To prevent the abuse of low-rate speech-based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low-bit-rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self-paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under-sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under-sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low-bit-rate speech-based steganography, and the steganalysis scheme based on the self-paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self-paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self-paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.

Wireless energy transfer is a promising technology to prolong the lifetime of wireless sensor networks (WSNs), by employing charging vehicles to replenish energy to lifetime-critical sensors. Existing studies on sensor charging assumed that one or multiple charging vehicles being deployed. Such an assumption may have its limitation for a real sensor network. On one hand, it usually is insufficient to employ just one vehicle to charge many sensors in a large-scale sensor network due to the limited charging capacity of the vehicle or energy expirations of some sensors prior to the arrival of the charging vehicle. On the other hand, although the employment of multiple vehicles can significantly improve the charging capability, it is too costly in terms of the initial investment and maintenance costs on these vehicles. In this paper, we propose a novel charging model that a charging vehicle can carry multiple low-cost removable chargers and each charger is powered by a portable high-volume battery. When there are energy-critical sensors to be charged, the vehicle can carry the chargers to charge multiple sensors simultaneously, by placing one portable charger in the vicinity of one sensor. Under this novel charging model, we study the scheduling problem of the charging vehicle so that both the dead duration of sensors and the total travel distance of the mobile vehicle per tour are minimized. Since this problem is NP-hard, we instead propose a (3+ϵ)-approximation algorithm if the residual lifetime of each sensor can be ignored; otherwise, we devise a novel heuristic algorithm, where ϵ is a given constant with 0 < ϵ ≤ 1. Finally, we evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the performance of the proposed algorithms are very promising.

Abstract Steganography in low bit‐rate speech streams is an important branch of Voice‐over‐Internet Protocol steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support‐vector‐machine‐based steganalysis of low bit‐rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long‐time distribution feature, extract Markov transition probabilities of pulse positions according to the short‐time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse‐to‐pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a‐encoded speech samples and compare it with the state‐of‐the‐art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy, false positive rate, and false negative rate at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods. Copyright © 2016 John Wiley & Sons, Ltd.

Traditional tracking solutions in wireless sensor networks based on fixed sensors have several critical problems. First, due to the mobility of targets, a lot of sensors have to keep being active to track targets in all potential directions, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected when moving into holes. Third, when targets stay at certain positions for a long time, sensors surrounding them have to suffer heavier work pressure than do others, which leads to a bottleneck for the entire network. To solve these problems, a few mobile sensors are introduced to follow targets directly for tracking because the energy capacity of mobile sensors is less constrained and they can detect targets closely with high tracking quality. Based on a realistic detection model, a solution of scheduling mobile sensors and fixed sensors for target tracking is proposed. Moreover, the movement path of mobile sensors has a provable performance bound compared to the optimal solution. Results of extensive simulations show that mobile sensors can improve tracking quality even if holes exist in the area and can reduce energy consumption of sensors effectively.

Steganography in low bit-rare speech streams is an important branch of Voice-over-IP steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support-vector-machine based steganalysis of low bit-rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long-time distribution feature, extract Markov transition probabilities of pulse positions according to the short-time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse-to-pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a encoded samples, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods.

Most recently, a DE framework with neighborhood and direction information (NDi-DE) was proposed to exploit the information of population and was demonstrated to be effective for most of the DE variants. However, the performance of NDi-DE heavily depends on the selection of direction information. In order to alleviate this problem, two adaptive operator selection (AOS) mechanisms are introduced to adaptively select the most suitable type of direction information for the specific mutation strategy during the evolutionary process. The new method is named as adaptive direction information based NDi-DE (aNDi-DE). In this way, the good balance between exploration and exploitation can be dynamically achieved. To evaluate the effectiveness of aNDi-DE, the proposed method is applied to the well-known DE/rand/1 algorithm. Through the experimental study, we show that aNDi-DE can effectively improve the efficiency and robustness of NDi-DE.

Tracking mobile targets is one of the most important applications in wireless sensor networks (WSNs). Traditional tracking solutions are based on fixed sensor nodes and have two critical problems. First, in WSNs, the energy constraint is a main concern, but due to the mobility of targets, lots of sensor nodes in WSNs have to switch between active and sleep states frequently, which causes excessive energy consumption. Second, when there are holes in the deployment area, targets may fail to be detected while moving in the holes. To solve these problems, this paper exploits a few of mobile sensor nodes to continuously track mobile targets because the energy capacity of mobile nodes is less constrained. Based on a realistic detection model, a solution for scheduling mobile nodes to cooperate with ordinary fixed nodes is proposed. When targets move, mobile nodes move along with them for tracking. The results of extensive simulations show that mobile nodes help to track the target when holes appears in the coverage area and extend the effective monitoring time. Moreover, the proposed solution can effectively reduce the energy consumption of sensor nodes and prolong the lifetime of the networks.

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm, successfully used in various scientific and engineering fields. Although DE has been studied by many researchers, the neighborhood and direction information is not fully and simultaneously exploited in the designing of DE. In order to alleviate this drawback and enhance the performance of DE, we first introduce two novel operators, namely, the neighbor guided selection scheme for parents involved in mutation and the direction induced mutation strategy, to fully exploit the neighborhood and direction information of the population, respectively. By synergizing these two operators, a simple and effective DE framework, which is referred to as the neighborhood and direction information based DE (NDi-DE), is then proposed for enhancing the performance of DE. This way, NDi-DE not only utilizes the information of neighboring individuals to exploit the regions of minima and accelerate convergence but also incorporates the direction information to prevent an individual from entering an undesired region and move to a promising area. Consequently, a good balance between exploration and exploitation can be achieved. In order to test the effectiveness of NDi-DE, the proposed framework is applied to the original DE algorithms, as well as several state-of-the-art DE variants. Experimental results show that NDi-DE is an effective framework to enhance the performance of most of the DE algorithms studied.