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For unmanned ships’ autonomous navigation and water traffic systems, trajectory planning is a critical problem. Because conventional optimization techniques only consider a single target, such as the shortest trajectory distance or lowest time, multi-objective optimization can generate trajectory solutions that are optimized in terms of both distance and smoothness, thereby better serving the control of unmanned vessels in real-world applications. To achieve this, we propose a multi-objective non-linear trajectory planning method based on an improved genetic algorithm. This method simultaneously considers both the trajectory distance and smoothness, while incorporating constraints related to the safety characteristics and inherent properties of the unmanned ship, including speed and steering angles. According to simulation data, the proposed multi-objective improved genetic algorithm in this paper is compared to the single-objective genetic algorithm that only considers path length in various environments, resulting in an average reduction of 18.4% in the number of turning points and 26.38% in average turning angles in the planned paths. Compared to the traditional $\text{A}\ast $ algorithm and PSO algorithm, it achieved an average reduction of 49.9% in the number of turning points and 78.48% in average turning angles. In practical cases, it demonstrated an average reduction of 30.47% in the number of turning points and 35.49% in average turning angles compared to other algorithms.

The block-term tensor decomposition model with multilinear rank-$(L_{r},L_{r},1)$ terms (or the “${\mathsf{LL1}}$ tensor decomposition” in short) offers a valuable alternative formulation for hyperspectral unmixing (HU), which ensures the identifiability of the endmembers/abundances in cases where classic matrix factorization (MF) approaches cannot provide such guarantees. However, the existing ${\mathsf{LL1}}$-tensor-decomposition-based HU algorithms use a three-factor parameterization of the tensor (i.e., the hyperspectral image cube), which causes difficulties in incorporating structural prior information arising in HU. Consequently, their algorithms often exhibit high per-iteration complexity and slow convergence. This article focuses on ${\mathsf{LL1}}$ tensor decomposition under structural constraints and regularization terms in HU. Our algorithm uses a two-factor reparameterization of the tensor model. Like in the MF-based approaches, the factors correspond to the endmembers and abundances in the context of HU. Thus, the proposed framework is natural to incorporate physics-motivated priors in HU. To tackle the formulated optimization problem, a two-block alternating gradient projection (GP)-based algorithm is proposed. Carefully designed projection solvers are proposed to implement the GP algorithm with a relatively low per-iteration complexity. An extrapolation-based acceleration strategy is proposed to expedite the GP algorithm. Such an extrapolated multiblock algorithm only had asymptotic convergence assurances in the literature. Our analysis shows that the algorithm converges to the vicinity of a stationary point within finite iterations, under reasonable conditions. Empirical study shows that the proposed algorithm often attains orders-of-magnitude speedup and substantial HU performance gains compared with the existing ${\mathsf{LL1}}$-decomposition-based HU algorithms.

Most greenhouse gases come from biological activities and industry which will lead to global warming and show an impact on human life. With the need of green transformation of the global economic structure and seeking for higher quality of human life, the detection and management of greenhouse gases, as well as most hazardous gases in the environment, are increasingly demanding. Applications in different fields require sensors that can detect gas volume fractions with magnitudes from 10–9 to 10–4. Greenhouse gas detection plays an important role both in the agriculture and industry field. In this review, we first summarize the mechanism of several common gas detectors used currently. Then, the advantages of nanostructured gas sensors are discussed. Finally, the applications of infrared gas sensors based on nanophotonic devices are described in detail. This review has been an outlook on the future development of infrared gas sensors based on nanophotonic devices.

Network slicing is a promising technology that can tailor the network to support diverse types of services over a common physical network infrastructure, and tenants can lease the customized slices from network providers to provide customized services for their users. This paper focuses on tenant profit optimization in 5G Radio Access Network (RAN) slicing and analyzes slice resource reservation and reconfiguration operation. A slice cost model is proposed, in which a base cost is included to compensate reconfiguration management overhead for the network providers. Increasing the accuracy of tenant resource requirement prediction can decrease tenant cost while guaranteeing QoS of its users. Further, we analyze the price strategy based on user traffic, and propose several alternative price scheme. In slicing across multiple Base Stations (BSs) scenario, we study two slice management operations, called slice mergence/split, similar but not identical to the breathing behavior of conventional cellular BS management. The tenant profit optimization problem across adjacent BSs is formulated, which can be used in slice mergence/splilt decision making. Taking VR (Virtual Reality) application as an example, simulation in allusion to the typical user arrival process is performed. The simulation indicates how these two operations can increase tenant profits.

