DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Table 1

Summary of literature review of the paper.

Author	Year	Subject	Method	Application	Results
Giang et al. [34]	2020	Large scale, dynamic fog computing in WSN	Distributed node-RED	Base study	Reduce costs
Hossan & Nower [26]	2020	Fog-based WSN dynamic	Neighboring impact factor	Efficient dynamic traffic light control algorithm for multiple intersections	Reduces wait time, lowers fuel consumption, and boosts system throughput
Sharma & Saini [30]	2020	Task allocation and secure deduplication using fog computing	Hybrid Multiplier. Multi-Objective based Whale Optimization algorithm	Base study	Enhancement in average latency, user satisfaction, network lifetime, energy consumption, and security strength
Tsipis et al. [27]	2020	Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive monitoring	Cloud/fog computing paradigm	Environmental Monitoring agricultural activity	Improve efficiency, flexibility, and scalability of the approach in terms of latency
Zeng et al. [35]	2020	Energy powered Cyber-Physical Fog Systems	Mixed-integer linear programming	Cyber-Physical application	The high energy efficiency of our algorithm
Rani & Saini [28]	2020	Secure data collection of fog computing in WSN	The combination of fog and cloud can handle extensive data collection.	Health monitoring	Reduce the cost of data transportation and storage
Bellavista et al. [36]	2020	SDN-based multi-layer routing in fog environments	Multi-Layer Advanced Networking Environment	Smart city	Determines the most suitable path and configures the proper MLR forwarding mechanism
Jain & Goel [37]	2020	Energy efficient fuzzy routing protocol	Fuzzy C-means	Wireless sensor network	High performance, low energy consumption
Tortonesi et al. [38]	2019	Innovative information-centric service model for fog computing	Fog-as-a-service	Smart city environments	An effective platform for running fog services on heterogeneous devices
Sun et al. [39]	2019	Presenting an energy-efficient clustering method for fog computing in WSNs	Cross-layer-sensing clustering method and particles swarm optimization	Base study	Optimize the data aggregation efficiency and improve the network performance
Maatoug et al. [40]	2019	Fog computing framework for energy management	Fog computing framework	Smart building	Decreases latency and improves energy-saving and the efficiency of services among things with different capabilities
Sahith et al. [41]	2019	Face identification in fog computing framework for WSN	Radio communication module XBee, ZigBee protocol	Face identification	Data collection and the functionality of the system are good.
Mihai et al. [42]	2018	Intelligent Data Processing in fog system and WSN	Fog and mist computing approaches	Base study	Improve the information to noise ratio
Bhargava et al. [43]	2017	Fog-enabled WSN system for animal behavior analysis	Edge mining concept	Animal behavior analysis	Accuracy and suitability of the methods