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Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.
Heena Rathore; Amr Mohamed; Mohsen Guizani; Shailendra Rathore. Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Computing and Applications 2021, 1 -14.
AMA StyleHeena Rathore, Amr Mohamed, Mohsen Guizani, Shailendra Rathore. Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Computing and Applications. 2021; ():1-14.
Chicago/Turabian StyleHeena Rathore; Amr Mohamed; Mohsen Guizani; Shailendra Rathore. 2021. "Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach." Neural Computing and Applications , no. : 1-14.
Cyber-physical systems (CPS) is a setup that controls and monitors the physical world around us. The advancement of these systems needs to incorporate an unequivocal spotlight on making these systems efficient. Blockchains and their inherent combination of consensus algorithms, distributed data storage, and secure protocols can be utilized to build robustness and reliability in these systems. Blockchain is the underlying technology behind bitcoins and it provides a decentralized framework to validate transactions and ensure that they cannot be modified. By distributing the role of information validation across the network peers, blockchain eliminates the risks associated with a centralized architecture. It is the most secure validation mechanism that is efficient and enables the provision of financial services, thereby giving users more freedom and power. This upcoming technology provides internet users with the capability to create value and authenticate digital information. It has the capability to revolutionize a diverse set of business applications, ranging from sharing economy to data management and prediction markets. In this paper, we present a holistic survey of various applications of CPS where blockchain has been utilized. Smart grids, health-care systems, and industrial production processes are some of the many applications that can benefit from the blockchain technology and will be discussed in the paper.
Heena Rathore; Amr Mohamed; Mohsen Guizani. A Survey of Blockchain Enabled Cyber-Physical Systems. Sensors 2020, 20, 282 .
AMA StyleHeena Rathore, Amr Mohamed, Mohsen Guizani. A Survey of Blockchain Enabled Cyber-Physical Systems. Sensors. 2020; 20 (1):282.
Chicago/Turabian StyleHeena Rathore; Amr Mohamed; Mohsen Guizani. 2020. "A Survey of Blockchain Enabled Cyber-Physical Systems." Sensors 20, no. 1: 282.
Deep brain stimulators, a widely used and comprehensively acknowledged restorative methodology, is a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worse case situations, it can even lead to the patient’s death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain or even modify the emotional pattern of the patient by giving fake stimulations through deep brain stimulators. This paper presents a deep learning methodology to predict different attack stimulations in deep brain stimulators. The proposed work uses long short term memory, a type of recurrent network for forecasting and predicting rest tremor velocity (a type of characteristic observed to evaluate the intensity of the neurological diseases). The prediction helps in diagnosing fake vs genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack.
Heena Rathore; Abdulla Al-Ali; Amr Mohamed; Xiaojiang Du; Mohsen Guizani. A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants. IEEE Access 2019, 7, 24154 -24164.
AMA StyleHeena Rathore, Abdulla Al-Ali, Amr Mohamed, Xiaojiang Du, Mohsen Guizani. A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants. IEEE Access. 2019; 7 (99):24154-24164.
Chicago/Turabian StyleHeena Rathore; Abdulla Al-Ali; Amr Mohamed; Xiaojiang Du; Mohsen Guizani. 2019. "A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants." IEEE Access 7, no. 99: 24154-24164.
Internet of Medical Things (IoMTs) is fast emerging, thereby fostering rapid advances in the areas of sensing, actuation and connectivity to significantly improve the quality and accessibility of health care for everyone. Implantable medical device (IMD) is an example of such an IoMT-enabled device. IMDs treat the patient’s health and give a mechanism to provide regular remote monitoring to the healthcare providers. However, the current wireless communication channels can curb the security and privacy of these devices by allowing an attacker to interfere with both the data and communication. The privacy and security breaches in IMDs have thereby alarmed both the health providers and government agencies. Ensuring security of these small devices is a vital task to prevent severe health consequences to the bearer. The attacks can range from system to infrastructure levels where both the software and hardware of the IMD are compromised. In the recent years, biometric and cryptographic approaches to authentication, machine learning approaches to anomaly detection and external wearable devices for wireless communication protection have been proposed. However, the existing solutions for wireless medical devices are either heavy for memory constrained devices or require additional devices to be worn. To treat the present situation, there is a requirement to facilitate effective and secure data communication by introducing policies that will incentivize the development of security techniques. This paper proposes a novel electrocardiogram authentication scheme which uses Legendre approximation coupled with multi-layer perceptron model for providing three levels of security for data, network and application levels. The proposed model can reach up to 99.99% testing accuracy in identifying the authorized personnel even with 5 coefficients.
