A selfish behavior detection technique is investigated to assist secure cooperative trans- mission at the physical layer. The detection technique calculates the correlation ratio between signals received from the dive...A selfish behavior detection technique is investigated to assist secure cooperative trans- mission at the physical layer. The detection technique calculates the correlation ratio between signals received from the diversity branches to determine the relay＇ s behavior in amplify and forward coop- eration strategy. The correlation ratio is considerably reduced because the relay＇ s selfish behavior makes the correlation between the received signals in the diversity branch degraded. Simulation re- sults show that the proposed mechanism can effectively detect selfish nodes and performance will be improved significantly with the destination＇ s detection technique.展开更多
There is relatively limited knowledge concerning our understanding of how our immune system and brain take most of the available energy in a selfish manner to compensate for their own needs on priority in high energy ...There is relatively limited knowledge concerning our understanding of how our immune system and brain take most of the available energy in a selfish manner to compensate for their own needs on priority in high energy demanding situations. The main objective of this review is to understand the energy allocation to immune system and brain in infections and/or fight or flight situations. The immune system and brain behave in a selfish manner as they allocate themselves majority of the total available energy. Insulin resistance (IR) is used as a tool for energy allocation by these systems. The immune system is activated as a response to stress and infection. Similarly, the brain gets activated as a response to any external environmental impulse, anxiety, and/or mental factor. These situations need to be dealt in a way to minimize their adverse health effects. The immune system and the brain in such situations need enormous energy for activation which is derived from the energy quota otherwise allocated to other organs. This maximum flux of energy towards these systems is achieved by making rest of the organs less responsive to insulin, a condition known as IR. As immune system and brain do not depend upon insulin for uptake of glucose, these systems are benefited from IR. IR is indicated as a beneficial role ensuring maximum energy allocation to these systems for improving health and well-being.展开更多
The cumulative sum （CUSUM） algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, ...The cumulative sum （CUSUM） algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum con- tention window size of I 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated： a single-hop network with nodes spreading out in 100 m^100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m~ 1 000 m. The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.展开更多
A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing r...A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing reputation based methods have limitation due to their stricter punishment strategy because they isolate nodes from network participation having lesser reputation value and thus reduce the total strength of nodes in a network. In this paper we have proposed a mathematical model for the classification of nodes in MANETs using adaptive decision boundary. This model classifies nodes in two classes: selfish and regular node as well as it assigns the grade to individual nodes. The grade is computed by counting how many passes are required to classify a node and it is used to define the punishment strategy as well as enhances the reputation definition of traditional reputation based mechanisms. Our work provides the extent of noncooperation that a network can allow depending on the current strength of nodes for the given scenario and thus includes selfish nodes in network participation with warning messages. We have taken a leader node for reputation calculation and classification which saves energy of other nodes as energy is a major challenge of MANET. The leader node finally sends the warning message to low grade nodes and broadcasts the classification list in the MANET that is considered in the routing activity.展开更多
The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized an...The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.展开更多
The main intention of developing cognitive radio technology is to solve the spectrum deficiency problem by allocating the spectrum dynamically to the unlicensed clients. An important aim of any wireless network is to ...The main intention of developing cognitive radio technology is to solve the spectrum deficiency problem by allocating the spectrum dynamically to the unlicensed clients. An important aim of any wireless network is to secure communication. It is to help the unlicensed clients to utilize the maximum available licensed bandwidth, and the cognitive network is designed for opportunistic communication technology. Selfish attacks cause serious security problem because they significantly deteriorate the performance of a cognitive network. In this paper, the selfish attacks have been identified using cooperative neighboring cognitive radio ad hoc network (COOPON). A novel technique has been proposed as ICOOPON (improvised COOPON), which shows improved performance in selfish attack detection as compared to existing technique. A comparative study has been presented to find the efficiency of proposed technique. The parameters used are throughput, packet delivery ratio and end to end delay.展开更多
