Search

Summary By controlling the network, the Internet of Things (IoT)‐connected software‐defined network (SDN) limits the scalability of IoT devices. Since SDN depends on a centralized controller that attackers can easily affect, it is incredibly susceptible to attacks. Secure access control to the SDN controller was the focus of the prior methods for controller scalability and restricted trust management. A framework called Safeguard Authentication Dynamic Access Control (SANDMAC) is suggested to safeguard and offer useful services to enterprises. Authentication confirms legitimacy after all users and applications have been registered. To improve network security, policies let users grant access to account attributes, legal activities, and temporal components. The administrator lessens conflicts between the methods by validating and saving the policies in the database. The services are provided to dependable customers using the forensic‐based investigation algorithm, depending on the quality of service and software level agreements requirements, decreasing reaction times and maximizing resource usage. Performance comparisons between the new and previous efforts are validated using a variety of parameters, and the proposed work is validated using the iFogSim application. According to the findings, SANDMAC significantly raises key performance indicators. SANDMAC specifically keeps false positives at 3.5% and accomplishes a low response time of 60 ms for roughly 800 authorized accesses. SANDMAC is a better option because of these enhancements, which result in longer network lifetimes and more dependable data transmission. [ABSTRACT FROM AUTHOR]

In an era characterized by the proliferation of the Internet of Things (IoT), the seamless interconnection of diverse devices has revolutionized various sectors. However, this rapid expansion has brought forth significant security concerns, making effective anomaly detection crucial for safeguarding IoT networks. This research introduces a pioneering framework for dynamic behavioral profiling and anomaly detection in Software-Defined IoT Networks (SD-IoT). By leveraging the inherent advantages of SDN, this framework enhances the security and resilience of IoT ecosystems. Through a meticulous step-by-step process, dynamic profiles of IoT devices' behavior are generated to capture evolving attributes over time. Machine learning-based anomaly detection algorithms analyzed profiles to identify deviations from normal behavior and to effectively detect potential security threats. Anomalies detected trigger real-time policy adaptations that contain and mitigate their impact. The SDN controller enforces these adapted policies, preventing anomaly propagation and ensuring network integrity. To evaluate the framework's efficacy, extensive experiments are conducted using a comprehensive datasets in the simulated IoT environment. The results demonstrated the accuracy of dynamic behavioral profiling, the adaptability of the policy adjustments, and the efficiency of the anomaly detection algorithm. The proposed framework holds immense promise in fortifying IoT networks against emerging threats, contributing to the security and stability of our increasingly interconnected world. The comparative analyses in the performance validation showed that the proposed model attained better anomaly detection performances than other existing models. [ABSTRACT FROM AUTHOR]

Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a major issue for SDN-based IoT applications. This paper uses the Controlled Service Scheduling Scheme (CS3) to address the challenge of simultaneous scheduling and power allocation. The suggested approach uses deep recurrent learning and probabilistic balancing for power allocation and service distribution during user-centric concurrent sharing intervals. The SDN control plane decides how much power to use for service delivery while forecasting user service demands directs the scheduling interval allocation. Power management is under the control plane of the SDN, whereas service distribution is under the data plane. Power-to-service requirements are evaluated probabilistically, and updates for both aircraft are obtained via the deep learning model. This allocation serves as the basis for training the learning model to alleviate power deficits across succeeding intervals. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption. [ABSTRACT FROM AUTHOR]

