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The growth of the number of connected devices in Internet of Things (IoT) systems causes a huge increase in network traffic. Thus, there is a significant demand for systems that can predict the measurements of the distributed IoT-based applications to mitigate the increasing network traffic. Existing methods utilized a distributed scheme, i.e., dual prediction schemes (DPS), to achieve this task. The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner. The state-of-the-art results can be simply reached using predictive models, e.g., adaptive filters. Deep learning-based approaches are not well utilized to address this problem. In this context, we proposed a distributed scheme utilizing fog and edge computing technologies. The proposed DPS includes an LSTM predictive model to reduce the data transmission instances of connected IoT devices; it is called LSTM-DPS. In addition, we proposed an updating mechanism that updates the LSTM model according to a set of tracking parameters that observes the model behavior during the deployment due to the changes of data properties through time. The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center. The LSTM-DPS is evaluated using two real datasets. The obtained results show that the LSTM-DPS outperforms state-of-the-art methods in terms of communication reduction ratios.

A cooperative molecular communication (CMC) system is considered inside a cylindrical-shaped channel where a few cooperative nodes (CNs) are intermediately placed between a transmitter (TX) and a fusion center (FC). The expressions for the maximum achievable rate and probability of error at the FC considering AND and OR rules are derived. Furthermore, the performance of the CMC system in a cylindrical channel is compared with the direct and CN-assisted systems. The CMC system with randomly-placed CNs is also analyzed and compared with the uniformly-placed CNs, and it is found that a lower probability of error is obtained in the case of uniform placement\ud of CNs. Furthermore, the system performance as a function of radial displacement of TX and FC under constant flow is compared with that under laminar flow and a higher probability of error is observed under laminar flow. The increased probability of error under laminar flow occurs due to the fact that the drift velocity decreases towards the walls of the cylindrical channel. The analytical expressions are verified using Monte-Carlo simulations.

Spectrum sensing is a fundamental component in a cognitive radio network to utilize the frequency bands effectively. In this article a fast Riemannian distance-based K-medoids (FRDK)-based cooperative spectrum sensing (CSS) method is developed to identify the state of primary user (PU). In particular, two CSS scenarios are considered, one is secondary users (SUs) with a single antenna and the other is SUs with multiple antennas. In the multiantenna case, a Riemannian mean-based data fusion method is proposed to fuse sensing data from SUs with multiple antennas. To implement CSS, we propose a FRDK-based framework, where SUs collect sensing data, preprocess, and upload these data to an appointed fusion center (FC). Then, the FC transforms these data as samples on a manifold and uses the FRDK algorithm to train a classifier for identifying the state of PU. Furthermore, the convergence and the complexity analysis of training process are presented. Finally, the effectiveness of the proposed FRDK-based CSS method is verified under different conditions.

Deep cooperative sensing is a cooperative spectrum sensing (CSS) algorithm based on a deep neural network (DNN). Since training DNN requires a large amount of sample data, what is worse existing algorithms directly send training data to the fusion center (FC), which greatly occupies the transmission channel. Motivated by this, in this paper, we introduce the federated learning framework to CSS and propose a federated learning-based spectrum sensing (FLSS) algorithm. In the federated learning framework, there is no need to gather data together. Each secondary user (SU) uses local data to train the neural network model and sends the gradient to FC to integrate the parameters. This framework can perform collaborative training while ensuring local data privacy and greatly reducing the traffic load between SU and FC. Besides, we adopt an efficient network model ShufflenetV2 to reduce the number of parameters and improve training efficiency. Simulation results demonstrate that the FLSS can achieve a detection probability of 98.78% with a false alarm probability of 1% at SNR = -15dB.

In this paper, we detect illegal spectrum access behaviors in the dynamic spectrum sharing of the cognitive wireless networks, in which multiple authorized users (AUs) work in the same channel. First, we detect whether there are users occupy the channel, and if the channel is occupied, then recognize whether there is the illegal user (IU) and infer the number of AUs roughly. In the light of the thorny challenge that legal spectrum utilization behaviors and illegal spectrum access behaviors coexist probabilistically, the spectrum sensing problem is formulated as a mathematical model of ternary hypothesis test. Moreover, when multiple AUs are working at the same time, it is extremely difficult to detect IU. To tackle the problem, taking into account the various potential combinations of AUs and IU, we exploit a two-step detector and derive related detection schemes based on the generalized likelihood ratio test (GLRT), the Rao test, and the Wald test following the generalized multi-hypothesis Neyman-Pearson (GMNP) criterion respectively. Additionally, we design a cooperative spectrum sensing (CSS) scheme on the basis of the global GMNP criteria, in which the distributed detection framework composed of K sensing nodes and a fusion center (FC). Finally, to verify the performance of the proposed detection scheme in a variety of parameter configurations, we provide comprehensive simulations and find that there will be at most (2N-1) detection valleys when N AUs work at the same time. Compared with single-sensing node detection, the CSS scheme can significantly improve the detection performance of the proposed detection schemes, especially in the upper bound and valley of the detection performance.

