Design and Development of Novel Hybrid Precoder for Millimeter-Wave MIMO System

Power consumption and hardware cost reduction with the use of hybrid beamforming in large-scale millimeter wave MIMO systems. The large dimensional analog precoding integrates with the hybrid beamforming based on the phase shifters including digital precoding with lower dimensionality. The reduction of Euclidean distance between the hybrid precoder and fully digital is the major problem to overcome the minimization of resultant spectral efficiency. The issue formulates as a fully digital precoder’s matrix factorization problem based on the analog RF precoder matrix and the digital baseband precoder matrix. An additional element-wise unit modulus constraint is imposed by the phase shifters on the analog RF precoder matrix. The traditional methods have a problem of performance loss in spectral efficiency. In the processing time and iteration, high complexities result in optimization algorithms. In this paper, a novel low complexity algorithm proposes which maximizes the spectral efficiency and reduces the computational processing time.

Preventing data leakage by securing chat session with randomized session ID

Messaging applications have become one of the largest and most popular smartphone applications. It includes the capacity for the users to communicate between themselves via text messages, photos and files. It is necessary to safeguard all messages. Privacy is one of the biggest issues which most individuals in the world of instant messaging ignore. Although several instant messaging applications offer varying security for users, the weaknesses and danger of data assault are increasing. Not just business discussions, our data must also be safeguarded during everyday discussions since data is very sensitive for everybody, and data protection is very crucial to prevent undesired loss of information. To address these types of weaknesses and hazards associated with data attacks, we require an encrypted messaging protocol and also hide IP address method for a safe interaction. This paper's goal is to protect conversations from targeted attacker by securing the communication between user and hide IP address from unauthorized access.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

Enhancing Route lifetime in Vehicular Ad Hoc ‎Networks Based on Skellam Distribution Model

The emergence of smart cities and the need to use intelligent transportation systems has led to an increased reliance on vehicle ad hoc networks (VANET). The topology of VANET is highly dynamic, which results in a short effective routing time. This paper presents a two-stage algorithm to select a route that can sustain communication between vehicles for as long as possible while taking into account the variables that affect the VANET topology. The first stage uses Skellam distribution model to assess the connectivity probability of paths in ‎a 2d road network based on traffic-flow and the number of vehicles ‎joining and leaving the ‎network, accordingly, the path with the highest connectivity is chosen. In the second stage, the control packets sent only to vehicles on the selected path to detect routes between source and destination, thus reducing the overhead of control packets and increasing network stability. ‎ the algorithm adopts the principle of global evaluation to ‎estimate the lifetime ‎of the ‎detected ‎routes within the chosen path. ‎the route with the ‎best estimated ‎lifetime ‎is ‎chosen to be ‎the active route. ‎in the event of route failure, the validity of the next route in lifetime is confirmed to be adopted as the alternate route. The proposed algorithm was compared with both on-‎demand distance ‎vector routing protocol (AODV) protocol and the modified location-aided routing ‎‎(LAR) ‎protocol. The proposed algorithm showed greater network stability, higher performance in terms of longer lifetime route detection, less energy consumption and higher throughput.

A MODEL OF A DISTRIBUTED INFORMATION SYSTEM BASED ON THE Z39.50 PROTOCOL

Based on the analysis of typical scenarios of information servers, the tasks that should be solved when organizing an access control system for distributed information resources are formulated. The possibilities of the Z39.50 technologies as the most suitable for building such a system are considered. Within the framework of this technology, three access control models are discussed, which differ in the degree of integration of information server functions with the Z39.50 technologies.The creation and support of distributed information systems and electronic libraries that integrate heterogeneous information resources and operate in various software and hardware environments requires special approaches to managing these systems. If the resources or data themselves can be managed locally, even for distributed information systems, then the task of managing access to distributed resources cannot be solved within the framework of local administration. The justification of the last thesis can be seen when considering typical scenarios of the information server, which we will describe below