Group management is practiced to deploy access control and to ease multicast and broadcast communication. However, the devices that constitute the Internet of Things (IoT) are resource-constrained, and the network of IoT is heterogeneous with variable topologies interconnected. Hence, to tackle heterogeneity, SDN-aided centralized group management as a service framework is proposed to provide a global network perspective and administration. Group management as a service includes a group key management function, which can be either centralized or decentralized. Decentralized approaches use complex cryptographic primitives, making centralized techniques the optimal option for the IoT ecosystem. It is also necessary to use a safe, scalable approach that addresses dynamic membership changes with minimal overhead to provide a centralized group key management service. A group key management strategy called a one-way Function Tree (OFT) was put forth to lower communication costs in sizable dynamic groups. The technique, however, is vulnerable to collusion attacks in which an appending and withdrawing device colludes and conspires to obtain unauthorized keys for an unauthorized timeline. Several collusion-deprived improvements to the OFT method are suggested; however, they come at an increased cost for both communication and computation. The Modified One-Way Function Tree (MOFT), a novel technique, is suggested in this proposed work. The collusion resistance of the proposed MOFT system was demonstrated via security analysis. According to performance studies, MOFT lowers communication costs when compared to the original OFT scheme. In comparison to the OFT's collusion-deprived upgrades, the computation cost is smaller. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has transformed our lives by introducing new services and enhancing productivity. However, the widespread adoption of IoT devices and communication units has posed challenges in network management. In response, there is a growing necessity to rethink and redesign IoT network control. Software-defined networking (SDN) has emerged as a promising solution, leveraging its programmability and centralized management capabilities. SDN can simplify network management, offer network abstraction, facilitate development, and efficiently handle the complexities of IoT networks. Despite these advantages, security concerns, particularly the threat of Distributed Denial of Service (DDoS) attacks, persist in the IoT landscape. This survey focuses on exploring the collaboration between SDN and IoT. It investigates various types of DDoS attacks and highlights different types of defense, detection, and mitigation methods employed to address DDoS threats in SDN-based IoT (SDN-IoT) networks.

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its "network brain" located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches. [ABSTRACT FROM AUTHOR]

Mobile users can access vital real-time services through wireless paradigms like software-defined network (SDN) topologies and the Internet of Things. Point-of-contact-based infrastructures and dynamic user densities increase resource access and service-sharing concurrency. Thus, controlling power consumption and network and device congestion becomes a major issue for SDN-based IoT applications. This paper uses the Controlled Service Scheduling Scheme (CS3) to address the challenge of simultaneous scheduling and power allocation. The suggested approach uses deep recurrent learning and probabilistic balancing for power allocation and service distribution during user-centric concurrent sharing intervals. The SDN control plane decides how much power to use for service delivery while forecasting user service demands directs the scheduling interval allocation. Power management is under the control plane of the SDN, whereas service distribution is under the data plane. Power-to-service requirements are evaluated probabilistically, and updates for both aircraft are obtained via the deep learning model. This allocation serves as the basis for training the learning model to alleviate power deficits across succeeding intervals. The simulation experiments are modeled using the Contiki Cooja simulator, where 200 mobile users are placed. The proposed plan delivers a 14.9% high-service distribution for various users, 18.29% less delay, 13.34% less failure, 5.54% less downtime, and 18.68% less power consumption.

Currently, the Internet of Thing has become an integral part of the world's Internet infrastructure. One of the important matters in this field is the security of the network. Some networks like Software-defined Networking are proposed to improve it. In this paper, cyber-attacks have been simulated and various parameters such as the average rate of receiving traffic, the average response time and network delay have been analyzed. In the following, a new plan for switch buffers and their effect on the state of CPU performance, the controller bandwidth and the amount of packet loss have been discussed. The new buffer structure is proposed using a hash table, which uses a DRAM combination structure. To implement the proposed technique, AS5710-54X-EC switch and floodlight controller were used, and also Eclipse Neon 3.1 was used for writing modules. The results imply that this method had a great impact on detecting attacks in SDN-based IoT frameworks. [ABSTRACT FROM AUTHOR]

Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous. [ABSTRACT FROM AUTHOR]

