Dr Gopikrishnan Sundaram
@vitap.ac.in
VIT-AP University, Amaravathi, AP
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K. Raja Sravan Kumar and S. Gopikrishnan
Elsevier BV
Kanneboina Ashok and S. Gopikrishnan
Springer Nature Singapore
Kanneboina Ashok and Sundaram Gopikrishnan
Abstract Internet of medical things (IoMT) network design integrates multiple healthcare devices to improve patient monitoring and real-time care operations. These networks use a wide range of devices to make critical patient care decisions. Thus, researchers have deployed multiple high-security frameworks with encryption, hashing, privacy preservation, attribute based access control, and more to secure these devices and networks. However, real-time monitoring security models are either complex or unreconfigurable. The existing models’ security depends on their internal configuration, which is rarely extensible for new attacks. This paper introduces a hybrid metaheuristic model to improve healthcare IoT security performance. The blockchain based model can be dynamically reconfigured by changing its encryption and hashing standards. The proposed model then continuously optimizes blockchain based IoMT deployment security and QoS performance using elephant herding optimization (EHO) and grey wolf optimization (GWO). Dual fitness functions improve security and QoS for multiple attack types in the proposed model. These fitness functions help reconfigure encryption and hashing parameters to improve performance under different attack configurations. The hybrid integration of EH and GW optimization models can tune blockchain based deployment for dynamic attack scenarios, making it scalable and useful for real-time scenarios. The model is tested under masquerading, Sybil, man-in-the-middle, and DDoS attacks and is compared with state-of-the-art models. The proposed model has 8.3% faster attack detection and mitigation, 5.9% better throughput, a 6.5% higher packet delivery ratio, and 10.3% better network consistency under attack scenarios. This performance enables real-time healthcare use cases for the proposed model.
S. Gopikrishnan, P. Priakanth, Gautam Srivastava, and C. Vijesh Joe
Elsevier BV
A. Baseera, Hari Kishan Kondaveeti, S. Gopikrishnan, B. J. Bejoy, C. G. Ravichandran, R. Santhosh, and R. Dhanapal
Springer Science and Business Media LLC
R. Sabitha, S. Gopikrishnan, B. J. Bejoy, V. Anusuya and V. Saravanan
S. Gopikrishnan, Abhiram Kalyan Madduru, Kaushik Karamsetty, and Dinesh Rohit Ravuri
Springer Nature Switzerland
Kanneboina Ashok and S. Gopikrishnan
Institute of Electrical and Electronics Engineers (IEEE)
Remote health monitoring-based Internet of Things (IoT) network security is a multi-domain task, that involves identification of network attack, evaluation of mitigation strategies, design of performance aware data security models, integration of privacy models, and modeling of device-level security methods. Internal designs for each of these models is highly complex, and varies in terms of quantitative & qualitative performance measures. This is due to their variation in terms of design nuances, functional advantages, context-based limitations, and possible deployment-specific future scopes. Due to this variation, it is highly ambiguous to select these models for performance-specific IoT deployments. Moreover, these models also vary in terms of security level, Quality of Service (QoS) parameters, scalability performance, computational complexity, deployment costs, and other performance metrics. Thus, to identify optimum models, researchers & network designers are required to test & validate multiple security models for their deployments. Due to which, the cost & time to market for IoT devices is increased, thereby affecting viability of IoT products. To overcome these selection issues, an empirical survey of different IoT security models including block-chains, encryption techniques, hashing models, privacy preservation techniques, machine learning based security methods, etc. are discussed in this text. This text also discusses various attack mitigation models that provide node-level security, network-level security, physical security, & route-level security. This discussion will assist in initially evaluating different operating characteristics of these models, which will allow readers to identify most suited models for their application-specific use cases. This article also assesses the models’ performance in terms of computational latency, energy consumption, security levels, deployment complexity, and scalability measures. These metrics are compared between different security models, which will further assist readers to identify optimum models for their performance-specific use cases. To further assist in model selection, this text proposes evaluation of a novel IoT Security Performance Rank (ISRP), that combines various performance metrics to form a singular rank which can be used to describe overall performance of these models. Readers will be able to consider optimal security approaches for new and current IoT installations based on this ranking.
Prabha Selvaraj, Vijay Kumar Burugari, S. Gopikrishnan, Abdullah Alourani , Gautam Srivastava, and Mohamed Baza
MDPI AG
Wireless sensors and actuator networks (WSNs) are the physical layer implementation used for many smart applications in this decade in the form of the Internet of Things (IoT) and cyber-physical systems (CPS). Even though many research concerns in WSNs have been answered, the evolution of the WSN into an IoT network has exposed it to many new technical issues, including data security, multi-sensory multi-communication capabilities, energy utilization, and the age of information. Cluster-based data collecting in the Internet of Things has the potential to address concerns with data freshness and energy efficiency. However, it may not offer reliable network data security. This research presents an improved method for data sharing and cluster head (CH) selection using the hybrid Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method in conjunction with glowworm swarm optimization (GSO) strategies based on the energy, trust value, bandwidth, and memory to address this security-enabled, cluster-based data aggregation in the IoT. Next, we aggregate the data after the cluster has been built using a genetic algorithm (GA). After aggregation, the data are encrypted and delivered securely using the TIGSO-EDS architecture. Cuckoo search is used to analyze the data and choose the best route for sending them. The proposed model’s analysis of the results is analyzed, and its uniqueness has been demonstrated via comparison with existing models. TIGSO-EDS reduces energy consumption each round by 12.71–19.96% and increases the percentage of successfully delivered data packets from 2.50% to 5.66%.
