The Research On Data Forwarding Problem In Opportunistic Network

Wireless ad hoc network is a kind of network that does not rely on a preexisting infrastructure and is self-organized in a distributed way by a group of nodes which communicate directly with each other to exchange information. With many advantages, such as flexible networking, rapid expandability and distributed control, wireless ad hoc network gains extensive research attention and has been developed rapidly. However, in many practical application environments, due to nodal mobility, low density, lossy link and so on, wireless ad hoc network often suffers from intermittent connectivity and network partition, therefore the conventional communication model of ad hoc network can not work well in these scenarios. To meet the communication requirements in such intermittently connected networks, opportunistic network is proposed. As a kind of special wireless ad hoc network, opportunistic network does not require the existence of a connected path from a message's source node to its destination node, and utilizes the communication opportunities arising from node movement to forward messages, which makes it more suitable for practical applications. The emergence of opportunistic network greatly expands the information transmission in both spatial and temporal scales, and is significant for the future implementation of ubiquitous computing.Data forwarding is a fundamental issue in opportunistic network, and almost all network applications are based on it. However, due to the link disconnection and the network partition, it has been a challenging research problem to achieve effective data forwarding in opportunistic network. Furthermore, opportunistic network has broad prospects for many application fields and different applications have different features, so opportunistic network is closely related to the specific scenarios, and it is very hard to find a general data forwarding scheme which is suitable for most realistic applications. Confronted with this problem, this dissertation focuses on two special forms of opportunistic network, Delay Tolerant Mobile Sensor Network (DTMSN) and Vehicular Ad hoc Network (VANET), and studies the data forwarding problem based on a systematical summary of the relevant works. The major contributions are as follows:1. Based on the intermittent connectivity of DTMSN, a novel Relative Distance-Aware Data delivery scheme (RDAD) is proposed. Utilizing the high-power broadcasting of the sink, RDAD can gain the relative distance from a node to the sink with small overhead, and then calculates the nodal delivery probability which provides guidance for message transmission. RDAD also employs the message survival time and the maximal replication to decide a message's transmission priority and the dropping principle for minimizing transmission overhead. Simulation results show that, compared with other DTMSN data delivering approaches, RDAD achieves not only a higher message delivery ratio with lower transmission overhead and data delivery delay, but also a relatively long network lifetime.2. According to the nodal mobility, this dissertation proposes an efficient Motion Tendency-based Adaptive Data delivery scheme (MTAD) tailored for DTMSN. Since the movement of a node changes its relative distance to the sink, a node's data forwarding ability is affected by its movement. Using the high-power broadcasting of the sink, MTAD obtains the relative distance from a node to the sink with small overhead, and further calculates the information about the nodal motion tendency (including the moving speed and direction). The information is then used to evaluate the node's effective delivery ability which provides guidance for message transmission. Simulation results show MTAD achieves good performance for data delivery and get a long network lifetime.3. This dissertation proposes a Travel Prediction-based Data forwarding scheme (TPD) for VANET, in which vehicles share their trajectory information in exchange of low delay and high reliability of data delivery in multi-hop carry-and-forward environments. The existing data forwarding algorithms forward messages mainly utilizing statistic information about road network traffic (e.g., traffic density and road section average speed), which becomes much less accurate when a VANET becomes sparse and thus the performance reduces. To solve this problem, TPD employs microscopic information about trajectories to predict pair-wise encounter probabilities and constructs a vehicular encounter graph, based on which an optimization is performed to guide message forwarding. Therefore, TPD could provide more accurate data forwarding than other schemes, and is less affected by the uncertainty of traffic statistics when the traffic density is low. Extensive simulations show that TPD acheves a higher data delivery ratio with a lower delivery delay than the existing algorithms.4. Based on the VADD algorithm, a new Delay Evaluation-based data Forwarding scheme (DEF) is proposed for VANET. DEF utilizes the road traffic statistics and a vehicle's driving direction to evaluate its expected delivery delay, which is used to select the best next-hop forwarding vehicle for data transmission. Simulation results demonstrate that DEF achieves better performance than VADD.