As fossil fuel stocks are being depleted,alternative sources of energy must be explored.Consequently,traditional thermal power plants must coexist with renewable resources,such as wind,solar,and hydro units,and all-da...As fossil fuel stocks are being depleted,alternative sources of energy must be explored.Consequently,traditional thermal power plants must coexist with renewable resources,such as wind,solar,and hydro units,and all-day planning and operation techniques are necessary to safeguard nature while meeting the current demand.The fundamental components of contemporary power systems are the simultaneous decrease in generation costs and increase in the available transfer capacity(ATC)of current systems.Thermal units are linked to sources of renewable energy such as hydro,wind,and solar power,and are set up to run for 24 h.By contrast,new research reports that various chaotic maps are merged with various existing optimization methodologies to obtain better results than those without the inclusion of chaos.Chaos seems to increase the performance and convergence properties of existing optimization approaches.In this study,selfish animal tendencies,mathematically represented as selfish herd optimizers,were hybridized with chaotic phenomena and used to improve ATC and/or reduce generation costs,creating a multi-objective optimization problem.To evaluate the performance of the proposed hybridized optimization technique,an optimal power flow-based ATC was enforced under various hydro-thermal-solar-wind conditions,that is,the renewable energy source-thermal scheduling concept,on IEEE 9-bus,IEEE 39-bus,and Indian Northern Region Power Grid 246-bus test systems.The findings show that the proposed technique outperforms existing well-established optimization strategies.展开更多
In this paper,we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorous mathematical setting.We first describe a more ...In this paper,we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorous mathematical setting.We first describe a more general model of blockchain selfish mining with both a two-block leading competitive criterion and a new economic incentive mechanism.Then we establish a pyramid Markov process and show that it is irreducible and positive recurrent,and its stationary probability vector is matrix-geometric with an explicitly representable rate matrix.Also,we use the stationary probability vector to study the influence of orphan blocks on the waste of computing resource.Next,we set up a pyramid Markov reward process to investigate the long-run average mining profits of the honest and dishonest mining pools,respectively.As a by-product,we build one-dimensional Markov reward processes and provide some new interesting interpretation on the Markov chain and the revenue analysis reported in the seminal work by Eyal and Sirer(2014).Note that the pyramid Markov(reward)processes can open up a new avenue in the study of blockchain selfish mining.Thus we hope that the methodology and results developed in this paper shed light on the blockchain selfish mining such that a series of promising research can be developed potentially.展开更多
We study asymmetric atomic selfish routing in ring networks, which has diverse practical applications in network design and analysis. We are concerned with minimizing the maximum latency of source-destination node-pai...We study asymmetric atomic selfish routing in ring networks, which has diverse practical applications in network design and analysis. We are concerned with minimizing the maximum latency of source-destination node-pairs over links with linear latencies. We show that there exists an optimal solution that is a 9-approximate Nash equilibrium, significantly improving the existing upper bound of 54 on the instability factor. We present fast implementation of the best response dynamics for computing a Nash equilibrium. Furthermore, we perform empirical study on the price of stability, narrowing the gap between the lower and upper bounds to 0.7436.展开更多
This paper considers a selfish routing based network improvement problem, in which the authors would like to find a modified latency function that results in a new Nash equilibrium flow satisfying all traffic demands ...This paper considers a selfish routing based network improvement problem, in which the authors would like to find a modified latency function that results in a new Nash equilibrium flow satisfying all traffic demands subject to the target capacity, while the total modification cost on edge latency is minimized. By using the reduction from the 3-Satisfiability （3-SAT） problem to our problem, the authors show that this problem is strongly NP-hard, even for the single commodity network.展开更多
In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation sig...In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.展开更多
In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation sig...In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.展开更多
As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. Ther...As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.展开更多
Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing de...Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.展开更多
基金Supported by the National High Technology Research and Development Program of China("863"Program) (2009AA011507)
文摘A selfish behavior detection technique is investigated to assist secure cooperative trans- mission at the physical layer. The detection technique calculates the correlation ratio between signals received from the diversity branches to determine the relay＇ s behavior in amplify and forward coop- eration strategy. The correlation ratio is considerably reduced because the relay＇ s selfish behavior makes the correlation between the received signals in the diversity branch degraded. Simulation re- sults show that the proposed mechanism can effectively detect selfish nodes and performance will be improved significantly with the destination＇ s detection technique.
文摘There is relatively limited knowledge concerning our understanding of how our immune system and brain take most of the available energy in a selfish manner to compensate for their own needs on priority in high energy demanding situations. The main objective of this review is to understand the energy allocation to immune system and brain in infections and/or fight or flight situations. The immune system and brain behave in a selfish manner as they allocate themselves majority of the total available energy. Insulin resistance (IR) is used as a tool for energy allocation by these systems. The immune system is activated as a response to stress and infection. Similarly, the brain gets activated as a response to any external environmental impulse, anxiety, and/or mental factor. These situations need to be dealt in a way to minimize their adverse health effects. The immune system and the brain in such situations need enormous energy for activation which is derived from the energy quota otherwise allocated to other organs. This maximum flux of energy towards these systems is achieved by making rest of the organs less responsive to insulin, a condition known as IR. As immune system and brain do not depend upon insulin for uptake of glucose, these systems are benefited from IR. IR is indicated as a beneficial role ensuring maximum energy allocation to these systems for improving health and well-being.