Economic power dispatch (EPD) is a significant managing problem in power systems. Control and management programs in large-scale systems, especially the power system, can be run centrally or in a distributed manner, each with its own advantages and disadvantages. If the power system is managed as a central fusion center, it will be vulnerable to natural disasters and cyber-attacks on the control center, despite its fast processing speed. On the other hand, if power system is managed in a distributed manner, information will be exchanged between all buses, and there is a risk that the security of the system will be compromised and intercepted by spreading information on all buses. The combination of security and system speed at the same time can ensure that power system needs are met. This paper addresses the solution of the EPD by defining a novel computational architecture, namely hybrid network architecture that merges both distributed and centralized architectures to speed up obtaining the solution of the EPD in large-scale power systems, while maintaining the necessary security level. This proposed method allows adjusting the convergence speed of the operating system along with setting the cybersecurity of the distributed system. This can play a key role in operating system reliability, which includes speed and security. To perform the optimization step, using the alternating direction method of multipliers (ADMM) framework and hybrid network architecture, a decentralized algorithm, named hybrid ADMM (HADMM), is developed to be used in solving the EPD problem. Besides, an acceleration scheme that exploits the relationship between ADMM and Douglas–Rachford splitting is proposed, which results in a faster algorithm called accelerated hybrid ADMM (AHADMM). The simulation results of the proposed algorithm applied to solve the EPD problem on the IEEE 118-bus system show the acceptable performance of the proposed algorithm under different scenarios.

In this paper, we consider a two-way network-coded relaying system consisting of two Primary Users (PUs) communicating via the relay node as the primary network with licensed spectrum. Multiple IoT devices capable of harvesting energy from a power beacon, utilize the same spectrum as Secondary Users (SUs). The SUs cooperate with the primary network and then transmit their own data. The devices harvest their required energy from a power beacon (PB), located near the primary relay node and store the energy in finite-size batteries. In phase one, the primary network communicate using the network coding technics and in the second phase, the SUs cooperate with the relay node by retransmitting the relay’s data to PUs. In the last phase, SUs transmit their gathered information to a fusion center (FC). We apply TDMA and Signals Combining (SC) schemes in cooperation phase and derive the closed-form expressions for the outage sumrate of the secondary network. To increase the outage sumrate of the secondary network, a new NOMA-based simultaneous cooperate and transmit (NBSCT) scheme with a new time schedule is proposed. The corresponding expressions are derived and compared with that of the mentioned methods for various network parameters. The numerical results verifies that the outage sumrate of secondary network in proposed NBSCT scheme outperforms the TDMA and SC schemes under Rician fading and improves the outage sumrate by approximately $$9.8\%$$ .

In this article, we investigate the analog–digital hybrid transceiver optimization for distributed Internet-of-Things (IoT) sensing networks consisting of a multiantenna fusion center (FC) and several multiantenna sensor nodes. Analog–digital hybrid transceiver is an economic way to realize tradeoffs between hardware cost and performance for multiantenna communications. Under the nonconvex unit modulus constraints and transmit power constraint at each sensor, two synchronization schemes are considered for the hybrid linear minimum mean-square error (LMMSE) transceiver optimization. First, a centralized algorithm is proposed, in which the hybrid transceivers are computed at the FC. Based on the framework of alternating direction method of multipliers (ADMMs), the unit modulus constraints can be satisfied by projecting the elements of analog transceivers onto the unit modulus circle. However, the centralized algorithm usually suffers from strict synchronous requirements and high communication overhead. In order to accommodate the inevitable computing and communication delays in distributed IoT sensing networks, an asynchronous distributed ADMM (AD-ADMM) algorithm is proposed. By using the aged information, the hybrid transceivers are computed at the sensors without the coordination of the FC. Thus, the AD-ADMM algorithm can greatly reduce the computation overhead of the FC and improve the scalability of IoT sensing networks. Simulation results are presented to show that both the centralized ADMM and AD-ADMM algorithms perform closely to the fully digital counterpart.

This article is concerned with the distributed networked fusion estimation problems for unstable systems with limited communication capacity, where the system and measurement noises are unknown but bounded. To overcome the unboundedness of measurement information for unstable systems, it is proposed to quantize the innovations that are sent to the fusion center over bandwidth constrained channels. By using the bounded recursive optimization idea, the design problems of local stable estimators and distributed fusion criterion under the quantized innovations are converted into two different convex optimization problems that can be easily solved by the standard software. The target tracking system is employed to demonstrate the effectiveness of the proposed methods.

