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Santosh Kumar earned his PhD in 2012 from the India Institute of Technology in Roorkee, India, his M. Tech. in Computer Science and Engineering in 2007 from Aligarh Muslim University in Aligarh, India, and his B.E. (IT) in 2003 from C.C.S. University in Meerut, India. He has served on the review boards of several national and international journals and conferences. He is a Senior Member of ACM, IEEE, IAENG, ACEEE, and ISOC (USA) and has published over 75 research papers in national and international journals/conferences on Wireless Communication Networks, WSN, IoT, Machine Learning, Grid Computing, and Software Engineering. He is currently working as a Professor at the Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun-UK, India. His research interests include Wireless Networks, WSN, IoT, Machine Learning, and Software Engineering.
Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network’s lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm.
Ankur Choudhary; Santosh Kumar; Sharad Gupta; Mingwei Gong; Aniket Mahanti. FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. Energies 2021, 14, 3935 .
AMA StyleAnkur Choudhary, Santosh Kumar, Sharad Gupta, Mingwei Gong, Aniket Mahanti. FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. Energies. 2021; 14 (13):3935.
Chicago/Turabian StyleAnkur Choudhary; Santosh Kumar; Sharad Gupta; Mingwei Gong; Aniket Mahanti. 2021. "FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks." Energies 14, no. 13: 3935.
People Counting in images is a worthwhile task as it is widely used for public safety, emergency people planning, intelligent crowd flow, and countless other reasons. Counting the objects manually in images does not make practical sense, since it is very time-consuming, and it never gives accurate results for dense crowded images. In crowded images, as the density of the people increases, object appear to be partially encircling each other. This occlusion problem of objects limits the crowd counting ability of any traditional computer vision model. To overcome this problem, here we addressed a dynamic kernel convolution neural network-linear regression (DKCNN-LR) model for counting the exact number of people in image frames even if crowd is very dense and occlusion problem. The proposed model works in two phases, first a DKCNN model use convolution layers in such a fashion that the kernel weight of each subsequent successive layer is half of its previous convolution layer’s weight. The first three heavy kernel weight layers identify far camera regions (low-level) features, and the later light kernel weight layers help identify near-camera region (high-level) features. Second, a linear regression model is employed to perform parametric regression between the actual people count (ground truth) and the estimated count (predicted values). The performance of the proposed model tested on three challenging and different quality benchmark datasets in terms of MAE, RMSE, Pearson-R and R2. The DKCNN-LR model secured MAE, RMSE on Mall dataset is 1.65, 2.76, on Beijing-BRT 1.43, 1.87 and on SmartCity dataset it is 2.69 and 10.69. These results confirm that the proposed model is quite reliable, effective and robust for real situations.
Ankit Tomar; Santosh Kumar; Bhaskar Pant; Umesh Kumar Tiwari. Dynamic Kernel CNN-LR model for people counting. Applied Intelligence 2021, 1 -16.
AMA StyleAnkit Tomar, Santosh Kumar, Bhaskar Pant, Umesh Kumar Tiwari. Dynamic Kernel CNN-LR model for people counting. Applied Intelligence. 2021; ():1-16.
Chicago/Turabian StyleAnkit Tomar; Santosh Kumar; Bhaskar Pant; Umesh Kumar Tiwari. 2021. "Dynamic Kernel CNN-LR model for people counting." Applied Intelligence , no. : 1-16.
Component-based software engineering emphasizes ‘development by means of reuse’ and ‘development meant for reuse’. Whether the system is simple or complex one, the estimation of better reliability remains a crucial concern. The main purpose of this work is to propose a method for reliability estimation and the computation of execution time of component-based software. In this work a metric named ‘reusability-ratio’ is introduced as a factor of reliability estimation. We focus on assessing and exploring reusability of components by defining reusability-ratio for newly developed, mutated (fully-qualified as well as partially-qualified) and off-the-shelf components. On the basis of interactions among components, one more metric is defined called ‘Interaction-ratio’. Interaction-ratio is used as another factor of reliability estimation. Based on the interactions made by components, a graph is constructed, namely ‘Component-Interaction Graph’. The structure of the component-interaction graph depends on the probability of interaction of components as well as the probability of the selection of different path execution-histories. Results obtained through experimental case study conclude that the reusability compete imperative function in the reliability of the component-based applications. Pre-tested, qualified and pre-configured artefacts consume lesser time and are more reliable as compared to the new component constructs. Metrics proposed in this work are well suited to estimate the reliability of component-based software and therefore proved promising to analyze the performance of the software.
