Providing security and privacy for the Internet of Things (IoT) applications while ensuring a minimum level of performance requirements is an open research challenge. Recently, Blockchain offers a promising solution to overcome the current peer-to-peer networks limitations. In the context of IoT, Byzantine Fault Tolerance-based (BFT) consensus protocols are used due to the energy efficiency advantage over other consensus protocols. The consensus process in BFT is done by electing a group of authenticated nodes. The elected nodes will be responsible for ensuring the data blocks' integrity through defining a total order on the blocks and preventing the concurrently appended blocks from containing conflicting data. However, the Blockchain Consensus Layer contributes the most performance overhead. Therefore, a performance study needs to be conducted especially for the IoT applications that are subject to maximum delay constraints. In this paper, we obtain a mathematical expression to calculate the end-to-end delay with different network configurations, i.e., number of network hops and replica machines. We validate the proposed analytical model with simulation. Our results show that the unique characteristics of IoT traffic have an undeniable impact on the end-to-end delay requirement.
Abstract-Frame aggregation is a major enhancement in the IEEE 802.11 family to boost the network performance. The increasing awareness about energy efficiency motivates the rethink of frame aggregation design. In this paper, we propose a novel Green Frame Aggregation (GFA) scheduling scheme that optimizes the aggregate size based on channel quality in order to minimize the consumed energy. GFA selects an optimal sub-frame size that satisfies the loss constraint for realtime applications as well as the energy budget of the ideal channel. This scheme is implemented and evaluated using a testbed deployment. The experimental analysis shows that GFA outperforms the conventional frame aggregation methodology in terms of energy efficiency by about 6× in the presence of severe interference conditions. Moreover, GFA outperforms the static frame sizing method in terms of network goodput while maintaining the same end-to-end latency.
Internet of Things (IoT) can be defined as the interconnection of any device to the Internet that collects and exchanges information. With the rapidly growing heterogenetic IoT applications and its associated devices, massive amount of data is being transmitted in the network. Often, a large spike in network traffic to a particular destination causes a widespread disruption of the Internet services for the end users, which can cause online businesses billions of dollars of losses. In this paper, we analyze an intelligent edge that can identify volumetric traffic and address them in real-time using an instantaneous detection method for IoT applications. This approach can easily detect a large surge and a potential variation in traffic patterns for an IoT application, which can contribute to safer and more efficient operation of the overall system. As per our results, we gave a closer insight on the advantage of having an intelligent edge to serve as a detection mechanism.
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