Ahmed Alzahrani

A survey on text mining in social networks

In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

Intrusion-aware trust model for vehicular ad hoc networks

In vehicular ad hoc networks, peers make decisions on the basis of the information provided by the other peers. If the received information is fake, then the result could be catastrophic, such as road accidents. Therefore, many researchers use the concept of trust to evaluate the trustworthiness of the received data. However, existing trust management schemes proposed so far for the vehicular networks are suffered from various limitations. For example, some schemes measure trust on the basis of the history of interactions, which is infeasible for vehicular networks due to its ephemeral nature. Also, none of the existing schemes operate in an identity anonymous environment. In order to overcome these limitations, we propose a novel trust management scheme for identity anonymous vehicular ad hoc networks. The proposed method is simple and completely decentralized that makes it easy to implement in the vehicular networks. Also, we prove that the proposed method is not only robust, but it also detects false location and time information. Furthermore, it introduces linear time complexity, which makes it suitable to use in real time. Copyright

Enabling Smarter Societies through Mobile Big Data Fogs and Clouds

Abstract: Smart societies require next generation mobility platforms and applications to enable the needed quality and pace of life. This paper proposes a mobile computing system that enables smarter cities with enhanced mobility information through big data technologies, fogs and clouds. The system includes a mobile application, a backend cloud-based big data analysis system, and a middleware platform based on fog computing. The system architecture and its component technologies are described in addition to a mobile application use case. The technologies used in this paper have been used in the literature in the past. However, we have not found any work where all these technologies have been brought together to develop a mobile application that provides uniquely focused information on user mobility. Google Maps notifications could provide information about nearby road closure or other events where relevant. However, we propose to pull in and provide information to the users about their travel locally, nationally, and internationally. More importantly, relevant information is pulled in from multiple news media and other sources and provided to the user in multimedia formats including text, voice and video.

Efficient techniques of key management and quantum cryptography in RFID networks

An efficient way of handling security keys using quantum cryptography QC for increasing security in radio frequency identification RFID networks is being investigated by network security industries. To establish secure RFID network, communication between any two nodes that hold RFID tags and/or readers merged with existing networks should be protected. In order to maximize the data security and secure transmission around RFID network, theoretical model of the quantum key management KM system based on RFID is introduced as a proposed research. This model not only manages the secure keys but also it defends passive eavesdropping attacks and other potential attacks. Novelties in this research are security keys of which QC is being utilized in RFID network with continuous key updates. To establish future security around RFID networks, efficient KM protocol and QC, which deal with quantum mathematical procedures and quantum physics, should be analyzed without affecting the legacy. Computational complexities in KM are increasing with the large key sizes, which are manageable through QC. To maximize the security and minimize the complexity in KM, QC with Grovers algorithm is introduced as a method in RFID network environments. So, we have proved that a number of operations with key sizes obtained in KM are reduced. In this proposal, QC will help to reduce the complexity of algorithms used in KM protocols and maximize the security. Copyright

Trust Management Method for Vehicular Ad Hoc Networks

In vehicular ad hoc networks, evaluating trustworthiness of data is utmost necessary for the receiver to make reliable decisions that are very crucial in safety and traffic-efficiency related applications. Existing trust management schemes that have been proposed so far for the vehicular networks has suffered from various limitations. For example, some schemes build trust based on the history of interactions. However, vehicular networks are ephemeral in nature, which makes that approach infeasible. Furthermore, in most of the existing approaches, unique identities of each vehicle must be known. This violates user privacy. In order to overcome these limitations, we have proposed a novel trust management scheme for the vehicular networks. The proposed method is simple and completely decentralized, which makes it easy to implement in the vehicular networks. We have analytically proved its robustness with respect to various security threats. Furthermore, it introduces linear time complexity, which makes it suitable to use in real-time.

Information and Communication Technology (ICT) Applications for Customer Relationship Management (CRM)

Information and communication technology (ICT) being developed for the next generation is growing in many dimensions for customer relationship management (CRM) that is looking to enhance modern facilities with minimum cost and maximum security in network communications. The application of e-Health over the next-generation wireless network is considered to be a Millennium Development Goal (MDG). Future wireless systems and ICT facilities based on next-generation networked radio frequency identification (NGN-RFID) systems are very useful for the health MDG, which is approached through CRM values. In this research, we consider customer facilities, relationship and management techniques between healthcare management committees of particular healthcare businesses in profitable healthcare industries or the health MDG in nonprofit projects through a NGN-RFID system that collects and handles all data from the CRM. The purpose of this research is to analyze possible ICT applications based on an NGN-RFID system for CRM, which focuses on the satisfaction of customers who are loyal to healthcare agencies authorized by the United Nations (UN). CRM limitations of selected CRM values and their strategic approaches considered for health MDGs are also briefly discussed.

Relevance of Energy Efficiency Gain in Massive MIMO Wireless Network

The massive MIMO and energy efficiency (EE) analysis for the next generation technology are the hottest topics in wireless network research. This paper explains about massive MIMO wireless networks and EE for manifold channel which is an evolution massive MIMO. This research will help to design and implement a practical system of next generation network based on massive MIMO where efficient processing provides EE gain. In order to approach this research, different types of manifolds are considered with efficient techniques that depend on the rank of the channel matrix. Employing the specific manifold that helps to analyze the rate of the feedback increases not only the overall performance of the MIMO system but also the EE. We studied the convergence techniques used for optimizing quantization errors which have influences with manifold feedback. Here, we have focused on relevant areas which are very important to analyze EE gain in the future massive network. According to the selected results obtained in this research, many challenges will be possible to make useful proposals.

Disaster Management in Smart Cities by Forecasting Traffic Plan Using Deep Learning and GPUs

The importance of disaster management is evident by the increasing number of natural and manmade disasters such as Irma and Manchester attacks. The estimated cost of the recent Irma hurricane is believed to be more than 80 billion USD; more importantly, more than 40 lives have been lost and thousands were misplaced. Disaster management plays a key role in reducing the human and economic losses. In our earlier work, we have developed a disaster management system that uses VANET, cloud computing, and simulations to devise city evacuation strategies. In this paper, we extend our earlier work by using deep learning to predict urban traffic behavior. Moreover, we use GPUs to deal with compute intensive nature of deep learning algorithms. To the best of our knowledge, we are the first to apply deep learning approach in disaster management. We use real-world open road traffic within a city available through the UK Department for Transport. Our results demonstrate the effectiveness of deep learning approach in disaster management and correct prediction of traffic behavior in emergency situations.