Umesh Kumar Tiwari; Santosh Kumar; Priya Matta. Execution-history based reliability estimation for component-based software: considering reusability-ratio and interaction-ratio. International Journal of System Assurance Engineering and Management 2020, 11, 1 -17.
AMA StyleUmesh Kumar Tiwari, Santosh Kumar, Priya Matta. Execution-history based reliability estimation for component-based software: considering reusability-ratio and interaction-ratio. International Journal of System Assurance Engineering and Management. 2020; 11 (5):1-17.
Chicago/Turabian StyleUmesh Kumar Tiwari; Santosh Kumar; Priya Matta. 2020. "Execution-history based reliability estimation for component-based software: considering reusability-ratio and interaction-ratio." International Journal of System Assurance Engineering and Management 11, no. 5: 1-17.
As a discipline, Machine Learning has been adopted and leveraged widely by researchers from several domains. There is a huge range of classifiers already available in machine learning and it has kept on growing with the advancement of this field. However, it is very hard to pick the best classifier among the several similar classifiers suitable for any problem. Recent advancement in this field for solving this issue is the Multiple Classifier System (MCS). It comes under the umbrella of ensemble learning and gives comparatively a better and definite result than a single classifier. MCS has two layers—(i) Base layer—contains a number of ML Classifiers appropriate for any specific task—and (ii) Meta Learner Layer—which aggregates the results from base layer classifiers by using techniques, such as Voting and Stacking. However, the job of selecting the appropriate classifiers from various classifiers or from a family of classifiers for a specific classification or prediction task on any dataset is still unraveling. This work emphasizes determining the characteristics of the selection method of base classifiers in the MCS. Moreover, which Meta Learner layer from Stacking and Voting aggregates the better result according to the different sizes of the base classifiers?
Vikas Tomer; Simon Caton; Santosh Kumar; Bhawnesh Kumar. A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System. Advances in Intelligent Systems and Computing 2020, 228 -236.
AMA StyleVikas Tomer, Simon Caton, Santosh Kumar, Bhawnesh Kumar. A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System. Advances in Intelligent Systems and Computing. 2020; ():228-236.
Chicago/Turabian StyleVikas Tomer; Simon Caton; Santosh Kumar; Bhawnesh Kumar. 2020. "A Selection Method for Computing the Ensemble Size of Base Classifier in Multiple Classifier System." Advances in Intelligent Systems and Computing , no. : 228-236.
Voice over Internet Protocol (VoIP) service is increasingly one of the most widespread applications since few decades. Data traffic of VoIP service faces various challenges and threats. The Session Initiation Protocol (SIP) flooding attack is one of the popular threats to this service. Flooding attack is the most severe attack as it is easy to generate and capture the networks and nodes that further lead to Denial of Service attack. Therefore, the performance analysis of SIP servers requires more focus. In this paper, effectiveness of SIP server under SIP flooding attack is evaluated using various message scenarios by setting up an experimental test bed. The stress testing of SIP server is carried out with CPU and memory utilization check. The quality of VoIP calls is measured in term of three quality metrics jitter, delay and packet loss. It is observed that with the increase of simultaneous calls, quality of VoIP calls degraded.
Santosh Kumar; Umesh Kumar Tiwari; Mandeep Kaur. Effectiveness of SIP Server Under SIP Flooding Attack During VoIP Calls. Wireless Personal Communications 2019, 108, 2229 -2239.
