Fog Computing and Efficient Resource Management in the era of Internet-of-Video Things (IoVT)

Author(s):  
Sai Saketh Nandan Perala ◽  
Ioannis Galanis ◽  
Iraklis Anagnostopoulos
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 115760-115773 ◽  
Author(s):  
Hina Rafique ◽  
Munam Ali Shah ◽  
Saif Ul Islam ◽  
Tahir Maqsood ◽  
Suleman Khan ◽  
...  

Author(s):  
M. Sudhakara ◽  
K. Dinesh Kumar ◽  
Ravi Kumar Poluru ◽  
R Lokesh Kumar ◽  
S Bharath Bhushan

Cloud computing is an emerging field. With its three key features and its natural favorable circumstances, it has had a few difficulties in the recent years. The gap between the cloud and the end devices must be reduced in latency specific applications (i.e., disaster management). Right now, fog computing is an advanced mechanism to reduce the latency and congestion in IoT networks. It emphasizes processing the data as close as possible to the edge of the networks, instead of sending/receiving the data from the data centre by using large quantity of fog nodes. The virtualization of these fog nodes (i.e., nodes are invisible to the users) in numerous locations across the data centres enabled the fog computing to become more popular. The end users need to purchase the computing resources from the cloud authorities to process their excessive workload. Since computing resources are heterogeneous and resource are constrained and dynamic in nature, allocating these resources to the users becomes an open research issue and must be addressed as the first priority.


2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Lingyun Lu ◽  
Tian Wang ◽  
Wei Ni ◽  
Kai Li ◽  
Bo Gao

This paper presents a suboptimal approach for resource allocation of massive MIMO-OFDMA systems for high-speed train (HST) applications. An optimization problem is formulated to alleviate the severe Doppler effect and maximize the energy efficiency (EE) of the system. We propose to decouple the problem between the allocations of antennas, subcarriers, and transmit powers and solve the problem by carrying out the allocations separately and iteratively in an alternating manner. Fast convergence can be achieved for the proposed approach within only several iterations. Simulation results show that the proposed algorithm is superior to existing techniques in terms of system EE and throughput in different system configurations of HST applications.


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