Biblio

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

This paper presents a new framework for SATCOM jamming resiliency in the presence of a smart adversary jammer that can prioritize specific channels to attack with a non-uniform probability of distribution. We first develop a model and a defense action strategy based on a Markov decision process (MDP). We propose a greedy algorithm for the MDP-based defense algorithm's policy to optimize the expected user's immediate and future discounted rewards. Next, we remove the assumption that the user has specific information about the attacker's pattern and model. We develop a Q-learning algorithm-a reinforcement learning (RL) approach-to optimize the user's policy. We show that the Q-learning method provides an attractive defense strategy solution without explicit knowledge of the jammer's strategy. Computer simulation results show that the MDP-based defense strategies are very efficient; they offer a significant data rate advantage over the simple random hopping approach. Also, the proposed Q-learning performance can achieve close to the MDP approach without explicit knowledge of the jammer's strategy or attacking model.

The disadvantages and inextensibility of the traditional network require more novel thoughts for the future network architecture, as for ICN (Information-Centric Network), is an information centered and self-caching network, ICN is deeply rooted in the 5G era, of which concept is user-centered and content-centered. Although the ICN enables cache replacement of content, an information distribution scheduling algorithm is still needed to allocate resources properly due to its limited cache capacity. This paper starts with data popularity, information epilepsy and other data related attributes in the ICN environment. Then it analyzes the factors affecting the cache, proposes the concept and calculation method of Gene value. Since the ICN is still in a theoretical state, this paper describes an ICN scenario that is close to the reality and processes a greedy caching algorithm named GDGCA (Gene Driven Greedy Caching Algorithm). The GDGCA tries to design an optimal simulation model, which based on the thoughts of throughput balance and satisfaction degree (SSD), then compares with the regular distributed scheduling algorithm in related research fields, such as the QoE indexes and satisfaction degree under different Poisson data volumes and cycles, the final simulation results prove that GDGCA has better performance in cache scheduling of ICN edge router, especially with the aid of Information Gene value.

To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.

Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Boğaziçi University network.

In the globalized knowledge economy, big data analytics have been widely applied in diverse areas. A critical issue in big data analysis on personal information is the possible leak of personal privacy. Therefore, it is necessary to have an anonymization-based de-identification method to avoid undesirable privacy leak. Such method can prevent published data form being traced back to personal privacy. Prior empirical researches have provided approaches to reduce privacy leak risk, e.g. Maximum Distance to Average Vector (MDAV), Condensation Approach and Differential Privacy. However, previous methods inevitably generate synthetic data of different sizes and is thus unsuitable for general use. To satisfy the need of general use, k-anonymity can be chosen as a privacy protection mechanism in the de-identification process to ensure the data not to be distorted, because k-anonymity is strong in both protecting privacy and preserving data authenticity. Accordingly, this study proposes an optimized multidimensional method for anonymizing data based on both the priority weight-adjusted method and the mean difference recommending tree method (MDR tree method). The results of this study reveal that this new method generate more reliable anonymous data and reduce the information loss rate.

In the globalized knowledge economy, big data analytics have been widely applied in diverse areas. A critical issue in big data analysis on personal information is the possible leak of personal privacy. Therefore, it is necessary to have an anonymization-based de-identification method to avoid undesirable privacy leak. Such method can prevent published data form being traced back to personal privacy. Prior empirical researches have provided approaches to reduce privacy leak risk, e.g. Maximum Distance to Average Vector (MDAV), Condensation Approach and Differential Privacy. However, previous methods inevitably generate synthetic data of different sizes and is thus unsuitable for general use. To satisfy the need of general use, k-anonymity can be chosen as a privacy protection mechanism in the de-identification process to ensure the data not to be distorted, because k-anonymity is strong in both protecting privacy and preserving data authenticity. Accordingly, this study proposes an optimized multidimensional method for anonymizing data based on both the priority weight-adjusted method and the mean difference recommending tree method (MDR tree method). The results of this study reveal that this new method generate more reliable anonymous data and reduce the information loss rate.