Heena Rathore; Chenglong Fu; Amr Mohamed; Abdulla Al-Ali; Xiaojiang Du; Mohsen Guizani; Zhengtao Yu. Multi-layer security scheme for implantable medical devices. Neural Computing and Applications 2018, 32, 4347 -4360.
AMA StyleHeena Rathore, Chenglong Fu, Amr Mohamed, Abdulla Al-Ali, Xiaojiang Du, Mohsen Guizani, Zhengtao Yu. Multi-layer security scheme for implantable medical devices. Neural Computing and Applications. 2018; 32 (9):4347-4360.
Chicago/Turabian StyleHeena Rathore; Chenglong Fu; Amr Mohamed; Abdulla Al-Ali; Xiaojiang Du; Mohsen Guizani; Zhengtao Yu. 2018. "Multi-layer security scheme for implantable medical devices." Neural Computing and Applications 32, no. 9: 4347-4360.
Diabetic therapy or insulin treatment enables patients to control the blood glucose level. Today, instead of physically utilizing syringes for infusing insulin, a patient can utilize a gadget, for example, a Wireless Insulin Pump (WIP) to pass insulin into the body. A typical WIP framework comprises of an insulin pump, continuous glucose management system, blood glucose monitor, and other associated devices with all connected wireless links. This takes into consideration more granular insulin conveyance while achieving blood glucose control. WIP frameworks have progressively benefited patients, yet the multifaceted nature of the subsequent framework has posed in parallel certain security implications. This paper proposes a highly accurate yet efficient deep learning methodology to protect these vulnerable devices against fake glucose dosage. Moreover, the proposal estimates the reliability of the framework through the Bayesian network. We conduct comparative study to conclude that the proposed method outperforms the state of the art by over 15% in accuracy achieving more than 93% accuracy. Also, the proposed approach enhances the reliability of the overall system by 18% when only one wireless link is secured, and more than 90% when all wireless links are secured.
Heena Rathore; Abdulla Al-Ali; Amr Mohamed; Xiaojiang Du; Mohsen Guizani. DLRT: Deep Learning Approach for Reliable Diabetic Treatment. GLOBECOM 2017 - 2017 IEEE Global Communications Conference 2017, 1 -6.
AMA StyleHeena Rathore, Abdulla Al-Ali, Amr Mohamed, Xiaojiang Du, Mohsen Guizani. DLRT: Deep Learning Approach for Reliable Diabetic Treatment. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. 2017; ():1-6.
Chicago/Turabian StyleHeena Rathore; Abdulla Al-Ali; Amr Mohamed; Xiaojiang Du; Mohsen Guizani. 2017. "DLRT: Deep Learning Approach for Reliable Diabetic Treatment." GLOBECOM 2017 - 2017 IEEE Global Communications Conference , no. : 1-6.
Security plays a vital role in Wireless Sensor Networks (WSN) for providing reliability to the network. In WSN, where nodes, in addition to having their inbuilt capability of sensing, processing, and communicating data, also possess certain risks. These risks expose them to attacks and bring in many security challenges. Many researchers are engaged in developing innovative design paradigms to address security issues by developing trust management systems. In WSN, trust is important for the establishment of cooperation among the sensor nodes. The article presents a sociopsychological model for detecting fraudulent nodes in WSN. The three factors, viz. ability, benevolence, and integrity, are used for the computation of trust. Furthermore, the article provides a novel consensus-aware sociopsychological approach to deal even in the presence of higher number of fraudulent nodes than benevolent nodes. The proposed work has been implemented in the LabVIEW platform and extensive simulations were carried out to study its performance. Additionally, it is experimentally evaluated on a testbed of size 16 nodes to obtain results that demonstrate the accuracy and robustness of the proposed model.
