A Low-Complexity Sequential Spectrum Sensing Algorithm for Cognitive Radio

Community detection is a dominant research field in social media mining. Numerous algorithms have been developed to detect community structure in network. Each of them focuses on different characteristic of data and generates different communities for same dataset. So cluster validation or comparative analysis of these algorithms is required to compare the efficiency of these algorithms. There are two known ways for cluster validation: Internal Quality Measure (IQM) and External Quality Measure (EQM). IQM evaluates the cluster without reference to external information while EQM requires already known clusters i.e. ground truth for comparison. Most of the researchers have compared Community Detection algorithms on the basis of either IQM or EQM but not both. In this paper, we have tried to compare nine existing sequential community detection algorithms on the basis of both IQM and EQM and we observed that overall performance of Louvain, Fastgreedy and Walktrap algorithm is more efficient than other algorithms. From this paper, researchers working in this area can get information about major sequential community detection algorithms and their efficiency on small real world datasets. They can incorporate the findings to develop more efficient algorithms.

Monte Carlo operating characteristic (OC) and average sample number (ASN) functions were compared with Wald's OC and ASN equations for sequential sampling plans based on Wald's sequential probability ratio test (SPRT) using the binomial, negative binomial, normal, and Poisson distributions. This comparison showed that the errors inherent in Wald's equations as a result of "overshooting" the decision boundaries of the SPRT can be large. Relative errors increased for the OC and ASN equations as the difference between the null (θ0)) and alternative (θ1) test parameter values increased. Relative errors also increased for the ASN equation as the probabilities of type I (α) and type II (β) errors increased. For discrete distributions, the relative errors also increased as θ0 increased with θ1/θ0 fixed. Wald's equations, in general, overestimate the true error probabilities and underestimate the true ASN. For the values of θ0, θ1, α, and β used in many sequential sampling plans in forestry, Wald's equations may not be adequate. For those cases where the errors in Wald's equations are important compared with the other errors associated with the sampling plan, two alternative Monte Carlo OC and ASN functions are proposed.

The operating characteristic (OC) and average sample number (ASN) of the sequential probability ratio test (SPRT) and multi-hypothesis SPRT (MSPRT) are studied. We consider the case where the observation sequence is independent but not necessarily identically distributed. Also, the thresholds for the test can be time varying. Based on the governing equations for OC and ASN of the SPRT developed in our previous work, a solution for the general case is proposed. The governing equations for OC and ASN of the MSPRT are also obtained. Numerical solutions for MSPRT are developed. Basically, the solutions rely on approximating the original test by truncation, that is, truncating the test at some finite time $K$ . We show that under some mild conditions, the approximation error diminishes as $K$ increases, at the cost of increased computation. Numerical examples are provided to demonstrate our solutions by comparing with Monte Carlo simulations, Simon’s lower bound, and Dragalin’s method (if available) for ASN.

At present, the communications industry is one of the fastest-growing sectors, as well as an important component of modern society, which has led to rapid growth in demand for the radio frequency spectrum (RF). Since virtually all RF is distributed among corporations providing various communication services, it is becoming increasingly difficult to introduce new communication systems and technologies. The deficit of RF has led to the development of new communication systems aimed at the efficient use of radio frequency resources. These include the Cognitive Radio System (CRS), which makes it possible to use the RF of the primary user efficiently and without reducing the quality of service for all subscribers. In the previous article, we proposed an algorithm for implementing CRS based on models of spectral occupancy. One stage of the algorithm is spectrum monitoring. Spectrum monitoring is used to obtain actual information about the spectrum occupation. There are many scientific works aimed at detecting the signals of primary users. Due to its implementation simplicity and low complexity, the main method of signal detection is the use of an energy detector, which was implemented in our previous article. However, this approach is sensitive to various types of interference and fluctuations signal-to-noise ratio (SNR), due to the dynamics of the radio environment. A new detection algorithm using an adaptive threshold is therefore proposed. The value of the adaptive threshold is selected taking into account the noise power fluctuations, thus avoiding the disadvantages of the classical energy detector. Simulation results show that an adaptive threshold algorithm is more resistant to the dynamic changes of SNR than a classical algorithm. The new algorithm also makes it possible to increase the performance of the algorithm proposed by us for the implementation of CRS based on models of spectral occupancy.

Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.

The mobility status of self and surrounding vehicles provides important information to various tasks in autonomous driving (AD) and intelligent transportation system (ITS). Accordingly, a precise, stable, and robust mobility tracking framework is essential. Compared with self-tracking that relies only on mobility observations from onboard sensors [e.g., global positioning system (GPS), inertial measurement unit (IMU), and camera], cooperative tracking markedly increases the precision and reliability of the mobility information by integrating observations from roadside units (RSUs) and nearby vehicles through vehicle-to-everything (V2X) communications in the Internet of Vehicles (IoV). Nevertheless, cooperative tracking can be quite vulnerable if there are malicious users sending bogus observations in the cooperative network. In this article, we present a malicious user detection framework, which includes two sequential detection algorithms and a secure mobility data exchange and fusion model to detect and remove bogus mobility information and integrate proposed detection algorithms with previous data fusion algorithms, which secures the cooperative mobility tracking in AD, ITS. Simulations validate the effectiveness and robustness of the proposed framework under different types of attacks.