An Efficient Authentication Scheme for Internet of Things

The Internet of Things (IoT) is increasingly affecting human lives in multiple profound ways. “Things” have the ability to communicate, generate, transmit and store data over the network connection. During each communication between “Things”, the data transmitted is potentially vulnerable to malicious attacks, loss, distortions and interruption which impair functionality, system efficiency and user satisfaction. Additionally, inappropriate user controls can cause problems in IoT services, such as granting anonymous users access to personal resources and enable legitimate users to access resources in an illegal manner or preventing legitimate users to access resources in an authorized manner. Therefore, communications between things need to be authenticated, authorized, secured and ensured to have high privacy by applying a strong authentication protocol. The aim of this research is to enhance the authentication protocol, starting by reducing the heavy use of storage in “Things”, and eliminating unnecessary messages during authentication steps, taking into consideration the network security analysis. This research represents a security performance analysis and enhancement authentication for the IoT. The results indicate that the enhanced protocol has a positive effect on minimizing packet length and time performance in authenticating users having once obtained access to the visited location area compared with the other two protocols used for comparative purposes, with 33% increased the proposed protocol performance.

Features-Aware DDoS Detection in Heterogeneous Smart Environments based on Fog and Cloud Computing

Nowadays, urban environments are deploying smart environments (SEs) to evolve infrastructures, resources, and services. SEs are composed of a huge amount of heterogeneous devices, i.e., the SEs have both personal devices (smartphones, notebooks, tablets, etc) and Internet of Things (IoT) devices (sensors, actuators, and others). One of the existing problems of the SEs is the detection of Distributed Denial of Service (DDoS) attacks, due to the vulnerabilities of IoT devices. In this way, it is necessary to deploy solutions that can detect DDoS in SEs, dealing with issues like scalability, adaptability, and heterogeneity (distinct protocols, hardware capacity, and running applications). Within this context, this article presents an Intelligent System for DDoS detection in SEs, applying Machine Learning (ML), Fog, and Cloud computing approaches. Additionally, the article presents a study about the most important traffic features for detecting DDoS in SEs, as well as a traffic segmentation approach to improve the accuracy of the system. The experiments performed, using real network traffic, suggest that the proposed system reaches 99% of accuracy, while reduces the volume of data exchanged and the detection time.

An Enhanced Block Pre-processing of PRESENT Algorithm for Fingerprint Template Encryption in the Internet of Things Environment

Many previous studies had proven that The PRESENT algorithm is ultra-lightweight encryption. Therefore, it is suitable for use in an IoT environment. However, the main problem with block encryption algorithms like PRESENT is that it causes attackers to break the encryption key. In the context of a fingerprint template, it contains a header and many zero blocks that lead to a pattern and make it easier for attackers to obtain an encryption key. Thus, this research proposed header and zero blocks bypass method during the block pre-processing to overcome this problem. First, the original PRESENT algorithm was enhanced by incorporating the block pre-processing phase. Then, the algorithm’s performance was tested using three measures: time, memory usage, and CPU usage for encrypting and decrypting fingerprint templates. This study demonstrated that the proposed method encrypted and decrypted the fingerprint templates faster with the same CPU usage of the original algorithm but consumed higher memory. Thus, it has the potential to be used in IoT environments for security.

Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

Channel Propagation Characteristics on the Performance of 4G Cellular Systems from High Altitude Platforms (HAPs)

In this paper, we investigated the effect of different channel propagation characteristics on the performance of 4G systems from high altitude platforms (HAPs). The use of High-Altitude Platforms for communication purpose in the past focused mostly on the assumption that the platform is quasi stationary. The technical limitation of the assumption was that of ensuring stability in the positioning of the platform in space. The use of antenna steering and other approaches were proposed as a solution to the said problem. In this paper, we proposed a channel model which account for the motion of the platform. This was done by investigating the effect of Doppler shift on the carrier frequency as the signals propagate between the transmitter and receiver while the High-Altitude Platform is in motion. The basic free space model was used and subjected to the frequency variation caused by the continuous random shift due to the motion of the HAPs. The trajectory path greatly affects the system performance. A trajectory of 30km, 100km and 500km radii were simulated. An acute elevation angle was used in the simulation. The proposed model was also compared to two other channel models to illustrate its performance. The results show that the proposed model behave similar to the existing models except at base station ID 35 and 45 where the highest deviation of 20dBm was observed. Other stations that deviated were less than 2dBm.