A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent devices.OppNets are characterized by a rough and dynamic topology as well as unpredictable contacts and contact times.Data is forwarded and stored in intermediate nodes until the next opportunity occurs.Therefore,achieving a high delivery ratio in OppNets is a challenging issue.It is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network performance.In this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social OppNets.The ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real time.The proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment simulator.Experiments show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Opportunistic Networks(OppNets)is gaining popularity day-by-day due to their various applications in the real-life world.The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication.Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks(Manets)and Wireless Sensor Networks(WSNs),therefore,it inherits most of the traits from both mentioned networking techniques.Apart from its popularity,Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network.These issues lower the overall performance of the network.Keeping this thing in mind,ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz.pattern prediction,decision tree prediction,adamic-adar method for complex networks.Apart from this,ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background.Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service(QoS)parameters.

Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

Mobile opportunistic network(MON)is an efficient way of communication when there is no persistent connection between nodes.Multicast in MONs can be used to efficiently deliver messages to multiple destination nodes.However,because multiple destination nodes are involved,multicast routing is more complex than unicast and brings a higher communication cost.Backbone-based routing can effectively reduce the network overhead and the complexity of routing scheme.However,the load of backbone nodes is larger than that of regular nodes.If the backbone node’s buffer is exhausted,it will have a significant impact on the performance of the routing scheme.Load balancing can improve the ability of backbone to deal with the change of network load,and backbone maintenance algorithm can provide backbone robustness.In this paper,we propose a robust load-balanced backbone-based multicast routing scheme in MONs.In the backbone construction algorithm,we transform the problem of backbone construction into a multi-objective optimization problem,and propose a multi-objective evolutionary algorithmbased backbone construction algorithm,namely LBMBCMOEA algorithm.In addition,in order to increase the robustness of the backbone-based routing scheme,we propose a localized multicast backbone maintenance algorithm(MBMA)to deal with the buffer exhaustion of backbone nodes.When a backbone node’s residual buffer is insufficient,MBMA algorithm selects other nodes to replace the backbone node.The results on extensive simulations show that when considering the node buffer size constraints,compared with previous backbone-based multicast routing schemes,our proposed algorithm has better performance,and when the node’s residual buffer is insufficient,MBMA algorithm can significantly improve the performance of the backbone-based multicast routing scheme.

Disjoint-Path Routing Mechanism in Mobile Opportunistic Networks

The prevalent multi-copy routing algorithms in mobile opportunistic networks(MONs)easily cause network congestion.This paper introduces a disjoint-path(DP)routing algorithm,where each node can only transmit packets once except the source node,to effectively control the number of packet copies in the network.The discrete continuous time Markov chain(CTMC)was utilized to analyze the state transition between nodes,and the copy numbers of packets with the DP routing algorithm were calculated.Simulation results indicate that DP has a great improvement in terms of packet delivery ratio,average delivery delay,average network overhead,energy and average hop count.

机会网络中基于身份门限签名的可信路由(英文)

In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.

A Mixture Model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.

Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration

Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication.Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies.The Prophet routing algorithm is an important routing algorithm in opportunistic networks.It forwards messages based on the encounter probability between nodes,and has good innovation significance and optimization potential.However,the Prophet routing algorithm does not consider the impact of the historical throughput of the node on message transmission,nor does it consider the impact of the encounter duration between nodes on message transmission.Therefore,to improve the transmission efficiency of opportunistic networks,this paper based on the Prophet routing algorithm,fuses the impact of the historical throughput of the node and the encounter duration between nodes on message transmission at the same time,and proposes the Prophet_TD routing algorithm based on the historical throughput and the encounter duration.This paper uses the Opportunistic Networks Environment v1.6.0(the ONE v1.6.0)as the simulation platform,controls the change of running time and the number of nodes respectively,conducts simulation experiments on the Prophet_TD routing algorithm.The simulation results show that compared to the traditional Prophet routing algorithm,on the whole,the Prophet_TD routing algorithm has a higher message delivery rate and a lower network overhead rate,and its average latency is also lower when node density is large.