Table of Contents

Wireless sensor networks (WSN) are designed to collect information by means of a large number of energy-limited battery sensor nodes. Therefore, it is important to minimize the energy consumed by each sensor, in order to extend the network life. The goal of this work is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent is sent to each sensor in order to process the information and to cooperate with neighboring sensors while mobile agents (MA) can be used to reduce information shared between source nodes (SN) and send them to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the multi-agent system (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the sensors of the network have been grouped into sectors. For each MA, we have established an optimal itinerary, consuming a minimum amount of energy with data aggregation efficiency in a minimum time. Successive simulations in large-scale wireless sensor networks through the SINALGO (published under a BSD license) simulator show the performance of the proposed method, in terms of energy consumption and package delivery rate. Full article

Cloud computing systems are popular in computing industry for their ease of use and wide range of applications. These systems offer services that can be used over the Internet. Due to their wide popularity and usage, cloud computing systems and their services often face issues resource management related challenges. In this paper, we present v-Mapper, a resource consolidation scheme which implements network resource management concepts through software-defined networking (SDN) control features. The paper makes three major contributions: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a cloud service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. We tested our scheme with other techniques in terms of average task processing time, service delay and bandwidth usage. Our results demonstrate that v-Mapper outperforms other techniques and delivers significant improvement in system’s performance. Full article

One of the most important research topics nowadays is human action recognition, which is of significant interest to the computer vision and machine learning communities. Some of the factors that hamper it include changes in postures and shapes and the memory space and time required to gather, store, label, and process the pictures. During our research, we noted a considerable complexity to recognize human actions from different viewpoints, and this can be explained by the position and orientation of the viewer related to the position of the subject. We attempted to address this issue in this paper by learning different special view-invariant facets that are robust to view variations. Moreover, we focused on providing a solution to this challenge by exploring view-specific as well as view-shared facets utilizing a novel deep model called the sample-affinity matrix (SAM). These models can accurately determine the similarities among samples of videos in diverse angles of the camera and enable us to precisely fine-tune transfer between various views and learn more detailed shared facets found in cross-view action identification. Additionally, we proposed a novel view-invariant facets algorithm that enabled us to better comprehend the internal processes of our project. Using a series of experiments applied on INRIA Xmas Motion Acquisition Sequences (IXMAS) and the Northwestern–UCLA Multi-view Action 3D (NUMA) datasets, we were able to show that our technique performs much better than state-of-the-art techniques. Full article

Software Defined Networking (SDN) is an evolving network architecture paradigm that focuses on the separation of control and data planes. SDN receives increasing attention both from academia and industry, across a multitude of application domains. In this article, we examine the current state of obtained knowledge on military SDN by conducting a systematic literature review (SLR). Through this work, we seek to evaluate the current state of the art in terms of research tracks, publications, methods, trends, and most active research areas. Accordingly, we utilize these findings for consolidating the areas of past and current research on the examined application domain, and propose directions for future research. Full article

This paper presents log likelihood ratio (LLR) based relay selection scheme for a cooperative amplify and forward relaying system. To evaluate the performance of the aforementioned system model, a three state Markov chain based fading environment has been presented to toggle among Rayleigh, Rician, and Nakagami-m fading environment. A simulation is carried out while assuming that there is no possibility of direct transmission from the source and destination terminal. Simulation results on the basis of Bit Error Rate (BER), Instantaneous Channel Capacity, and Outage probability have been presented and compared for different cases. In each case, the best performance of the proposed algorithm is obtained with a Binary Phase Shift Keying (BPSK) modulation scheme. Full article

The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem. Full article

A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate. Full article

Frame repetition (FR) is a common temporal-domain tampering operator, which is often used to increase the frame rate of video sequences. Existing methods detect FR forgery by analyzing residual variation or similarity between video frames; however, these methods are easily interfered with by noise, affecting the stability of detection performance. This paper proposes a noise-level based detection method which detects the varying noise level over time to determine whether the video is forged by FR. Wavelet coefficients are first computed for each video frame, and median absolute deviation (MAD) of wavelet coefficients is used to estimate the standard deviation of Gaussian noise mixed in each video frame. Then, fast Fourier transform (FFT) is used to calculate the amplitude spectrum of the standard deviation curve of the video sequence, and to provide the peak-mean ratio (PMR) of the amplitude spectrum. Finally, according to the PMR obtained, a hard threshold decision is taken to determine whether the standard deviation bears periodicity in the temporal domain, in which way FR forgery can be automatically identified. The experimental results show that the proposed method ensures a large PMR for the forged video, and presents a better detection performance when compared with the existing detection methods. Full article

A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze data packets entering the data center network and calculate the Renyi entropies base on IP of data packets, and then combine them with the hidden Markov model to get a probability model HMM-R to detect L-DDoS attacks at different rates. Compared with the four common attack detection algorithms (KNN, SVM, SOM, BP), HMM-R is superior to them in terms of the true positive rate, the false positive rate, and the adaptivity. Full article

Passive Radio Frequency IDentification (RFID) tags are generally highly constrained and cannot support conventional encryption systems to meet the required security. Hence, designers of security protocols may try to achieve the desired security only using limited ultra-lightweight operations. In this paper, we show that the security of such protocols is not provided by using rotation functions. In the following, for an example, we investigate the security of an RFID authentication protocol that has been recently developed using rotation function named ULRAS, which stands for an Ultra-Lightweight RFID Authentication Scheme and show its security weaknesses. More precisely, we show that the ULRAS protocol is vulnerable against de-synchronization attack. The given attack has the success probability of almost ‘1’, with the complexity of only one session of the protocol. In addition, we show that the given attack can be used as a traceability attack against the protocol if the parameters’ lengths are an integer power of 2, e.g., 128. Moreover, we propose a new authentication protocol named UEAP, which stands for an Ultra-lightweight Encryption based Authentication Protocol, and then informally and formally, using Scyther tool, prove that the UEAP protocol is secure against all known active and passive attacks. Full article