List of Titles

An AI-enabled lightweight data fusion and load optimization approach for internet of things

- Jan, Mian, Zakarya, Muhammad, Khan, Muhammad, Mastorakis, Spyridon, Balasubramanian, Venki

Authors: Jan, Mian , Zakarya, Muhammad , Khan, Muhammad , Mastorakis, Spyridon , Balasubramanian, Venki
Date: 2021
Type: Text , Journal article
Relation: Future Generation Computer Systems Vol. 122, no. (2021), p. 40-51
Full Text: false
Reviewed:
Description: In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications. © 2021 Elsevier B.V. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.

An intelligent heart disease prediction system based on swarm-artificial neural network

- Nandy, Sudarshan, Adhikari, Mainak, Balasubramanian, Venki, Menon, Varun, Li, Xingwang, Zakarya, Muhammad

Authors: Nandy, Sudarshan , Adhikari, Mainak , Balasubramanian, Venki , Menon, Varun , Li, Xingwang , Zakarya, Muhammad
Date: 2023
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14723-14737
Full Text: false
Reviewed:
Description: The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

PFARS : Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme

- Khan, Rahim, Zakarya, Muhammad, Tan, Zhiyuan, Usman, Muhammad, Jan, Mian, Khan, Mukhtaj

Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
Date: 2019
Type: Text , Journal article
Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
Full Text:
Reviewed:
Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

Quick View

Smart sensing-enabled decision support system for water scheduling in orange orchard

- Khan, Rahim, Zakarya, Muhammad, Balasubramanian, Venki, Jan, Mian, Menon, Varun

Authors: Khan, Rahim , Zakarya, Muhammad , Balasubramanian, Venki , Jan, Mian , Menon, Varun
Date: 2021
Type: Text , Journal article
Relation: IEEE Sensors Journal Vol. 21, no. 16 (2021), p. 17492-17499
Full Text:
Reviewed:
Description: The scarcity of water resources throughout the world demands its optimum utilization in various sectors. Smart Sensing-enabled irrigation management systems are the ideal solutions to ensure the optimum utilization of water resources in the agriculture sector. This paper presents a wireless sensor network-enabled Decision Support System (DSS) for developing a need-based irrigation schedule for the orange orchard. For efficient monitoring of various in-field parameters, our proposed approach uses the latest smart sensing technology such as soil moisture, leaf-wetness, temperature and humidity. The proposed smart sensing-enabled test-bed was deployed in the orange orchard of our institute for approximately one year and successfully adjusted its irrigation schedule according to the needs and demands of the plants. Moreover, a modified Longest Common SubSequence (LCSS) mechanism is integrated with the proposed DSS for distinguishing multi-valued noise from the abrupt changing scenarios. To resolve the concurrent communication problem of two or more wasp-mote sensor boards with a common receiver, an enhanced RTS/CTS handshake mechanism is presented. Our proposed DSS compares the most recently refined data with pre-defined threshold values for efficient water management in the orchard. Irrigation activity is scheduled if water deficit criterion is met and the farmer is informed accordingly. Both the experimental and simulation results show that the proposed scheme performs better in comparison to the existing schemes. © 2001-2012 IEEE.

Quick View

Technology-assisted decision support system for efficient water utilization : a real-time testbed for irrigation using wireless sensor networks

- Khan, Rahim, Ali, Ihsan, Zakarya, Muhammad, Ahmad, Mushtaq, Imran, Muhammad, Shoaib, Muhammad

Authors: Khan, Rahim , Ali, Ihsan , Zakarya, Muhammad , Ahmad, Mushtaq , Imran, Muhammad , Shoaib, Muhammad
Date: 2018
Type: Text , Journal article
Relation: IEEE Access Vol. 6, no. (2018), p. 25686-25697
Full Text:
Reviewed:
Description: Scientific organizations and researchers are eager to apply recent technological advancements, such as sensors and actuators, in different application areas, including environmental monitoring, creation of intelligent buildings, and precision agriculture. Technology-assisted irrigation for agriculture is a major research innovation which eases the work of farmers and prevents water wastage. Wireless sensor networks (WSNs) are used as sensor nodes that directly interact with the physical environment and provide real-time data that are useful in identifying regions in need, particularly in agricultural fields. This paper presents an efficient methodology that employs WSN as a data collection tool and a decision support system (DSS). The proposed DSS can assist farmers in their manual irrigation procedures or automate irrigation activities. Water-deficient sites in both scenarios are identified by using soil moisture and environmental data sensors. However, the proposed system's accuracy is directly proportional to the accuracy of dynamic data generated by the deployed WSN. A simplified outlier-detection algorithm is thus presented and integrated with the proposed DSS to fine-tune the collected data prior to processing. The complexity of the algorithm is O(1) for dynamic datasets generated by sensor nodes and O(n) for static datasets. Different issues in technology-assisted irrigation management and their solutions are also addressed. © 2013 IEEE.

Showing items 1 - 5 of 5