## 2 Related Work
Therefore, the use of virtual networks for service function chains in cloud-fog computing under multiple network domains, leads to saving network resources [21]. In recent times, the cloud of things (CoT) has gained immense popularity, due to its ability to offer an enormous amount of resources to wireless networks and heterogeneous mobile edge computing systems. The CoT makes the opportunistic decision-making during the online processing of tasks for load sharing, and makes the overall network reliable and efficient [22]. The cloud of things framework can significantly improve communication gaps between cloud resources and other mobile devices. In this paper, the author(s) proposed a methodology for offloading computation in mobile devices, which might reduce failure rates. This algorithm reduces failure rates by improving the control policy. In recent times, WSN used virtualization techniques to offer energy-efficient and fault-tolerant data communication to the immensely growing service domain for IoT [23]. With the application of WSN in e-healthcare, the wireless body area network (WBAN) gained a huge response in the healthcare domain. The WBAN is used to monitor patient data by using body sensors, and transmits the acquired data, based on the severity of the patients' symptoms, by allocating a channel without contention or with contention [24]. EEOR [15] is an energy-efficient protocol that works on transmission power as a major parameter. This protocol discussed two cases that involved constant and dynamic power consumption models. These models are known as non-adjustable and adjustable power models. In the first model, the algorithm calculated the expected cost at each node and made a forwarder list on the source node based on this cost. The forwarder list was sorted in increasing order of expected cost and the first node on the list became the next-hop forwarder. As EEOR is an opportunistic routing protocol, broadcasting is utilized and the packets transmitted might be received by each node on the forwarder list. In this, the authors propose algorithms for fixed-power calculation, adjustable power calculation,and opportunistic power calculation. This algorithm was compared with ExOR [11] by simulation in the TOSSIM simulator. The results showed that EEOR always calculated the end-to-end cost based on links from the source to destination. EEOR followed distance vector routing for storing the routing information inside each sensor node. The expected energy consumption cost was updated inside each node, after each round of packet transmission. Data delivery was guaranteed in this protocol. Additionally, according to the simulation results, packet duplication was significantly decreased. The MDOR [25; 26] protocol worked on the distance between the source to relay nodes. In this, the authors proposed an algorithm that calculated the distance to each neighbor from the source node and found out the average distance node. The average distance node was used by the source as a next-hop forwarder. The authors also stated that, to increase the speed and reliability of transmission, the strength of the signal was very important. The signal power depended on the distance between the sender and receiver. If a node sent a packet to the nearest node, then it might take more hops and this would decrease the lifetime of the network. Another problem addressed in this protocol was to reduce energy consumption at each node through the dynamic energy consumption model. This model consumed energy according to the packet size and transmitted the packet by amplifying it according to the distance between the source and the relay nodes. MDOR always chose the middle position node to optimize energy consumption in amplifying the packets. The MDOR simulation results showed that the energy consumption was optimized and it was suitable for certain applications of WSN like environment monitoring, forest fire detection, etc.