The advent of Internet of Things (IoT) and the evident inadequacy of Cloud networks concerning management of numerous end nodes have brought about a shift of paradigm giving birth to Fog computing. Fog computing is an extension of Cloud computing that extends Cloud resources at the edge of the network, closer to the user. Cloud computing has become one of the essential needs of people over the Internet but with the emerging concept of IoT, traditional Clouds seem inadequate. IoT entails extremely low latency and for that, the Cloud servers that are distant and unknown to the user appear to be unsuitable. With the help of Fog computing, the Fog devices installed would be closer to the user that will provide an immediate storage for the frequently needed data. This paper discusses data migration between different storage types especially between Cloud devices and then presents a mechanism to migrate data between Cloud and Fog Layer. We call this mechanism Adaptive Deadline-Aware Scheme (ADAS) for Data migration between Cloud and Fog. We will demonstrate that we can access and process latency sensitive "hot" data through the proposed ADAS more efficiently than with a traditional Cloud setup.

In this paper, an error-detection-coding-aided iterative hard decision interference cancellation (EDC-IHIC) scheme for multiple-input multiple-output systems employing hybrid automatic repeat request (HARQ) for multi-packet transmission is developed and investigated. In the EDC-IHIC scheme, only packets identified as error-free by the EDC are submitted to the interference cancellation (IC) stage for cancellation from the received signals. Therefore, the possibility of error propagation, including inter-transmission error propagation, can be eliminated using EDC-IHIC. Because EDC must be implemented in systems that employ HARQ to determine packet retransmission, error propagation can be prevented without the need for additional redundancy. The results of simulations conducted herein verify that the EDC-IHIC scheme outperforms conventional hard decision IC schemes in terms of the packet error rate in various environments.

Vehicular ad hoc network (VANET) is a dedicated network to connect vehicles without any centralized administration or infrastructure. The wireless access in vehicular environments (WAVE) protocol leveraging IEEE 1609/802.11p is widely implemented for VANETs. However, in congested traffic situation, the performance of the WAVE system degrades significantly due to serious collision, especially for safety related broadcast services on the control channel (CCH) interval due to the inherent drawback of its collision avoidance mechanisms called carrier sense multiple access with collision avoidance (CSMA/CA). In this paper, we propose a method that can decrease the number of frame collisions in CCH with a few modifications to the IEEE 802.11p protocol. In the paper, vehicles still employ CSMA/CA to compete for the channel access opportunity. However, by taking advantage of periodicity of synchronization interval, a two-state switching scheme introducing two new inter frame space (IFS) is proposed to reduce the number of competing vehicles substantially and as a result, the collision probability is significantly decreased. The simulation results demonstrate the superiority of the proposed method in packet collision rate.

In this paper the problem of massive multiple input multiple output (MIMO) channel estimation with sparsity aware adaptive algorithms for $5^{th}$ generation mobile systems is investigated. These channels are shown to be non-stationary along with being sparse. Non-stationarity is a feature that implies channel taps change with time. Up until now most of the adaptive algorithms that have been presented for channel estimation, have only considered sparsity and very few of them have been tested in non-stationary conditions. Therefore we investigate the performance of several newly proposed sparsity aware algorithms in these conditions and finally propose an enhanced version of RZA-LMS/F algorithm with variable threshold namely VT-RZA-LMS/F. The results show that this algorithm has better performance than all other algorithms for the next generation channel estimation problems, especially when the non-stationarity gets high. Overall, in this paper for the first time, we estimate a non-stationary Rayleigh fading channel with sparsity aware algorithms and show that by increasing non-stationarity, the estimation performance declines.

The concept of SDN (Software Defined Networking) endows the network with programmability and significantly improves the flexibility and extensibility of networks. Currently a plenty of research works on introducing SDN into wireless networks. Most of them focus on the innovation of the SDN based architectures but few consider how to realize the global perception of the network through the controller. In order to address this problem, a software defined carrier grade Wi-Fi framework called SWAN, is proposed firstly. Then based on the proposed SWAN architecture, a blueprint of introducing the traditional NSA (Network Situation Awareness) into SWAN is proposed and described in detail. Through perceiving various network data by a decentralized architecture and making comprehension and prediction on the perceived data, the proposed blueprint endows the controllers with the capability to aware of the current network situation and predict the near future situation. Meanwhile, the extensibility of the proposed blueprint makes it a universal solution for software defined wireless networks SDWNs rather than just for one case. Then we further research one typical use case of proposed NSA blueprint: network performance awareness (NPA). The subsequent comparison with other methods and result analysis not only well prove the effectiveness of proposed NPA but further provide a strong proof of the feasibility of proposed NSA blueprint.

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

Achievable sum rate and energy efficiency (EE) are investigated for the massive multiple-input multiple-output (Massive MIMO) downlink with channel aging in the time varying Ricean fading channel. Specifically, the expression of the achievable sum rate of the system for the maximum ratio transmission (MRT) precoder with aged channel state information (CSI) in the time varying Ricean fading channel is first presented. Based on the expression, the effect of both channel aging and the Ricean factor on the power scaling law are studied. It is found that the transmit power of base station (BS) is scaled down by $1/{\sqrt{M}}$(where M is the number of the BS antennas) when the Ricean factor K is equal to zero (i.e., time varying Rayleigh fading channel), indicating that aged CSI does not affect the power scaling law. However, the transmit power of the BS is scaled down by 1/M for the time varying Ricean fading channel (where $K{\neq}0$) indicating that the Ricean factor affects the power scaling law and sum rate, and channel aging only leads to a reduction of the sum rate. Second, the EE of the system is analyzed based on the general power consumption model. Both the theoretical analysis and the simulations show that the channel aging could degrade the sum rate and the EE of the system, and it does not affect the power scaling law.

