Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Table 4

Work summary of resource allocation and management in IoT.

Problems	Solutions	Literatures	Advantages
Resource management	A distributed cloud network framework	[66]	Replaces the centralized architecture with a distributed cloud architecture, solves the defects of the centralized cloud architecture, and brings people better experience
	A MILP model	[68]	Assigns the requirement of services’ resource to heterogeneous network equipment interface
	A framework for communication used in 5G	[69]	Transforms the resource allocation problem into a power and channel allocation problem, minimizes the total energy consumption, and improves QoS levels
	A low complexity channel allocation algorithm	[70]	Improves throughput of the network and allocates finite resources to a large group of users
	A resource allocation framework with several bands under cognitive 5G IoT	[71]	Manages resources more flexibly and reduces energy consumption than common single-band approach
	A protocol which is distributed and optimal to allocate resources	[72]	Has excellent adaptability when changing topology of network and dynamically manages resources in the heterogeneous IoT environment
	A multilevel allocating resources algorithm for IoT communication	[73]	Has fast data processing rate and very low latency in both saturated and nonsaturated environment
	A new algorithm to allocate bandwidth dynamically	[74]	Reduces the error of signal reconstruction under the same bandwidth and makes the bandwidth allocation of IoT more reasonable
	A reinforcement learning mechanism	[75]	Effectively solves the resource allocation problems caused by the mismatch of service quality and complex service providing condition in the IoT