S. S. Iyengar

A Novel Cloud-Based Platform for Implementation of Oblivious Power Routing for Clusters of Microgrids

There has been an increasing demand for connectivity of the clusters of microgrids to increase their flexibility and security. This paper presents a framework for implementation, simulation, and evaluation of a novel power routing algorithm for clusters of microgrids. The presumed cluster is composed of multiple direct current (dc) microgrids connected together through multi-terminal dc system in a meshed network. In this structure, the energy is redirected from the microgrid with excessive power generation capacity to the microgrid which has power shortage to supply its internal loads. The key contribution of this paper is that each microgrid in the cluster is unaware of the current state and other flows of the cluster. In this approach, the optimal power flow problem is solved for the system while managing congestion and mitigating power losses. The proposed methodology works for both radial and non-radial networks regardless of the network topology, scale, and number of microgrids involved in the cluster. Therefore, it is also well suited for large-scale optimal power routing problems that will emerge in the future clusters of microgrids. The effectiveness of the proposed algorithm is verified by MATLAB simulation. We also present a comprehensive cloud-based platform for further implementation of the proposed algorithm on the OPAL-RT real-time digital simulation system. The communication paths between the microgrids and the cloud environment can be emulated by OMNeT++.

Determination of the minimum-variance unbiased estimator for DC power-flow estimation

One of the most important features of the Smart Grid (SG) is real-time self-assessment which may threat that target power system stability. In order to improve robustness of power systems against such attacks, accurate estimation of the power system operation is required and conventional power flow methods should be upgraded. In this paper, we derive minimum variance unbiased estimators (MVUEs) for active power based on the voltage phase at each node of the power system. The state variables are the voltage phases and the received measurement signals are active power measurements. The proposed method is implemented on a four-bus test system. Three scenarios are defined to investigate the effect of covariance matrix topology on the estimation accuracy. The results shows that lower correlation between the noise vector elements leads to a more accurate estimation of power system operation.

A Panorama of Future Interdependent Networks: From Intelligent Infrastructures to Smart Cities

In this chapter, we briefly provide a big picture of emerging challenges in the interdependent networks. The introduced networks will collaborate together to achieve sustainability in terms of upgrading the infrastructures to more intelligent and efficient systems, providing more realistic models of interdependent networks, and modernizing the conventional urban areas to smart cities. Then, we provide the potential trends to address the challenges caused by integration of these networks. We also introduce smart cities as a prominent example of sustainable interdependent networks. We then provide the motivations for studying theory and applications of interdependent networks while capturing the requirements of the sustainable development. Finally, we explain the general structure of the book and provide a brief overview of the chapters. For more information please visit www.interdependentnetworks.com.

Game Theory for Cyber Security and Privacy

In this survey, we review the existing game-theoretic approaches for cyber security and privacy issues, categorizing their application into two classes, security and privacy. To show how game theory is utilized in cyberspace security and privacy, we select research regarding three main applications: cyber-physical security, communication security, and privacy. We present game models, features, and solutions of the selected works and describe their advantages and limitations from design to implementation of the defense mechanisms. We also identify some emerging trends and topics for future research. This survey not only demonstrates how to employ game-theoretic approaches to security and privacy but also encourages researchers to employ game theory to establish a comprehensive understanding of emerging security and privacy problems in cyberspace and potential solutions.

PPVC: Privacy Preserving Voronoi Cell for location-based services

The proliferation of GPS-enabled mobile devices has made location-based services (LBSs) very popular in mobile networks and protecting users location information has become a critical issue of it. In a typical location-based service framework, the LBS requires the user to provide precise location information to assure better Quality of Services(QoS), while the user wants to hide this information as much as possible. This dilemma of privacy and quality of service trade-off has been studied in literature extensively. In this paper, we propose a spatial obfuscation framework, Privacy Preserving Voronoi Cell (PPVC) based on Voronoi diagram. In this framework users region of interest (ROI) is divided into n sectors and each sector is concealed with an irregular quadrilateral, generated from a n-gon Voronoi cell and users location is confounded within a convex region called Anonymity Zone. We evaluate PPVC with simulated environment, showing the efficiency and efficacy of the proposed framework. Finally, we implement it on Android phone with a user centric privacy scale.

The Next Generation of Artificial Intelligence: Synthesizable AI

While AI is expanding to many systems and services from search engines to online retail, a revolution is needed, to produce rapid, reliable “AI everywhere” applications by “continuous, cross-domain learning”. We introduce Synthesizable Artificial Intelligence, and discuss its uniqueness by its five advanced “abilities”; (1) continuous learning after training by “connecting the dots”; (2) measuring quality of success; (3) correcting concept drift; (4) “self-correcting” for new paradigms; and (5) retroactively applying new learning for development of “long-term self-learning”. SAI can retroactively apply new concepts to old examples, “self-learning” in a new way by considering recent experiences similar to the human experience. We demonstrate its current and future applications in transferring seamlessly from one domain to another, and show its use in commercial applications, including engine sound analysis, providing real-time indications of potential engine failure.

A Survey on Deep Learning: Algorithms, Techniques, and Applications

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

Preventing Colluding Identity Clone Attacks in Online Social Networks

Nowadays, Online Social Networks (OSNs) has become one of the most common ways among people to facilitate communication. This has made it a target for attackers to steal information from influential users and has brought new forms of customized attacks for OSNs. Attackers take advantage of the users trustworthiness when using OSN. This exploitation leads to attacks with a combination of both classical and modern threats. Specifically, colluding attackers have been taken advantage of many OSNs by creating fake profiles of friends of the target in the same OSN or others. Colluders impersonate their victims and ask friend requests to the target in the aim to infiltrate her private circle to steal information. These types of attacks are difficult to detect in OSNs because multiple malicious users may have a similar purpose to gain information from their targeted user. The purpose of this paper is to overcome this type of attack by addressing the problem of matching user profiles across multiple OSNs. Then, we will extract both features and text from a users profile and build a classifier based on supervised learning techniques. Simulation and experimental results are provided to validate the accuracy of our findings.

A novel cleaning approach of environmental sensing data streams

With recent widespread usage of state-of-the-art technology (e.g., various mobile devices), environmental sensing is getting popular. The sensors used for sensing are small and due to the mobility they become more error-prone, which results in data corruption or loss from sensor. Therefore, cleaning of the sensed data is of high importance to recover the lost or corrupted data. In this paper, we propose a novel data cleaning mechanism to ensure better accuracy in environmental sensing applications. Based on the sensed data and the context relationship of each sensor, we update the credibility (or alternatively reliability) of the sensed data. We consider mobility pattern of the mobile sensor nodes while selecting the candidate sensor nodes for data stream cleaning. Through simulations, we evaluate the performance of our proposed approach. We compare our proposed sensor data stream cleaning approach with Influence Mean Cleaning (IMC) (a recent algorithm in data stream cleaning) and Mean-based cleaning. Simulation results show up to 24% reduction in root mean square error (RMSE) over IMC and up to 30% over Mean-based cleaning.