Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOn the enhancement of non-functional requirements for cloud-assisted middleware-based IoT and other applications
423
0
0.0
(
0
)
Added by
Samir Medjiah
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The Internet of objects (IoT) will have to meet the non-functional needs (QoS, security, etc.) of new business applications supported by the cloud. To do this, the interactions between the underlying application software and the communicating objects will rely on networks and communication middleware with configurable, programmable and dynamically deployable capabilities. These capabilities will be available both on pre-existing entities but also on virtual entities, i.e. that will be dynamically created in the Cloud according to the need. In this new ecosystem, meeting the end-to-end QoS needs of these future applications is a major challenge. This challenge has particularly to be tackled both at the level of the Middleware intermediary entities and at the level of the networks interconnecting these entities. In this context, this paper presents our approach for a self-adaptive QoS management at the middleware level for IoT applications. This approach is aimed at: 1) taking advantage of the technological opportunities offered by the Cloud, the dynamic deployment of processing functions and the autonomic computing paradigm, 2) taking into account the hetero-geneity of the solutions that will coexist in this landscape, and 3) ensuring the consistency of the (re) configuration choices thanks to appropriate theoretical tools.
rate research
Read More
In the recent years, telecom and computer networks have witnessed new concepts and technologies through Network Function Virtualization (NFV) and Software-Defined Networking (SDN). SDN, which allows applications to have a control over the network, and NFV, which allows deploying network functions in virtualized environments, are two paradigms that are increasingly used for the Internet of Things (IoT). This Internet (IoT) brings the promise to interconnect billions of devices in the next few years rises several scientific challenges in particular those of the satisfaction of the quality of service (QoS) required by the IoT applications. In order to address this problem, we have identified two bottlenecks with respect to the QoS: the traversed networks and the intermediate entities that allows the application to interact with the IoT devices. In this paper, we first present an innovative vision of a network function with respect to their deployment and runtime environment. Then, we describe our general approach of a solution that consists in the dynamic, autonomous, and seamless deployment of QoS management mechanisms. We also describe the requirements for the implementation of such approach. Finally, we present a redirection mechanism, implemented as a network function, allowing the seamless control of the data path of a given middleware traffic. This mechanism is assessed through a use case related to vehicular transportation.
Internet of Things (IoT) is an Internet-based environment of connected devices and applications. IoT creates an environment where physical devices and sensors are flawlessly combined into information nodes to deliver innovative and smart services for human-being to make their life easier and more efficient. The main objective of the IoT devices-network is to generate data, which are converted into useful information by the data analysis process, it also provides useful resources to the end users. IoT resource management is a key challenge to ensure the quality of end user experience. Many IoT smart devices and technologies like sensors, actuators, RFID, UMTS, 3G, and GSM etc. are used to develop IoT networks. Cloud Computing plays an important role in these networks deployment by providing physical resources as virtualized resources consist of memory, computation power, network bandwidth, virtualized system and device drivers in secure and pay as per use basis. One of the major concerns of Cloud-based IoT environment is resource management, which ensures efficient resource utilization, load balancing, reduce SLA violation, and improve the system performance by reducing operational cost and energy consumption. Many researchers have been proposed IoT based resource management techniques. The focus of this paper is to investigate these proposed resource allocation techniques and finds which parameters must be considered for improvement in resource allocation for IoT networks. Further, this paper also uncovered challenges and issues of Cloud-based resource allocation for IoT environment.
The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control. The requirements of the IoT applications vary due to different tasks in the smart grid. In this paper, we propose a new computing paradigm to offer location-aware, latencysensitive monitoring and intelligent control for IoT applications in the smart grid. In particular, a new fog-based architecture and programming model is designed. Fog computing extends computing to the edge of a network, which has a perfect match to IoT applications. However, existing schemes can hardly satisfy the distributed coordination within fog computing nodes in the smart grid. In the proposed model, we introduce a new distributed fog computing coordinator, which periodically gathers information of fog computing nodes, e.g., remaining resources, tasks, etc. Moreover, the fog computing coordinator also manages jobs so that all computing nodes can collaborate on complex tasks. In addition, we construct a working prototype of intelligent electric vehicle service to evaluate the proposed model. Experiment results are also presented to demonstrate that our proposed model exceed the traditional fog computing schemes for IoT applications in the smart grid.
The introduction of Narrowband Internet of Things (NB-IoT) as a cellular IoT technology aims to support massive Machine-Type Communications applications. These applications are characterized by massive connections from a large number of low-complexity and low-power devices. One of the goals of NB-IoT is to improve coverage extension beyond existing cellular technologies. In order to do that, NB-IoT introduces transmission repetitions and different bandwidth allocation configurations in uplink. These new transmission approaches yield many transmission options in uplink. In this paper, we propose analytical expressions that describe the influence of these new approaches in the transmission. Our analysis is based on the Shannon theorem. The transmission is studied in terms of the required Signal to Noise Ratio, bandwidth utilization, and energy per transmitted bit. Additionally, we propose an uplink link adaptation algorithm that contemplates these new transmission approaches. The conducted evaluation summarizes the influence of these approaches. Furthermore, we present the resulting uplink link adaptation from our proposed algorithm sweeping the devices coverage.
Internet of Things is one of the most promising technology of the fifth-generation (5G) mobile broadband systems. Data-driven wireless services of 5G systems require unprecedented capacity and availability. The millimeter-wave based wireless communication technologies are expected to play an essential role in future 5G systems. In this article, we describe the three broad categories of fifth-generation services, viz., enhanced mobile broadband, ultra-reliable and low-latency communications, and massive machine-type communications. Furthermore, we introduce the potential issues of consumer devices under a unifying 5G framework. We provide the state-of-the-art overview with an emphasis on technical challenges when applying millimeter-wave (mmWave) technology to support the massive Internet of Things applications. Our discussion highlights the challenges and solutions, particularly for communication/computation requirements in consumer devices under the millimeter-wave 5G framework.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
