A conceptual architecture for integrating software defined network and network virtualization with internet of things

Software defined network (SDN) and network function virtualization (NFV) are new paradigms and technologies of the network which support the best experience of providing functions and services, managing network traffic, and a new way of control. They support virtualization and separating data from control in network devices, as well as provide services in a software-based environment. Internet of things (IoT) is a heterogeneous network with a massive number of connected devices and objects. IoT should be integrated with such technologies for the purpose of providing the capabilities of dynamic reconfiguration with a high level of integration. This paper proposes a conceptual architecture for integrating software defined network (SDN) and NFV with IoT. The proposed work combines the three technologies together in one architecture. It also presents the previous works in this area and takes a look at the theoretical background of those technologies in order to give a complete view of proposed work

IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PD

Towards the Softwarization of Content Delivery Networks for Component and Service Provisioning

Content Delivery Networks (CDNs) are common systems nowadays to deliver content (e.g. Web pages, videos) to geographically distributed end-users over the Internet. Leveraging geographically distributed replica servers, CDNs can easily help to meet the required Quality of Service (QoS) in terms of content quality and delivery time. Recently, the dominating surge in demand for rich and premium content has encouraged CDN providers to provision value-added services (VAS) in addition to the basic services. While video streaming is an example of basic CDN services, VASs cover more advanced services such as media management. Network softwarization relies on programmability properties to facilitate the deployment and management of network functionalities. It brings about several benefits such as scalability, adaptability, and flexibility in the provisioning of network components and services. Technologies, such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN) are its key enablers. There are several challenges related to the component and service provisioning in CDNs. On the architectural front, a first challenge is the extension of the CDN coverage by on-the-fly deployment of components in new locations and another challenge is the upgrade of CDN components in a timely manner, because traditionally, they are deployed statically as physical building blocks. Yet, another architectural challenge is the dynamic composition of required middle-boxes for CDN VAS provisioning, because existing SDN frameworks lack features to support the dynamic chaining of the application-level middle-boxes that are essential building blocks of CDN VASs. On the algorithmic front, a challenge is the optimal placement of CDN VAS middle-boxes in a dynamic manner as CDN VASs have an unknown end-point prior to placement. This thesis relies on network softwarization to address key architectural and algorithmic challenges related to component and service provisioning in CDNs. To tackle the first challenge, we propose an architecture based on NFV and microservices for an on-the-fly CDN component provisioning including deployment and upgrading. In order to address the second challenge, we propose an architecture for on-the-fly provisioning of VASs in CDNs using NFV and SDN technologies. The proposed architecture reduces the content delivery time by introducing features for in-network caching. For the algorithmic challenge, we study and model the problem of dynamic placement and chaining of middle-boxes (implemented as Virtual Network Function (VNF)) for CDN VASs as an Integer Linear Programming (ILP) problem with the objective of minimizing the cost while respecting the QoS. To increase the problem tractability, we propose and validate some heuristics

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude