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A novel dynamic software-defined networking approach to neutralize traffic burst

- Sharma, Aakanksha, Balasubramanian, Venki, Kamruzzaman, Joarder

  • Authors: Sharma, Aakanksha , Balasubramanian, Venki , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Computers Vol. 12, no. 7 (2023), p.
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  • Description: Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. © 2023 by the authors.

Adaptive capacity task offloading in multi-hop D2D-based social industrial IoT

- Ibrar, Muhammad, Wang, Lei, Akbar, Aamir, Jan, Mian, Balasubramanian, Venki, Muntean, Gabriel-Miro, Shah, Nadir

  • Authors: Ibrar, Muhammad , Wang, Lei , Akbar, Aamir , Jan, Mian , Balasubramanian, Venki , Muntean, Gabriel-Miro , Shah, Nadir
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Network Science and Engineering Vol. 10, no. 5 (2023), p. 2843-2852
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  • Description: Traditional communication technologies such as cellular networks are facing problems to support high service quality when used for time-critical applications in an Industrial Internet-of-Things (IIoT) context, including real-time data transmission, route dependability, and scalability. To address these problems, device-to-device (D2D) communications based on social relationships can be used, which allow for task-offloading: resource-rich devices share unused computing resources with resource constraint devices. However, unbalanced task offloading in Social IIoT (SIIoT) might actually degrade the overall system performance, which is not desirable. In this paper, we propose an adaptive capacity task offloading solution for D2D-based social industrial IoT (ToSIIoT) which considers devices utilization ratio and strength of social relationships in order to improve resource utilization, increase QoS and achieve better task completion rate. The proposed approach consists of three aspects: social-aware relay selection in a multi-hop D2D communication context, choice of a resource-rich SIIoT device for task offloading, and adaptive redistribution of tasks. The paper proposes heuristic algorithms, as finding optimal solutions to the problems are NP-hard. Extensive experimental results show that the proposed ToSIIoT performs better than existing approaches in terms of utilization ratio, QoS violation, average execution delay, and task completion ratio. © 2013 IEEE.

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
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  • 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.

Classification of methods to reduce clinical alarm signals for remote patient monitoring : a critical review

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew, Shenhan, Mai, Buyya, Rajkumar, Islam, Sardar

Clinically prioritized data visualization in remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew, Neupane, Arun

Cognitive AmBC-NOMA IoV-MTS networks with IQI : reliability and security analysis

- Li, Xingwang, Zheng, Yike, Alshehri, Mohammad, Hai, Linpeng, Balasubramanian, Venki, Zeng, Ming, Nie, Gaofeng

  • Authors: Li, Xingwang , Zheng, Yike , Alshehri, Mohammad , Hai, Linpeng , Balasubramanian, Venki , Zeng, Ming , Nie, Gaofeng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 24, no. 2 (2023), p. 2596-2607
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  • Description: Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security. © 2000-2011 IEEE.

DQN-based resource allocation for NOMA-MEC-aided multi-source data stream

- Ling, Jing, Xia, Junjuan, Zhu, Fusheng, Gao, Chongzhi, Lai, Shiwei, Balasubramanian, Venki

  • Authors: Ling, Jing , Xia, Junjuan , Zhu, Fusheng , Gao, Chongzhi , Lai, Shiwei , Balasubramanian, Venki
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Eurasip Journal on Advances in Signal Processing Vol. 2023, no. 1 (2023), p.
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  • Description: This paper investigates a non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) network with multiple sources and one computing access point (CAP), in which NOMA technology is applied to transmit multi-source data streams to CAP for computing. To measure the performance of the considered NOMA-aided MEC network, we first design the system cost as a linear weighting function of energy consumption and delay under the NOMA-aided MEC network. Moreover, we propose a deep Q network (DQN)-based offloading strategy to minimize the system cost by jointly optimizing the offloading ratio and transmission power allocation. Finally, we design experiments to demonstrate the effectiveness of the proposed strategy. Specifically, the designed strategy can decrease the system cost by about 15% compared with local computing when the number of sources is 5. © 2023, The Author(s).

Missing health data pattern matching technique for continuous remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew

Service deployment strategy for predictive analysis of FinTech IoT applications in edge networks

- Munusamy, Ambigavathi, Adhikari, Mainak, Balasubramanian, Venki, Khan, Mohammad, Menon, Varun, Rawat, Danda, Srirama, Satish

  • Authors: Munusamy, Ambigavathi , Adhikari, Mainak , Balasubramanian, Venki , Khan, Mohammad , Menon, Varun , Rawat, Danda , Srirama, Satish
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Internet of Things Journal Vol. 10, no. 3 (2023), p. 2131-2140
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  • Description: The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users' satisfaction ratio. Besides that, an efficient service deployment strategy is developed using $Hall's$ theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively. © 2014 IEEE.