Heena Rathore; Venkataramana Badarla; Supratim Shit. Consensus-Aware Sociopsychological Trust Model for Wireless Sensor Networks. ACM Transactions on Sensor Networks 2016, 12, 1 -27.
AMA StyleHeena Rathore, Venkataramana Badarla, Supratim Shit. Consensus-Aware Sociopsychological Trust Model for Wireless Sensor Networks. ACM Transactions on Sensor Networks. 2016; 12 (3):1-27.
Chicago/Turabian StyleHeena Rathore; Venkataramana Badarla; Supratim Shit. 2016. "Consensus-Aware Sociopsychological Trust Model for Wireless Sensor Networks." ACM Transactions on Sensor Networks 12, no. 3: 1-27.
Initially, mapping of biological systems with network systems was made using different protocols and rules that were derived structurally. The structural analogy is required for building a framework between the two systems. Looking for specific behavior that optimally maps to networks provides better understanding for modeling and systematic development of novel methods in network systems. Instigation of novel methodologies requires identification of the analogies and understanding of the realistic biological behavior. This chapter deals with the structural and operational correlation between biological systems and network systems. Initially, it discusses how the network domain can be compared to biological systems. Later it discusses the models developed in this regard.
Heena Rathore. Inceptive Findings. Mapping Biological Systems to Network Systems 2016, 27 -35.
AMA StyleHeena Rathore. Inceptive Findings. Mapping Biological Systems to Network Systems. 2016; ():27-35.
Chicago/Turabian StyleHeena Rathore. 2016. "Inceptive Findings." Mapping Biological Systems to Network Systems , no. : 27-35.
Biological immune systems have immense potential in fighting with foreign bodies that encounter the body. It has two protective layers, viz, innate layer and adaptive layer, which helps in dealing with the different types of attacks where the innate layer consists of skin, mucus, and tears and adaptive layer comprises of B-cells and T-cells . Additionally, it has the interesting characteristic of remembering the foreign bodies that attack the human body. This feature helps them in fighting with the foreign bodies at a faster rate. This chapter throws light on the human immune system. Additionally, it explains how the system can help in dealing with different problems of computer networks .
Heena Rathore. Immunology and Immune System. Mapping Biological Systems to Network Systems 2016, 51 -65.
AMA StyleHeena Rathore. Immunology and Immune System. Mapping Biological Systems to Network Systems. 2016; ():51-65.
Chicago/Turabian StyleHeena Rathore. 2016. "Immunology and Immune System." Mapping Biological Systems to Network Systems , no. : 51-65.
Bio-inspired network systems are a field of biology and computer science. These systems have gained importance in recent past for their tremendous amount of potential in solving many challenging networking issues. Bio-inspired systems take inspiration and develop model from biological organisms or biological systems. They not only offer a complete parallelism but also mimic the characteristics, laws, and dynamics between biological systems and network systems. Biological organisms have self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease. Software-defined networking (SDN) provides a breakthrough in network transformation. It decouples the software from hardware firmware by disengaging the data plane and control plane of the networking device. Evolution of SDN in the current network scenario has enabled programmable networking that has provided a radical new way of networking. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. The two systems, viz., SDN and biological organisms aim for self-organization and self-healing properties and thus, there exists a match between the two. The study provides a list of bio-inspired solutions in various issues of SDN.
Heena Rathore. Bio-inspired Software-Defined Networking. Mapping Biological Systems to Network Systems 2016, 107 -115.
AMA StyleHeena Rathore. Bio-inspired Software-Defined Networking. Mapping Biological Systems to Network Systems. 2016; ():107-115.