This paper concentrates on a globally sequential fusion state estimation problem for clustered wireless sensor networks (WSNs). Therein, frequent data communications in WSN will significantly increase energy consumption and communication burden, and it needs to be taken into account. To reduce unnecessary data transmissions and save energy, an event-triggered mechanism that decides whether the current measurement should be transmitted or not, therefore, is introduced. Then a novel variational Bayesian based event-triggered sequential measurement fusion (VB-ESMF) estimator is proposed to produce the local fused results of the clustered WSNs, where the variational Bayesian approach is used to infer the measurement noise covariance matrices of pseudo measurement noises. Therein, the local fused measurements of the clusters in the clustered WSNs are used sequentially by the remote fusion center, and thus a global fusion state estimation, which is globally optimal when all measurements are transmitted successfully, is computed. Additionally, certain boundedness and convergence conditions of the proposed estimator are derived, and an expected compromise between communication rate and estimation accuracy can be obtained by properly turning the trigger threshold. Finally, the effectiveness of both the VB-ESMF estimation and the globally sequential fusion state estimation is illustrated by simulation results.

In this article, the problem of power allocation in a wireless multiple access channel with two sensors is addressed, where squared channel coefficients, and, hence, signal-to-noise ratios are assumed to have Gaussian mixture distributions and the dependence between channel coefficients is described by using copula theory. For this model, outage probability is analyzed as the objective function for formulating the power allocation, and for the product copula function or in the independent channels case, a closed form of outage probability is obtained. Considering that the corresponding optimizations do not lead to convex optimization problems, MATLAB simulation is employed to solve the power allocation problem. Various Gaussian mixture distribution parameters are exploited for the links between the sensors and the fusion center with different dependencies and the results are compared.

Distributed detection in wireless sensor networks is considered, where the decisions made by local sensors are transmitted towards a fusion center through parallel binary symmetric channels. Our contribution is to show that, when the number of local sensors approaches infinity and the channel capacity from the local sensor to the fusion center is larger than the entropy of any local decision, there exists one method in theory that the local decision can be sent to the fusion center without distortion. Correspondingly, the asymptotically optimal link bit error probability between each sensor and the FC is determined. The resulting distributed detection system can reach asymptotically the same detection performance as that with ideal communication. Although the proposed method is asymptotically optimal in nature, it is valid to the distributed detection system when the quantity of the sensors is above dozens. The simulation confirms its validity. Moreover, when compared with the ideal one, simulations confirmed that the proposed scheme achieves a significant saving of communication resource at the expense of a small reduction in the detection performance for the system with dozens of sensors.

Multi-sensor millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor networks (WSNs) relying on both distributed (D-MIMO) and centralized (C-MIMO) configurations are conceived. Hybrid combining based low complexity fusion rules are constructed for the fusion center (FC) for both D-MIMO and C-MIMO systems employing a partially connected structure (PCS) and a fully connected structure (FCS), respectively. The decision rules are based on the transmission of local binary sensor decisions and also take into account the accuracy of local detection at the individual sensors. Closed-form analytical expressions are derived for the probabilities of false alarm and correct detection to analyze the system’s performance. Furthermore, the asymptotic distributed detection (DD) performance corresponding to both antenna architectures is analyzed in the large-scale antenna regime along with the pertinent power scaling laws. Additionally, digital signaling matrices are designed for enhancing the system performance. Our simulation results quantify the performance gains of the proposed architectures, which closely match the analytical results.

This work conceives the robust linear transceivers for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) multi-sensor network under imperfect channel state information (CSI) at the fusion center (FC). Both the popular stochastic (S-) and norm ball CSI uncertainty (N-CSIU) models are considered for robust design. The proposed techniques are based on two design criterion, the first being, minimizing the mean squared error (MSE) of the estimate at the FC subject to total network power or individual sensor power constraints. Second, minimizing the total power consumption in the network while meeting a predefined level of MSE performance. Furthermore, the framework for precoder and combiner optimization is based on results from majorization theory, which leads to non-iterative closed-form solutions for the transceivers. While the most general scenario with correlated parameters and arbitrary observation SNR is considered to begin with, scenarios with uncorrelated parameters and high observation SNR are also considered as special cases, which makes the analysis comprehensive. Simulation results are presented to demonstrate the efficacy of the proposed schemes.

Cognitive radio network is an intelligent and adaptive communication technology used to effectively utilize the radio spectrum to provide better communication. It enables the secondary (unlicensed)...