For most conventional bionic signal design methods, they cannot construct high-similarity bionic signals to match those complex cetacean sounds because they are only based on relatively simple bionic signal models. Besides, although very few methods based on the weighted signal superposition technology can construct high-similarity bionic signals, it's very difficult to adjust relevant parameters to match different cetacean sounds or synthesize other desired bionic signals. To solve these problems, firstly, two bionic signal models are proposed individually to mimic cetacean sounds with a simple time-frequency (TF) structure, and then they are combined to mimic cetacean sounds with complex TF structures based on a designed piecewise construction strategy. Based on the two models, the parameters of the synthesized bionic signals can be adjusted to improve detection and communication performance of the bionic signals. The experimental results show that the Pearson correlation coefficient (PCC) results between 13 true cetacean sounds and their corresponding bionic signals are higher than 0.97, and 11 results of them are no less than 0.99. Four key performance indicators of a bionic signal are improved by more than 40% when bandwidth increases by 1kHz. Experimental results demonstrate that the proposed method cannot only efficiently imitate all kinds of simple and complex cetacean tonal sounds with high similarity, but also construct a variety of the same type of bionic signals by simply adjusting model parameters. In addition, the proposed method can also be applied to other areas, such as constructing a new cetacean sound database and so on.

With the increasing demand of location-based services, channel state information (CSI) has attracted great interest because of the fine-grained information it provides. In this paper, we propose an original network structure, which exploits both the local information and global information in CSI amplitude for fingerprint localization. First, we validate the correlation between adjacent subcarriers and introduce the position-dependent local feature (PDL-feature). Next, local connection based deep neural network (LC-DNN) is designed to improve positioning performance by extracting and exploiting the correlation between adjacent subcarriers for indoor localization. LC-DNN consists of locally-connected layer and fully-connected layer. In the locally-connected layer, the variation of CSI amplitude in local frequency range is extracted and spliced for rich information. The frequency range and the times of extraction are determined by receptive field length and step size respectively. In the fully-connected layer, not only global features of CSI amplitude are further extracted, but also the function between features and position coordinates is obtained. Experiments are conducted to validate the effectiveness of LC-DNN and investigate the influence of hyper parameters on localization. Moreover, the positioning performance of LC-DNN is compared with four methods based on deep neural networks (DNNs). Results show that LC-DNN performs well in positioning accuracy and stability, with the mean error of 0.78m.

When the radio communication of an armed squad fighting in a jungle is interfered with by enemy's radio jamming equipment and their command and intelligence cannot be transmitted and exchanged, are there any other communication methods that can be used to solve such an emergency and covert communication problem? Focusing on this problem, a camouflage covert communication method (CCCM) in air is proposed by imitating cricket's sound. As well-known, there exist all kinds of animals' sounds in the jungle; however, hearing these animals' sounds, people generally considers them as background noise and ignores them. Based on this fact, the proposed CCCM uses the cricket's sound as the carrier wave, imitates the features of the cricket's call sequence to construct the camouflage communication sequence, and utilizes the time interval (TI) between two adjacent pulses to encode information. Meanwhile, other animals' sound is superimposed to the camouflage communication sequence to improve the camouflage ability of the generated communication sequence. Experimental results are provided to demonstrate the performance of the proposed CCCM.

Recently, the Infrastructure as a Service Cloud (IaaS) (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Owing to the fact that it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on the long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with the disturbances from normal processes.

Attractive monetary or other kinds of incentives are needed to motivate the relayers in device-to-device (D2D) communications. In this paper, little attention has been paid to D2D charge and incentive mechanisms that are also compatible with prevailing mobile service charging models. To help lay a foundation for commercial applications of the D2D, this paper studies the charge, reward, and penalty modes of D2D communication under operator control. The charge and reward mechanisms of D2D services are analyzed from the perspective of the commercial relationships between operators and end users participating in D2D links, deriving the incentive principles based on which a number of examples of practical reward/penalty and charging modes are presented. For unicast services, the user perception of charging and rewards in a D2D relay service is studied in detail for an end-to-end communication process and a reward and penalty metering method, compatible with a base-station-to-device (B2D) billing mode is analyzed. Specifically, in a typical congestion scenario, the probability of a relayer deliberately disconnecting the D2D is estimated and modeled, and the effect of reward and penalty policies on the reliability of D2D services is analyzed quantitatively. For a directional content multicast service, the process of establishing a D2D relay connection with reward status awareness is presented. Furthermore, the prevalence of free-riding can be reduced by measures such as giving users high reward credits with a higher priority in obtaining B2D and D2D services. Finally, following the derived principles, some segmented D2D application scenarios with commercial or social utility are identified as avenues to promote the commercial use of practical D2D relaying.