Chicago/Turabian StyleHeena Rathore. 2016. "Bio-inspired Software-Defined Networking." Mapping Biological Systems to Network Systems , no. : 107-115.
Genetic algorithms are the heuristic search and enhancement upgrade structures that impersonate the undertaking of natural evolution . It is a machine learning algorithm which comprises a populace of individuals represented by chromosomes (present in individual’s DNA). The number of inhabitants in people contends with one another for assets to reach to another era of people, a procedure commonly known as evolution. The people capable of surviving spread their hereditary material. This chapter provides details of how genetic algorithms work and the way they are used in computer networks for solving issues such as sensor networks, traveling salesman problem, etc.
Heena Rathore. Genetic Algorithms. Mapping Biological Systems to Network Systems 2016, 97 -106.
AMA StyleHeena Rathore. Genetic Algorithms. Mapping Biological Systems to Network Systems. 2016; ():97-106.
Chicago/Turabian StyleHeena Rathore. 2016. "Genetic Algorithms." Mapping Biological Systems to Network Systems , no. : 97-106.
Nature is the physical world around us in particular and life in general. It has immense matter to explore, analyze, and investigate. For instance, plants perform photosynthesis, bees search for nectar, birds fly in a synchronized way; the sun rises and sets in a specific way. On observing nature keenly, it can be inferred that it is perfect, divine, structured, and mannered. It is because of the intrinsic appealing characteristics of biological systems that nowadays many researchers are engaged in producing novel design paradigms to address the challenges in current network systems based on biological systems. Biologically inspired approaches seem promising when high level of robustness and adaptability is required. The chapter begins by exploring why biology and computer network research are such a natural match. This is followed by presenting a broad overview of biologically inspired research in network systems. It is classified by the biological field that inspired each topic and by the area of networking in which that topic lies. Each case elucidates how biological concepts have been most successfully applied in various domains.
Heena Rathore. Introduction: Bio-inspired Systems. Mapping Biological Systems to Network Systems 2016, 1 -10.
AMA StyleHeena Rathore. Introduction: Bio-inspired Systems. Mapping Biological Systems to Network Systems. 2016; ():1-10.
Chicago/Turabian StyleHeena Rathore. 2016. "Introduction: Bio-inspired Systems." Mapping Biological Systems to Network Systems , no. : 1-10.
Study of artificial neural network (ANN) is a branch of machine learning and data mining. They are a group of measurable learning models inspired by biological neural networks, i.e., brain. The system is utilized to gauge or estimate capacities that can rely upon a substantial number of inputs which are obscure. ANNs are for the most part introduced as frameworks of interconnected “neurons” which trade messages between one another. The associations have numeric weights that can be tuned in view of experience, making neural networks versatile to inputs and fit for learning. The chapter provides details on the ANN and how these frameworks have tackled numerous issues for computer engineers.
Heena Rathore. Artificial Neural Network. Mapping Biological Systems to Network Systems 2016, 79 -96.
AMA StyleHeena Rathore. Artificial Neural Network. Mapping Biological Systems to Network Systems. 2016; ():79-96.
Chicago/Turabian StyleHeena Rathore. 2016. "Artificial Neural Network." Mapping Biological Systems to Network Systems , no. : 79-96.
In Wireless Sensor Network (WSN), where nodes besides having its inbuilt capability of sensing, processing and communicating data, also possess some risks. These risks expose them to attacks and bring in many security challenges. Therefore, it is imperative to have a secure system where there is perfect confidentiality and correctness to the data being sent from one node to another. Cooperation among the nodes is needed so that they could confidently rely on other nodes and send the data faithfully. However, owing to certain hardware and software faults, nodes can behave fraudulently and send fraudulent information. Nevertheless, since the network is openly accessible, anybody can access the deployment area which breaches the security of WSN. Therefore, it is required to have correct and accurate secure model for WSN to protect the information and resources from attacks and misbehavior. Many researchers are engaged in developing innovative design paradigms to address such nodes by developing key management protocols , secure routing mechanisms and trust management systems. Key management protocols and secure routing cannot itself provide security to WSNs for various attacks. Trust management system can improve the security of WSN. The case study begins by explaining the security issues and challenges in WSN. It discusses the goals, threat models and attacks followed by the security measures that can be implemented in detection of attacks. Here, various types of trust and reputation models are also reviewed. The intent of this case study is to investigate the security related issues and challenges in wireless sensor networks and methodologies used to overcome them. Furthermore, the present case study provides details on how bio-inspired approaches in WSN prove a benefactor in many ways.