Survey : self-empowered wireless sensor networks security taxonomy, challenges, and future research directions

- Adil, Muhammad, Menon, Varun, Balasubramanian, Venki, Alotaibi, Sattam, Song, Houbing, Jin, Zhanpeng, Farouk, Ahmed

  • Authors: Adil, Muhammad , Menon, Varun , Balasubramanian, Venki , Alotaibi, Sattam , Song, Houbing , Jin, Zhanpeng , Farouk, Ahmed
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Sensors Journal Vol. 23, no. 18 (2023), p. 20519-20535
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  • Description: In the recent past, patient-wearable devices and implantable biosensors revealed exponential growth in digital healthcare, because they have the capability to allow access to information anywhere and every time to improve the life standard of multifarious disease-affected patients followed by healthy people. Following these advantages, digital healthcare demands a secure wireless communication infrastructure for interconnected self-empowered biosensor devices to maintain the trust of patients, doctors, pharmacologists, nursing staff, and other associated stakeholders. Several authentications, privacy, and data preservation schemes had been used in the literature to ensure the security of this emerging technology, but with time, these counteraction prototypes become vulnerable to new security threats, as the hackers work tirelessly to compromise them and steal the legitimate information of user's or disrupt the operation of an employed self-empowered wireless sensor network (SWSN). To discuss the security problems of SWSN applications, in this review article, we have presented a detailed survey of the present literature from 2019 to 2022, to familiarize the readers with different security threats and their counteraction schemes. Following this, we will highlight the pros and cons of these countermeasure techniques in the context of SWSN security requirements to underscore their limitations. Thereafter, we will follow-up on the underlined limitations to discuss the open security challenges of SWSNs that need the concerned authorities' attention. Based on this, we will pave a road map for future research work that could be useful for every individual associated with this technology. For the novelty and uniqueness of this work, we will make a comparative analysis with present survey papers published on this topic to answer the question of reviewers, readers, editors, and students why this article is in time and needed in the presence of rival papers. © 2022 IEEE.

Whose data are reliable : sensor declared data reliability

- Shafin, Sakib, Karmakar, Gour, Mareels, Iven, Balasubramanian, Venki, Kolluri, Ramachandra

  • Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven , Balasubramanian, Venki , Kolluri, Ramachandra
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21-23 June 2023, International Conference on Wireless and Mobile Computing, Networking and Communications Vol. 2023-June, p. 249-254
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  • Description: Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications. © 2023 IEEE.

A supervised learning model to identify the star potential of a basketball player

- Srinivasan, Ram, Balasubramanian, Venki, Vidyasagar, Abhishek

  • Authors: Srinivasan, Ram , Balasubramanian, Venki , Vidyasagar, Abhishek
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Expert Systems Vol. 39, no. 5 (2022), p.
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  • Description: Basketball is a mathematical game with many abstract data interpretations. An average fan ceases to witness the revolution in sports, which is influenced using data science and analytics unless someone brings it to light. Nowadays, teams look at data and tend to make decisions on scouting the player for the team. The decision making for the coaches can be made easier using machine learning algorithms to identify the star potential of players. The paper provides a novel algorithm by building a machine learning model on all players to predict whether the player is a star or not. Besides, an interactive user interface is developed for coaches to input the player's data and to make an informed decision based on the prediction. © 2021 John Wiley & Sons Ltd.

An ensemble of machine learning and clinician set thresholds for vital signs alarms

- Mai, Shenhan, Balasubramanian, Venki, Arora, Teena

Blockchain adaptation of remote patient monitoring with internet of medical things

- Godly, Cinthia, Balasubramanian, Venki, Jinila, Bevesh

Deep learning model to empower student engagement in online synchronous learning environment

- Godly, Cinthia, Balasubramanian, Venki, Stranieri, Andrew, Saikrishna, Vidya, Mohammed, Rehena, Chackappan, Godly

  • Authors: Godly, Cinthia , Balasubramanian, Venki , Stranieri, Andrew , Saikrishna, Vidya , Mohammed, Rehena , Chackappan, Godly
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 19th IEEE India Council International Conference, INDICON 2022, Kochi India, 24-26 November 2022, INDICON 2022 - 2022 IEEE 19th India Council International Conference
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  • Description: Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback. © 2022 IEEE.

Deep reinforcement learning-based multi-objective edge server placement in Internet of Vehicles

- Lu, Jiawei, Jiang, Jielin, Balasubramanian, Venki, Khosravi, Mohammad, Xu, Xiaolong

  • Authors: Lu, Jiawei , Jiang, Jielin , Balasubramanian, Venki , Khosravi, Mohammad , Xu, Xiaolong
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computer Communications Vol. 187, no. (2022), p. 172-180
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  • Description: In the typical scenario of the Internet of Vehicles (IoV), the edge servers (ESs) are laid out near the road side units (RSUs) to process the collected data for a variety of IoV services in real time. Generally, because ESs are lightweight compared with cloud servers, if the ESs are not appropriately distributed, it will cause the unbalanced workload of the ESs. Thus, developing an ES plan to avoid the risk of overload and improve the quality of service (QoS) remains a challenge. To tackle it, a deep reinforcement learning-based multi-objective edge server placement strategy, named DESP, is fully explored, to promote the coverage rate, the workload balancing and reduce the average delay of finishing tasks in the IoV. In particular, the Markov Decision Process (MDP) of the ES placement problem is formulated and the deep reinforcement learning, i.e., Deep Q-Network (DQN) is applied to obtain the optimal placement scheme achieving the multiple objectives above. At last, a real vehicular data set is used for assessing the validity of DESP. © 2022 Elsevier B.V.