AMA StyleSantosh Kumar, Umesh Kumar Tiwari, Mandeep Kaur. Effectiveness of SIP Server Under SIP Flooding Attack During VoIP Calls. Wireless Personal Communications. 2019; 108 (4):2229-2239.
Chicago/Turabian StyleSantosh Kumar; Umesh Kumar Tiwari; Mandeep Kaur. 2019. "Effectiveness of SIP Server Under SIP Flooding Attack During VoIP Calls." Wireless Personal Communications 108, no. 4: 2229-2239.
Wireless sensor network (WSN) is one of the most evolving technologies. WSN involves collecting, processing, transferring and storing information about objects with the help of sensor nodes. Tracking and detection of targets is one of the most attractive applications of WSN in surveillance systems. To resolve the problem of target tracking, it is essential to deploy a system model. It has been observed that clustering algorithms play an important role in cluster head selection, but they consume significant amount of energy. In this paper an energy efficient system model is deployed with a novel target tracking algorithm to track the target around the vicinity of the WSN. As there is more possibility of collision proximate to the base station, a new collision avoidance method is introduced. The lifetime of the network on the basis of congestion around the sink node, packet density and path loss are also measured efficiently.
Santosh Kumar; Sudhir; Umesh Kumar Tiwari. Energy Efficient Target Tracking with Collision Avoidance in WSNs. Wireless Personal Communications 2018, 103, 2515 -2528.
AMA StyleSantosh Kumar, Sudhir, Umesh Kumar Tiwari. Energy Efficient Target Tracking with Collision Avoidance in WSNs. Wireless Personal Communications. 2018; 103 (3):2515-2528.
Chicago/Turabian StyleSantosh Kumar; Sudhir; Umesh Kumar Tiwari. 2018. "Energy Efficient Target Tracking with Collision Avoidance in WSNs." Wireless Personal Communications 103, no. 3: 2515-2528.
Nowadays an important issue as well as challenge in data mining is obviously is outlier detection. Outlier detection has been used in many areas such as Fraud detection, Intrusion detection, Health care, Fault detection, etc., where detection of outliers is based on the different characteristics of data or datasets. In this current age of ‘Information Technology’, large numbers of processes are obtainable in the domain of data mining to discover the outliers by successfully creating the clusters and after that detecting the outliers from these created clusters. In data mining, cluster methods are highly essential and have been applied from micro- to macro-applications. Basically clusters are a pool of similar data objects put together grounded on the attributes and district features they have. Specifically outlier detection is used to recognize and exclude inconsistency from the available data sets. In the presented work an algorithm has been suggested which is based on clustering approach to the given data sets. The proposed algorithm efficiently detects outliers inside the clusters by using clustering algorithm and weight based approach.
Manish Mahajan; Santosh Kumar; Bhasker Pant. A Novel Cluster Based Algorithm for Outlier Detection. Advances in Intelligent Systems and Computing 2018, 449 -456.
AMA StyleManish Mahajan, Santosh Kumar, Bhasker Pant. A Novel Cluster Based Algorithm for Outlier Detection. Advances in Intelligent Systems and Computing. 2018; ():449-456.
Chicago/Turabian StyleManish Mahajan; Santosh Kumar; Bhasker Pant. 2018. "A Novel Cluster Based Algorithm for Outlier Detection." Advances in Intelligent Systems and Computing , no. : 449-456.
Researchers shows a huge interest in Latent Class Analysis (LCA) in various domains over the last two decades. We proposed a new Latent Class Data Analysis using Statistical modeling approach to categorize better and worst Hotel to Stay. The main objective of this study was to find the unobserved classes in the Trap Advisor dataset. The results allow to identify new entry of the Hotel and detects whether it lies in Good Hotel category or in worst Hotel category. For evaluation and demonstration purpose freely, available Trip Advisor dataset is used.
Vijay Singh; Bhasker Pant; D. P. Singh; Santosh Kumar. Latent Class Analysis (LCA) Based Approach for Finding Best Hotels. Advances in Intelligent Systems and Computing 2018, 205 -213.
