SPArTaCuS: Service priority adaptiveness for emergency traffic in smart cities using software-defined networking

Abhishek, Rohit; Zhao, Shuai; Medhi, Deep

doi:10.1109/isc2.2016.7580854

Cited by 28 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, Du and Nakao has applied deep learning models to the RAN using application specific radio spectrum scheduling [6]. Abhishek et al [10] has propose a priority management solution for smart cities where the prioritization of network traffic is managed. Some other approaches suggested in the proposed field are listed in Table 1 below.…”

Section: Related Workmentioning

confidence: 99%

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Khan

Ali

et al. 2022

J Netw Syst Manage

View full text Add to dashboard Cite

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

show abstract

Section: Related Workmentioning

confidence: 99%

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Khan

Ali

et al. 2022

J Netw Syst Manage

View full text Add to dashboard Cite

show abstract

“…SDN was actually one of the leading foundational technologies being leveraged to shape the smart city vision. For example, Abhishek et al [6] that presented a service priority adaptive approach to handling emergency traffic in smart cities and He et al [4] proposed an SDN-based solution to improve the mobile edge computing and caching for a smart city using a big data deep reinforcement learning approach. In addition to its advantage in the most basic network functions such as the routing and end-to-end performance optimization [7], what makes SDN appealing to the smart city vision also lies in its three unique characteristics: (1) a logically centralized control plane to enable efficient global view and control, (2) programmability to enable insitu configuration (3)virtualization that provides isolation and resource sharing between applications running in the same physical infrastructure.…”

Section: A Need Of Sdn In Smart City Communication Infrastructuresmentioning

confidence: 99%

Secure Software-Defined Networking Communication Systems for Smart Cities: Current Status, Challenges, and Trends

2021

View full text Add to dashboard Cite

“…The primary motivation behind implementing VNs is to obtain different service levels such as bandwidth, security, QoS, or availability. In [15], a framework was proposed to prioritize network traffic adaptively for smart cities using a softwaredefined network (SDN) approach, where services that require priority are placed in virtualized networks and the mechanism is accomplished through a priority management layer in the SDN architecture. Here, we suggest the use of VNs in the wireless mesh network in order to prioritize and tailor services to different smart infrastructure and smart city applications.…”

Section: B Communication Networkmentioning

confidence: 99%

Disaster Recovery Power and Communications for Smart Critical Infrastructures

Alqahtani

Abhishek

Tipper

et al. 2018

2018 IEEE International Conference on Communications (ICC)

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a framework to leverage electrical microgrids and cellular networks to support postdisaster communications for the public, government and critical infrastructure operation. The framework involves both policy and technical components. The proposed approach is an integration of electrical microgrids to provide power together with self configuring wireless mesh communication networks and local edge computing infrastructure to support critical communications and smart infrastructure services/applications in a specific geographic area. Hence, geographic zones which are resilient safe havens are created in a city. We outline the basic components of our approach and discuss open challenges to realizing the vision.

show abstract

SPArTaCuS: Service priority adaptiveness for emergency traffic in smart cities using software-defined networking

Cited by 28 publications

References 3 publications

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Secure Software-Defined Networking Communication Systems for Smart Cities: Current Status, Challenges, and Trends

Disaster Recovery Power and Communications for Smart Critical Infrastructures

Contact Info

Product

Resources

About