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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 $$9.8\%$$ .

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.

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.

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 a large-scale wireless sensor network, weak event signals spread over a certain region that contains a subset of sensors located in close proximity, thus forming a spatial cluster around the event. Only this subset of sensors receives credible information from the event and information of the remaining far away sensors will be dominated by background noise, therefore less credible and faulty. We exploit this fact for improving the performance of multi-sensor fusion rule by proposing novel techniques that give importance to the nearby sensors and utilize their proximity information. The proposed mechanism is demonstrated to improve the performance of fusion rule when (1) fusion center receives soft information from the local sensors and (2) when sensors send binary hard information in the form of 1/0 to the fusion center. For the assessment of the proposed schemes, simulation results are provided to show that the proposed schemes have better performance compared to the traditional schemes.

In this paper, we investigate the physical layer security (PLS) performance for the Internet of Things (IoT), which is modeled as an IoT sensor network (ISN). The considered system consists of multiple power transfer stations (PTSs), multiple IoT sensor nodes (SNs), one legitimate fusion center (LFC) and multiple eavesdropping fusion centers (EFCs), which attempt to extract the transmitted information at SNs without an active attack. The SNs and the EFCs are equipped with a single antenna, while the LFC is equipped with multiple antennas. Specifically, the SNs harvest energy from the PTSs and then use the harvested energy to transmit the information to the LFC. In this research, the energy harvesting (EH) process is considered in the following two strategies: 1) the SN harvests energy from all PTSs, and 2) the SN harvests energy from the best PTS. To guarantee security for the considered system before the SN sends the packet, the SN’s power is controlled by a suitable power policy that is based on the channel state information (CSI), harvested energy, and security constraints. An algorithm for the nearly optimal EH time is implemented. Accordingly, the analytical expressions for the existence probability of secrecy capacity and secrecy outage probability (SOP) are derived by using the statistical characteristics of the signal-to-noise ratio (SNR). In addition, we analyze the secrecy performance for various system parameters, such as the location of system elements, the number of PTSs, and the number of EFCs. Finally, the results of Monte Carlo simulations are provided to confirm the correctness of our analysis and derivation.

The lack of spectrum resources restricts the development of the wireless communication-oriented applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, cognitive radio is regarded as an effective technology. Cooperative spectrum sensing with multi cognitive users can improve the low detection performance caused by channel fading or shadow effect. However, it also may lead to poor detection accuracy due to poor channel conditions of individual users. In order to solve the above problems, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

In this paper, we derive the detection probability of cooperative spectrum sensing using the energy detector with multiple branches and multihops in each branch. The primary signal passes through $$L_{i}$$ hops in the ith branch. Two cooperation protocols are considered. In the first one, the fusion center combines the signal of all branches, and it is known as all-participating relaying. It requires multiples channels for branch transmissions to avoid interferences. In the second one, only the branches with the highest SNR are activated. The results are valid for any number of hops and branches. We also consider situations where the direct link is available or not. We have studied the evolution of the detection probability with respect to the signal-to-noise ratio for a constant false alarm rate.

Cognitive radio (CR) is a key enabling technology of dynamic spectrum access to achieve better utilization of frequency spectrum. Cooperative spectrum sensing (CSS) exploits the spatial diversity between CR users to improve the accuracy of spectrum sensing. But, in practice, the reliability of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In this type of attack, some malicious CR sensors intentionally report falsified local sensing results to a fusion center and disrupt the global decision-making process. In this study, we present a smart and robust defense scheme called attack-aware CSS (ACSS). The idea is based on the estimation of attack strength where the attack strength corresponds to the attack population. The attack strength is defined as the ratio of the number of malicious sensors to the total number of sensors which is equal to the probability that a specific sensor is malicious. Assuming the malicious sensor behavior or attack strategy, the proposed approach estimates attack strength and uses the Bayesian hypothesis test to improve the collaborative sensing performance. Computer simulation results illustrate the effectiveness of the proposed ACSS approach against SSDF attacks, especially for CR networks located in hostile environments.