This paper presents collaborative spectrum sensing for optical wireless scattering communications. The received signal is characterized by the conditional Poisson distribution, given the mean photon count following Malaga distributed correlated fading. We consider multi-scattered non-coplanar links and use the stochastic analytical method to model the channel. A system model is developed, utilizing the cooperative diversity drawn from the fact that the users are randomly distributed. A novel centralized cooperative spectrum sensing technique is developed that all the raw data available at each collaborative user are combined at the fusion center to make a decision. Another novel decentralized technique is also proposed where only one-bit information is required to be sent to the fusion center. For this decentralized technique, we propose a unique voting rule that leverages the location flexibility of randomly distributed users with each user experiencing correlated fading. Results demonstrate their effectiveness and provide insights into the proposed cooperative spectrum sensing system. Interestingly, it is also found that the use of the proposed voting rule requires fewer than the total number of collaborative users while satisfying a given error rate, and yields the Bernoulli distributed output of the fusion center.

Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users’ sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback–Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU’s reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

The paper considers distributed detection problem basis on using soft decision scheme both in the local sensors and in the fusion center (FC). The algorithm for making soft decisions when receiving data from local sensors in the fusion center and its performance characteristics, which are necessary for the formation decision fusion rule, are presented. The dependencies of the total error probability on the energy parameter taking into account signal-to-noise ratio at the level of local sensors and the channel’s signal-to-noise ratio are given. The gain of the fusion rule basis on the aggregation of soft decisions in the FC when receiving data about soft local decisions, in efficiency compared to hard fusion rule.

Spectrum sensing is used to detect whether primary user are using authorized spectrums, which can be regarded as a key and core issue for opportunistic spectrum access in cognitive radio networks. In traditional information theory and clustering algorithm-based spectrum sensing methods, they need to evaluate noise environment for constructing a reference point. However, the reference point is fixed, which is unreasonable in dynamic cognitive radio environment. Moreover, these methods convert signal feature from manifold onto Euclidean space, which will cause to overall performance degradation, since sensing information losses. To address this problem, an information geometry (IG)-based K-means clustering algorithm, namely IGK, is developed, it clusters samples on manifold instead of Euclidean space with an unsupervised way to train a classifier for spectrum sensing. Specifically, secondary users observe and collect data from a selected authorized spectrum, which needs to be detected, and send these sensing data to a fusion center (FC). Then, the FC transforms these data into samples on the manifold to obtained a classifier by using the proposed IGK algorithm. According to the trained classifier, we can get related result of the authorized spectrum. Finally, in simulation section, the effectiveness of the proposed scheme is verified under different conditions.

This article studies the performance evaluation of distributed linear regression Kalman filtering fusion for nonlinear systems. Sufficient conditions are established for the convergence of the centralized fusion (CF) under the assumption of bounded estimation error covariance, and a measure of performance is derived from the convergence conditions. By the performance analysis, it can be found that the CF has a better performance than the distributed fusion with feedback, especially at the beginning of the estimation. Moreover, the performance of the local estimator can be improved by receiving the fused estimate from the fusion center, which is different from the fusion estimation in linear systems. Finally, by simulations of a target tacking example, the comparisons of the centralized fusion and the distributed fusion with and without feedback are presented to show the accuracy of the performance analysis.

In this article, we address the problem of distributed detection of a noncooperative (unknown emitted signal) target with a wireless sensor network. When the target is present, sensors observe a (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things, one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The fusion center receives sensor bits via noisy binary symmetric channels and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e., the target position $\boldsymbol {x}_{T}$ ) observable only under $\mathcal {H}_{1}$ hypothesis. Davies’ framework is exploited herein to design the generalized forms of Rao and locally optimum detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold optimization is also proposed. The simulation results confirm the promising performance of our proposed approaches.

This paper deals with spectrum sensing using the energy detector (ED) with multiple hops multibranch relaying where the primary user (PU) and relays harvest energy from radio frequency signal transmitted from a given node A. The harvested energy is used by PU and relays to transmit signals to the fusion center where the ED is used to detect the PU. The study is valid for amplify and forward relaying, any number of hops and any number of branches. We also suggest a new lower bound of the detection probability using the cumulative distribution function of signal-to-noise ratio (SNR). When there are many available branches, only the best one is activated. The activated branch offers the highest end-to-end SNR.

Change-point detection (CPD) from streaming data is a fundamental problem in statistical signal processing and has wide applications in wireless sensor networks and the Internet of Things (IoT), such as environmental monitoring and physical activity detection. In various detection schemes, decentralized detection without fusion center is becoming increasingly popular for IoT applications since it enjoys the benefit of strong robustness and security. However, it faces the big challenge of high energy consumption. In this work, we propose an energy-efficient decentralized CPD algorithm called request–response and censoring-based cumulative sum. Specifically, we design a novel communication strategy based on request–response and censoring scheme, which could help the sensors extract useful information from the neighbor sets with a low amount of communication. Furthermore, to provide a guideline on selecting the parameters involved in our algorithm, we theoretically analyze the performance of the proposed algorithm under a simplification for the most interesting cases, in terms of two standard criteria average running length (ARL) and expected detection delay (EDD). Numerical simulations are conducted to verify the effectiveness of the proposed algorithm. Moreover, we test the feasibility of the proposed algorithm in the physical activity CPD task. The experimental results on the real-world data set demonstrate its high energy efficiency and small detection delay.

