Due to recent advancements and the success of versatile mobile applications, more and more services around the globe are being moved to the cloud. As a result, its limitations have become more evident. The major issues that cloud-based applications face include large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. The drawbacks of cloud computing are reduced by fog and edge computing, where data are processed near the places where it is generated-at network edges or fog nodes-most importantly in a distributed way. Smart agriculture is an approach based on the Internet of Things (IoT) where cloud computing is not an option as the internet is usually not available at remote sites. In addition, pure edge computing also is not practical, as most sensor nodes are very small and they do not have enough computing power. Intermediate fog computing also is not a good choice, as fixed fog nodes (getaway nodes) do not work well with high node fluctuation caused by bad weather or harsh conditions. Considering these issues and limitations, we have proposed the idea of flying edge computing where an unmanned aerial vehicle (UAV) acts as an edge-computing machine. This can be an ideal solution for smart agriculture, given the size and remoteness of many agricultural areas. This technique can be called "wind or breeze computing" because the data are "blown" or moved by the current of computing. The Flying-Edge offers fast deployment of edge facilities in challenging locations and it can be a major step to accomplish the goal of IoT-based smart agriculture.
Despite the fact that the ocean plays a role in everything from the air we breathe to daily weather and climate patterns, we know very little about our ocean. Underwater wireless sensor network (UWSN) is one of the options helping us to discover some domains such as natural assets and underwater resource exploration. However, the acoustic signal is the only suitable option in underwater communication in the absence of radio waves, which face a number of challenges under this environment. To overcome these issues, many routing schemes are introduced by researchers though energy consumption is still a challenge in underwater communication. To overcome the issue of rapid energy consumption, a reliable and energy-efficient routing method is introduced that avoids the redundant forwarding of data; hence, it achieves energy efficiency and eventually prolongs the network lifetime. Simulation results support the claim that the proposed scheme achieves energy efficiency along higher delivery ratio by reducing the data transmission error rate during the routing decisions.
The most unmanned area of this planet is sheltered with water; that is roughly 71.9% of the total area of this planet. A large quantity of marine life is present in this area. That is the reason underwater research is bounded due to unexplored benefits. Due to the addition of sensors and growing interests in the exploration and monitoring of marine life Underwater Wireless Sensor Network (UWSN) can play an important role. A variety of routing protocols has been deployed in order to get information between deployed nodes. Providing stable data transmission, maximum throughput, minimum consumption of the energy and delay are challenging tasks in the UWSN. These routing protocols can be Layer-by-Layer Angle-Based Flooding (L2-ABF) and Diagonal and Vertical Routing Protocol (DVRP). In order to get stable data transmission, the node density plays our role in shallow and deep water. Several parameters are employed to evaluate the output efficiency of these routing protocols. In this paper, like an end to end delay, loss of data packets during transmission and data delivery ratio within communication are considered the major parameters for evaluation. For this, the network simulator is used with the aqua sim package. The results, we have produced during this study; guides us about the best routing protocol for data transmission. It finally reveals that the L2-ABF performs better then DVRP in a different situation, further the tradeoffs relationship is achieved against multiple situations.
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