Software defined wireless sensor networks application opportunities for efficient network management : a survey

Wireless Sensor Networks (WSNs) are commonly used information technologies of modern networking and computing platforms. Today's network computing applications are faced with a high demand of powerful network functionalities. Functional network reach is central to customer satisfaction such as in mobile networks and cloud computing environments. However, efficient management of WSNs remains a challenge, due to problems supplemental to them. Recent technology shift proposes Software Defined Networking (SDN) for improving computing networks. This review paper highlights application challenges faced by WSNs for monitored environments and those faced by the proposed approaches, as well as opportunities that can be realized on applications of WSNs using SDN. We also highlight Implementation considerations by focusing on critical aspects that should not be disregarded when attempting to improve network functionalities. We then propose a strategy for Software Defined Wireless Sensor Network (SDWSN) as an effort for application improvement in monitored environments.The National Research Foundation (NRF) of South Africa (grant number: RDYR160404161474 and IFR160118156967).http://www.elsevier.com/locate/compeleceng2019-02-01hj2018Electrical, Electronic and Computer Engineerin

Low-Power Wireless for the Internet of Things: Standards and Applications: Internet of Things, IEEE 802.15.4, Bluetooth, Physical layer, Medium Access Control,coexistence, mesh networking, cyber-physical systems, WSN, M2M

International audienceThe proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the Internet of Things (IoT) to bridge the gap between the virtual and physical world through enabling the monitoring and actuation of the physical world controlled by data processing systems. Wireless technologies, despite their offered convenience, flexibility, low cost, and mobility pose unique challenges such as fading, interference, energy, and security, which must be carefully addressed when using resource-constrained IoT devices. To this end, the efforts of the research community have led to the standardization of several wireless technologies for various types of application domains depending on factors such as reliability, latency, scalability, and energy efficiency. In this paper, we first overview these standard wireless technologies, and we specifically study the MAC and physical layer technologies proposed to address the requirements and challenges of wireless communications. Furthermore, we explain the use of these standards in various application domains, such as smart homes, smart healthcare, industrial automation, and smart cities, and discuss their suitability in satisfying the requirements of these applications. In addition to proposing guidelines to weigh the pros and cons of each standard for an application at hand, we also examine what new strategies can be exploited to overcome existing challenges and support emerging IoT applications

Flexible network management in software defined wireless sensor networks for monitoring application systems

Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.Thesis (PhD)--University of Pretoria, 2018.National Research Foundation (NRF)Telkom Centre of ExcellenceElectrical, Electronic and Computer EngineeringPhDUnrestricte

Constructive Interference in 802.15.4: A Tutorial

International audienceConstructive Interference (CI) can happen when multiple wireless devices send the same frame at the same time. If the time offset between the transmissions is less than 500 ns, a receiver will successfully decode the frame with high probability. CI can be useful for achieving low-latency communication or low-overhead flooding in a multi-hop low-power wireless network. The contribution of this article is threefold. First, we present the current state-of-the-art CI-based protocols. Second, we provide a detailed hands-on tutorial on how to implement CI-based protocols on TelosB motes, with well documented open-source code. Third, we discuss the issues and challenges of CI-based protocols, and list open issues and research directions. This article is targeted at the level of practicing engineers and advanced researchers and can serve both as a primer on CI technology and a reference to its implementation

Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions

Software defined networking based resource management and quality of service support in wireless sensor network applications

To achieve greater performance in computing networks, a setup of critical computing aspects that ensures efficient network operation, needs to be implemented. One of these computing aspects is, Quality of Service (QoS). Its main functionality is to manage traffic queues by means of prioritizing sensitive network traffic. QoS capable networking allows efficient control of traffic especially for network critical data. However, to achieve this in Wireless Sensor Networks (WSN) is a serious challenge, since these technologies have a lot of computing limitations. It is even difficult to manage networking resources with ease in these types of technologies, due to their communication, processing and memory limitations. Even though this is the case with WSNs, they have been largely used in monitoring/detection systems, and by this proving their application importance. Realizing efficient network control requires intelligent methods of network management, especially for sensitive network data. Different network types implement diverse methods to control and administer network traffic as well as effectively manage network resources. As with WSNs, communication traffic and network resource control are mostly performed depending on independently employed mechanisms to deal with networking events occurring on different levels. It is therefore challenging to realize efficient network performance with guaranteed QoS in WSNs, given their computing limitations. Software defined networking (SDN) is advocated as a potential paradigm to improve and evolve WSNs in terms of capacity and application. A means to apply SDN strategies to these compute-limited WSNs, formulates software defined wireless sensor networks (SDWSN). In this work, a resource-aware OpenFlow-based Active Network Management (OF-ANM) QoS scheme that uses SDN strategies is proposed and implemented to apply QoS requirements for managing traffic congestion in WSNs. This scheme uses SDN programmability strategies to apply network QoS requirements and perform traffic load balancing to ensure congestion control in SDWSN. Our experimental results show that the developed scheme is able to provide congestion avoidance within the network. It also allows opportunities to implement flexible QoS requirements based on the system’s traffic state. Moreover, a QoS Path Selection and Resource-associating (Q-PSR) scheme for adaptive load balancing and intelligent resource control for optimal network performance is proposed and implemented. Our experimental results indicate better performance in terms of computation with load balancing and efficient resource alignment for different networking tasks when compared with other competing schemes.Thesis (PhD)--University of Pretoria, 2018.National Research FoundationUniversity of PretoriaElectrical, Electronic and Computer EngineeringPhDUnrestricte

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Robust, Resilient Networked Communication in Challenged Environments

In challenged environments, digital communication infrastructure may be difficult or even impossible to access. This is especially true in rural and developing regions, as well as in any region during a time of political or environmental crisis. We advance the state of the art in wireless networking and security to design networks and applications that rapidly assess changing networking conditions to restore communication and provide local situational awareness. This dissertation examines new systems for responding to current and emerging needs for wireless networks. This work looks across the wireless ecosystem of widely deployed standards. We develop new tools to improve network assessment and to provide robust and reliable network communication. By incorporating new technological breakthroughs, such as the wide commercial success of Unmanned Aircraft Systems (UAS), we introduce novel methods and systems for existing wireless standards for these challenged networks. We assess how existing technologies and standards function in difficult environments: lacking end-end Internet connectivity, experiencing overload or other resource constraints, and operating in three dimensional space. Through this lens, we demonstrate how to optimize networks to serve marginalized communities outside of first world urban cities and make our networks resilient to natural and political crisis that threaten communication