scholarly journals The Performance Evaluation of Adaptive Guard Channel Scheme in Wireless Network

2021 ◽  
Vol 40 (1) ◽  
pp. 109-114
Author(s):  
O.A. Ojesanmi ◽  
O.A. Lawal ◽  
F.T. Ibharalu ◽  
I.A. Adejumobi
Keyword(s):  
Fuzzy Logic ◽  
Performance Evaluation ◽  
Wireless Network ◽  
Performance Metrics ◽  
Channel Allocation ◽  
Model Performance ◽  
Arrival Rate ◽  
Radio Spectrum ◽  
Guard Channel ◽  
Guard Channels

Dynamic Guard Channels (DCG) reduces the dropping and blocking rates in a network. However, most of the existing DGC allocations are not quite efficient because there were consideration for only the Handoff (HO) calls while the New calls (NC) were not considered; this leads to poor Quality of Service (QoS) for NC. Although it is better to give priority to HO calls over NC since the breaking of the connection of an established communicationis more annoying than blocking a NC. Thus, there is need to provide an alternative approach that guarantees an acceptable QoS in terms of both the HC and the NC. This paper presents the performance evaluation of an adaptive guard channel allocation; the scheme made use of two different models (1) guard channel with fuzzy logic (2) guard channel without fuzzy logic. Priority is given to handoff call due to the scarcity of radio spectrum. When all the guard channels have been allocated and the arrival rate of handoff calls keeps on increasing, new set of threshold values would be estimated by fuzzy logic model. Performance metrics are; Call Blocking Rate (CBR), Call Dropping Rate (CDR) and Throughput. Results showed that guard channel with fuzzy logic has the CBR values range from 24.02% to 69.015 and CDR values range from 12.025 to 18.90% while guard channel without fuzzy logic has CBR values range from 28.22% to 75.65% and CDR values range from 19.06% to 36.50%. The scheme proved to be more efficient in congestion control in wireless network.

Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

Leveraging Collaborative-Filtering for Personalized Behavior Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 1-27
Author(s):  
Xuhai Xu ◽  
Prerna Chikersal ◽  
Janine M. Dutcher ◽  
Yasaman S. Sefidgar ◽  
Woosuk Seo ◽  
...  
Keyword(s):  
College Students ◽  
Collaborative Filtering ◽  
Human Behavior ◽  
Work Performance ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Model Performance ◽  
Majority Voting ◽  
Behavior Modeling ◽  
Depression Detection

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

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