalman-filter-based link quality estimator has been designed to track both the specular and scattered power in the distribution parameter space with constant noise covariance matrices. Experiments from industrial sites show significantly increased accuracy.

In modern wireless communication systems, wireless channel modeling has always been a fundamental task in system design and performance optimization. Traditional channel modeling methods, such as ray-tracing and geometry- based stochastic channel models, require in-depth domain-specific knowledge and technical expertise in radio signal propagations across electromagnetic fields. To avoid these difficulties and complexities, a novel generative adversarial network (GAN) framework is proposed for the first time to address the problem of autonomous wireless channel modeling without complex theoretical analysis or data processing. Specifically, the GAN is trained by raw measurement data to reach the Nash equilibrium of a MinMax game between a channel data generator and a channel data discriminator. Once this process converges, the resulting channel data generator is extracted as the target channel model for a specific application scenario. To demonstrate, the distribution of a typical additive white Gaussian noise channel is successfully approximated by using the proposed GAN-based channel modeling framework, thus verifying its good performance and effectiveness.

In order to address the challenges in MIMO channel measurement and modeling for 5G communication scenarios, a CDM-based parallel channel sounder is developed and evaluated in this article. Compared to traditional TDM-based channel sounders, our parallel channel sounder supports eight-by-eight (8 x 8) MIMO communications, 200 MHz RF bandwidth, 5 ns multipath time resolution, 36 dB dynamic range, and 2 kHz maximum Doppler shift at a speed of 350 km/h. To achieve these performance targets, a series of sophisticated technical solutions are proposed and analyzed to realize 32 ps synchronization accuracy, 51.36 Gb/s parallel data streaming, and high-precision calibration across multiple channels. Key functions and performance metrics are evaluated and verified in a microwave chamber. Real measurement results in a complex campus environment show that our CDM-based parallel channel sounder can capture more detailed channel characteristics, and therefore offer accurate PDPs and RMS delay spreads at both LoS and NLoS positions under MIMO communication scenarios.

With the increasing number of wearable sensors and mobile devices, human activity recognition (HAR) based on multiple sensors has attracted more and more attention in recent years. On account of the diversity of human actions, the analysis of multivariate signals of activities is still a challenging task. Clustering is an unsupervised classification technique which can directly work on unlabeled data and automatically identify unknown activities. Therefore, a new wavelet tensor fuzzy clustering scheme (WTFCS) for multi-sensor activity recognition is proposed in this paper. Firstly, feature tensors of multiple activity signals are constructed using the discrete wavelet packet transform (DWPT). Then Multilinear Principal Component Analysis (MPCA) is utilized to reduce the dimensionality of feature tensors so as to keep the inherent data structure. On the basis of the principal feature initialization and the tensor fuzzy membership, a new fuzzy clustering (PTFC) is developed to identify different activity feature tensor groups. Finally, the open HAR dataset (DSAD) is used to verify the efficiency of the WTFCS. Clustering results of seventeen activities of eight subjects show that potential useful features of human activities can be captured through combining DWPT-based feature extraction with MPCA-based dimensionality reduction. The PTFC is capable of discriminating various human activities effectively. Its correctness rate of activity recognition is higher than those of fuzzy c-means clustering and the fuzzy clustering based on the tensor distance.

Wireless local area network (WLAN) is an indivisible part of the next generation wireless system. In this paper, an open Wi-Fi platform is designed and developed with special consideration of real-time signal processing. Such system can help accelerate research and development of future wireless network, especially in the case of cellular/ Wi-Fi co-existing networks. This platform is based on the Intel general-purpose processor and the universal software radio peripheral (USRP) radio front end. The design including the physical layer implementations is purely software and is optimized for real-time signal processing on the general purpose processor. In the lab experiment, this platform supports baseband rate up to 700 Mbps with 2 transmitters in 80 MHz bandwidth. A cellular-Wi-Fi signaling interface between the Wi-Fi access point (AP) and the 5G core network is also developed and validated as an example for wireless resource allocation.