This paper investigates the problem of Cognitive Radio Network (CRN) with Cooperative Spectrum Sensing (CSS), when multiple idle channels are available. In this work CRN-CSS is modeled to resolve the problems of sensing, grouping and decision making. To enlarge network connectivity and provide larger coverage for users, we integrate CRN-CSS with 5G. Trinary partitioning is performed to group user and perform sensing in cooperative manner. Sensing of multiple channels leads to interference that is overwhelmed by the novel Dynamic Multi-Channel Slot Allocation (DMCSA) algorithm which allocates channel effectively. To address the challenges of spectrum decision, we have presented a special entity (i.e.) Spectrum Agent which is deployed to perform only spectrum sensing and report to fusion center. Fusion center is responsible for decision making and spectrum allocation, for global decision fusion center constructs a graph based on the reports obtained from secondary users and spectrum agent. These reports are compared for making final decision about spectrum. On the whole we describe with a detailed architecture of the proposed CRN-CSS model with seamless integration of 5G cellular networks that achieves higher throughput efficiencies. The obtained simulation results demonstrate the proposed CRN-CSS model with 5G is a remarkable cellular network design to improve throughput, detection probability, delay and sensing overhead.

In this paper we investigate the problem of cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) in the presence of malicious attacks from secondary users (SUs). We propose a novel CSS approach based on a new adaptive reputation and evidential reasoning theory (ERT) to detect the malicious SUs, to strengthen the sensing ability, and to keep good stability and generalization ability. Different from the previous work, we assume that the fusion center has no prior information of the radios in CRNs except the condition that the honest SUs are in majority. Specifically, to counter different attack strategies, we present an adaptive reputation which fully considers the signal-to-noise ratio, historical sensing data information, and current local sensing results via an energy detection method. The proposed adaptive reputation can effectively identify the malicious SUs when the CRNs are with single-attack or multiple-attack models. Furthermore, we present a CSS scheme via the proposed adaptive reputation and ERT on the basis of local sensing results derived from the energy detection method. The new CSS scheme has good robustness and generality and can decrease the computational complexity and overcome the evidence conflict problems of Dempster–Shafer theory-based CSS schemes. Extensive simulations are carried out to demonstrate the performance and the superiority of the proposed approach in contrast with previous methods.

In this paper, we evaluate the detection and false alarm probabilities for relay based spectrum sensing techniques using the energy detector. The communication process consists of two phases. In the first phase, the primary user P transmits and K relays listen. In the second phase, the relays amplify the signal to the fusion center where spectrum sensing based on the energy detector is performed. All relays transmit over orthogonal channels. We also consider cooperative spectrum sensing with best relay selection. Both opportunistic amplify and forward, partial and reactive relay selection are considered. The results are valid for Rayleigh fading channels in the absence or presence of a direct link between P and the fusion center D.

Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.

To overcome the frequency spectrum scarcity, cognitive radio (CR) network is proposed where secondary users opportunistically use vacant frequency band. CR intrinsic properties impose threats to these wireless networks. Smart primary user emulation attack (PUEA) is one of these threats where a malicious user performs spectrum sensing and after being aware of radio environment sends fake signals identical to the primary user signals with the desired signal occurrences in both vacant and occupied licensed frequency bands. In this paper, while considering smart PUEA, we propose a soft cooperative spectrum sensing method. In this method, the sensing information values of N secondary users are transmitted to the fusion center (FC) and combined with some coefficients with the aim of minimizing the error probability of primary user detection, under the given constant false alarm probability. We also propose a method for quantization of this sensing information that should be sent to FC. Furthermore, we have worked on the investigation of channel estimation error impact on detection performance. Simulation results show that our method significantly improves the spectrum sensing probability of the error, in comparison with hard combination scheme, AND fusion and OR fusion rules, either considering quantization or in the presence of channel estimation error.

