Conventional networking devices require that each is programmed with different rules to perform specific collective tasks. Next generation networks are required to be elastic, scalable and secured to connect millions of heterogeneous devices. Software defined networking (SDN) is an emerging network architecture that separates control from forwarding devices. This decoupling allows centralized network control to be done network-wide. This paper analyzes the latency and jitter of SDN against a conventional network. Through simulation, it is shown that SDN has an average three times lower jitter and latency per packet that translate to improved throughput under varying traffic conditions.
In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An l-th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the l-th layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the 600×600 hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles (1-σ) and 95-th percentiles (2-σ) respectively.
Hidden node problem sometimes referred to as frequent packets collision that mostly leads to loss of packets is no longer new in wireless networks because it affects the previous IEEE802.11 standards. The new IEEE802.11ah standard which is also a sub-standard of IEEE 802.11 is no exemption. As a matter of fact, IEEE802.11ah suffers from a hidden node problem more than networks (IEEE 802.11a/b/n/ac) due to their wider coverage which is up to 1km, high number of devices they can support (over 8000 nodes to one AP) and frequent simultaneous sleeping and sending of the nodes (power saving mode). A few researchers have worked on this hidden node problem in IEEE802.11ah but could not get a lasting solution to it. Therefore, this paper proposes an algorithm which detects hidden nodes and also proposes a theoretical solution based on previous works which was also experimentally verified through the BIHD-CM.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.