As a key component of the information sensing and aggregating for big data, cloud computing, and Internet of Things (IoT), the information security in wireless sensor network (WSN) is critical. Due to constrained resources of sensor node, WSN is becoming a vulnerable target to many security attacks. Compared to external attacks, it is more difficult to defend against internal attacks. The former can be defended by using encryption and authentication schemes. However, this is invalid for the latter, which can obtain all keys of the network. The studies have proved that the trust management technology is one of effective approaches for detecting and defending against internal attacks. Hence, it is necessary to investigate and review the attack and defense with trust management. In this paper, the state-of-the-art trust management schemes are deeply investigated for WSN. Moreover, their advantages and disadvantages are symmetrically compared and analyzed in defending against internal attacks. The future directions of trust management are further provided. Finally, the conclusions and prospects are given.

The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility.

With the seamless coverage of wireless cellular networks in modern society, it is interesting to consider the shape of wireless cellular coverage. Is the shape a regular hexagon, an irregular polygon, or another complex geometrical shape? Based on fractal theory, the statistical characteristic of the wireless cellular coverage boundary is determined by the measured wireless cellular data collected from Shanghai, China. The measured results indicate that the wireless cellular coverage boundary presents an extremely irregular geometrical shape, which is also called a statistical fractal shape. Moreover, the statistical fractal characteristics of the wireless cellular coverage boundary have been validated by values of the Hurst parameter estimated in angular scales. The statistical fractal characteristics of the wireless cellular coverage boundary can be used to evaluate and design the handoff scheme of mobile user terminals in wireless cellular networks., 16 pages, 4 figures

In this paper, we first challenge the accuracy of the so-called innovative assumption that is widely adopted in delay performance analysis of network-coding-based epidemic routing (NCER) in delay-tolerant networks (DTNs). We demonstrate that this optimistic assumption severely underestimates data delivery delay and solves this problem successfully by introducing an extra encounter factor $\delta$ . Based on this, we propose a coloring process (CP)-based analytical model to evaluate the delay performance of NCER. Numerical results show that our CP-based method outperforms traditional ordinary differential equation (ODE)-based methods on estimating the delay performance under different network sizes. Furthermore, to mitigate potential competition among multiple data streams from different nodes, we propose a feedback-based recovery protocol (FRP) that takes advantage of finite buffer space and significantly reduces the number of ineffective transmissions between nodes. As a result, FRP can achieve much better delay performance compared with existing protocols in prior work.

To overcome the inter-carrier interference (ICI) of orthogonal frequency division multiplexing (OFDM) systems subject to unknown carrier frequency offset (CFO) and multipath, this paper develops a blind adaptive interference suppression scheme based on independent component analysis (ICA). Taking into account statistical independence of subcarriers' signals of OFDM, the signal recovery mechanism is investigated to achieve the goal of blind equalization. The received OFDM signals can be considered as the mixed observation signals. The effect of CFO and multipath corresponds to the mixing matrix in the problem of blind source separation (BSS) framework. In this paper, the ICA-based OFDM system model is built, and the proposed ICA-based detector is exploited to extract source signals from the observation of a received mixture based on the assumption of statistical independence between the sources. The blind separation technique can increase spectral efficiency and provide robustness performance against erroneous parameter estimation problem. Theoretical analysis and simulation results show that compared with the conventional pilot-based scheme, the improved performance of OFDM systems is obtained by the proposed ICA-based detection technique.

Compressive sensing (CS)-based data aggregation has become an increasingly important research topic for large-scale wireless sensor networks since conventional data aggregations are shown to be inefficient and unstable in handling huge data traffic. However, for CS-based techniques, the discrete cosine transform, which is the most widely adopted sparsification basis, cannot sufficiently sparsify real-world signals, which are unordered due to random sensor distribution, thus weakening advantages of CS. In this paper, an energy-efficient CS-based scheme, which is called “treelet-based clustered compressive data aggregation” (T-CCDA), is proposed. Specifically, as a first step, treelet transform is adopted as a sparsification tool to mine sparsity from signals for CS recovery. This approach not only enhances the performance of CS recovery but reveals localized correlation structures among sensor nodes as well. Then, a novel clustered routing algorithm is proposed to further facilitate energy saving by taking advantage of the correlation structures, thus giving our T-CCDA scheme. Simulation results show that the proposed scheme outperforms other reference approaches in terms of communication overhead per reconstruction error for adopted data sets.