Emerging point of care devices and artificial intelligence : prospects and challenges for public health

- Stranieri, Andrew, Venkatraman, Sitalakshmi, Minicz, John, Zarnegar, Armita, Firmin, Sally, Balasubramanian, Venki, Jelinek, Herbert

  • Authors: Stranieri, Andrew , Venkatraman, Sitalakshmi , Minicz, John , Zarnegar, Armita , Firmin, Sally , Balasubramanian, Venki , Jelinek, Herbert
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Smart Health Vol. 24, no. (2022), p.
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  • Description: Risk assessments for numerous conditions can now be performed cost-effectively and accurately using emerging point of care devices coupled with machine learning algorithms. In this article, the case is advanced that point of care testing in combination with risk assessments generated with artificial intelligence algorithms, applied to the universal screening of the general public for multiple conditions at one session, represents a new kind of in-expensive screening that can lead to the early detection of disease and other public health benefits. A case study of a diabetes screening clinic in a rural area of Australia is presented to illustrate its benefits. Universal, poly-aetiological screening is shown to meet the ten World Health Organisation criteria for screening programmes. © Elsevier Inc.

Physical-layer security based mobile edge computing for emerging cyber physical systems

- Chen, Lunyuan, Tang, Shunpu, Balasubramanian, Venki, Xia, Junjuan, Zhou, Fasheng, Fan, Lisheng

  • Authors: Chen, Lunyuan , Tang, Shunpu , Balasubramanian, Venki , Xia, Junjuan , Zhou, Fasheng , Fan, Lisheng
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Computer Communications Vol. 194, no. (2022), p. 180-188
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  • Description: This paper studies a secure mobile edge computing (MEC) for emerging cyber physical systems (CPS), where there exist K eavesdroppers in the network, which can threaten the task offloading. These K eavesdroppers can work either in a colluding mode where they cooperate to decode the secret message, or in a non-colluding mode where the eavesdroppers decode the message individually. For both eavesdropping nodes, we design the secure MEC system by devising a computation offloading ratio, transmit power and computational capability allocation to optimize the system performance mainly measured by the latency. In particular, a novel deep reinforcement learning (DRL) together with convex optimization (DRCO) is proposed, where the DRL is used to find a proper solution to the offloading ratio, while the convex optimization is implemented to solve the allocation of transmission power and computational capability. Simulation results show that the proposed DRCO method is superior to other conventional methods, and can provide a guaranteed secrecy and latency. © 2022

Prioritization of clinical alarms using semantic features of vital signs in remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Mai, Shenhan

  • Authors: Arora, Teena , Balasubramanian, Venki , Mai, Shenhan
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 242-245
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  • Description: In recent years remote patient monitoring applications have emerged that can monitor the patient continuously and remotely with the help of wearable sensors that collect physiological data and send it to a telemedicine platform. Sensitivity of the sensor, patient's movement, electromagnetic interference, and data processing algorithms are a few factors that affect the collected data, leading to false alarms, and consequent false alarm leads to alarm fatigue. This study presents novel factors such as trust, frequency, slope, and trend that transform the vital signs raw data from the sensors into semantic data in a remote monitoring application. Experimental results have shown that data transformations lead to a reduction in clinically non-significant alarms and the prioritization of clinically significant alarms. © 2022 ACM.

Remote patient monitoring for healthcare : a big challenge for big data

- Stranieri, Andrew, Balasubramanian, Venki

  • Authors: Stranieri, Andrew , Balasubramanian, Venki
  • Date: 2022
  • Type: Text , Book chapter
  • Relation: Research Anthology on Big Data Analytics, Architectures, and Applications Chapter 50 p. 1054-1070
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  • Description: Remote patient monitoring involves the collection of data from wearable sensors that typically requires analysis in real time. The real-time analysis of data streaming continuously to a server challenges data mining algorithms that have mostly been developed for static data residing in central repositories. Remote patient monitoring also generates huge data sets that present storage and management problems. Although virtual records of every health event throughout an individual’s lifespan known as the electronic health record are rapidly emerging, few electronic records accommodate data from continuous remote patient monitoring. These factors combine to make data analytics with continuous patient data very challenging. In this chapter, benefits for data analytics inherent in the use of standards for clinical concepts for remote patient monitoring is presented. The openEHR standard that describes the way in which concepts are used in clinical practice is well suited to be adopted as the standard required to record meta-data about remote monitoring. The claim is advanced that this is likely to facilitate meaningful real time analyses with big remote patient monitoring data. The point is made by drawing on a case study involving the transmission of patient vital sign data collected from wearable sensors in an Indian hospital. © 2022 by IGI Global. All rights reserved.