Query: Software Defined Networking in IoT

title: Secure Data Transportation With Software Defined Networking and k n Secret Sharing for High Confidence IoT Services
abstract: Internet of Things (IoT) has become a critical infrastructure in smart city services. Unlike traditional network nodes, most of the current IoT devices are constrained with limited capabilities. Moreover, frequent changes in the network status (e.g. nodes turns into the sleep mode to save battery) make it even more difficult to set up a stable, secure transmission among smart city IoT devices. On the one hand, these weaknesses make the IoT more vulnerable to attacks, such as data eavesdropping, which can monitor, tamper, and obtain the transporting data. On the other hand, the high confidence smart city service strongly relies on the security of data transporting among the IoT devices, e.g. data being tempered would reduce the reliability of smart city services and data being monitored or stolen would infringe the privacy of smart city services. Toward high confidence smart city IoT services, we proposed an approach to secure the data transportation among the smart city IoT devices, which combines a k n secret sharing mechanism and software defined networking (SDN) technique to securely transport IoT data. Specifically, the data are transported by multiple routes calculated by the SDN controller adaptively. Data safety is guaranteed by the all or nothing feature of the k n secret sharing mechanism. Two SDN based transmission strategies, which leverage the SDN's advantages on network management, and scheduling, are applied to overcome the challenges of the unstable network state in IoT. Extensive experiments conducted from many aspects show that the proposed approach can remarkably reduce the attack success rate with reasonable and acceptable overhead.

title: Blockchain Enabled Distributed Security Framework for Next Generation IoT: An Edge Cloud and Software Defined Network Integrated Approach
abstract: The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day to day life. It is important to do research on security frameworks for next generation IoT and develop state of the art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain enabled distributed security framework using edge cloud and software defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

title: A Software Defined IoT Device Management Framework for Edge and Cloud Computing
abstract: In this article, we present the design and implementation of the software defined IoT management (SDIM) framework based on software defined networking (SDN) enabled architecture that is purposely built for the edge computing multidomain wireless sensor networks (WSNs) This framework can dynamically provision the IoT devices to enable machine to machine (M2M) communication as well as continuous operational fault detection for WSNs. Unlike the existing approaches in the literature, SDIM is mainly deployed at multiaccess edge computing (MEC) nodes and is integrated with the cloud by aggregating multidomain topology information. Backed by the experimental results over the University of Bristol 5G test network, we demonstrate in practice that our framework outperforms the implementations of the lightweight M2M (LWM2M) and NETCONF Light IoT device management protocols when deployed autonomously at the network edge and/or the cloud. Specifically, SDIM edge deployments can lower the average device provisioning time as high as 46% compared to LWM2M and 60.3% compared to NETCONF Light. Moreover, it can decrease the average operational fault detection time by approximately 33% compared to LWM2M and roughly 40% compared to NETCONF Light. Also, SDIM reduces control operations time up to 27% posing a powerful feature for use cases with time critical control requirements. Last, SDIM manages to both reduce CPU consumption and to have important energy consumption gains at the network edge, which can reach as high as 20% during device provisioning and 4.5% 4.9% during fault detection compared to the benchmark framework deployments.

title: Spatiotemporal Congestion Aware Path Planning Toward Intelligent Transportation Systems in Software Defined Smart City IoT
abstract: In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid based model to quantify the traffic congestion probability of the transportation network; 2) we propose a polynomial time solvable algorithm to recognize the grids that affect the traffic congestion probability of the network links or paths; and 3) we utilize the time expanded network technology to expand the time slots in the spatial dimension and propose a polynomial time path planning algorithm that can seek for a congestion aware path to schedule the traffic within a given time threshold.

title: Securing the Internet of Things in the Age of Machine Learning and Software Defined Networking
abstract: The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next generation IoT challenges will require a new secure by design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning (ML) and software defined networking (SDN) will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of ML and SDN to address existing and next generation IoT security threats.

title: Software Defined Networking for Energy Harvesting Internet of Things
abstract: Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. The overall network performance of existing IoT is restricted by limited network lifetime. Hence, energy harvesting technology with energy replenishment from mobile charger is proposed to prolong the network lifetime. Energy harvesting IoT is emerged. Nodes can not only request energy replenishment from the mobile charger, but also transfer surplus energy to the mobile charger for improving energy utilization. This gives rise to bidirectional energy flows in the network. A new paradigm that energy flows coexist with data flows is further resulted in. But there exist great challenges on controlling these flows. Toward centralized flow control, we exploit software defined networking to simplify and optimize network management, thus introduce software defined energy harvesting IoT (SEANET) In our proposed architecture, the data plane, energy plane, and control plane are decoupled to support enhanced communications and flexible energy scheduling. We consider reliable communications for SEANET, and propose to relay data packets among the nodes with high reputation values and sufficient energy. In particular, reputation values of nodes are computed by the multiweighted subjective logic for higher accuracy. Besides, a Nash bargaining game is formulated to solve the benefit allocation problem for energy trading in SEANET. Numerical results indicate that SEANET improves data traffic by reducing packet loss, optimizes energy utilization, and saves energy.

title: Software Defined Networking for Internet of Things: A Survey
abstract: Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost effective manner. Besides, the advent of software defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects edge, access, core, and data center networking. In these areas, the utility of SDN based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN based technologies.

title: SD MIoT: A Software Defined Networking Solution for Mobile Internet of Things
abstract: The Internet of Things (IoT) brings new potentials in humans' everyday life activities through enabling applications with stringent communication demands, including mobile IoT applications. State of the art routing protocols for the IoT, such as IPv6 routing protocol for low power and lossy networks (RPL) have not been originally designed for such challenging applications. Recent proposals blend the software defined networking (SDN) paradigm with IoT, enabling better informed and bespoke routing control, matching the application requirements. In this article, we propose SD MIoT, an open source SDN solution for mobile IoT environments that consists of a modular SDN controller and an OpenFlow like protocol. SD MIoT detects passively in real time, the network's mobile nodes through mobility detector (MODE) an intelligent algorithm that utilizes the connectivity graph of the SDN controller. It incorporates novel mobility aware topology discovery mechanisms, routing policies, and flow rule establishment methods, all of them balancing control overhead with routing robustness, according to nodes' mobility behavior detected by MODE. We provide extensive evaluation results over two realistic scenarios, further confirming the suitability of SD MIoT for mobile IoT environments and demonstrating reliable operation in terms of successful data packet delivery with low control overhead.

title: Software Defined Wireless Networking Opportunities and Challenges for Internet of Things: A Review
abstract: With the emergence of Internet of Things (IoT) there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.

title: Load Balanced and QoS Aware Software Defined Internet of Things
abstract: Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality of Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP hard. Then, a predictive and proactive heuristic mechanism based on time series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.