In this letter, we investigate the problem of power allocation in wireless sensor networks, where transceiver hardware impairments (HWIs) at both the sensors and fusion center (FC) are considered. In addition, the received signal at FC is corrupted with the additive non-Gaussian noise, which is characterized by a Gaussian mixture distribution. For this purpose, the Bhattacharyya distance is analyzed as the objective function for formulating the transmit power allocation. Then, we obtain an approximate closed-form expression for the Bhattacharyya distance between two Gaussian mixture distributions. Moreover, the empirical impulsive noise model, i.e., the Bernoulli–Gaussian and the Poisson–Gaussian models, are exploited for the simulation, and the results are compared with each other.

Cooperative spectrum sensing allows users to detect available spectrum and utilize it. However, it is known that a few users can easily affect the cooperative decision at the fusion center by reporting falsified sensing data. The problem of detecting malicious users in cooperative spectrum sensing has been studied by numerous researchers. The basic approach is to compute the credibility of the reported data of each user and declare those with low credibility (below some threshold) as malicious. The computation of credibility can be based on several things such as pattern of historic behavior, entropy of the reported data, signal-to-noise ratio, etc. The credibility of the reported data of some user node can be expressed as an attack probability of that node. A node with higher attack probability will have lower credibility while a node with lower attack probability a higher credibility. Then, the problem of computing credibility of a node becomes the problem of computing attack probability of a node given a set of reported data. We can enumerate a list of all possible attack probability vectors for a set of nodes and the list of all possible energy level vectors of the channel for the observed time period, and compute which combination of attack probability and channel energy level vector can have the maximum probability to produce the reported channel energy level. However, the search space is quite large and grows exponentially as the number of user nodes and the number of time slots to observe increase. In this paper, we suggest algorithms that reduce the search space considerably and detect malicious users in linear time instead of exponential time. The suggested algorithms have been implemented and show promising results.

Cooperative spectrum sensing (CSS) is an efficient method to detect the reliability of the free spectrum, however, with great overhead and energy consumption. In this paper, we study a much broader application of CSS, where the sensors are used to sense multiple channels. On the one hand, since the tiny and low-cost sensors do not have high-speed analog-to-digital-convertors, they cannot simultaneously sense more than one channel. Therefore, the simultaneous multi-channel CSS is an important issue in a cognitive sensor network (CSN). On the other hand, these tiny sensors do not have high-power batteries, which makes the network lifetime as an important metric. In this paper, node selection is proposed for the multi-channel CSS to maximize the lifetime of a CSN under some detection constraints with lower overhead than cooperative sensing by all the sensors simultaneously. We analyze the problem for the OR and the AND rules, which can be implemented at the fusion center. The problem is solved by using convex optimization methods where assignment indices for every sensor are assumed. We provide a performance analysis through simulations using MATLAB, which shows that the sensor selection scheme provides a significant long lifetime for a CSN compared to the case where sensors are selected randomly and where all sensors are just classified to sense the channels simultaneously.

This paper proposes a novel centralized Constant false alarm ratio (CFAR) detector for multistatic sonar systems. The detector employs the idea of Variability index (Ⅵ) CFAR detection, to adaptively select the matched detection algorithm in diversified undersea environments. All the echo data from mutistatic sonar receivers are transmitted into the centralized fusion center. Firstly, the background statistics of reference cells from different nodes are analyzed. Then choose one appropriate centralized detection algorithm according to the background statistics, which refers to the centralized Cell averaging CFAR (CA-CFAR), greatest of CFAR, order statistic CFAR detection algorithms. The performance of the proposed detector is analyzed by computer simulation and measured sonar data. The results show that, compared to the centralized CA-CFAR detector, the introduced centralized detector achieves a better robustness in multiply heterogeneous undersea environments.

In nano-networking, many nano-machines need to share common communication media, in order to achieve information exchange and data fusion. Multiple-access is an important technique for multiple nano-machines to send information to one access point (AP) or fusion center. Built on the Molecular Shift Keying (MoSK) modulation, this article proposes a novel Molecular Type Spread MoSK (MTS-MoSK) scheme for achieving multiple-access transmission in diffusion-based molecular communications (DMC). Correspondingly, two detection schemes are introduced and investigated, which are the Maximum Selection assisted Majority Voter Detection (MS-MVD) and Equal-Gain Combining Detection (EGCD). The error performance of the MTS-MoSK DMC systems with respectively the two detection schemes is analyzed, when both Multiple-Access Interference (MAI) and Inter-Symbol Interference (ISI) are taken into account. Furthermore, the performance of MTS-MoSK DMC systems is investigated and compared with the aid of analytical results as well as Monte-Carlo and particle-based simulations. Our studies and performance results show that MTS-MoSK constitutes a promising candidate for implementing multiple-access DMC, and MS-MVD has the potential to outperform EGCD in DMC.

