Node Localization in Wireless Sensor Networks Using Multi-output Random Forest Regression

Madhumathi, K.; Suresh, T.

doi:10.1007/978-981-15-0184-5_16

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analyzing the wine quality is presented by [ 66 ]. In the network field, Madhumathi and Suresh [ 67 ] develop a model to predict the future location of a dynamic sensor node in wireless communications. Fang et al [ 68 ] propose an encrypted malicious traffic identification method.…”

Section: Related Workmentioning

confidence: 99%

“…At each instant n, the action probability vector pi(n) is updated by the linear learning algorithm given in equation ( 13) if the chosen action ai(k) is rewarded by the environment, and it is updated according to equation ( 14) if the chosen action is penalized [104]. [11], [12], [13] Global problem [26], [27], [28] Healthcare [32], [33], [34], [35], [36], [41], [98], [37], [39], [40], [42], [43], [45], [46], [47], [48], [49], [50], [51], Industrial [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62] Network [63], [67], [68], [69], [99], [100] Physics [71], [72] Text processing …”

Section: Learning Automatamentioning

confidence: 99%

See 1 more Smart Citation

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Learning Automatamentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…One of the several approaches such as distance mapping (Tan et al, 2020), distance vectors (Kanwar & Kumar, 2020), Amri et al 2019 regression analysis (Madhumathi & Suresh, 2020; Singh et al, 2020) clustering has widely been implemented to overcome the node localization issues (Rani & Jayakumar, 2017; El Khediri et al, 2020). Distance mapping is based on distance from the transmitters present near neighbour nodes and distance vector is based on created vector matrix comprising of various parameters in addition to the distance such as residual energy, energy consumption, drop rate, transmission rate, etc.…”

Section: Introductionmentioning

confidence: 99%

Node localization and data aggregation scheme using cuckoo search and neural network

Kaur

Bhatia

et al. 2022

Expert Systems

View full text Add to dashboard Cite

Among multi‐hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)‐hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%–20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission.

show abstract