Group management is practiced to deploy access control and to ease multicast and broadcast communication. However, the devices that constitute the Internet of Things (IoT) are resource-constrained, and the network of IoT is heterogeneous with variable topologies interconnected. Hence, to tackle heterogeneity, SDN-aided centralized group management as a service framework is proposed to provide a global network perspective and administration. Group management as a service includes a group key management function, which can be either centralized or decentralized. Decentralized approaches use complex cryptographic primitives, making centralized techniques the optimal option for the IoT ecosystem. It is also necessary to use a safe, scalable approach that addresses dynamic membership changes with minimal overhead to provide a centralized group key management service. A group key management strategy called a one-way Function Tree (OFT) was put forth to lower communication costs in sizable dynamic groups. The technique, however, is vulnerable to collusion attacks in which an appending and withdrawing device colludes and conspires to obtain unauthorized keys for an unauthorized timeline. Several collusion-deprived improvements to the OFT method are suggested; however, they come at an increased cost for both communication and computation. The Modified One-Way Function Tree (MOFT), a novel technique, is suggested in this proposed work. The collusion resistance of the proposed MOFT system was demonstrated via security analysis. According to performance studies, MOFT lowers communication costs when compared to the original OFT scheme. In comparison to the OFT’s collusion-deprived upgrades, the computation cost is smaller.

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller's bandwidth and find that the system throughput decreases when the controller's bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) is revolutionizing communication technology by facilitating the sharing of information between different physical devices connected to a network. To improve control, customization, flexibility, and reduce network maintenance costs, a new Software-Defined Network (SDN) technology must be used in this infrastructure. Despite the various advantages of combining SDN and IoT, this environment is more vulnerable to various attacks due to the centralization of control. Most methods to ensure IoT security are designed to detect Distributed Denialof-Service (DDoS) attacks, but they often lack mechanisms to mitigate their severity. This paper proposes a Multi-Attack Intrusion Detection System (MAIDS) for Software-Defined IoT Networks (SDN-IoT). The proposed scheme uses two machine-learning algorithms to improve detection efficiency and provide a mechanism to prevent false alarms. First, a comparative analysis of the most commonly used machine-learning algorithms to secure the SDN was performed on two datasets: the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD) and the Canadian Institute for Cybersecurity Intrusion Detection Systems (CICIDS2017), to select the most suitable algorithms for the proposed scheme and for securing SDN-IoT systems. The algorithms evaluated include Extreme Gradient Boosting (XGBoost), KNearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Second, an algorithm for selecting the best dataset for machine learning in Intrusion Detection Systems (IDS) was developed to enable effective comparison between the datasets used in the development of the security scheme. The results showed that XGBoost and RF are the best algorithms to ensure the security of SDN-IoT and to be applied in the proposed security system, with average accuracies of 99.88% and 99.89%, respectively. Furthermore, the proposed security scheme reduced the false alarm rate by 33.23%, which is a significant improvement over prevalent schemes. Finally, tests of the algorithm for dataset selection showed that the rates of false positives and false negatives were reduced when the XGBoost and RF algorithms were trained on the CICIDS2017 dataset, making it the best for IDS compared to the NSL-KDD dataset. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices have become part of today's human life and the amplified use of smart phones as well as number of IoT devices in everyday life has made network security more important. These IoT devices are less secure and often abandoned making them a easy target of DDoS attacks which caused by extreme network packet flow threaten vital network services. The DoS is a single-server attack in which the DDoS is a multi-server attack and IoT devices are managed through Software-defined networks (SDN). This research is proposed to improve the Firefly method in optimizing the Convolution neural network (CNN) for detecting DDoS attacks in Software Defined-IoT (SD-IoT). The suggested SD-IoT framework has been structured into three layers, namely application layer, control layer and user infrastructure layer. The SDN-IoT architecture layer model, Security apps identify DDoS attack in Application layer C-DAD attack detection. The second layer manages the SD-IoT network with the SDNWISE controller and the IoT controller. The infrastructure layer contains SOFS (Sensor Open Flow Switch) and IoT devices. However, this proposal has considered each firefly as a single hyperparameter and by updating the position of firefly periodically results in the reduction of the search iteration. Hence, the proposed model is evaluated by Root mean square and used for measuring the training accuracy of the proposed model. DDoS attacks use a backtracking technique to pinpoint the source, which increases response time. Four CNN models by varying layers and parameters has implemented to improve DDoS detection yielded results with 98% accuracy. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used in SDN. To obtain the best and most relevant features, a feature selection technique is used. Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies. The performance of our proposed model is also measured in terms of accuracy, recall, and precision. F1 rating and detection time Furthermore, a lightweight model for training is proposed, which selects fewer features while maintaining the model’s performance. Experiments show that the adopted methodology outperforms existing models. [ABSTRACT FROM AUTHOR]