Smartphones equipped with motion sensors can be manipulated as a community seismic network for earthquake detection. But, there still have many challenges such as the limited sensing capability of the off-the-shelf sensors and unpredictable diversity of daily operations by phone users in current smartphones, which yield poor monitoring quality. So we present a suite of algorithms towards detecting anomalous seismic events from sampling data contaminated by users operations, including a lightweight signal preprocessing method, a two-phase events picking and timing scheme on local smartphones, and a decision fusion scheme to maximize anomaly detection performance at the fusion center while meeting the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smartphones and shake tables. The results verify the effectiveness of our approach in distinguishing anomalous seismic events from noises due to the normal daily operation.

In this work, we propose a joint collaboration-compression framework for the random signal detection problem in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a linear collaboration matrix) with each other. Then a subset of sensors linearly compress their aggregated information before communicating with the fusion center (FC). We propose a novel metric called generalized deflection coefficient (GDC) for evaluating the detection performance which is shown to be tightly upper bounded by the Kullback-Leibler divergence for Gaussian observations. We jointly design the linear collaboration and compression strategies under power constraints via alternating maximization of the proposed GDC metric. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

We consider a distributed detection problem where measurements at each sensor follow a general parametric distribution. The network does not have a central processing unit or fusion center (FC). Thus, each node takes some measurements, does some processing, exchanges messages with its neighbors and finally makes a decision (typically the same for all nodes) about the phenomenon of interest. The problem can be formulated as a composite hypothesis test with unknown parameters where, in general, a uniformly most powerful test does not exist. This leads naturally to the use of the Generalized Likelihood Ratio (GLR) test. As the measurements follow a general parametric distribution (which could model spatial dependence of the data), the implementation of fully-distributed detection procedures could be demanding in network resources. For this reason, we study the use of a simpler test (referred as L-MP) which uses the product of the marginals of the measurements taken at each node, where the unknown parameters are easily estimated with only local measurements. Although this simple proposal still requires network-wide cooperation between nodes, the number of communications is significantly reduced with respect to the GLR test, making it a suitable choice in severely resource-constrained sensor networks. This simpler test does not exploit the full parametric model of data, so, it becomes important to analyze its statistical properties and its potential performance loss. This is done through the analysis of the L-MP asymptotic distribution. Interestingly, despite the fact that the L-MP is simpler and more efficient to implement than the GLR test, we obtain some conditions under which the L-MP has superior asymptotic performance to the GLR test. Finally, we present numerical results for a fully-distributed spectrum sensing application for cognitive radios., 13 pages, 6 figures. Submitted to the IEEE Transactions on Signal and Information Processing over Networks

Multisource information perception is the basic means for humans to explore the universe, and information decision fusion has become a crucial technique in some fields. Limited by the unknown distribution of multisource information, this article proposes a decision fusion method based on the distributed wavelet neural network (DWNN) and the Bayesian inference. The proposed fusion decision framework is a parallel network that consists of an empirical wavelet filtering layer, feature extraction layer, local decision layer, and decision fusion layer. Notice that the activation function of the feature extraction layer is a wavelet, and this nonlinear operation can be considered as a wavelet transform of the multisource data. Subsequently, an iterative learning method is adopted to minimize the estimated loss of the subnetwork and approximate the optimum decision model for local data. Furthermore, a decision fusion rule with the minimum Bayesian-like cost based on local evidence is adopted in the fusion center. Finally, a typical multisource decision fusion experiment of human surface electromyography (sEMG) and time series classification is presented to show the effectiveness of the proposed decision fusion structure.

In this paper, we develop a new algorithm for centralized target detection in passive MIMO radar (PMR) using sparse recovery technique. PMRs use a network of receivers and illuminators of opportunity to detect and localize targets. We consider a widely separated PMR network assuming the availability of reference channels. We first transform the collected information of all receivers to a common space and combine them to attain a unified model. The problem of target detection in the extracted model is equal to a block sparse recovery problem. Since employing the generic sparse recovery tools are impractical due to the ultra-large dimension of the sensing matrix, we exploit the structure of the involving matrices and propose a very efficient distributed algorithm which extracts all scatterers, including targets and clutter simultaneously with a unified procedure. The proposed algorithm is highly efficient, and it does not require a high bandwidth link to transfer raw data from nodes to the fusion center. Moreover, the algorithm inherently benefits from parallel processing and distributes the extensive computations among receivers. Our simulation results demonstrate that the proposed algorithm outperforms the popular PMR detection algorithm, especially in the presence of interfering targets and any strong clutter residue.

