Computation of PoA for Selfish Node Detection and Resource Allocation Using Game Theory

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.

Selfish behavior detection of cooperative relay

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.

Energy Metabolism and Allocation in Selfish Immune System and Brain: A Beneficial Role of Insulin Resistance in Aging

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.

Classification of Selfish and Regular Nodes Based on Reputation Values in MANET Using Adaptive Decision Boundary

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.

Efficient Selfish Attack Detection in Cognitive Radio Network

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.

Detection of Selfish Behavior in Wireless Ad Hoc Networks Based on CUSUM Algorithm

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 contention window size of 1 024, 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.

Calculation of Available Transfer Capability Using Hybrid Chaotic Selfish Herd Optimizer and 24 Hours RES-thermal Scheduling

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.

基于演化博弈的车联网分发消息激励机制

由于车联网中节点资源有限,车联网中存在不愿意传递消息的自私车辆节点,严重影响网络性能。为了改善网络性能,提出了一种基于演化博弈的车联网消息合作分发的激励机制,利用节点始终追求最大收益的有限理性的特点,根据节点传递消息的数量以及节点在固定时间内的空闲程度设计收益函数,引导自私节点努力参与到车联网中消息的传递过程,同时引入路边单元作为监管者加速车辆节点的策略更新过程。仿真结果表明自私节点比例越多,网络性能越差,而该激励机制可以有效地改善存在自私节点的车联网的网络性能。

DTN中基于虚拟货币交易的高效率路由算法

由于容迟网络的间歇性连接等特点,以及节点自身缓存、能量等资源受限,DTN中的节点往往会表现出一定的自私性。自私节点的存在可能会提高网络的开销,降低消息的成功投递率。为了促进自私节点参与合作,提出了一种基于虚拟货币交易的高效率路由算法PVCT(Efficient Routing Algorithm Based on Virtual Currency Transaction in DTN),并结合容迟网络的小世界特性,提高了路由算法的效率。该算法利用虚拟货币交易的方式,并根据节点的基本属性、位置属性、社会属性等进行定价,节点根据设计的价格函数给出对应的报价,并利用价格函数合理地分配消息副本数。在PVCT策略中,节点根据判断情况分为正常节点和自私节点,当消息的跳数小于或等于两跳时,按照概率路由的策略进行转发;反之,当消息的跳数大于两跳时,若遇到的为自私节点,则执行虚拟货币交易的路由算法。携带消息节点的出价若高于转发节点的价格,则进行交易,更新各自的收益状态;否则,进入二次价格调整阶段以协调节点双方之前的虚拟报价。仿真实验表明,PVCT路由算法在DTN中能更好地促进消息的转发,从而提升网络的整体性能。

A New Theoretical Framework of Pyramid Markov Processes for Blockchain Selfish Mining

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.

考虑个体自私的热电联产系统完全分布式经济调度

针对传统集中式经济调度策略需要获取全局信息以及无法适应系统灵活拓扑的缺陷,同时考虑能量以电能和热量形式并存出现的情况,基于一致性算法提出了一种完全分布式热电联产经济调度策略.将热电联产系统中各机组电价和热价的增量成本作为一致性变量进行迭代计算,直至增量成本达到收敛,实现系统经济最优.此外,还分析了个别机组为了提升自身利益,向相邻机组传输带有偏差增量成本值的情况,设计了一个增量成本补偿项来消除该自私行为的影响.在3个仿真算例中得到的结果表明,所提策略在解决热电联产系统经济调度问题中具有有效性和可行性.

动态SHO-ACO的焊接机器人路径规划

移动焊接机器人在户外进行路径规划时的高效性和安全性尤为重要。针对蚁群算法(ACO)信息素以总长度为单个影响因子的缺陷,加入转向次数要素,建立环境适应度函数,从而改进轨迹上信息素增值状况;针对基本蚁群收敛速度慢的问题,借鉴自私羊群算法(SHO)的空间因素,改进启发函数;针对局部最优问题,将SHO的吸引力函数融入信息素变化中再结合环境适应度函数,动态指引蚁群朝向更加优良的轨迹前行;而且针对停滞僵局问题,提出撤离行动与预警行动,确保蚂蚁探路效率;针对传统轮转方法随机性问题,提出了评判拐弯机制以在有目的选择下一节点的同时,计算路径距离方法,降低了算法的复杂程度。SHO-ACO与势场蚁群和传统蚁群算法进行仿真对比实验,结果表明,SHO-ACO在简单环境与复杂环境中均具有优越性。

Stability vs.Optimality in Selfish Ring Routing

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.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks(SMPISCS)

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.