AMA StyleVijay Singh, Bhasker Pant, D. P. Singh, Santosh Kumar. Latent Class Analysis (LCA) Based Approach for Finding Best Hotels. Advances in Intelligent Systems and Computing. 2018; ():205-213.
Chicago/Turabian StyleVijay Singh; Bhasker Pant; D. P. Singh; Santosh Kumar. 2018. "Latent Class Analysis (LCA) Based Approach for Finding Best Hotels." Advances in Intelligent Systems and Computing , no. : 205-213.
For a decent yield, the farmer needs to monitor the field continuously. Internet of Things (IoT) plays an important role in enhancing the yield by changing the conventional method of monitoring. This paper centers on crop monitoring using IoT which will help farmers to continuously monitor the crops and take various decisions according to the needs. The system will provide data of soil moisture, leaf area index, leaf water area index, plant water content, and vegetation water mass. The data are collected using Arduino microcontroller along with the sensors deployed in the field remotely. The data once received are analyzed by applying feed forward, cascade forward, and function fitting neural network. After analyzing the data, cascade forward neural network gave the best result.
Rajneesh Kumar Pandey; Santosh Kumar; Ram Krishna Jha. Crop Monitoring Using IoT: A Neural Network Approach. Robotics in Education 2018, 123 -132.
AMA StyleRajneesh Kumar Pandey, Santosh Kumar, Ram Krishna Jha. Crop Monitoring Using IoT: A Neural Network Approach. Robotics in Education. 2018; ():123-132.
Chicago/Turabian StyleRajneesh Kumar Pandey; Santosh Kumar; Ram Krishna Jha. 2018. "Crop Monitoring Using IoT: A Neural Network Approach." Robotics in Education , no. : 123-132.
A healthy mind requires healthy body and healthy body needs healthy food. Therefore, to provide healthy food to the rapidly growing population of a country is a challenge within the limited fertile land. To fulfill the healthy food demand requires the high production of yield. To achieve high production, it is essential for the farmers to monitor the field from time to time for a good yield, as a little miss can cause disaster and the entire efforts could go waste. The manual monitoring of the field is quite tough and expensive. Therefore, in this paper Internet of Things (IoT) applications are addressed to monitor the field. An Arduino microcontroller board with the soil moisture, temperature, and humidity sensors is used to collect the data from the remote field on the fly. The data once received is analyzed by applying cascade forward and function fitting neural network. Further, the data is tested against an already trained dataset of the field in normal conditions collected over a period of 1 year. The test data, when applied to the trained data, provides a dataset which is used for analysis of the ideal condition of the field. In case of alarming changes in the field properties, a signal is generated and the farmer is informed to take necessary action. A preventive action can be taken to save the crop and also maintain the productivity of the field.
Ram Krishna Jha; Santosh Kumar; Kireet Joshi; Rajneesh Pandey. Field Monitoring Using IoT: A Neural Network Approach. Advances in Intelligent Systems and Computing 2018, 639 -650.
AMA StyleRam Krishna Jha, Santosh Kumar, Kireet Joshi, Rajneesh Pandey. Field Monitoring Using IoT: A Neural Network Approach. Advances in Intelligent Systems and Computing. 2018; ():639-650.
Chicago/Turabian StyleRam Krishna Jha; Santosh Kumar; Kireet Joshi; Rajneesh Pandey. 2018. "Field Monitoring Using IoT: A Neural Network Approach." Advances in Intelligent Systems and Computing , no. : 639-650.
Component-based software development is one of the proficient models of constructing eminent-quality software products. Reusability is the basic concept behind the component-based software. Reusability suggests re-use of existing software artifacts rather than developing them from the beginning. Components interact to share services and information in component-based software environment. In this paper we have focused on the interaction characteristics of the components. We define a metric called Interaction-metric as a factor of measurement of actual execution time of component-based software. We introduce the role of Interaction-metrics of individual components to estimate the execution-time of the component-based software. Interaction-metric are easy to compute, and are informative to analyze the performance of the components as well as the component-based software.