Hidden Primary User problem caused by fading and shadowing severely affects the detection rate of the cognitive radio systems with a single spectrum sensor. Cooperative Spectrum Sensing has been introduced to tackle this problem by using the spatial diversity of spectrum sensors. It is shown that the use of soft decision algorithms in fusion center has a better performance than hard decision algorithms. The problem of soft decision based on sensor measurements perfectly matches the Machine Learning paradigm. In this brief, a novel fast soft decision algorithm is proposed based on Machine Learning theory for wideband Cooperative Spectrum Sensing, which finds a decision boundary to classify the Power Spectral Density (PSD) measurement vectors of sensors into the empty and occupied channel classes. The statistical characteristics of sensors PSD samples are employed to derive a fast solution, which outperforms the SVM-linear algorithm. By solving an optimization problem a Constant False Alarm Rate algorithm is introduced and then it is improved to the Adaptive False Alarm Rate algorithm. The proposed algorithm reduces the average training time to one tenth of the SVM-linear training time, while the detection probability is the same. The hardware implementation of the proposed algorithm is described in Verilog HDL and the corresponding simulation results are presented.

This paper is concerned with the Tobit Kalman fusion estimation problem for a class of multi-sensor systems subject to parametric uncertainties and measurement censoring. The parametric uncertainty is characterized by the multiplicative noise appearing in both state and observation equations, and the measurement censoring is governed by the Tobit observation model. The fusion estimation is implemented via two stages: at the first stage, each sensor sends its observations to the local estimator and, at the second stage, the local estimates are then transmitted to the fusion center so as to generate the fused estimate. The local estimator realizes a Tobit Kalman filtering algorithm which is devised in accordance with a modified regression model, whilst the fusion center carries out the fusion estimation by resorting to the federated fusion rule. Furthermore, the monotonicity of the fused error covariance with respect to the censoring threshold is discussed and, subsequently, the boundedness property is also examined where both lower and upper bounds are acquired for the fused error covariance. The validity of the fusion estimator is finally shown via two numerical examples.

This paper considers the problem of sequential binary hypothesis testing based on observations from a network of $m$ sensors where a subset of the sensors is compromised by a malicious adversary. The asymptotic average sample number required to reach a certain level of error probability is selected as the performance metric of the system. We propose an asymptotically optimal voting algorithm for the sensor network with a fusion center and generalize it to fully-distributed networks, where the algorithm stays asymptotically optimal under the weak assumption that the sensor network is connected. Moreover, we prove that both of the proposed algorithms are asymptotically optimal in the presence of Byzantine sensors, in the sense that each of them forms a Nash equilibrium with the worst-case attack (flip-attack). Compared to existing distributed detection strategies, the proposed scheme has a low message complexity, which is independent of the error probability and the sample number, by taking advantage of the sparsity of votes. The results are corroborated by numerical simulations.

We address the problem of distributed detection in multiple sensor systems in the context of underwater target detection. A number of sensor nodes are employed to detect the presence or absence of ...

This paper studies the distributed dimensionality reduction fusion estimation problem for cyber-physical systems with limited bandwidth in presence of eavesdroppers. Since wireless communication is implemented by broadcasting, the eavesdroppers can collude to collect the data through anther communication networks. To protect data privacy, based on the physical processes and local estimation error covariance (EEC) matrix, an insertion method of artificial noise (AN) is developed such that only eavesdroppers’ fusion EEC becomes worse. Meanwhile, the fusion center needs to decode the received signal due to the noise interference, while the successful decoding probability varies with signal to noise ratio. Subsequently, some criteria for the selection probabilities and the successful decoding probabilities are given to guarantee the effectiveness of the AN insertion strategy. Moreover, a sufficient condition of the designed AN power is derived to guarantee the confidentiality. Simulation examples are given to show the effectiveness of the proposed methods.

In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

Trajectory planning of an automated vehicle requires behavior knowledge of preceding target vehicles (PTVs), and lateral states, such as lateral velocity and yaw rate are key enablers for precise behavior description. However, lateral states of a PTV can hardly be measured directly by common onboard sensors. In addition, although these states can be transmitted via vehicle to vehicle (V2V), the accuracy is limited by the state holding process during the communication interval. Aiming at improved PTV lateral states, this article considers the estimation of these states using visual and V2V measurements, and proposes a three-stage fusion architecture consisting of input estimator, heterogeneous model-based local estimators, and fusion center. Specifically, to cope with the low rate of the received steering angle, the input estimator is constructed to obtain high rate control inputs. In the local estimator design, unlike the conventional modeling problems mixing control input errors with model errors in process noise, this paper separates them and develops a model adaption algorithm to compensate the time-varying covariance of the errors of the estimated control inputs. Then, to fuse the heterogeneous local estimates in the fusion center, a subfull model-based information matrix filter is designed to address this specific heterogeneous fusion problem, in which the local models are treated as submodels of a common full model. Hardware-in-the-loop experimental results show that the proposed method gives more accurate estimates in comparison with other approaches.

