Node-selfishness And Resource Allocation Analysis In Wireless Networks

With the dramatic development of data processing,wireless communication and intelli-gent computing,the individual node in the multi-hop wireless network always equipments smart units for realizing some autonomous functions,such as sensitive sensing,accurate judgment and determent,quickly performing,etc.The autonomous functions of nodes endow wireless multi-hop network some novel characteristics(scalability,self-organizing,flexibility and so on),which makes the wireless multi-hop network get common attention and wide application in numerous areas,such as urgent assistance,military application and personal communication.Due to node's limited transmission range,the remote data transmission in the wireless multi-hop networks depends on the cooperation forwarding of network nodes.However,considering the features of portable and mobility,individual node,which generally depends on the battery,is limited by energy resource,small memory and so on.The node with lim-ited resources will naturally present selfish behaviors.Specifically,the node just focuses on its own profit,and refuses to provide cooperation forwarding services for other nodes at the expense of its valuable resources if there is no incentive.In this paper,such selfish behavior of node is defined as node-selfishness.The present of node-selfishness will decrease the transmission probability,network throughput etc.,which may impact the network perfor-mance of the whole system and restrict the further development of the wireless multi-hop network.Consequently,it is important to effectively deal with the node-selfishness in the wireless network to reduce the negative effect of node-selfishness and improve the network performance.Combining some optimizations,i.e.the forwarding protocol and resource allocation,the paper designs incentive mechanism,model and manage node-selfishness to comprehensively deal with the node-selfishness in the wireless multi-hop networks.The main research works and contributions are summarized as follows.Firstly,the paper investigates the adaptive packet forwarding for mobile social net-works(MSNs)afflicted with potential selfish nodes.In the absence of end-to-end paths and without the knowledge of the whole network,packet forwarding including forwarding decision(i.e.forwarding or dropping the packet)and relaying selection is crucial to be made by the individual of the node based on the packet-forwarding protocol in autonomous MSNs.When considering the various selfish behaviors of network nodes in multi-hop M-SNs,a novel forwarding protocol-Incentive Compatible Multiple-copy Packet Forwarding(ICMPF)protocol is proposed to maintain a satisfied packet delivery probability whilst re-ducing the delivery overhead.Considering the fact that the relay's forwarding decision in the ICMPF protocol is affected by its available resources(i.e.bandwidth,location priva-cy)and network environment(i.e.other relays' actions,social ties),an evolutionary game framework is exploited for modeling the complicated interactions among nodes to guide their forwarding behaviors.Meanwhile,the paper analyzes the evolutionary stable strategy(ESS)for this game-theoretic framework by employing replicator dynamics to portray the updates of nodes' forwarding strategies.Then,the paper proves that the nodes' strategy dy-namics converge to the ESS,and further develop a distributed learning algorithm for nodes to approach to the ESS.Simulation results show that our system converges to the ESS and also is robust to the learning error induced by the communication noise.Secondly,as for the time-varying node-selfishness effected by the factors,the paper investigates the dynamic data transmission in the selfish wireless networks(SeWNs).Based on the variation of node-selfishness,the paper builds the virtual receiving selfishness queue(v-RSQ)and virtual forwarding selfishness queue(v-FSQ)to model the node's dynamic receiving and forwarding selfishness,respectively.After having combined nodes' v-RSQ and v-FSQ information,two dynamic rate allocation schemes are developed to maximize the throughput-related network utility while limiting the negative effect of the node's selfishness.Simulation results show that,both allocation schemes can improve the network performance and suppress the node-selfishness(receiving selfishness and forwarding selfishness)in the wireless networks.Next,the paper proposes a new dynamic transmission scheme to improve the data trans-mission over wireless relay networks,by combing the flow allocation and relaying strategy adaption.To reduce the node-selfishness damage and enhance the network performance,the paper develops a novel virtual node-selfishness queue(VSQ)to portray the node's dy-namic selfish characteristic in terms of its energy resource and incentives.Then,based on the built VSQs,a stochastic optimization model is employed to maximize the average network throughput whilst keeping the network stable and bounding the node-selfishness.The problem of stochastic optimization is further decomposed into two subproblems via the Lyapunov optimization theory,which corresponds to the flow allocation for source and the relaying strategy adaption for autonomous relay,respectively.After that,a Joint flow Rate allocation and Relaying strategy Adaption(JRRA)algorithm is developed to accommodate the wireless network only according to the current network state information.The explicit tradeoff between network throughput and average data transmission delay is theoretically given.Simulation results validate the theoretical analysis of our proposed scheme.Finally,considering the continue time-varying node-selfishness,the paper investigates the dynamic flow allocation and forwarding strategy adaption scheme for wireless networks with potential selfish nodes.Aided by an incentive mechanism,the paper develops a stochas-tic differential equation(SDE)to portray the dynamic node-selfishness in terms of node's energy resource and incentives.Then,a stochastic optimization model is employed to max-imize the average network utility whilst bounding the node-selfishness.To solve the opti-mization problem,a Dynamic flow Rate Allocation and Forwarding strategy update(DRAF)algorithm is developed to accommodate the dynamic network state via the continuous-time Lyapunov optimization theory.Compared with the traditional optimization algorithm,the DRAF algorithm decreases the algorithm complexity,but also introduces the tracking er-rors.After having investigated the tracking errors between the output of DRFA algorithm and that of the optimal,an Adaptive-Compensation flow Rate Allocation and Forwarding strategy update(ACRAF)algorithm is designed,which iterates only once when the network state changes,for reducing the iteration costs.Finally,the paper provides a sufficient condi-tion that the ACRAF algorithm asymptotically tracks the moving equilibrium point with no tracking errors.Simulation results validate the theoretical analysis of our proposed scheme.