Influence Maximization is an important research content in the dissemination process of information and behavior in social networks. Because Hill Climbing and Greedy Algorithm have good dissemination effect on this topic, researchers have used it to solve this NP problem for a long time. These algorithms only consider the number of active nodes in each round, ignoring the characteristic that the influence will be accumulated, so its effect is still far from the optimal solution. Also, the time complexity of these algorithms is considerable. Aiming at the problem of Influence Maximization, this paper improves the traditional Hill Climbing and Greedy Algorithm. We propose a Hybrid Distribution Value Accumulation Algorithm for Influence Maximization, which has better activation effect than Hill Climbing and Greedy Algorithm. In the first stage of the algorithm, the region is numerically accumulating rapidly and is easy to activate through value-greed. Experiments are conducted on two data sets: the voting situation on Wikipedia and the transmission situation of Gnutella node-to-node file sharing network. Experimental results verify the efficiency of our methods.

Shortest path queries on road networks are widely used in location-based services (LBS), e.g., finding the shortest route from my home to the airport through Google Maps. However, when there are a large number of path queries arrived concurrently or in a short while, an LBS provider (e.g., Google Maps) has to endure a high workload and then may lead to a long response time to users. Therefore, path caching services are utilized to accelerate large-scale path query processing, which try to store the historical path results and reuse them to answer the coming queries directly. However, most of existing path caches are organized based on nodes of paths; hence, the underlying road network topology is still needed to answer a path query when its querying origin or destination lies on edges. To overcome this limitation, we propose an edge-based shortest path cache in this paper that can efficiently handle queries without needing any road information, which is much more practical in the real world. We achieve this by designing a totally new edge-based path cache structure, an efficient R-tree-based cache lookup algorithm, and a greedy-based cache construction algorithm. Extensive experiments on a real road network and real point-of-interest datasets are conducted, and the results show the efficiency, scalability, and applicability of our proposed caching techniques.

The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.

Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.

Fog-Cloud framework is being regarded as a more promising technology to provide performance guarantee for IoT applications, which not only have higher requirements on computation resources, but also are delay and/or security sensitive. In this framework, a delay and security-sensitive computation task is usually divided into several sub-tasks, which could be offloaded to either fog or cloud computing servers, referred to as offloading destinations. Sub-tasks may exchange information during their processing and then have requirement on transmission bandwidth. Different destinations produce different completion delays of a sub-task, affecting the corresponding task delay. The existing offloading approaches either considered only a single type of offloading destinations or ignored delay and/or security constraint. This paper studies a computation offloading problem in the fog-cloud scenario where not only computation and security capabilities of offloading destinations may be different, but also bandwidth and delay of links may be different. We first propose a joint offloading approach by formulating the problem as a form of Mixed Integer Programming Multi-Commodity Flow to maximize the fog-cloud provider's revenue without sacrificing performance and security requirements of users. We also propose a greedy algorithm for the problem. Extensive simulation results under various network scales show that the proposed computation offloading mechanism achieves higher revenue than the conventional single-type computation offloading under delay and security constraints.

Differential privacy is an approach that preserves patient privacy while permitting researchers access to medical data. This paper presents mechanisms proposed to satisfy differential privacy while answering a given workload of range queries. Representing input data as a vector of counts, these methods partition the vector according to relationships between the data and the ranges of the given queries. After partitioning the vector into buckets, the counts of each bucket are estimated privately and split among the bucket's positions to answer the given query set. The performance of the proposed method was evaluated using different workloads over several attributes. The results show that partitioning the vector based on the data can produce more accurate answers, while partitioning the vector based on the given workload improves privacy. This paper's two main contributions are: (1) improving earlier work on partitioning mechanisms by building a greedy algorithm to partition the counts' vector efficiently, and (2) its adaptive algorithm considers the sensitivity of the given queries before providing results.

