Energy Management for Internet of Things-enabled Smart Cities in the UAE (Working Paper)

The drastic increase in urbanization over the past few years in UAE requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, etc. Smart city solutions must have the ability to efficiently utilize energy and handle associated challenges. Electric Vehicles (EVs) are considered as a step forward towards the green environmental and economical transportation. In order to increase the penetration of EVs in the future transportation system, the smart charging management for EVs becomes necessary to fulfill the charging needs efficiently. Internet of Things (IoT) is an enabling technology through which efficient charging management for EVs can be done in order to manage EVs, efficiently utilize consumer resources, and save money. In this work, we will present a brief overview of charging management for EVs and associated challenges in smart cities. We will further investigate placement of charging stations and scheduling optimization for EVs charging in smart cities. We will present experimental and simulation results to exhibit the tremendous impact of the proposed schemes/ algorithms on the performance of IoT-enabled smart charging management for EVs

Recommending with limited number of trusted users in social networks

© 2018 IEEE. To estimate the reliability of an unknown node in social networks, existing works involve as many opinions from other nodes as possible. Though this makes it possible to approximate the real property of the unknown nodes, the computational complexity increases as the scale of social networks getting bigger and bigger. We therefore propose a novel method which involve only limited number of social relations to predict the trustworthiness of the unknown nodes. The proposed method involves four rating prediction mechanisms: FM use the recommendation given by the most reliable recommender with the shortest trust propagation distance from the active user as the predicted rating, FMW weights the recommendation in FM, FA uses the mean value of recommendations with the shortest trust propagation distance from the active user as the predicted rating, and FAW weights recommendations in FA. The simulation results show that the proposed method can greatly reduce the rating prediction calculation, while the rating prediction losses are reasonable

Efficient Detection of Skin Cancer Using Deep Learning Techniques and a Comparative Analysis Study

Many skin lesions may result in the wrong diagnosis of skin cancer, leading to delays and ultimately making the cure impossible. Framed within this statement, this article proposes an efficient skin cancer detection model and compares the six pre-trained models, used for transfer learning in ISIC 2019 dataset. Three most common types of skin cancer—melanoma, nevus, and basal cell carcinoma—are classified by using the transfer learning on the pre-trained models of the ISIC 2019 dataset, to conclude the most accurate detection results with training and test accuracy of 99.73% and 93.79%, respectively

Smart meter: Toward client centric energy efficient smartphone based solution

© 2016 IEEE. Smart city applications are developed to facilitate the urbanization and massive development all over the world. This is achieved with real time responses to challenges faced by different sectors, such as health, transportation, water and energy. Smart meter is one of the smart city applied solutions, which facilitates to overcome the increased demand on electricity. This research examines smart meter in the context of energy sector to exploit its related features in the process of Demand Side Management (DSM) to facilitate energy efficiency. A smartphone application is developed that facilitate integration of client in DSM for energy efficiency. The feasibility of such application is reflected on the smart meter business model adopted in Abu Dhabi. Consequently, fundamentals are established to initiate cost-benefit analysis to evaluate the rolling out of advanced metering infrastructure

Fuzzy Query Routing in Unstructured Mobile Peer-to-Peer Networks

© 2016 IEEE. Due to the disparity between the peer-to-peer (P2P) and the physical networks, we study the challenging problems of mobile routing in unstructured P2P networks over mobile ad hoc networks (MANETs). To route queries and objects of interest, the existing mobile P2P protocols widely adopted an inflexible techniques which experience a relatively high delivery time due to remarkable network traffic, nodes mobility and broken links. The bond between routing and mobility is crucial to obtain efficient searching in mobile P2P network. To solve this problem, we proposed fuzzy search controller [1] which reduced search time but due to peer mobility the protocol causes low hit rate and high overhead. Thus, this article proposes novel fuzzy controller based possibilistic routing for unstructured mobile P2P networks to reduce routing time. The possibilistic routing is based on ultrapeer mobility, active time and location. The inference rules are defined to select the best route to forward query walker. Simulations show that the fuzzy search controller gives better performance than the competing protocols in terms of reducing response time and increasing hit rate in different mobility scenarios

ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs

A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure

A fuzzy logic scheme for real-time routing in wireless sensor networks

© 2015 IEEE. Real-time communications is still remain a major research challenge due to the limited energy supply, limited computing power, and limited bandwidth of the wireless links connecting sensor nodes in wireless sensor networks (WSNs). Due to these limitations, sensor nodes are not powerful enough to accommodate the complexity of the real-time WSNs protocol. In real-time WSNs, to increase reliability of data delivery the high hit rate and reduction of packet losses is required. To solve these problems, we proposed a new fuzzy controller based routing algorithm to optimize real-time communication in WSNs. The main objectives of the proposed fuzzy controller are 1) to reduce energy consumption among nodes in order to increase network lifetime, 2) to meet real-time packet deadline, reduce end-to-end delay and packet losses. Furthermore, the proposed scheme adjusting the transmission range of surplus nodes to shortened end-to-end delay. We show through extensive simulation results that our proposed fuzzy algorithm considerably reduces energy consumption and noticeable improved real-time performance in comparison with existing schemes

Ontology Evolution Using Recoverable SQL Logs

Logs of SQL queries are useful for building the system design, upgrading, and checking which SQL queries are running on certain applications. These SQL queries provide us useful information and knowledge about the system operations. The existing works use SQL query logs to find patterns when the underlying data and database schema is not available. For this purpose, a knowledge-base in the form of an ontology is created which is then mined for knowledge extraction. In this paper, we have proposed an approach to create and evolve an ontology from logs of SQL queries. Furthermore, when these SQL queries are transformed into the ontology, they loose their original form/shape i.e., we do not have original SQL queries. Therefore, we have further proposed a strategy to recover these SQL queries in their original form. Experiments on real world datasets demonstrate the effectiveness of the proposed approach

Database auditing and forensics: Exploration and evaluation

© 2015 IEEE. Database auditing is a prerequisite in the process of database forensics. Log files of different types and purposes are used in correlating evidence related to forensic investigation. In this paper, a new framework is proposed to explore and implement auditing features and DBMS-specific built-in utilities to aid in carrying out database forensics. The new framework is implemented in three phases, where ideal forensic auditing settings are suggested, techniques and approaches to conduct forensics are evaluated, and finally database forensic tools are investigated and evaluated. The research findings serve as guidelines toward focusing on database forensics. There is a crucial need to fill in the gap where forensic tools are few and not database specific

Design guidelines for SaaS development process

© 2018 IEEE. A novel and widespread business model in cloud computing is to provide on-demand software as a service (SaaS) over the Internet. The software runs on a server and the user access it through an Internet connection. A single application instance can be shared by multiple users which provide a cost-effective solution to SaaS providers. Varying requirements from multiple users increase complexity in SaaS application design. The success of SaaS depends on its design. SaaS is different than traditional web-based application, so traditional application design model cannot full fill many SaaS specific design requirements. This paper provides a better understanding of key design factors in SaaS development process which results in a successful SaaS product following an improved design process. This study identifies key design factors through literature review and provides guidelines for key design factors on the SaaS application development. Ultimately, it will be beneficial for SaaS developers to improve the SaaS application development process and have a positive impact on the final product