Currently, solutions based on the Internet of Things are becoming widely in demand. The concept of the Internet of Things implies the construction of a software-defined network (SDN) of physical devices with integrated mechanisms of interaction both among themselves and with a cloud platform, a software system and objects of the outside world. The aim of the work is to develop a cloud platform and a visual software system for configuring Internet of Things devices. The paper proposes a four-level architecture of a software-defined network, including an additional layer of Internet of Things devices containing actuators for executing commands and sensors for collecting environmental parameters. To aggregate data about the network structure, as well as to obtain information from the end devices of the Internet of Things and store their configuration, a cloud platform has been developed that allows using an external REST API interface and a deployed socket server to interact with the visual software system and end devices of the Internet of Things. A visual IoT Map software system has been developed for configuring a software-defined network of Internet of Things devices, visualizing the network topology and data received from end devices. Special attention is paid to the mechanisms of interaction between the visual software system, the cloud platform and the end devices of the Internet of Things.

Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its “network brain” located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches.

IoT infrastructures are becoming increasingly more difficult to manage. One of the main issues is the high volatility present in the infrastruture, which increasingly demands self-adaptive solutions. As a proposal to handle this challenge, this paper presents STEER (Sdn-based inTEnt drivEn iot netwoRks), a new approach for the dynamic adaptation of IoT networks for indoor monitoring applications, based on the unification of Intent-Driven Networks (IDN) and Software-Defined Networks (SDN). Particularly, we explore the ability of IDNs to dynamically interpret an application's intent, using an IDN-based mediator attached to an SDN-controller to autonomously adapt the IoT network behavior at runtime, thus realizing the intent according to the current operating context of the network. We demonstrate the approach using a representative application scenario related to IoT indoor environment monitoring in the domain of indoor air quality monitoring. The experiments allowed us to validate the applicability of the approach and show the system-wide effect of dynamic adaptation to the current operating environment on improving performance according to the metric under consideration, in this case, the number of application-level messages exchanged in the network. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed. [ABSTRACT FROM AUTHOR]

The first edition of the International Conference on Trends and Innovations in Smart Technologies (ICTIST'22) was held on 7-8 October 2022, bringing together researchers and experts from the fields of communication networks and security, computational intelligence, and engineering. The conference provided a platform for participants to share their research and discuss the latest trends and innovations in these areas. The present report will start by providing an overview on the keynote speeches and the main axes around which the communication sessions revolved before moving on to more detailed abstracts, presenting each of the topics presented during the ICTIST'22 conference. [ABSTRACT FROM AUTHOR]

Digital twins and the Internet of Things (IoT) have gained significant research attention in recent years due to their potential advantages in various domains, and vehicular ad hoc networks (VANETs) are one such application. VANETs can provide a wide range of services for passengers and drivers, including safety, convenience, and information. The dynamic nature of these environments poses several challenges, including intermittent connectivity, quality of service (QoS), and heterogeneous applications. Combining intelligent technologies and software-defined networking (SDN) with VANETs (termed intelligent software-defined vehicular networks (iSDVNs)) meets these challenges. In this context, several types of research have been published, and we summarize their benefits and limitations. We also aim to survey stochastic modeling and performance analysis for iSDVNs and the uses of machine-learning algorithms through digital twin networks (DTNs), which are also part of iSDVNs. We first present a taxonomy of SDVN architectures based on their modes of operation. Next, we survey and classify the state-of-the-art iSDVN routing protocols, stochastic computations, and resource allocations. The evolution of SDN causes its complexity to increase, posing a significant challenge to efficient network management. Digital twins offer a promising solution to address these challenges. This paper explores the relationship between digital twins and SDN and also proposes a novel approach to improve network management in SDN environments by increasing digital twin capabilities. We analyze the pitfalls of these state-of-the-art iSDVN protocols and compare them using tables. Finally, we summarize several challenges faced by current iSDVNs and possible future directions to make iSDVNs autonomous.

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics.

