Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Spatial Domain Management and Massive MIMO Coordination in 5G SDN

In 5G mobile communication systems, massive multiple-input multiple-output (MIMO) and heterogeneous networks (HetNets) play crucial roles to achieve expected coverage and capacity across venues. This paper correspondingly addresses software-defined network (SDN) as the central controller of radio resource management in massive MIMO HetNets. In particular, we identify the huge spatial domain information management and complicated MIMO coordination as the grand challenges in 5G systems. Our work accordingly distinguishes itself by considering more network MIMO aspects, including flexibility and complexity of spatial coordination. In our proposed scheme, SDN controller first collects the user channel state information in an effective way, and then calculates the null-space of victim users and applies linear precoding to that null-space. Simulation results show that our design is highly beneficial and easy to be deployed, due to its high quality of service performance but low computation complexity

Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

State-of-the-art emergency navigation approaches are designed to evacuate civilians during a disaster based on real-time decisions using a pre-defined algorithm and live sensory data. Hence, casualties caused by the poor decisions and guidance are only apparent at the end of the evacuation process and cannot then be remedied. Previous research shows that the performance of routing algorithms for evacuation purposes are sensitive to the initial distribution of evacuees, the occupancy levels, the type of disaster and its as well its locations. Thus an algorithm that performs well in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms for evacuees where results are affected by the choice of certain parameters. Therefore, this paper proposes a simulation-based evacuee routing algorithm that optimises evacuation by making use of the high computational power of cloud servers. Rather than guiding evacuees with a predetermined routing algorithm, a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner, and any "simulated casualties" are then re-routed using a variant of Dijkstra's algorithm to obtain new safe paths for them to exits. This approach can be iterated as long as corrective action is still possible.Comment: Submitted to PerNEM'15 for revie

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Analyzing Methods and Opportunities in Software-Defined (SDN) Networks for Data Traffic Optimizations

Computer networks are dynamic and require constant updating and monitoring of operations to meet the growing volume of data trafficked. This generates a number of cost issues as well as performance management and tuning to deliver granular quality of service (QoS), balancing data load, and controlling the occurrence of bottlenecks. As an alternative, a new programmable network paradigm has been used under the name of Software Defined Networks (SDN). The SDN consists of decoupling the data plane and controlling the network, where a programmable controller is responsible for managing rules for routing the data to various devices. Thus, the hardware that remains in the network data stream simply addresses the routing of the packets quickly according to these rules. In this context, this article conducts a study on different methods and approaches that are being used in the literature to solve problems in the optimization of data traffic in the network through the use of SDN. In particular, this study differs from other reviews of SDN because it focuses on issues such as QoS, load balancing, and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented. load balancing and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented. load balancing and congestion control. Finally, in addition to the review of the SDN's state-of-the-art in the areas mentioned, a survey of future challenges and research opportunities in the area is also presented