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 characterize...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.展开更多
In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding a...In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers (nodes) and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.展开更多
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 requi...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.展开更多
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.Ho...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.展开更多
Opportunistic networks are derived from delay tolerant networks, where mobile nodes have no end-to-end connections. Nodes are represented by people, which means that opportunistic networks can be regarded as social ne...Opportunistic networks are derived from delay tolerant networks, where mobile nodes have no end-to-end connections. Nodes are represented by people, which means that opportunistic networks can be regarded as social networks. Human mobility plays an important role in affecting the performance of forwarding protocols in social networks, furthermore, the trajectory of people's movements are driven by social characteristics. However, current routing protocols rely on simple mobility models, and rarely consider social characteristics. Considering two heterogeneous network models, an social opportunistic networks routing(SONR) was proposed which brings an adapted discrete Markov chain into nodes' mobility model and calculates the transition probability between successive status. Comparison was made between Spray, Wait and Epidemic protocol. Simulation show that SONR can improve performance on delivery ratio, delivery latency and network overhead, meanwhile. SONR approaches the performance of Epidemic routing.展开更多
The unique characteristics of opportunistic networks (ONs), such as intermittent connectivity and limited network resources, makes it difficult to support quality of service (QoS) provisioning, particularly to gua...The unique characteristics of opportunistic networks (ONs), such as intermittent connectivity and limited network resources, makes it difficult to support quality of service (QoS) provisioning, particularly to guarantee delivery ratio and delivery delay. In this paper, we propose a QoS-oriented packet scheduling scheme (QPSS) to make decisions for bundle transmissions to satisfy the needs for the delivery ratio and delivery delay constraints of bundles. Different from prior works, a novel bundle classification method based on the reliability and latency requirements is utilized to decide the traffic class of bundles. A scheduling algorithm of traffic class and bundle redundancy is used to maintain a forwarding and dropping priority queue and allocate network resources in QPSS. Simulation results indicate that our scheme not only achieves a higher overall delivery ratio but also obtains an approximate 14% increase in terms of the amount of eligible bundles.展开更多
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 on...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.展开更多
Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a re...Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.展开更多
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-...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.展开更多
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 ...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.展开更多
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...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.展开更多
Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwardin...Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwarding messages in such a network is trapped by many particular challenges, and some protocols have contributed to solve them partly. In this paper, we propose a Context-Aware Adaptive opportunistic Routing algorithm(CAAR). The algorithm firstly predicts the approximate location and orientation of the destination node by using its movement key positions and historical communication records, and then calculates the best neighbor for the next hop by using location and velocity of neighbors. In the unpredictable cases, forwarding messages will be delivered to the more capable forwarding nodes or wait for another transmission while the capable node does not exist in the neighborhood. The proposed algorithm takes the movement pattern into consideration and can adapt different network topologies and movements. The experiment results show that the proposed routing algorithm outperforms the epidemic forwarding(EF) and the prophet forwarding(PF) in packet delivery ratio while ensuring low bandwidth overhead.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China under Grants No.61373139Postdoctoral Science Foundation of China under Grant No.2014M560379 and No.2015T80484Natural Science Foundation of Jiangsu Province under Grant No.BK2012833
文摘In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers (nodes) and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.
文摘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.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61972044 and 61732017)the Fundamental Research Funds through the Central Universities(2020XDA09-3)+1 种基金the Funds for International Cooperation and Exchange of NSFC(Grant No.61720106007)the 111 Project(B18008).
文摘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.
基金supported by the National Natural Science Foundation of China(61171097)the State Major Science and Technology Special Projects(2012ZX03004001)
文摘Opportunistic networks are derived from delay tolerant networks, where mobile nodes have no end-to-end connections. Nodes are represented by people, which means that opportunistic networks can be regarded as social networks. Human mobility plays an important role in affecting the performance of forwarding protocols in social networks, furthermore, the trajectory of people's movements are driven by social characteristics. However, current routing protocols rely on simple mobility models, and rarely consider social characteristics. Considering two heterogeneous network models, an social opportunistic networks routing(SONR) was proposed which brings an adapted discrete Markov chain into nodes' mobility model and calculates the transition probability between successive status. Comparison was made between Spray, Wait and Epidemic protocol. Simulation show that SONR can improve performance on delivery ratio, delivery latency and network overhead, meanwhile. SONR approaches the performance of Epidemic routing.
基金supported by the NSFC-Guangdong Joint Found (U1501254)the Co-Construction Program with the Beijing Municipal Commission of Education and the Ministry of Science and Technology of China (2012BAH45B01)+1 种基金the Fundamental Research Funds for the Central Universities (BUPT2011RCZJ16,2014ZD03-03)the China Information Security Special Fund (NDRC)
文摘The unique characteristics of opportunistic networks (ONs), such as intermittent connectivity and limited network resources, makes it difficult to support quality of service (QoS) provisioning, particularly to guarantee delivery ratio and delivery delay. In this paper, we propose a QoS-oriented packet scheduling scheme (QPSS) to make decisions for bundle transmissions to satisfy the needs for the delivery ratio and delivery delay constraints of bundles. Different from prior works, a novel bundle classification method based on the reliability and latency requirements is utilized to decide the traffic class of bundles. A scheduling algorithm of traffic class and bundle redundancy is used to maintain a forwarding and dropping priority queue and allocate network resources in QPSS. Simulation results indicate that our scheme not only achieves a higher overall delivery ratio but also obtains an approximate 14% increase in terms of the amount of eligible bundles.
基金the National Natural Science Foundation of China under Grants U1804164,61902112 and U1404602in part by the Science and Technology Foundation of Henan Educational Committee under Grants 19A510015,20A520019,20A520020.
文摘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.
基金supported by the Shaanxi Natural Science Foundation Research Plan (No. 2015JQ6238)the China Scholarship Council+3 种基金the National Natural Science Foundation of China(Nos. 61373083 and 61402273)the Fundamental Research Funds for the Central Universities of China (No. GK201401002)the Program of Shaanxi Science and Technology Innovation Team of China (No. 2014KTC18)the 111 Programme of Introducing Talents of Discipline to Universities (No. B16031)
文摘Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.
基金the Major national S&T program under Grant No. 2011ZX03005-002National Natural Science Foundation of China under Grant No. 60872041,61072066the Fundamental Research Funds for the Central Universities under Grant No. JY10000903001,JY10000901034
文摘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.
基金The authors wish to thank the Natural Science Foundation of China under Grant Nos.61841109,61662054Natural Science Foundation of Inner Mongolia under Grand No.2019MS06031.
文摘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.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-20-11A)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(Grant No.BK19AF005).
文摘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.
基金Supported by the National Natural Science Foundation of China(61373040,61173137)the Ph.D.Programs Foundation of Ministry of Education of China(20120141110002)the Key Project of Natural Science Foundation of Hubei Province(2010CDA004)
文摘Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwarding messages in such a network is trapped by many particular challenges, and some protocols have contributed to solve them partly. In this paper, we propose a Context-Aware Adaptive opportunistic Routing algorithm(CAAR). The algorithm firstly predicts the approximate location and orientation of the destination node by using its movement key positions and historical communication records, and then calculates the best neighbor for the next hop by using location and velocity of neighbors. In the unpredictable cases, forwarding messages will be delivered to the more capable forwarding nodes or wait for another transmission while the capable node does not exist in the neighborhood. The proposed algorithm takes the movement pattern into consideration and can adapt different network topologies and movements. The experiment results show that the proposed routing algorithm outperforms the epidemic forwarding(EF) and the prophet forwarding(PF) in packet delivery ratio while ensuring low bandwidth overhead.