The usage of the Software-Defined Sensor Networks (SD-WSNs) as support for applications of the Internet of Things (IoT) has been the focus of recent research to reduce the complexity of configuration and management. This simplification allows greater flexibility for the configuration of these networks in operating time. However, although more flexible than traditional approaches, the existing SD-WSN architectures found in the literature have limitations mainly with regard to the reprogramming of devices in operating time. In this article, we propose a novel approach called Extended Software-Defined Wireless Sensor Network (ESD-WSN), in which the concept of the SDNs is extended to the application layer allowing the reprogramming of low-end devices in operation time without the need for firmware update. The proposed architecture was validated by simulation and an experimental study using very constrained low-end devices. The simulation/experimental results showed the effectiveness, flexibility, and efficiency of the proposal. [ABSTRACT FROM AUTHOR]

Software-defined networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The controller in the control plane is the fundamental element used to manage all operations of the data plane. Hence, the performance and capabilities of the controller itself are essential in achieving optimal performance. Furthermore, the tools used to benchmark their performance must be accurate and useful in measuring different evaluation parameters. There are dozens of controller proposals for general and specialized networks in the literature. However, there is a very limited comprehensive quantitative analysis for them. In this article, we present a comprehensive qualitative comparison of different SDN controllers, along with a quantitative analysis of their performance in different network scenarios. We categorize and classify 34 controllers and present a qualitative comparison. We also present a comparative analysis of controllers for specialized networks such as the Internet of Things, blockchain networks, vehicular networks, and wireless sensor networks. We also discuss in-depth capabilities of benchmarking tools and provide a comparative analysis of their capabilities. This work uses three benchmarking tools to compare 9 controllers and presents a detailed analysis of their performance, along with discussion on performance of specialized network controllers. [ABSTRACT FROM AUTHOR]

The data being generated by the Internet of Things needs to be stored, monitored, and analyzed for maximum IoT resource utilization. Software Defined Networking has been extensively utilized to address issues such as heterogeneity and scalability. However, for small-scale IoT application, sometimes it is considered an inefficient approach. This paper proposes an alternate lightweight mechanism to the design and implementation of a dynamic virtual network based on user requirements. The key idea is to provide users a virtual interface that enables them to reconfigure the communication flowbetween the sensors and actuators at runtime. The throughput of the communication flow depends on the data traffic load and optimal routing. Users can reconfigure the communication flow, and virtual agents find the optimal route to handle the traffic load. The virtual network provides a user-friendly interface to allowphysical devices to be mappedwith the corresponding virtual agents. The proposed network is applicable for all systems that lie in the Internet of Things domain. Results conclude that the proposed network is efficient, reliable, and responsive to network reconfiguration at runtime. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is gradually incorporating multiple environmental, people, or industrial monitoring deployments with diverse communication and application requirements. The main routing protocols used in the IoT, such as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), are focusing on the many-to-one communication of resource-constraint devices over wireless multi-hop topologies, i.e., due to their legacy of the Wireless Sensor Networks (WSN). The Software-Defined Networking (SDN) paradigm appeared as a promising approach to implement alternative routing control strategies, enriching the set of IoT applications that can be delivered, by enabling global protocol strategies and programmability of the network environment. However, SDN can be associated with significant network control overhead. In this paper, we propose VERO-SDN, an open-source SDN solution for the IoT, bringing the following novelties in contrast to the related works: (i) programmable routing control with reduced control overhead through inherent protocol support of a long-range control channel; and (ii) a modular SDN controller and an OpenFlow-like protocol improving the quality of communication in a wide range of IoT scenarios through supporting two alternative topology discovery and two flow establishment mechanisms. We carried out simulations with various topologies, network sizes and high-volume transmissions with alternative communication patterns. Our results verified the robust performance and reduced control overhead of VERO-SDN for alternative IoT deployments, e.g., achieved up to 47% reduction in network's overall end-to-end delay time compared to RPL and a packet delivery ratio of over 99.5%.