The national network of fusion centers, of which there are currently 78 nationwide, was created in response to the terrorist attacks of September 11, 2001 and continue to play an integral role in contemporary law enforcement. Their mission, put simply, is to facilitate information sharing across disparate agencies and organizations. Despite a significant presence within the law enforcement landscape, fusion centers have received relatively minimal scholarly attention. This limited literature alludes to operational challenges and public concerns that inhibit fusion center effectiveness. More specifically, little information is known about how fusion centers develop relationships with external partners as well as institute mechanisms to safeguard against violations of individual privacy. The present research employs a combination of national survey data and three in-depth case studies of fusion centers in Florida, Nevada, and Michigan to provide initial answers to these questions. Implications for improved policy and practice are discussed.

The eigenvalue (EV) fusion technique was recently proposed for detecting idle subchannels of OFDMA signals in centralized cooperative spectrum sensing for cognitive radio (CR). It has been shown that the technique outperforms the conventional decision fusion, in spite of the larger volume of data reported to the fusion center. It has been conjectured, though, that bit errors in the reporting channel could be more disastrous to the data carrying CR decisions than to the data carrying EVs. In this paper we investigate this conjecture and conclude that it is partially true: CR decisions can be more sensitive to channel errors, but the amount of redundancy inserted to protect the decisions does not always lead to a larger number of bits compared to the EV fusion. Then, performance and traffic in the reporting channel must be traded when deciding upon the fusion scheme to be adopted. We also suggest a modified version of the original EV fusion and show that it can achieve approximately the same performance of the original one, with a significant reduction in the reporting channel traffic.

In order to improve the throughput of cognitive radio (CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.

We present a novel distributed cooperative spectrum sensing algorithm from compressive sampling in wideband cognitive radio (CR) networks. Each CR utilizes compressive sampling to reduce data acquisition costs. A subspace method is then adopted to directly detect occupied channels without reconstructing the sparse spectrum. To obtain the spatial diversity gain, global signal subspace is estimated by the distributed projection approximation subspace tracking (DPAST) algorithm in which the CRs exchange information locally and cooperate without the need for a fusion center. Then, the orthogonality property of the signal subspace and noise subspace can be exploited to find spectral support to complete the spectrum sensing. We study the convergence behavior of the DPAST algorithm and evaluate the performance of spectrum sensing. Simulation results indicate that the DPAST can effectively estimate the global signal subspace, and the proposed compressed wideband spectrum sensing scheme performs better than spectrum sensing at a single CR.

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods.

Cognitive radio (CR) provides a mechanism for effective spectrum usage. Spectrum sensing is an essential component of CR which comprises intelligent signal processing algorithms to identify the spectrum holes. In this paper, we propose a novel soft combining scheme, called mean cumulative sum (MCS), at the cognitive base station or Fusion center (FC), and we have investigated our proposed method through closed form expressions of probability of detection and probability of false alarm, respectively. We have also provided a method of obtaining an optimum threshold to minimize the sensing error which occurs in FC. Our simulation results show that proposed MCS combining scheme outperforms single user spectrum sensing and performs equally well as that of equal gain soft combining scheme. In addition, our results show that sensing error is reduced when optimum threshold is chosen at the FC.