Raja Sravan Kumar Kovvali and Gopikrishnan Sundaram
MDPI AG
The large scale of the Internet of Things necessitates using long-lasting physical layer devices for data collection. Deploying large numbers of Wi-Fi-enabled devices is expensive, so the Internet of Everything (IoE) is equipped with multiple communication modules to collect data where Wi-Fi is unavailable. However, because of their extended communication capabilities, IoE devices face energy limitations. As a result, IoE devices must be provided with the necessary energy resources. This paper introduces a novel multi-hop cooperation communication mechanism for Wireless Energy Transfer (WET) in the Wireless Powered-Internet of Everything (WP-IoE). IoE devices are outfitted here with various communication devices such as RF, Bluetooth, and Wi-Fi. This research proposes a two-phase energy transmission schedule to address the energy requirements. For data collection, the first phase provides a distributed tree-based data communication plan. The proposed model’s second phase used the reverse data collection protocol to implement wireless energy transmission. By combining these two phases, an optimized WET framework was created without unmanned aerial vehicles or robots. The experimental findings show that the proposed method in this research increases the average lifetime of the network and has a more significant charge latency and average charge throughput than other models.
S. Gopikrishnan, Gautam Srivastava, and P. Priakanth
Elsevier BV
M. Veera Brahmam, S. Gopikrishnan, K. Raja Sravan Kumar, and M. Seshu Bhavani
Springer Singapore
S. Gopikrishnan, Cheemakurthi Srujan, V. N. Siva Praneeth, and Sagar Mousam Parida
Springer Singapore
K. Raja Sravan Kumar, S. Gopikrishnan, M. Veera Brahmam, and M. Gargi
Springer Singapore
S. Gopikrishnan, P. Priakanth, Gautam Srivastava, and Giancarlo Fortino
Institute of Electrical and Electronics Engineers (IEEE)
S. Gopikrishnan, P. Priakanth, and Gautam Srivastava
Elsevier BV
Abstract The distributed tree-based data communication protocol is a widely accepted efficient method in wireless sensor network-enabled Internet of Things (IoT). Due to increasing demand for IoT applications, cognitive radio sensors play a vital role in spectrum utilization. This short communication proposes a delay aware, energy-efficient data communication protocol (DEDC) for cognitive radio sensor networks (CRSN). This research proposes a first communication protocol that integrates the solution for delay and energy issues in CRSNs specific to IoT application. The respective simulation results and comparative studies with the existing models on other parameters prove the novel contribution of this research in terms of delay and energy utilization.
P. Priakanth and S. Gopikrishnan
IGI Global
The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?
S. Gopikrishnan, P. Priakanth, and Rolly Maulana Awangga
Springer Science and Business Media LLC
Internet of things is the backbone of the smart applications, which attracts many types of research on the state-of-the-art network applications. Enormous research on sensor networks left more devices that are sensible in the day-to-day life. Hence, implementing new sensor networks for smart applications is not necessary. Many researchers have accepted and utilized existing networks for their request. In this case, techniques for identifying and registering existing sensible things are on demand. This paper proposed a hybrid framework for sensor identification and registration (HSIR) for new IoT applications. This research proposing HSIR as a framework aimed for user-friendliness in the IoT as well as addressed toward the scalability requirement of IoT applications. This model uses content- and context-based multicast communication instead of broadcast to reduce energy and time consumption in sensor identification. HSIR also proposed a public key to register the new network for application requirements. The behaviour of the proposed model has been assayed in realistic with simulations and proved by comparing other models.
S. Gopikrishnan, C. Krishnaraj, and K. Kokilavani
MAFTREE
Vehicular ad hoc network (VANET) is a novice technique which has drawn the attention of several industries and academics. Security parameters in VANET are now receiving popularity in the research community. A defensive mechanism provides a solution to control the attacks across the VANET security. However, a single defence mechanism is unable to provide solution to the attack models as more sophisticated method is required for VANETs. This paper proposed a method termed heuristic approach for ant colony optimization (HAAC) for improved security in addition to better transportation, reliability and management. The heuristic based ant colony optimization is used to reduce the problem in finding known and unknown opponents in providing security to VANET. The characteristic of real ant colonies is used in VANET security in order to solve attack problems with shortest path. The Reinforcing VANET security using vehicle mode analysis is evaluated in an efficient manner using NS2 simulator. The excellent outcomes are obtained by an HAAC approach combined with a dynamic heuristic.
S. Gopikrishnan and P. Priakanth
Springer Science and Business Media LLC