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
This article puts forward a new scheme to control message redundancy efficiently in delay tolerant mobile Ad-hoc networks （MANET）. The class of networks generally lacks end-to-end connectivity. In order to improve t...This article puts forward a new scheme to control message redundancy efficiently in delay tolerant mobile Ad-hoc networks （MANET）. The class of networks generally lacks end-to-end connectivity. In order to improve the efficiency that messages are delivered successfully, multiple message copies routing protocols are usually used, but the network load is increased due to a large number of message redundancies. In the study, by using counter method, every node adds an encounter counter based on epidemic routing scheme. The counter records the number which the node encounters other nodes with the same message copy. If the counter of a node reaches tbe installed threshold, the node removes the copy. Theoretical analysis gives a lower bound of threshold in delay tolerant MANET. According to the lower bound of threshold, a rational threshold is installed in real environment. With proposed scheme message copies decrease obviously and are removed completely finally. The successful delivery efficiency is still the same as epidemic routing and the redundant copies are efficiently controlled to a relatively low level Computer simulations give the variation of message copies concerning different thresholds in fast and slow mobility scenes.展开更多
Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, rou...Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.展开更多
In Delay Tolerant Networks （DTNs）, some routing algorithms ignore that most nodes are selfish, i.e., nodes are willing to use their own resources to forward messages to nodes with whom they have a relationship. In v...In Delay Tolerant Networks （DTNs）, some routing algorithms ignore that most nodes are selfish, i.e., nodes are willing to use their own resources to forward messages to nodes with whom they have a relationship. In view of this phenomenon, we propose a routing algorithm based on Geographic Information and Node Selfishness （GINS）. To choose a forwarding node, GINS combines nodes＇ willingness to forward and their geographic information to maximize the possibility of contacting the destination. GINS formulates the message forwarding process as a 0-1 Knapsack Problem with Assignment Restrictions to satisfy node demands for selfishness. Extensive simulations were conducted, and results show that GINS can achieve a high delivery ratio and a lower hop count compared with GRONE and LPHU. Furthermore, its overhead ratio is 25% and 30% less than that of GRONE and LPHU, respectively.展开更多
Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile ...Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.展开更多
Epidemic routing (Flooding) is considered as a simple routing protocol for opportunistic networks where the participants attempt to transmit whatever information they have to everyone who does not already have that in...Epidemic routing (Flooding) is considered as a simple routing protocol for opportunistic networks where the participants attempt to transmit whatever information they have to everyone who does not already have that information. However, it is plagued with disadvantages of resource scarcity as it exerts stress on available bandwidth as well as storage capacity of the devices in the network. Cognitive radio (CR) is one of the emerging technologies that can improve the bandwidth utilization by smart allocation of spectrum radio bands. Ideally speaking, a spectrum-aware cognitive radio is able to sense the local spectrum usage and adapt its own radio parameters accordingly. In this study, we have performed experiments to analyze the gains achieved by flooding protocol using cognitive radios of varying capabilities in opportunistic networks. We have performed experiments on three opportunistic networks obtained from real-life traces from different environments and presented results showing variance in delivery efficiency as well as cost incurred on those scenarios. Our results show that performance of flooding can be significantly improved using CRs in bandwidth-scarce environments;however, the improvement is not uniform with the increase in a number of available bands.展开更多
Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a devic...Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.展开更多
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 Hi-Tech Research and Development Program of China (2007AA01Z429,2007AA01Z405)the National Natural Science Foundation of China (60702059,60872041,11061035)
文摘This article puts forward a new scheme to control message redundancy efficiently in delay tolerant mobile Ad-hoc networks （MANET）. The class of networks generally lacks end-to-end connectivity. In order to improve the efficiency that messages are delivered successfully, multiple message copies routing protocols are usually used, but the network load is increased due to a large number of message redundancies. In the study, by using counter method, every node adds an encounter counter based on epidemic routing scheme. The counter records the number which the node encounters other nodes with the same message copy. If the counter of a node reaches tbe installed threshold, the node removes the copy. Theoretical analysis gives a lower bound of threshold in delay tolerant MANET. According to the lower bound of threshold, a rational threshold is installed in real environment. With proposed scheme message copies decrease obviously and are removed completely finally. The successful delivery efficiency is still the same as epidemic routing and the redundant copies are efficiently controlled to a relatively low level Computer simulations give the variation of message copies concerning different thresholds in fast and slow mobility scenes.
文摘Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.
基金supported in part by the National Natural Science Foundation of China(Nos.61502261,61572457,and 61379132)the Science and Technology Plan Project for Colleges and Universities o Shandong Province(No.J14LN85)
文摘In Delay Tolerant Networks （DTNs）, some routing algorithms ignore that most nodes are selfish, i.e., nodes are willing to use their own resources to forward messages to nodes with whom they have a relationship. In view of this phenomenon, we propose a routing algorithm based on Geographic Information and Node Selfishness （GINS）. To choose a forwarding node, GINS combines nodes＇ willingness to forward and their geographic information to maximize the possibility of contacting the destination. GINS formulates the message forwarding process as a 0-1 Knapsack Problem with Assignment Restrictions to satisfy node demands for selfishness. Extensive simulations were conducted, and results show that GINS can achieve a high delivery ratio and a lower hop count compared with GRONE and LPHU. Furthermore, its overhead ratio is 25% and 30% less than that of GRONE and LPHU, respectively.
基金supported by the National Natural Science Foundation of China (Nos. 61370192, 61432015, 61428203, and 61572347)the US National Science Foundation (Nos. CNS-1319915 and CNS-1343355)
文摘Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.
文摘Epidemic routing (Flooding) is considered as a simple routing protocol for opportunistic networks where the participants attempt to transmit whatever information they have to everyone who does not already have that information. However, it is plagued with disadvantages of resource scarcity as it exerts stress on available bandwidth as well as storage capacity of the devices in the network. Cognitive radio (CR) is one of the emerging technologies that can improve the bandwidth utilization by smart allocation of spectrum radio bands. Ideally speaking, a spectrum-aware cognitive radio is able to sense the local spectrum usage and adapt its own radio parameters accordingly. In this study, we have performed experiments to analyze the gains achieved by flooding protocol using cognitive radios of varying capabilities in opportunistic networks. We have performed experiments on three opportunistic networks obtained from real-life traces from different environments and presented results showing variance in delivery efficiency as well as cost incurred on those scenarios. Our results show that performance of flooding can be significantly improved using CRs in bandwidth-scarce environments;however, the improvement is not uniform with the increase in a number of available bands.
文摘Most of the existing opportunistic network routing protocols are based on some type of utility function that is directly or indirectly dependent on the past behavior of devices. The past behavior or history of a device is usually referred to as contacts that the device had in the past. Whatever may be the metric of history, most of these routing protocols work on the realistic premise that node mobility is not truly random. In contrast, there are several oracles based methods where such oracles assist these methods to gain access to information that is unrealistic in the real world. Although, such oracles are unrealistic, they can help to understand the nature and behavior of underlying networks. In this paper, we have analyzed the gap between these two extremes. We have performed max-flow computations on three different opportunistic networks and then compared the results by performing max-flow computations on history generated by the respective networks. We have found that the correctness of the history based prediction of history is dependent on the dense nature of the underlying network. Moreover, the history based prediction can deliver correct paths but cannot guarantee their absolute reliability.