Fog-based MANET (Mobile Ad hoc networks) is a novel paradigm of a mobile ad hoc network with the advantages of both mobility and fog computing. Meanwhile, as traditional routing protocol, ad hoc on-demand distance vector (AODV) routing protocol has been applied widely in fog-based MANET. Currently, how to improve the transmission performance and enhance security are the two major aspects in AODV’s research field. However, the researches on joint energy efficiency and security seem to be seldom considered. In this paper, we propose a source anonymity-based lightweight secure AODV (SAL-SAODV) routing protocol to meet the above requirements. In SAL-SAODV protocol, source anonymous and secure transmitting schemes are proposed and applied. The scheme involves the following three parts: the source anonymity algorithm is employed to achieve the source node, without being tracked and located; the improved secure scheme based on the polynomial of CRC-4 is applied to substitute the RSA digital signature of SAODV and guarantee the data integrity, in addition to reducing the computation and energy consumption; the random delayed transmitting scheme (RDTM) is implemented to separate the check code and transmitted data, and achieve tamper-proof results. The simulation results show that the comprehensive performance of the proposed SAL-SAODV is a trade-off of the transmission performance, energy efficiency, and security, and better than AODV and SAODV.

The power consumption of mobile devices (MDs) has been increasing dramatically since more functions and bigger screens are adopted in the latest MDs. To extend the usage time of MDs and improve customer satisfaction levels, this research study, for the first time, studies the influence of different deployment strategies of a few low-power base stations (BSs) to reduce the power consumption of a large number of MDs. For both microcell and picocell scenarios, the authors’ analyse the power reductions at the MD side with the deployment of a low-power BS. The optimum low-power cell radius is derived for achieving the maximum MDs power reduction. The tradeoff relationships between MD power reduction and the key parameters of low-power BSs are investigated and discussed. It is shown that authors’ proposed method can achieve about 18% power reduction for microcell MDs.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types. © 2012 IEEE.

A compact wideband high-gain microstrip grid array antenna is presented.The antenna is printed on a dielectric substrate, backed by a metal board, and directly fed from a 50 Ω coaxial cable. It adopts elliptical radiation elements to enhance its impedance, gain bandwidths, and sinusoid lines to reduce its size. Structural parameters of a proposed antenna with seven radiation elements were optimized by a parallel genetic algorithm on a cluster system. A prototype antenna was fabricated and tested. Results of simulation and measurement agree well and show the antenna exhibits encouraging properties, for example, a S11 < −10 dB bandwidth of 25.1% and a 3 dB gain-drop bandwidth of 16.5%, both of which are much wider than that of conventional grid array antennas, as well as a maximum gain of approximately 13.8 dBi with an area-reduction factor of 46%. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1144–1147, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25895

IEEE 802.11p and 1609 standards are currently under development to support Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications in vehicular networks. For infrastructure-based vehicular relay networks, access probability is an important measure which indicates how well an arbitrary vehicle can access the infrastructure, i.e. a base station (BS). On the other hand, connectivity probability, i.e. the probability that all the vehicles are connected to the infrastructure, indicates the service coverage performance of a vehicular relay network. In this paper, we develop an analytical model with a generic radio channel model to fully characterize the access probability and connectivity probability performance in a vehicular relay network considering both one-hop (direct access) and two-hop (via a relay) communications between a vehicle and the infrastructure. Specifically, we derive close-form equations for calculating these two probabilities. Our analytical results, validated by simulations, reveal the tradeoffs between key system parameters, such as inter-BS distance, vehicle density, transmission ranges of a BS and a vehicle, and their collective impact on access probability and connectivity probability under different communication channel models. These results and new knowledge about vehicular relay networks will enable network designers and operators to effectively improve network planning, deployment and resource management. © 2006 IEEE.