In this paper, the performance of cooperative spectrum sensing (CSS) with censoring of cognitive radio (CR) users under several schemes of fusions at fusion center (FC) has been assessed in the presence of Rayleigh fading. Improved energy detector (IED) with multiple antennas has been used at each CR user. The CR users utilize selection combining (SC) of the decision statistics obtained by IEDs with multiple antennas for making hard decisions about PU and transmit the decisions to the FC using BPSK if the CR user is selected to transmit. Censoring of CR users has been considered based on quality of the reporting channels between CRs and a fusion center (FC). A training based channel estimator is used at FC to estimate the fading coefficients characterizing the channels between the CR users and The FC. Several types of fusion rules such as OR-logic, AND-logic, majority-logic and maximal ratio combining (MRC) fusion rules are considered at the FC for estimating the performance in terms of missed detection (Qm), total error (Qm + Qf) and proposed weighted total error probabilities. Performance of CSS in terms of missed detection is evaluated for various values of IED parameter (p) and number of antennas (M) at each CR. The performance in terms of total error probability has also been estimated for several values of normalized detection thresholds, sensing and reporting channel SNRs under both perfect and imperfect channel estimation. Performance comparison amongst the fusion rules have also been presented under both perfect and imperfect channel estimation under different channel and network conditions.

In this work, we propose a multi-camera object tracking method with surprisal observations based on the cubature information filter in visual sensor networks. In multi-camera object tracking approaches, multiple cameras observe an object and exchange the object’s local information with each other to compute the global state of the object. The information exchange among the cameras suffers from certain bandwidth and energy constraints. Thus, allowing only a desired number of cameras with the most informative observations to participate in the information exchange is an efficient way to meet the stringent requirements of bandwidth and energy. In this paper, the concept of surprisal is used to calculate the amount of information associated with the observations of each camera. Furthermore, a surprisal selection mechanism is proposed to facilitate the cameras to take independent decision on whether their observations are informative or not. If the observations are informative, the cameras calculate the local information vector and matrix based on the cubature information filter and transmit them to the fusion center. These cameras are called as surprisal cameras. The fusion center computes the global state of the object by fusing the local information from the surprisal cameras. Moreover, the proposed scheme also ensures that on average, only a desired number of cameras participate in the information exchange. The proposed method shows a significant improvement in tracking accuracy over the multi-camera object tracking with randomly selected or fixed cameras for the same number of average transmissions to the fusion center.

This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to a fusion center while the process noise does exist. We present a specific condition-properly short intermittent communication period/packet loss under which a proposed optimal distributed algorithm can be equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, using some recent results, a suboptimal distributed fusion algorithm is motivated. Numerical examples show that this algorithm still works well and significantly better than the standard distributed Kalman filtering fusion with a proper modification due to packet loss even if the condition is violated strongly.

This paper considers a cognitive radio (CR) system in non-ideal fading wireless channels and proposes cooperative spectrum sensing schemes based on coherent multiple access channels (MAC), serving as an alternative way to improve the cooperative spectrum sensing performance and provide space diversity for spectrum sensing. Sufficient statistics are transmitted using a common channel from the secondary users (SUs) to a fusion center (FC) where the global decision is obtained. The optimal scaling factors of the proposed schemes are obtained by maximizing the detection probability under a target false alarm probability and a transmit power constraint. Because the proposed optimal MAC scheme has high computational complexity, a sub-optimal solution based on maximization of the deflection coefficient (DC) is also proposed. Simulation results show that the proposed algorithms can significantly improve the spectrum sensing performance and approach the detection baseline.

A novel networked data-fusion method is developed for the target tracking in wireless sensor networks (WSNs). Specifically, this paper investigates data fusion scheme under the communication constraint between the fusion center and each sensor. Such a message constraint is motivated by the bandwidth limitation of the communication links, fusion center, and by the limited power budget of local sensors. In the proposed scheme, each sensor collects one noise-corrupted sample, performs a quantizing operation, and transmits quantized message to the fusion center. Then the fusion center combines the received quantized messages to produce a final estimate. The novel data-fusion method is based on the quantized measurement innovations and decentralized Kalman filtering (DKF) with feedback. For the proposed algorithm, the performance analysis of the estimation precision is provided. Finally, Monte Carlo simulations show the effectiveness of the proposed scheme.

Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the malicious secondary users (SUs). In this paper we propose a reputation-based collaborative spectrum sensing scheme to improve the security of cooperative sensing by mitigating the impacts of misbehaviors. The fusion center calculates the reputation rating of each SU according to their history reports to weight their sensing results in the proposed scheme. We analyze and evaluate the performance of the proposed scheme and its advantages over previous schemes in expansibility and integrity. Simulation results show that the proposed scheme can minimize the harmful influence from malicious SUs.

Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth. We consider the problem of target tracking in a WSN with quantized measurements in this paper. Attention is focused on the design of measurement quantizer with adaptive thresholds. Based on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy, an adaptive design method for quantization thresholds is proposed. Due to the nonlinear measuring and quantization models, particle filtering (PF) is adopted in the fusion center (FC) to estimate the target state. Posterior Cramer-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also derived. Finally, a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach.

The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. As the cognitive users (secondary users) are only allowed to use a licensed spectrum in the absence of its rightful owner, the ability to accurately sense the presence of the rightful owners (primary users) is highly essential. The traditional way of having individual secondary users perform their own spectrum sensing is vulnerable to the presence of noise and shadowing of propagation channel. Cooperative spectrum sensing emerges as an attractive alternative that exploits the inherent geospatial diversity of multiple cognitive radios to enhance the robustness of sensing accuracy. Two specific issues in cooperative spectrum sensing are discussed in this paper. The first issue is on dynamic detection of primary user’s bands. A dynamic band clustering (DBC) algorithm that uses K-means clustering technique is proposed in this paper. The proposed algorithm reduces the number of erroneous narrow subbands resulting from spurious noise. This in turn minimizes the number of subbands to be detected and hence the overall sensing time. The second issue is on reducing the overheads required to facilitate fusion center operation. A novel entropy-based maximal ratio combining for decision-fusion center is also proposed in this paper. Based on extensive simulation studies, the proposed fusion technique is shown to be comparable to conventional information-fusion techniques. The performance offered by the proposed technique is achieved with significant reduction in the bandwidth overheads.

Compressed sensing offers a new wideband spectrum sensing scheme in Cognitive Radio (CR). A major challenge of this scheme is how to determinate the required measurements while the signal sparsity is not known a priori. This paper presents a cooperative sensing scheme based on sequential compressed sensing where sequential measurements are collected from the analog-to-information converters. A novel cooperative compressed sensing recovery algorithm named Simultaneous Sparsity Adaptive Matching Pursuit (SSAMP) is utilized for sequential compressed sensing in order to estimate the reconstruction errors and determinate the minimal number of required measurements. Once the fusion center obtains enough measurements, the reconstruction spectrum sparse vectors are then used to make a decision on spectrum occupancy. Simulations corroborate the effectiveness of the estimation and sensing performance of our cooperative scheme. Meanwhile, the performance of SSAMP and Simultaneous Orthogonal Matching Pursuit (SOMP) is evaluated by Mean-Square estimation Errors (MSE) and sensing time.

In a multi-sensor central level tracking system, owing to random delay in transmission and varying preprocessing time for different sensor platforms, an earlier measurement from the same target can arrive at the fusion center after a later one. Practical data fusion schemes are challenged by the inevitable appearance of measurements that are out of sequence, called, “out-of-sequence measurements” (OOSMs). The question is how to incorporate these OOSMs in a track that has already been updated with a later observation in order to enhance the performance of the tracking system. Several approaches for a sequential algorithm to find a solution for the OOSM problem have been discussed in previous papers. An approach to address the OOSM problem in the probabilistic multi-hypothesis tracker (PMHT), being a batch algorithm, was proposed in previous paper. However, the situation of this approach was not an OOSM case but, rather, an out of sequence scan (OOSS) where a batch of data was lost and then only one scan of measurements from the lost batch arrived with the present batch. In this paper, we propose an approach that has a measurement reordering step to address the OOSM problem in the PMHT within the framework of the OOSM case and report on the performance with the simulation results. The simulation results indicate that the proposed approach may be a suitable solution for the OOSM problem in PMHT under the proper conditions of length of batch, amount of lag, density of clutter, and probability of detection for the target.