基金Supported by National Natural Science Foundation of China (No. 60702038)National High Technology Research and Development Program of China ("863"Program, No. 2007AA01Z220)Cultivation Fund of Innovation Project,Ministry of Education of China (No. 708024)
文摘The cumulative sum （CUSUM） algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum con- tention window size of I 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated： a single-hop network with nodes spreading out in 100 m^100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m~ 1 000 m. The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.
文摘A MANET is a cooperative network in which each node has dual responsibilities of forwarding and routing thus node strength is a major factor because a lesser number of nodes reduces network performance. The existing reputation based methods have limitation due to their stricter punishment strategy because they isolate nodes from network participation having lesser reputation value and thus reduce the total strength of nodes in a network. In this paper we have proposed a mathematical model for the classification of nodes in MANETs using adaptive decision boundary. This model classifies nodes in two classes: selfish and regular node as well as it assigns the grade to individual nodes. The grade is computed by counting how many passes are required to classify a node and it is used to define the punishment strategy as well as enhances the reputation definition of traditional reputation based mechanisms. Our work provides the extent of noncooperation that a network can allow depending on the current strength of nodes for the given scenario and thus includes selfish nodes in network participation with warning messages. We have taken a leader node for reputation calculation and classification which saves energy of other nodes as energy is a major challenge of MANET. The leader node finally sends the warning message to low grade nodes and broadcasts the classification list in the MANET that is considered in the routing activity.
文摘The introduction of new technologies has increased communication network coverage and the number of associating nodes in dynamic communication networks(DCN).As the network has the characteristics like decentralized and dynamic,few nodes in the network may not associate with other nodes.These uncooperative nodes also known as selfish nodes corrupt the performance of the cooperative nodes.Namely,the nodes cause congestion,high delay,security concerns,and resource depletion.This study presents an effective selfish node detection method to address these problems.The Price of Anarchy(PoA)and the Price of Stability(PoS)in Game Theory with the Presence of Nash Equilibrium(NE)are discussed for the Selfish Node Detection.This is a novel experiment to detect selfish nodes in a network using PoA.Moreover,the least response dynamic-based Capacitated Selfish Resource Allocation(CSRA)game is introduced to improve resource usage among the nodes.The suggested strategy is simulated using the Solar Winds simulator,and the simulation results show that,when compared to earlier methods,the new scheme offers promising performance in terms of delivery rate,delay,and throughput.
文摘The main intention of developing cognitive radio technology is to solve the spectrum deficiency problem by allocating the spectrum dynamically to the unlicensed clients. An important aim of any wireless network is to secure communication. It is to help the unlicensed clients to utilize the maximum available licensed bandwidth, and the cognitive network is designed for opportunistic communication technology. Selfish attacks cause serious security problem because they significantly deteriorate the performance of a cognitive network. In this paper, the selfish attacks have been identified using cooperative neighboring cognitive radio ad hoc network (COOPON). A novel technique has been proposed as ICOOPON (improvised COOPON), which shows improved performance in selfish attack detection as compared to existing technique. A comparative study has been presented to find the efficiency of proposed technique. The parameters used are throughput, packet delivery ratio and end to end delay.
文摘As fossil fuel stocks are being depleted,alternative sources of energy must be explored.Consequently,traditional thermal power plants must coexist with renewable resources,such as wind,solar,and hydro units,and all-day planning and operation techniques are necessary to safeguard nature while meeting the current demand.The fundamental components of contemporary power systems are the simultaneous decrease in generation costs and increase in the available transfer capacity(ATC)of current systems.Thermal units are linked to sources of renewable energy such as hydro,wind,and solar power,and are set up to run for 24 h.By contrast,new research reports that various chaotic maps are merged with various existing optimization methodologies to obtain better results than those without the inclusion of chaos.Chaos seems to increase the performance and convergence properties of existing optimization approaches.In this study,selfish animal tendencies,mathematically represented as selfish herd optimizers,were hybridized with chaotic phenomena and used to improve ATC and/or reduce generation costs,creating a multi-objective optimization problem.To evaluate the performance of the proposed hybridized optimization technique,an optimal power flow-based ATC was enforced under various hydro-thermal-solar-wind conditions,that is,the renewable energy source-thermal scheduling concept,on IEEE 9-bus,IEEE 39-bus,and Indian Northern Region Power Grid 246-bus test systems.The findings show that the proposed technique outperforms existing well-established optimization strategies.