Heena Rathore. Case Study: A Review of Security Challenges, Attacks and Trust and Reputation Models in Wireless Sensor Networks. Mapping Biological Systems to Network Systems 2016, 117 -175.
AMA StyleHeena Rathore. Case Study: A Review of Security Challenges, Attacks and Trust and Reputation Models in Wireless Sensor Networks. Mapping Biological Systems to Network Systems. 2016; ():117-175.
Chicago/Turabian StyleHeena Rathore. 2016. "Case Study: A Review of Security Challenges, Attacks and Trust and Reputation Models in Wireless Sensor Networks." Mapping Biological Systems to Network Systems , no. : 117-175.
A computer network, in general, comprises of numerous computers that are linked together to communicate with each other. The goal of a computer network is to enable two or more computers to share and exchange data with one another for various purposes. Users can access remote resources by either logging into the appropriate remote computer or transfer data from the remote computer to their own computers. To understand what a network is all about, this chapter provides details on topologies, design, and usage of a network. Furthermore, since present network demands future technologies to be self-adaptive and self-healed, the chapter provides details on issues and challenges faced by it. Additionally, the chapter provides ground details on the future of networking technologies.
Heena Rathore. Computer Networks. Mapping Biological Systems to Network Systems 2016, 11 -25.
AMA StyleHeena Rathore. Computer Networks. Mapping Biological Systems to Network Systems. 2016; ():11-25.
Chicago/Turabian StyleHeena Rathore. 2016. "Computer Networks." Mapping Biological Systems to Network Systems , no. : 11-25.
Swarm intelligence is a behavior shown by a collection of social insects and animals which exhibit spatial arrangement and synchronized motion. These animals control and manage their position with the help of local interactions among companions of the same species. The work is performed in a systematic manner such that exchange of information occurs. The information which is gathered is shared with the other species. This ability benefits them in many aspects of social life, such as the need to protect themselves from predators and to perform well-organized locomotion and foraging (Nicole in Fish, networks, and synchronization 199:3518–3562, 2012).
Heena Rathore. Swarm Intelligence and Social Insects. Mapping Biological Systems to Network Systems 2016, 37 -50.
AMA StyleHeena Rathore. Swarm Intelligence and Social Insects. Mapping Biological Systems to Network Systems. 2016; ():37-50.
Chicago/Turabian StyleHeena Rathore. 2016. "Swarm Intelligence and Social Insects." Mapping Biological Systems to Network Systems , no. : 37-50.
Nature has always inspired scientific research to flourish in one way or other. Nature is one of those greatest expansions that have shown extraordinary results in every respect. The biological activities that are occurring around us have splendid and magnificent characteristics that have inspired us in many ways. For instance, ants coordinate themselves to perform foraging; birds synchronize themselves to show beautiful patterns; fireflies show impressive patterns of luminosity at night, etc. Therefore, engineers and scientists of diverse domains are engaging themselves in instigating innovative design architectures in resolving different difficulties and challenges. This chapter deals with providing elaborative details on bio-inspired research in streams, such as energy, agriculture, aerospace, electrical, mechanical.
Heena Rathore. Bio-inspired Approaches in Various Engineering Domain. Mapping Biological Systems to Network Systems 2016, 177 -194.
AMA StyleHeena Rathore. Bio-inspired Approaches in Various Engineering Domain. Mapping Biological Systems to Network Systems. 2016; ():177-194.