Named Data Networking (NDN) is a future Internet architecture, NDN forwarding strategy is a hot research topic in MANET. At present, there are two categories of forwarding strategies in NDN. One is the blind forwarding(BF), the other is the aware forwarding(AF). Data packet return by the way that one came forwarding strategy(DRF) as one of the BF strategy may fail for the interruptions of the path that are caused by the mobility of nodes. Consumer need to wait until the interest packet times out to request the data packet again. To solve the insufficient of DRF, in this paper a Forwarding Strategy, called FN based on Neighbor-aware is proposed for NDN MANET. The node maintains the neighbor information and the request information of neighbor nodes. In the phase of data packet response, in order to improve request satisfaction rate, node specifies the next hop node; Meanwhile, in order to reduce packet loss rate, node assists the last hop node to forward packet to the specific node. The simulation results show that compared with DRF and greedy forwarding(GF) strategy, FN can improve request satisfaction rate when node density is high.

Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important for an efficient representation of compositionality in object-based representations. With the perspective of feature learning as a data compression operation, we propose a new greedy inference algorithm for hierarchical sparse coding. Convolutional matching pursuit with a L0-norm constraint was used to encode the input signal into compact and non-redundant codes distributed across levels of the hierarchy. Simple and complex synthetic datasets of temporal signals were created to evaluate the encoding efficiency and compare with the theoretical lower bounds on the information rate for those signals. Empirical evidence have shown that the algorithm is able to infer near-optimal codes for simple signals. However, it failed for complex signals with strong overlapping between objects. We explain the inefficiency of convolutional matching pursuit that occurred in such case. This brings new insights about the NP-hard optimization problem related to using L0-norm constraint in inferring optimally compact and distributed object-based representations.

Feature selection is an important step in data analysis to address the curse of dimensionality. Such dimensionality reduction techniques are particularly important when if a classification is required and the model scales in polynomial time with the size of the feature (e.g., some applications include genomics, life sciences, cyber-security, etc.). Feature selection is the process of finding the minimum subset of features that allows for the maximum predictive power. Many of the state-of-the-art information-theoretic feature selection approaches use a greedy forward search; however, there are concerns with the search in regards to the efficiency and optimality. A unified framework was recently presented for information-theoretic feature selection that tied together many of the works in over the past twenty years. The work showed that joint mutual information maximization (JMI) is generally the best options; however, the complexity of greedy search for JMI scales quadratically and it is infeasible on high dimensional datasets. In this contribution, we propose a fast approximation of JMI based on information theory. Our approach takes advantage of decomposing the calculations within JMI to speed up a typical greedy search. We benchmarked the proposed approach against JMI on several UCI datasets, and we demonstrate that the proposed approach returns feature sets that are highly consistent with JMI, while decreasing the run time required to perform feature selection.

We present a new method for mitigating wall return and a new greedy algorithm for detecting stationary targets after wall clutter has been cancelled. Given limited measurements of a stepped-frequency radar signal consisting of both wall and target return, our objective is to detect and localize the potential targets. Modulated Discrete Prolate Spheroidal Sequences (DPSS's) form an efficient basis for sampled bandpass signals. We mitigate the wall clutter efficiently within the compressive measurements through the use of a bandpass modulated DPSS basis. Then, in each step of an iterative algorithm for detecting the target positions, we use a modulated DPSS basis to cancel nearly all of the target return corresponding to previously selected targets. With this basis, we improve upon the target detection sensitivity of a Fourier-based technique.

Fingerprint-based Audio recognition system must address concurrent objectives. Indeed, fingerprints must be both robust to distortions and discriminative while their dimension must remain to allow fast comparison. This paper proposes to restate these objectives as a penalized sparse representation problem. On top of this dictionary-based approach, we propose a structured sparsity model in the form of a probabilistic distribution for the sparse support. A practical suboptimal greedy algorithm is then presented and evaluated on robustness and recognition tasks. We show that some existing methods can be seen as particular cases of this algorithm and that the general framework allows to reach other points of a Pareto-like continuum.