Umesh Kumar Tiwari; Santosh Kumar; Nikhil Kumar. Estimating actual execution time of Component-based software: Considering Interaction-metric. 2017 International Conference on Computing, Communication and Automation (ICCCA) 2017, 870 -875.
AMA StyleUmesh Kumar Tiwari, Santosh Kumar, Nikhil Kumar. Estimating actual execution time of Component-based software: Considering Interaction-metric. 2017 International Conference on Computing, Communication and Automation (ICCCA). 2017; ():870-875.
Chicago/Turabian StyleUmesh Kumar Tiwari; Santosh Kumar; Nikhil Kumar. 2017. "Estimating actual execution time of Component-based software: Considering Interaction-metric." 2017 International Conference on Computing, Communication and Automation (ICCCA) , no. : 870-875.
Network security management is a big challenge for network administrators due to increasing vulnerabilities. Vulnerabilities are the weakness of the network and allow malicious attackers access to resources controlled by an organization. To keep networks secure network administrators should be aware of all vulnerabilities through which an attacker can gain access. In this paper, we have considered the attack graph which describes how an attacker can compromise with the security of a network. To generate the attack graph, Multihost Multistage Vulnerability Analysis (MulVAL) tool is used. The generated graphs by this tool are logical attack graphs. These graphs are based on logical programming and based on dependencies among attack goal and configuration information. We have taken two security metrics, namely, exploitability metric and impact metric to analyze the risk associated with the network. Our preliminary results suggest that the size of the network has an impact on the vulnerability of a network.
Santosh Kumar; Anuradha Negi; Keshav Prasad; Aniket Mahanti. Evaluation of Network Risk Using Attack Graph Based Security Metrics. 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) 2016, 91 -93.
AMA StyleSantosh Kumar, Anuradha Negi, Keshav Prasad, Aniket Mahanti. Evaluation of Network Risk Using Attack Graph Based Security Metrics. 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). 2016; ():91-93.
Chicago/Turabian StyleSantosh Kumar; Anuradha Negi; Keshav Prasad; Aniket Mahanti. 2016. "Evaluation of Network Risk Using Attack Graph Based Security Metrics." 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) , no. : 91-93.
Testing plays a vital role in the evolution and establishment of any quality product as well as any quality system. Testing is essential to prove the correctness (valid output when input is valid, and proper handling techniques when input is invalid) of the system and it is crucial to prove the compatibility of the system with the operating environment. In component-based software systems, various components interact with each other to access as well as provide required functionalities. In such complex systems testing is one of the most important activities. Since component-based software engineering relies on the concept of “use of pre-built and pre-tested components”, our focus here, is on functional testing rather than structural testing. Functional testing emphasizes the behavioural attributes of the components when they interact with each other. Components interact through operands and parameters. In this paper we suggest functional testing strategy and test case generation technique for component-based software. When two components are integrated then they generate some specific effect. This strategy is named Integration-effect graph. This strategy is a black-box technique as it covers the input and output domains only. We have used the graph theory notations to show the integration and interaction among the components.
Umesh Kumar Tiwari; Santosh Kumar. Components integration-effect graph: a black box testing and test case generation technique for component-based software. International Journal of System Assurance Engineering and Management 2016, 8, 393 -407.
AMA StyleUmesh Kumar Tiwari, Santosh Kumar. Components integration-effect graph: a black box testing and test case generation technique for component-based software. International Journal of System Assurance Engineering and Management. 2016; 8 (2):393-407.
Chicago/Turabian StyleUmesh Kumar Tiwari; Santosh Kumar. 2016. "Components integration-effect graph: a black box testing and test case generation technique for component-based software." International Journal of System Assurance Engineering and Management 8, no. 2: 393-407.