Sequential sampling plans based on the negative binomial distribution were developed for egg mass density of spruce budworm, Choristoneura fumiferana (Clemens), in Michigan's Upper Peninsula. Plans developed were modifications of Wald's sequential probability ratio test (SPRT) based on Monte Carlo simulation. Parameters of the negative binomial distribution were estimated by the maximum likelihood method. Sampling models developed classify egg mass populations into low and high categories using the number of egg masses on whole branch samples of balsam fir, Abies balsamifera (L.) Voss. Plans are presented for three pairs of population density hypotheses to give forest pest managers the flexibility to select a plan that best meets management objectives. Monte Carlo estimates indicated that Wald's average sample number (ASN) and operating characteristic (OC) equations underestimated the actual ASN values, and the actual alpha and beta error probabilities were smaller than the values prescribed by Wald's equations. Errors were sufficiently large to have serious economic implications during implementation of plans developed from Wald's approximate procedure. The sequential plans developed by modifying the decision boundaries of Wald's SPRT using the Monte Carlo procedure had error probabilities, OC functions, and ASN values that were approximately equal to those desired.

Energy detection is popularly used in the detection of idle spectrum because of its simple implement and low complexity. But, the detection performance can be observably affected due to noise uncertainty. Papers have proposed some solutions to resist the effects of noise uncertainty. However, most of these papers are assumed that the noise uncertainty interval is known to the detector, designing algorithms to detect the primary user signal. Few algorithms are designed to estimate the range of the noise uncertainty. This paper presents an algorithm for energy detection based on noise variance estimation that can be relatively accurate estimated the noise uncertainty interval. The simulation results show that in the case of the noise uncertainty interval is unknown to the detector, our proposed algorithm can detect well, which is similar to the theoretical detection, and its complexity is also very low.

The suitability of using the presence of eggs or hatched eggs as a predictor of larval damage from Cryptophlebia spp. in macadamia and litchi was evaluated. Although larval damage or presence was not independent of egg presence, eggs were not suitable as a predictor of damage. Less than 51% of the macadamia nuts with eggs had larvae or larval damage, and 25%, the average sample number was 10%, then the average sample number is <50 fruit.

In this paper, we present a low-complexity low data rate (LDR) ultra wideband (UWB) transceiver prototype CMOS chip suitable for a differential binary phase shift keying (DBPSK) modulation applied data transmission with 500 MHz-bandwidth signals. This solution, which is mainly based on "1.5-bit" direct sub-sampling at 1 Gsps and differential digital correlations, enjoys good immunity to clock drift and interferers. It is also likely to offer an interesting trade-off between fine performance in dense multipath environments, low implementation complexity, and hence low power consumption. At first, the 1.5 bits concept is discussed. Then, the implementation choices for some critical blocks of the RX data path are discussed to justify the adopted low-complexity approach. Finally, some preliminary implementation results are provided.

Practical and low complexity implementation of receivers is of vital importance for the successful penetration of the ultra-wideband (UWB) technology. The role of a synchronization technique is essentially to determine the relative delay of the received signal with respect to a template signal in the receiver, and it is difficult to implement due to the ultra-fine time-resolvability of the UWB signal. In this paper, a new method for synchronization based on sliding correlation theory and energy detection is proposed, which can reduce the average acquisition time considerably. The algorithm is shown to have enhanced efficiency compared to the conventional serial search algorithm and the degradation in performance is limited. For binary PPM modulation, the key design parameter selections that minimize the BER are derived and analyzed. Simulations corroborate our results.

In this paper, we propose a DDoS defense architecture, named NEIF (Network Egress and Ingress Filtering), which is deployed at the Internet Service Provider's (ISP) edge routers to prohibit DDoS attacks into and from the ISPs' networks. The main challenge is how to implement NEIF with a small fixed amount of memory and low implementation complexity so that it may be acceptable by ISPs. We first design a bloom filter based data structure to identify and measure a few relatively large flows instead of all flows, where the amount of required memory is independent of link speeds and the number of flows. Then, the relatively large flows are rate-limited to their fair share based on the packet symmetry-the ratio of received and transmitted packets of a host. The dropping decisions of each flow are made on the observed counters directly that are with low implementation complexity. Finally, we implement NEIF with Click and perform experiments on PlanetLab. The experimental results validate our analysis and show that the Internet can benefit from NEIF even under partial deployment.

This paper considers the problem of synchronization in frequency hopping systems. A simple digital signal processing block along with the existing RF circuits forms a phase detector that generates a signal corresponding to the phase difference between the hop timings of the local and received signals. The two hop-timing acquisition algorithms proposed in this paper - based on energy detection - utilize the phase detector and result in low-complexity implementations. In the first one, a simple cost function relying on the power sum definition estimates the hop timing. This estimator allows for only a limited acquisition range. Based on this study, we propose a hop-timing estimator with a full acquisition range which still maintains low-complexity. Furthermore, this paper analyzes the optimal threshold to detect the signal existence based on the maximum likelihood detection rule.

Sequential analysis of variance was first dealt with by Johnson (1953) for equal numbers in groups and the results were extended to unequal numbers by Jones (1968) and Jones and Hinkelmann (1969). The average sample number (A.S.N.) obtained for an irregular design is usually lower than that for equal subclass numbers and this advantage is enhanced if sampling starts with the design. In this article Monte Carlo results are given for two classifications models with fixed effects, namely, one-way classification and twoway classification without interaction. Different methods are adopted to obtain the minimum starting design for unbalanced models and the resulting A.S.N. of these are compared. In all cases the probabilities of errors of the two kinds are taken to be 0.05.

The expression for the Operating Characteristics (OC) and Average Sample Number (ASN) functions are derived under inspection error. Two types of error are defined i.e. (i) failing to find one or more of the nonconformities in the sample and (ii) declaring one or more nonconformities when none exist. It is seen by OC and ASN curves that inspector errors have considerable effects on the results of a sequential test for Poisson distribution.