One of the most critical challenges of the Internet of Things (IoT) is to provide real-time services. Therefore, to provide a secure, efficient, and stable communication platform in the Internet of Things, emerging architectures such as software-defined networks (SDN) being significant. This paper proposes a comprehensive SDN based fault-tolerant architecture in IoT environments. In the proposed scheme, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the main and backup non-overlapping paths between network equipment. In addition to the fault tolerance (FT) discussion in the proposed scheme, service quality is considered in the proposed scheme. Putting the percentage of link usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths. The end-to-end delay and the degree of link congestion are minimal. We compare our proposed scheme with two policies for building routes from source to destination. The simulation results indicate that, while reducing the error recovery time, the proposed method leads to improved services quality parameters such as packet loss, delay, and packet jitter. The results show that in case of a link failure in the network, the recovery time in large scenarios is a maximum of 16 ms, which improved by 20%, compared to the disjoint paths (DP) method. Our Approach can decrease packet loss by approximately 30% compared to the Dijkstra's algorithm and roughly 24% compared to the DP method. Also, SLRG reduces latency by approximately 36% compared to the Dijkstra's algorithm and roughly 19% compared to the DP method. Last, the proposed scheme reduces Jitter by approximately 49% compared to the Dijkstra's algorithm, and roughly 26% compared to the DP method. [ABSTRACT FROM AUTHOR]

The Routing over Low Power and Lossy Networks (RPL) protocol is an important standardized routing solution for the Internet of Things (IoT), characterized by significant benefits, including IPv6 support and efficient low-power operation over noisy channels, especially for many-to-one communication scenarios (i.e., a set of deployed devices gathering periodic measurements destined to a single sink node). However, the RPL faces performance issues in networks with mobility or point-to-point communication requirements, raising applicability constraints in a number of real-world IoT applications, spanning from environmental monitoring to industrial and urban networks. In this paper, we approach RPL from the Software-Defined Networking (SDN) perspective, exploiting its high customization features to address the above inefficiencies. We apply an evolutionary methodology, i.e., building over the widely deployed RPL protocol, while maintaining compliance with its standard. More precisely, we investigate two routing control strategies exploiting the global view of the network: (i) the Moderate RPL control that enables dynamic reconfigurability of crucial protocol parameters to improve its operation in mobile environments; and (ii) the Deep RPL control that utilizes a new RPL Objective Function (OF) we proposed that enforces direct point-to-point paths through link-coloring. We implemented and evaluated the two strategies based on a novel centralized routing control facility. Our experimental analysis considers hybrid scenarios with both fixed and mobile nodes, as well as many-to-one and point-to-point communication. Compared to the standard RPL in mobile topologies, the proposed solution achieves improved packet delivery ratio of up to 33 percent and 21 percent for the mobile nodes and for the whole network, respectively, while maintaining RPL compliance. In case of point-to-point communication in a random topology, the improvements rise up to 32.7 percent for the trip-time and 42 percent for the round-trip time, while the packet loss ratio for the same experiment is improved up to 75 percent in the non-storing mode.

Software-defined networking (SDN) offers potential pathways to overcome the management complexity of the Internet of Things (IoT). Previous studies have often been limited to software simulations or general proposals only. In this article, we design and evaluate an SDN-based management system for wireless sensor networks (WSNs) using IPv6 over low-power wireless personal area networks (6LoWPAN). The framework is described in detail covering different data-, control-, and application-plane implementations, and includes a novel addressing scheme and packet format. It also uses a centralized routing protocol, located at the SDN controller, based on the shortest path algorithm. We compare our approach with the routing protocol for low-power and lossy networks (RPL), which uses a distributed routing protocol. Hardware tests were carried out in a dynamic environment, with multiple sources of interference for different payload sizes to evaluate the impacts and practicality of SDN in WSNs. The performance comparison shows that the proposed SDN management system for IP-enabled WSNs using a centralized routing protocol outperforms the RPL protocol in terms of round-trip time, jitter, memory consumption, and packet loss rate (PLR), despite the control overhead introduced. [ABSTRACT FROM AUTHOR]