With the increasing popularity of location-based services, channel state information (CSI) has received widespread attention in positioning due to the fine-grained information it provides. However, the raw amplitudes of CSI are especially sensitive to noise and easily effected by the frequency-selective fading. As the expanding of test area, positioning is disturbed by random noise and insufficient resolution of features severely. In this paper, we propose Cluster-Mapping (C-Map), an adaptive pre-processing system for CSI amplitude-based fingerprint localization. This system models positioning as a classification problem, an adaptive processing mechanism is constructed according to the characteristics of CSI amplitude. C-Map mainly contains two parts: dynamic denoising and feature enhancement. In the dynamic denoising, effective features are screened out by clustering iteratively, without specifying parameters according to the experimental environment. In the feature enhancement, polynomial fitting with regularization is used to reduce jagged fluctuations, then the trend of variation over 30 sub-carriers are described by combining derivation with nonlinear kernel function. Extensive experiments are conducted in typical environments to verify the superior performance of C-Map for the preprocessing of CSI amplitude. Compared with the combination of mean and multidimensional scaling (MDS), the average positioning error of C-Map is reduced 28.1% in comprehensive indoor environment, and 19% in garage. Furthermore, compared with the combination of DBSCAN and principal component analysis (PCA), the average positioning error of C-Map is reduced 33.1% in comprehensive indoor environment, and 28% in garage.

The number of applications based on the Android platform is increasing rapidly now. However, as the supervision and review of Android applications are inadequate, a reasonable chance exists that users will download malware. This malware can lead to information leakage, monetary loss, and other damages. At present, a variety of applications exist for detecting malware, but most of these applications cannot show specific malicious behaviors. Moreover, the operation of this detection software is based on the database of viruses, and thus, it cannot identify unknown malware. To solve these problems, we implemented a system to detect the behaviors of Android applications and identify known or unknown malware. Our system can monitor specified applications utilizing loading a kernel module. After the detection process, the related documents are uploaded to the server, and the dynamic behaviors are reconstructed. As a result, a behavior diagram is generated. In addition, if the user needs to know whether the application is malware, the related Android package is sent to the server and analyzed. Then, the server calculates the results and the results are returned to the client.

The Augmented Intelligence of Things enables many edge or end devices in the Internet of Things (IoT) to perform machine reasoning to make decisions, thus become more intelligent. For healthcare enterprises, huge physical data generated by smart devices facilitate to iterate their products. However, traditional data sharing models based on cloud outsourcing meet many security challenges, such as data confidentiality, reliability, and privacy protection, and most existing schemes have high computational complexity for the utilization of time-consuming cryptographic operations, such as bilinear pairing, which is not suitable for those resource-constrained IoT devices. To tackle the abovementioned issues, we present Double Rainbows, a promising data sharing scheme based on pairing-free searchable encryption. It is constructed on the cloud-edge-end architecture, supporting computing task transfer and reliable data storage and retrieval. The experimental results show that it outperforms in efficiency and exhibit more security functionalities. [ABSTRACT FROM AUTHOR]

In the fog computing, it is difficult to satisfy the security and privacy requirement for traditional access control system, such as attribute-based access control system. Risk-based access control system can adapt to the dynamic fog environment. However, the existing risk schemes are modeled for users in majority, not for fog nodes, and the context and privacy sensitivity are rarely considered. In this article, we propose a risk assessment scheme for fog nodes in access control system. Firstly, to improve the accuracy of risk score, the risk assessment is modeled with the subject, object, and context for fog nodes; Secondly, we address the risk assessment computing module for every component. Moreover, we depict system model for risk assessment and implement its prototype system risk assessment model. In the end, the reasonability and correctness of computing model are analyzed by proving and simulation. According to the experiments, the accuracy of risk score is higher than that of the work-based access control and dynamic access control for IoT. Therefore, the feasibility and effectiveness of this scheme are proved through the experiments. [ABSTRACT FROM AUTHOR]

Once started, existing hash tables cannot change their pre-defined hash functions, even if the incoming data cannot be evenly distributed to the hash table buckets. In this paper, we present DHash, a type of hash table for shared memory systems, that can change its hash function and rebuild the hash table on the fly, without noticeably degrading its service. The major technical novelty of DHash stems from an efficient distributing mechanism that can atomically distribute every node when rebuilding, without locking the corresponding hash table buckets. This not only enables non-blocking lookup, insert, and delete operations, but more importantly, makes DHash independent of the implementation of hash table buckets, such that DHash allows programmers to select the set algorithms that meet their requirements best from a variety of existing lock-free and wait-free set algorithms. Evaluations show that DHash can efficiently change its hash function on the fly. Moreover, when rebuilding, DHash consistently outperforms the state-of-the-art hash tables in terms of throughput and response time of concurrent operations, at different concurrency levels, and with different operation mixes and average load factors. [ABSTRACT FROM AUTHOR]

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS. [ABSTRACT FROM AUTHOR]

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces. [ABSTRACT FROM AUTHOR]