Existing trust evaluation models for wireless sensor networks can accurately and objectively evaluate trust value of nodes, but the nodes' energy saving problem was ignored. Especially when there are a few malicious nodes in a network, the overall trust value calculation for all nodes would waste lots of energy. Beside that, the network failure caused by nodes death was also not considered. In this paper, we proposed a method for avoiding energy hole which applied trust evaluation models and a trust evaluation method based on information entropy, so as to achieve the purpose of improving nodes utilization. Simulation results show that the proposed method can effectively improve nodes utilization, and it has reasonable detection rate and lower false alert rate compared to other classical methods.

This paper proposes a new hybrid scheme to decrease the high peak to average power ratio (PAPR) of optical orthogonal frequency division multiplexing (OFDM) signals in visible light communication (VLC) systems. The PAPR causes nonlinear signal distortions and high power requirements for the VLC transmitter (light emitting diode, LED). The proposed method is applicable for both direct current-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM). In the proposed scheme, the PTS method is firstly modified to fit both optical OFDM approaches transmission and then combined with signal clipping method for further PAPR reduction and bit error rate (BER) improvement of the VLC system. The performance of hybrid scheme has been evaluated and compared with the original OFDM based VLC system, conventional PTS and clipping methods. The results show that the hybrid scheme outperforms other methods in terms of both the PAPR reduction and BER performance.

In this paper, I propose an application layer cooperative congestion control scheme for safety message broadcast in vehicular networks, called CMS, that adaptively controls a vehicle's safety message rate and transmit timing based on the channel congestion state. Motivated by the fact that all vehicles should transmit and receive an application layer safety message in a periodic manner, I directly exploit the message itself as a means of estimating the channel congestion state. In particular, vehicles can determine wider network conditions by appending their local channel estimation result onto safety message transmissions and sharing them with each other. In result CMS realizes cooperative congestion control without any modification of the existing MAC protocol. I present extensive NS-3 simulation results which show that CMS outperforms conventional congestion control schemes in terms of the packet collision rate and throughput, especially in a high-density traffic environment.

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.

In this paper, we present a low-cost fire detection system using a thermal camera and a smartphone. The developed system collects thermal and RGB videos from the developed camera. To detect fire, candidate fire regions are extracted from videos obtained using a thermal camera. The block mean of variation of adjacent frames is measured to analyze the dynamic characteristics of the candidate fire regions. After analyzing the dynamic characteristics of regions of interest, a fire is determined by the candidate fire regions. In order to evaluate the performance of our system, we compared with a smoke detector, a heat detector, and a flame detector. In the experiments, our fire detection system showed the excellent performance in detecting fire with an overall accuracy rate of 97.8 %.

Ciphertext-policy attribute-based encryption (CP-ABE) associates ciphertext with access policies. Only when the user's attributes satisfy the ciphertext's policy, they can be capable to decrypt the ciphertext. Expressivity and security are the two directions for the research of CP-ABE. Most of the existing schemes only consider monotonic access structures are selectively secure, resulting in lower expressivity and lower security. Therefore, fully secure CP-ABE schemes with non-monotonic access structure are desired. In the existing fully secure non-monotonic access structure CP-ABE schemes, the attributes that are set is bounded and a one-use constraint is required by these projects on attributes, and efficiency will be lost. In this paper, to overcome the flaw referred to above, we propose a new fully secure non-monotonic access structure CP-ABE. Our proposition enforces no constraints on the scale of the attributes that are set and permits attributes' unrestricted utilization. Furthermore, the scheme's public parameters are composed of a constant number of group elements. We further compare the performance of our scheme with former non-monotonic access structure ABE schemes. It is shown that our scheme has relatively lower computation cost and stronger security.

Mobile Ad hoc Network (MANET) is a collection of nodes or communication devices that wish to communicate without any fixed infrastructure and predetermined organization of available links. The effort has been made by proposing a scheme to overcome the critical security issue in MANET. The insufficiency of security considerations in the design of Ad hoc On-Demand Distance Vector protocol makes it vulnerable to the threats of collaborative black hole attacks, where hacker nodes attack the data packets and drop them instead of forwarding. To secure mobile ad hoc networks from collaborative black hole attacks, we implement our scheme and considered sensor's energy as a key feature with a better packet delivery ratio, less delay time and high throughput. The proposed scheme has offered an improved solution to diminish collaborative black hole attacks with high performance and benchmark results as compared to the existing schemes EDRIAODV and DRIAODV respectively. This paper has shown that throughput and packet delivery ratio increase while the end to end delay decreases as compared to existing schemes. It also reduces the overall energy consumption and network traffic by maintaining accuracy and high detection rate which is more safe and reliable for future work.

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

UOV is one of the most important signature schemes in Multivariate Public Key Cryptography (MPKC). It has a strong security guarantee and is considered to be quantum-resistant. However, it suffers from large key size and its signing procedure is relatively slow. In this paper, we propose a new secure UOV variant (Circulant UOV) with shorter private key and higher signing efficiency. We estimate that the private key size of Circulant UOV is smaller by about 45% than that of the regular UOV and its signing speed is more than 14 times faster than that of the regular UOV. We also give a practical implementation on modern x64 CPU, which shows that Circulant UOV is comparable to many other signature schemes.