Rstercaecntt trends have shown a migration of software from local machines to server-based services. These servicebased networks depend on high up-times and heavy resistance in order to compete in the market. Along with this growth, denial of service attacks have equally grown. Defending against these attacks has become increasingly difficult with the growth of Internet of Things and the different varieties of denial of service attacks. For this, our research offers a solution of implementing software-defined networking and real-time metric based techniques to mitigate a denial of service attack within a smaller time window than other comparable solutions. The use of our method offers both efficient attack handling and also flexibility to fit a variety of implementations. The end result being a network that can automatically adapt against new attacks based on previous network activity. [ABSTRACT FROM AUTHOR]

Abstract Visible light communication (VLC) is a wireless technology complementary to well-understood radio frequency (RF) communication that is promising to help alleviate the spectrum crunch problem in overcrowded RF spectrum bands. While there has been significant advancement in recent years in understanding physical layer techniques for visible light point-to-point links, the core problem of developing efficient networking technology specialized for visible-light networks is substantially unaddressed. This article discusses the current existing techniques as well as the main challenges for the design of visible-light ad hoc networks - referred to as LANETs. The paper discusses typical architectures and application scenarios for LANETs and highlights the major differences between LANETs and traditional mobile ad hoc networks (MANETs). Enabling technologies and design principles of LANETs are analyzed and existing work is surveyed following a layered approach. Open research issues in LANET design are also discussed, including long-range visible light communication, full-duplex LANET MAC, blockage-resistant routing, VLC-friendly TCP and software-defined prototyping, among others. [ABSTRACT FROM AUTHOR]

Software-defined Network (SDN) is a new networking paradigm enabling innovation through network programmability. Over past few years, many applications have been built using SDN such as server load balancing, virtual-machine migration, traffic engineering and access control. In this paper, we focus on using SDN for energy-aware routing (EAR). Since traffic load has a small influence on the power consumption of routers, EAR allows putting unused links into sleep mode to save energy. SDN can collect traffic matrix and then computes routing solutions satisfying QoS while being minimal in energy consumption. However, prior works on EAR have assumed that the SDN forwarding table switch can hold an infinite number of rules. In practice, this assumption does not hold since such flow tables are implemented in Ternary Content Addressable Memory (TCAM) which is expensive and power hungry. We consider the use of wildcard rules to compress the forwarding tables. In this paper, we propose optimization methods to minimize energy consumption for a backbone network while respecting capacity constraints on links and rule space constraints on routers. In details, we present two exact formulations using Integer Linear Program (ILP) and introduce efficient heuristic algorithms. Based on simulations on realistic network topologies, we show that using this smart rule space allocation, it is possible to save almost as much power consumption as the classical EAR approach. [ABSTRACT FROM AUTHOR]

The lack of privileged access to emergent and operational deployments is one of the key matters during validation and testing of novel telecommunication systems and technologies. This matter jeopardizes the repeatability of experiments, which results in burdens for innovation and research in these areas. In this light, we present a method and architecture to make the software-driven definition of virtual testbeds easier. As distinguishing features, our proposal can mimic operational deployments by using high-dimensional activity patterns. These activity patterns shape the effect of a control module that triggers agents for the generation of network traffic. This solution exploits the capabilities of network emulation and virtualization systems, which nowadays can be easily deployed in commodity servers. With this, we accomplish a reproducible definition of realistic experimental conditions and the introduction of real agent implementations in a cost-effective fashion. We evaluate our solution in a case study that is comprised of the validation of a network-monitoring tool for Voice over IP (VoIP) deployments. Our experimental results support the viability of the method and illustrate how this formulation can improve the experimentation in emergent technologies. [ABSTRACT FROM AUTHOR]

The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.

Venture capital experts explore current opportunities to fuel and benefit from the innovation economy. Venture capital remains a vibrant force in the US, funding a broad array of disruptive technologies and playing a major role in fueling a startup-based economy. A survey of Austin Technology Incubator's recent startup portfolio suggests promising trends for venture investment in the innovation economy. Next-generation data analytics, offering strategic business applications that integrate data and technology into innovative and affordable tools, provide significant opportunities for venture investment. Corporations worldwide increasingly look to Silicon Valley's culture of entrepreneurship and venture investment to create new models for successful disruptive innovation. [ABSTRACT FROM PUBLISHER]