In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from electrocardiogram (ECG) based biometrics, a classification task which aims to identify a subject from a given time-series sequential data. Despite having a major issue in traditional RNN networks which they learn representations from previous time sequences, bidirectional is designed to learn the representations from future time steps which enables for better understanding of context, and eliminate ambiguity. Moreover, GRU cell in RNNs deploys an update gate and a reset gate in a hidden state layer which is computationally efficient than a usual LSTM network due to the reduction of gates. The experimental results suggest that our proposed BGRU model, the combination of RNN with GRU cell unit in bidirectional manner, achieved a high classification accuracy of 98.55%. Various neural network architectures with different parameters are also evaluated for different approaches, including one-dimensional Convolutional Neural Network (1D-CNN), and traditional RNNs with LSTM and GRU for non-fiducial approach. The proposed models were evaluated with two publicly available datasets: ECG-ID Database (ECGID) and MIT-BIH Arrhythmia Database (MITDB). This paper is expected to demonstrate the feasibility and effectiveness of applying various deep learning approaches to biometric identification and also evaluate the effect of network performance on classification accuracy according to the changes in percentage of training dataset.

Localization is the basic feature of wireless sensor networks (WSN) and estimating the time difference of arrival (TDoA), and angle of arrival (AoA) is the most used schemes for localization. Underwater wireless sensor network (UWSN) is extensively used for data collection in an underwater environment for both military and civilian applications. Nevertheless, efficient and accurate localization algorithms are essential for the UWSN because of the dynamical property of the underwater surrounding. Also, data management, collection, and processing for WSNs have become a more active topic nowadays in computer science, such as database system and data mining. The objective of deploying the WSNs applications is to collect the real-time data, which has very challenging due to the capacity of communication and high data generated by WSNs. For this purpose, the time and location are the basic aspects when a sensor collects data, especially for the case of location-aware data. Many researchers have studied the underwater sensor nodes localization and they have considered the sensor location where the data is collected and most of them focused on the fixed sensor nodes. In this research work, energy efficient and accurate localization schemes are presented named as distance-based and angle-based schemes for the underwater environment with relatively less energy consumption and mean estimation errors (MEEs). The proposed schemes mainly focus on the localization of underwater nodes and especially on the MEEs in localization. The extensive simulation is performed to compare the proposed schemes with other counterpart schemes. The results show that the proposed schemes outperform other counterpart schemes in terms of MEEs of localization and energy consumption.

Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint power. Hence, in this paper, a new ant-based path planning approach that considers UGV energy consumption in its planning strategy is proposed. This method is called Green Ant (G-Ant) and integrates an ant-based algorithm with a power/energy consumption prediction model to reach its main goal, which is providing a collision-free shortest path with low power consumption. G-Ant is evaluated and validated via simulation tools. Its performance is compared with ant colony optimization, genetic algorithm, and particle swarm optimization approaches. Various scenarios were simulated to evaluate G-Ant performance in terms of UGV travel time, travel length, computational time by taking into account different numbers of iterations, different numbers of obstacle, and different population sizes. The obtained results show that the G-Ant outperforms the existing methods in terms of travel length and number of iteration.

Pervasive social networking (PSN) aims at bridging the gap between the services and users by providing a platform for social communication irrespective of the time and location. With the advent of a new era of high-speed telecommunication services, mobile users have evolved to a large extent demanding secure, private, and trustworthy services. Online social networks have evolved as pervasive online social networks (POSNs), which uses a common platform to connect users from hybrid applications. Trust has always been a concern for these networks. However, existing approaches tend to provide application-specific trust management, thus resulting in the cost of excessive network resource utilization and high computations. In this paper, a pervasive trust management framework is presented for POSNs, which is capable of generating high trust value between the users with a lower cost of monitoring. The proposed approach uses a flexible mixture model to develop the system around six different properties, and then utilizes the concept of osmotic computing to perform computational offloading, which reduces the number of computations as well as computational time. The novel concepts of lock door policy and intermediate state management procedure are used to allow trust visualization by providing efficient identification of trustworthy and untrustworthy users. The proposed approach is capable of predicting user ratings efficiently with extremely low errors, which are in the range of ±2. The effectiveness of the proposed approach is demonstrated using theoretical and numerical analyses along with data set-based simulations.