A localization algorithm based on compressive sensing by K-nearest Neighbor classification

Yang, Sixing; Guo, Yan; Liu, Xi; Niu, Dawei; Sun, Baoming

doi:10.1109/icsp.2016.7877953

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The proposed HELP protocol performance is in a two-tier fashion, which is discussed in this section. In the first tier, performance metrics of proposed Extreme Learning Machines (ELM) has been evaluated and compared with the other existing algorithms such as Reinforcement Learning Algorithms (RL) [8], "Support Vector machines" (SVM) [9], Artificial Neural Networks (ANN) [19], Naïve Bayes Algorithms (NB) [20], K-Nearest Neighborhood algorithm (KNN) [21][22][23].…”

Section: Resultsmentioning

confidence: 99%

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Sampathkumar¹,

Malmurugan²

2022

Computers, Materials &Amp; Continua

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Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.

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