Attack graph describes how an attacker can compromise with network security. To generate the attack graph, we required system as well as vulnerability information. The system information contains scanned data of a network, which is to be analyzed. The vulnerability data contain information about, how exploits can be generated due to multiple vulnerabilities and what effects can be of such exploitation. Multihost multistage vulnerability analysis (MulVAL) tool is used for generating attack graph in this work. MulVAL generated graphs are logical attack graphs based on logical programming and based on dependencies among attack goal and configuration information. The risk of network attack graph is measured through graph topology theoretic properties (connectivity, cycles, and depth), and analysis of possible attacks paths is carried out in this paper.
Keshav Prasad; Santosh Kumar; Anuradha Negi; Aniket Mahanti. Generation and Risk Analysis of Network Attack Graph. Advances in Intelligent Systems and Computing 2015, 507 -516.
AMA StyleKeshav Prasad, Santosh Kumar, Anuradha Negi, Aniket Mahanti. Generation and Risk Analysis of Network Attack Graph. Advances in Intelligent Systems and Computing. 2015; ():507-516.
Chicago/Turabian StyleKeshav Prasad; Santosh Kumar; Anuradha Negi; Aniket Mahanti. 2015. "Generation and Risk Analysis of Network Attack Graph." Advances in Intelligent Systems and Computing , no. : 507-516.
RSSI method is used to measure the distance between beacon nodes and unknown node. This technique is not more relevant because the radio frequency (RF) signals are mostly affected with the noise available in the environment. Therefore, the accurate distance evaluation cannot possible between the known nodes and unknown nodes. RSSI method is having many accuracy challenges which need improvements. Similarly, IRSSI method has also some faults regarding the calculation of model parameter. In this paper, a New Received Signal Strength Indicator (NRSSI) method is proposed. The new proposed RSSI method is achieved with improvement of the parameter values and introduction of the noise factor (or thermal noise). The thermal noise influences the RF signal severely, therefore, it is fused as a part of noise in RSSI method to reduce the measurement error effectively. In this paper, the NRSSI method improves the accuracy of the distance estimation for unknown nodes than RSSI and IRSSI.
Akhand Pratap Singh; Devesh Pratap Singh; Santosh Kumar. NRSSI: New proposed RSSI method for the distance measurement in WSNs. 2015 1st International Conference on Next Generation Computing Technologies (NGCT) 2015, 296 -300.
AMA StyleAkhand Pratap Singh, Devesh Pratap Singh, Santosh Kumar. NRSSI: New proposed RSSI method for the distance measurement in WSNs. 2015 1st International Conference on Next Generation Computing Technologies (NGCT). 2015; ():296-300.
Chicago/Turabian StyleAkhand Pratap Singh; Devesh Pratap Singh; Santosh Kumar. 2015. "NRSSI: New proposed RSSI method for the distance measurement in WSNs." 2015 1st International Conference on Next Generation Computing Technologies (NGCT) , no. : 296-300.
Mobile adhoc network (MANET) routing protocols performance are perceptive to mobility and scalability of network, therefore, the objectives of paper is to describe mobility metrics into direct and derived mobility metrics and impact of these metrics on routing performance metrics in MANET. An effort for analyzing derived mobility metrics with direct mobility metrics are considered across Manhattan and Freeway mobility model in this article. This article extends an intuitive study to analyze impact of mobility models on two prominent reactive routing protocols i.e. ad-hoc on demand distance vector (AODV) and dynamic source routing (DSR) with fixed network size, varying node speed and identical traffic load
S. C. Sharma; Santosh Kumar. Estimation of Mobility Models Based on Mobility Metrics and Their Impact on Routing Protocols in MANET. International Journal of Computer and Communication Engineering 2013, 386 -389.
AMA StyleS. C. Sharma, Santosh Kumar. Estimation of Mobility Models Based on Mobility Metrics and Their Impact on Routing Protocols in MANET. International Journal of Computer and Communication Engineering. 2013; ():386-389.
Chicago/Turabian StyleS. C. Sharma; Santosh Kumar. 2013. "Estimation of Mobility Models Based on Mobility Metrics and Their Impact on Routing Protocols in MANET." International Journal of Computer and Communication Engineering , no. : 386-389.