Semi Markov process inspired selfish aware co-operative scheme for wireless sensor networks(SMPISCS)

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.

A SELFISH ROUTING BASED NETWORK IMPROVEMENT PROBLEM

这篇论文考虑自私路由基于网络改进问题，作者想在发现导致新纳什平衡流动的一个修改潜伏函数令人满意所有交通要求题目到目标能力，当全部的修正花费了时紧张不安潜伏被最小化。由从 3 可满足性(3-SAT ) 使用减小问题到我们的问题，作者证明这个问题是强烈 NP 难的，甚至为单个商品网络。

从隐性与显性作用角度探析“三只手”调节经济机理——兼论构建中国特色社会主义宏观经济理论

总结“三只手”特别是“第三只手”调节经济的力量组成与功能划分;深入探析“市场之手”“政府之手”“第三只手”用力的隐性与显性作用机理,着力探讨其驱力来源、力量组成、主要目标、运行规律等,对于我们深刻认知“三只手”调节经济的力量构造,并在实践中主动应用“三只手”调节经济的手段与方法,意义重要。“三只手”调节经济的理论与方法,必将成为在市场经济实践中、充分展示社会主义制度优越性“中国之治”的璀璨亮点。

基于交易记录特征的自私挖矿检测方案

自私挖矿攻击是一种挖矿策略,存在基于工作量证明(PoW,proof of work)机制的区块链中。自私矿工通过延迟区块广播的时机来截取其他矿工的挖矿奖励,以此获得比正常情况下更多的收益。这种攻击对工作量证明机制的激励相容性造成了破坏。近年来,自私挖矿这一攻击行为被研究者从许多角度进行了研究和分析。然而这一挖矿策略利用了工作量证明机制区块链在网络延迟方面存在的缺陷,使得采用这一挖矿策略的矿工在行为上与正常挖矿行为没有明显的区别,导致当前缺少可以有效检测出自私挖矿行为的方案。因此,提出了一种自私挖矿的检测方案,该检测方案创新性地利用了区块链中区块的高度和区块中的交易记录特征,可以做到对网络中的自私挖矿行为进行实时检测,有一定的实用价值。所提检测方案为生成的新区块定义了一个状态值,这个状态值与区块中包含的交易数量、支付给矿工的交易费用等数据有关。根据这些特征值间的数学关系,可以判断出该区块是否来自自私矿工。通过仿真实验测试了攻击者在不同算力下,该检测方案的检测情况。结果表明,所提检测方案在判断区块是否来自自私挖矿时,有86.02%以上的检测准确率,可以有效地对自私挖矿产生的区块进行检测。

容迟网络中基于节点社会属性的激励机制

针对容迟网络(DTN)中存在部分节点不合作的问题,提出基于节点社会属性的激励机制(IMNSA)。首先,定义节点协作度、奉献值衡量节点协作程度,其中,协作度体现节点的合作情况,奉献值量化节点在数据传输中的贡献。其次,设置奖励与惩罚策略以提高节点协作的积极性。奖励策略是基于节点协作度与奉献值的消息管理策略;惩罚策略通过强制自私节点转发若干条其他节点的消息迫使节点合作。最后,为提高消息投递率,提出节点羁绊值的概念,通过羁绊值筛选朋友节点以加大消息的扩散程度。仿真实验结果表明,所提机制可有效提高节点协作度,相较于Spray and Wait(SaW)算法,节点的平均协作度提高了112%,同时在消息投递率方面比Prophet平均提高106.48%,负载率比Prophet平均降低86.8%。可见,所提机制在激励节点合作方面具有不错的效果,同时可以保持较高的投递率和较低的网络负载,在社交DTN方面具有良好的应用前景。

Study of Hassan's Tragedy from Three Aspects in The Kite Runner

This paper analyzes a novel Kite Runner of Khaled Hosseini, published in 2003. It is the first novel of Hosseini which had been one of the bestselling in New York Times ranking list. It studies Hassan's tragedy from three aspects, including the social environment in Afghanistan, the loyalty of Hassan, and the selfishness of Amir. Besides, it also discusses the friendship and betrayal between two boys-Hassan and Amir. Finally, the terrible life of Hassan in Afghanistan is under investigated.