In this paper, in order to maximize a long-term average system transmission rate, we propose an optimal energy cooperation policy for the sustainable wireless sensor networks where each pair of nodes can harvest energy and transfer energy in the pair. We formulate this optimization problem into a Markov decision process (MDP). We define a node pairing metric-relative energy distance ratio (REDR)-to design a node pairing algorithm which can decouple the original MDP into a multiple low dimension MDP. Then, value iteration and relative value iteration algorithms are brought to find optimal energy cooperation policies over finite and infinite horizons for each pair of nodes respectively. Simulation results show that the system transmission rate of the proposed policy with REDR based pairing algorithm outperforms the non-cooperation policies as well as the policies with straightforward pairing algorithms.

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output (MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound (PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization (MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search (ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

In reality, multiple radar system (MRS) may have some limited resource, such as the data computation capacity of the fusion center and the transmit energy of each radar. To better exploit its limited system resource, a collaborative detection and power allocation (CDPA) scheme is developed for the application of target tracking in clutter. The basis of the CDPA scheme is to use optimization technique to control the false alarm rate (FAR) and transmit power of each radar in view of the aforementioned resource constraints, while achieving better target state estimation accuracy. The Bayesian Cramer–Rao lower bound is derived, relaxed, and subsequently utilized, as the optimization criterion for the CDPA strategy. The resulting nonlinear and nonconvex optimization problem consists of two adaptable vectors, one for FAR selection and the other for power allocation. By introducing an auxiliary vector, a fast two-step solution technique is presented to jointly select the FAR and distribute the transmit power. Simulation results demonstrate that, with given data computation capability and system total power budget, the CDPA scheme can evidently expand the detection range, increase the resource utilization efficiency of the MRS, and improve the target tracking accuracy.

Monitoring the road condition has gain significant importance in last few years. Among existing road monitoring technologies, wireless sensor network (WSN) is favored by researchers because of its low cost and flexibility in deployment. Specifically, it can collect road condition data spontaneously and transmit the information to a fusion center that can determine the damage level or status of the road surface. However, there is a big challenge preventing the wide deployment of WSN that the sensor nodes can only survive for a limited time in practice. It is because majority of the power has been spent on the continuous wireless transmission of the extensive road data to the fusion center. In this study, we propose a low-power temperature data transmission scheme based on compressed sensing in combination with self-powered road surface wireless monitoring sensor system to realize data sparseness and compression and reduce system power consumption. Experimental results show that the compressed data can reduce the power consumption requirement by 50.78% under the allowable reconstruction error by comparison with the existing approaches.

Sequential reporting has the potential of reducing the duration of the reporting phase of the cooperative spectrum sensing (CSS) schemes, thereby increasing the overall system throughput. In this article, we study decision-based CSS using random medium-access sequential reporting and a K-out-of-M decision rule at the fusion center. Closed-form expressions are derived for the detection and false alarm probabilities as well as the average reporting time of two early termination sequential reporting schemes, assuming a reporting channel shared with the primary user (PU). Furthermore, a heuristic energy-based reporting priority scheme that favors cognitive radio (CR) terminals based on their capacity to detect the PU is proposed. Computer simulations are used to assess the accuracy of the derived expressions and to evaluate the proposed heuristic reporting priority scheme.

Instead of only considering the radar estimation error in the traditional radar system (TRS), for the integrated radar and communication system (IRCS), we investigate the Cramer-Rao bound (CRB) of the localization estimation, which is influenced by both radar estimation error and communication transmission error. The functions of radar and communication are operated simultaneously by embedding the communication symbols into the multicarrier radar waveforms. Firstly, we derive the CRB of time/direction of arrival (TOA/DOA) estimation. To minimize the estimation error, we maximize the signal-to-interference-plus-noise ratio (SINR) of radar by iteratively optimizing the radar transmit and receive beamformers (with the constraint of available transmit power). Then, the CRB of localization estimation is derived using hybrid TOA/DOA measurement. The local CRBs from different IRCSs are fused according to the linear fusion rule at the fusion center (FC). Finally, numerical results demonstrate that the additional estimation errors for the IRCS are mainly determined by the channel conditions of communication and available transmit power; the estimation accuracy for both IRCS and TRS can be improved through the iterative transmit and receive beamforming (ITRB) technique.

Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a nonlinear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods---an advantage that becomes more pronounced when the sensor network grows large., Accepted for publication in IEEE Transactions on Signal and Information Processing over Networks

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.