基金This work is supported by the National Key R&D Program of China under Grant No.2020AAA0103801Quanlin Li is supported by the National Natural Science Foundation of China under Grant Nos.71671158 and 71932002+1 种基金the Beijing Social Science Foundation Research Base Project under Grant No.19JDGLA004Xiaole Wu is supported by the National Natural Science Foundation of China under Grant No.72025102.
文摘In this paper,we provide a new theoretical framework of pyramid Markov processes to solve some open and fundamental problems of blockchain selfish mining under a rigorous mathematical setting.We first describe a more general model of blockchain selfish mining with both a two-block leading competitive criterion and a new economic incentive mechanism.Then we establish a pyramid Markov process and show that it is irreducible and positive recurrent,and its stationary probability vector is matrix-geometric with an explicitly representable rate matrix.Also,we use the stationary probability vector to study the influence of orphan blocks on the waste of computing resource.Next,we set up a pyramid Markov reward process to investigate the long-run average mining profits of the honest and dishonest mining pools,respectively.As a by-product,we build one-dimensional Markov reward processes and provide some new interesting interpretation on the Markov chain and the revenue analysis reported in the seminal work by Eyal and Sirer(2014).Note that the pyramid Markov(reward)processes can open up a new avenue in the study of blockchain selfish mining.Thus we hope that the methodology and results developed in this paper shed light on the blockchain selfish mining such that a series of promising research can be developed potentially.
基金Supported in part by China 973 Project(Grant No.2011CB80800)National Natural Science Foundation of China(Grant Nos.10531070,10721101,11222109 and 71101006)CAS Program for Cross & Cooperative Team of Science & Technology Innovation
文摘We study asymmetric atomic selfish routing in ring networks, which has diverse practical applications in network design and analysis. We are concerned with minimizing the maximum latency of source-destination node-pairs over links with linear latencies. We show that there exists an optimal solution that is a 9-approximate Nash equilibrium, significantly improving the existing upper bound of 54 on the instability factor. We present fast implementation of the best response dynamics for computing a Nash equilibrium. Furthermore, we perform empirical study on the price of stability, narrowing the gap between the lower and upper bounds to 0.7436.
基金The work is supported Hohai University Funds under Grant Nos. XZX/08B002-02, 2009428211, and the US National Science Foundation under Grant No. DMI-0553310.
文摘This paper considers a selfish routing based network improvement problem, in which the authors would like to find a modified latency function that results in a new Nash equilibrium flow satisfying all traffic demands subject to the target capacity, while the total modification cost on edge latency is minimized. By using the reduction from the 3-Satisfiability （3-SAT） problem to our problem, the authors show that this problem is strongly NP-hard, even for the single commodity network.
文摘In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.
文摘In Wireless Sensor Network(WSN),energy and packet forwarding tendencies of sensor nodes plays a potential role in ensuring a maximum degree of co-operation under data delivery.This quantified level of co-operation signifies the performance of the network in terms of increased throughput,packet delivery rate and decreased delay depending on the data being aggregated and level of control overhead.The performance of a sensor network is highly inclined by the selfish behaving nature of sensor nodes that gets revealed when the residual energy ranges below a bearable level of activeness in packet forwarding.The selfish sensor node needs to be identified in future through reliable forecasting mechanism for improving the lifetime and packet delivery rate.Semi Markov Process Inspired Selfish aware Co-operative Scheme(SMPISCS)is propounded for making an attempt to mitigate selfish nodes for prolonging the lifetime of the network and balancing energy consumptions of the network.SMPISCS model provides a kind of sensor node’s behavior for quantifying and future forecasting the probability with which the node could turn into selfish.Simulation experiments are carried out through Network Simulator 2 and the performance are analyzed based on varying the number of selfish sensor nodes,number of sensor nodes and range of detection threshold.
基金supported by the Ministry of Trade,Industry and Energy(MOTIE)under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT)the Ministry of Science and ICT(MSIT)under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP).
文摘As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.
基金The author Dr.Arshiya S.Ansari extends the appreciation to the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1538).
文摘Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.