Chicago/Turabian StyleHeena Rathore. 2016. "Bio-inspired Approaches in Various Engineering Domain." Mapping Biological Systems to Network Systems , no. : 177-194.
Communicable disease models have been studied using classical mathematical differential equations for a long time now. It is important to study the communicable disease models, so that one can come up with a good response system to contain the spread of viruses. Social networks are susceptible to the rapid spread of malicious information, commonly referred to as rumors. Rumors often spread rapidly through the network and, if not contained quickly, can be harmful. This chapter describes a method for identifying highly connected nodes in a social network and using these nodes to build immunity against such malicious information. To describe this method, this chapter draws inspiration from two well-established topics in the area of biology: one is the spread of communicable diseases in human population and second is how human body builds immunity against diseases as described in Chap. 5. In case of communicable diseases, it would be very simplistic if we only consider that an infected node can transmit its disease to its nearest neighbors. More realistically speaking, it is possible that an infected node can develop random links with other nodes in the system. The spread of communicable diseases is controlled by both these factors. An infected node with capability to have several random links is capable of spreading the disease through the network faster. We can postulate that certain nodes in a social network exhibit similar behavior and can be defined as highly connected nodes in the network. Once such nodes are identified, the concept of weighting functions is introduced that can be attached to messages passing through such nodes. This chapter describes how the spread of malicious information can be controlled by a community of such highly connected nodes, using the concept of weighted functions.
Heena Rathore. Information Epidemics and Social Networking. Mapping Biological Systems to Network Systems 2016, 67 -78.
AMA StyleHeena Rathore. Information Epidemics and Social Networking. Mapping Biological Systems to Network Systems. 2016; ():67-78.
Chicago/Turabian StyleHeena Rathore. 2016. "Information Epidemics and Social Networking." Mapping Biological Systems to Network Systems , no. : 67-78.
Heena Rathore; Venkataramana Badarla; George K J. Sociopsychological trust model for Wireless Sensor Networks. Journal of Network and Computer Applications 2016, 62, 75 -87.
AMA StyleHeena Rathore, Venkataramana Badarla, George K J. Sociopsychological trust model for Wireless Sensor Networks. Journal of Network and Computer Applications. 2016; 62 ():75-87.
Chicago/Turabian StyleHeena Rathore; Venkataramana Badarla; George K J. 2016. "Sociopsychological trust model for Wireless Sensor Networks." Journal of Network and Computer Applications 62, no. : 75-87.
Heena Rathore. Mapping Biological Systems to Network Systems. Mapping Biological Systems to Network Systems 2016, 1 .
AMA StyleHeena Rathore. Mapping Biological Systems to Network Systems. Mapping Biological Systems to Network Systems. 2016; ():1.
Chicago/Turabian StyleHeena Rathore. 2016. "Mapping Biological Systems to Network Systems." Mapping Biological Systems to Network Systems , no. : 1.
Biological Immune Systems have intelligent capabilities of detecting foreign bodies which attack our body. Moreover they have inherent insightful capabilities to remember them, when they hit the body again. Primary response is the initial response instantiated by the body to the attack and secondary response is the response hence forth. Secondary response is naturally faster because of its characteristic of remembering the cure of the attack. Similar type of perspicacious nature can be adapted in removal of fraudulent nodes in wireless sensor network. The work proposes a novel algorithm for the detection and removal of the fraudulent nodes. It first detects the fraudulent nodes by machine learning module and then removes these nodes by immune-inspired module. Eventually if the same type of malicious nature is seen again, analogy of secondary response of immune system is instigated in sensor network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy and robustness of the proposed model.
Heena Rathore; Venkataramana Badarla. Primary-secondary immune response adaptation for wireless sensor network. 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) 2014, 164 -166.
AMA StyleHeena Rathore, Venkataramana Badarla. Primary-secondary immune response adaptation for wireless sensor network. 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). 2014; ():164-166.
Chicago/Turabian StyleHeena Rathore; Venkataramana Badarla. 2014. "Primary-secondary immune response adaptation for wireless sensor network." 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) , no. : 164-166.