We consider sensor networks with a specific signal processing objective. The networks are organized in architectures comprised of sensor clusters whose cluster heads are connected via a backbone network. The data collected by the sensors are finally fused at a fusion center to satisfy the designated signal processing objective. Data operations and their time limitations are dictated by the signal processing objective, in conjunction with the power and life-span constraints of the sensors. The limited life-span of the sensors induce time-varying cluster traffic rates, and, thus dynamics in the operation of any rate allocation schemes. In this paper, we introduce a traffic monitoring algorithmic system, which detects changes in cluster traffic rates and dictates subsequent adaptations in the deployed traffic allocation techniques. We analyze the performance of the monitoring algorithmic system. We also analyze the stability of the coupled monitoring-rate allocation system. We finally present numerical results for some specific system parameters.

In this paper we propose fusion methods for tracking a single target in a sensor network. The sensors use sequential Monte Carlo (SMC) techniques to process the received measurements and obtain random measures of the unknown states. We apply standard particle filtering (SPF) and cost-reference particle filtering (CRPF) methods. For both types of filtering, the random measures contain particles drawn from the state space. Associated to the particles, the SPF has weights representing probability masses, while the CRPF has user-defined costs measuring the quality of the particles. Summaries of the random measures are sent to the fusion center which combines them into a global summary. Similarly, the fusion center may send a global summary to the individual sensors that use it for improved tracking. Through extensive simulations and comparisons with other methods, we study the performance of the proposed algorithms.

In order to resolve the multisensor multiplied maneuvering target tracking problem, this paper presents a distributed interacted multiple model multisensor joint probabilistic data association algorithm (DIMM-MSJPDA). First of all, the interacted multiple model joint probabilistic data association algorithm is applied to each sensor, and then the state estimation, estimation covariance, model probability, combined innovation, innovation covariance are delivered to the fusion center. Then, the tracks from each sensor are correlated and the D-S evidence theory is used to gain the model probability of an identical target. Finally, the ultimate state estimation of each target is calculated according to the new model probability, and the state estimation is transmitted to each sensor. Simulations are designed to test the tracking performance of DIMM-MSJPDA algorithm. The results show that the use of DIMM-MSJPDA algorithm enables the distributed multisensor system to track multiplied maneuvering targets and its tracking performance is much better than that of IMMJPDA algorithm.

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.

In this paper, we propose a two-layer sensor fusion scheme for multiple hypotheses multisensor systems. To reflect reality in decision making, uncertain decision regions are introduced in the hypotheses testing process. The entire decision space is partitioned into distinct regions of “correct”, “uncertain” and “incorrect” regions. The first layer of decision is made by each sensor indepedently based on a set of optimal decision rules. The fusion process is performed by treating the fusion center as an additional “virtual” sensor to the system. This “virtual” sensor makes decision based on the decisions reached by the set of sensors in the system. The optimal decision rules are derived by minimizing the Bayes risk function. As a consequence, the performance of the system as well as individual sensors can be quantified by the probabilities of correct, incorrect and uncertain decisions. Numerical examples of three hypotheses, two and four sensor systems are presented to illustrate the proposed scheme.

We consider the decentralized detection problem, in whichN independent, identical sensors transmit a finite-valued function of their observations to a fusion center which then decides which one ofM hypotheses is true. For the case where the number of sensors tends to infinity, we show that it is asymptotically optimal to divide the sensors intoM(M-1)/2 groups, with all sensors in each group using the same decision rule in deciding what to transmit. We also show how the optimal number of sensors in each group may be determined by solving a mathematical programming problem. For the special case of two hypotheses and binary messages the solution simplifies considerably: it is optimal (asymptotically, asN→∞) to have all sensors perform an identical likelihood ratio test, and the optimal threshold is very easy to determine numerically.