## 2 Related Work  
Routing strategies under delay tolerant scenarios have been explored by a number of research groups. The most basic and simple protocol is direct transmission, where data would only be delivered when sensors are in direct proximity of the sinks. That protocol has very little communication overhead, given that messages are only sent directly from the source sensor node to the sink node. However, depending on how frequently sensor nodes meet the sink nodes, the delivery of the data is very poor and the delivery ratio might be very low. This is particularly true if the sink nodes are very few and spread out. Another basic protocol is the flooding protocol [8], where a sensor always broadcasts the data messages in its buffer to all the nearby sensors, which receive the data messages, keep them in the buffer queue, and rebroadcast them. If the queue is large enough, the flooding protocol could achieve a low data delivery delay and high delivery ratio at the cost of more traffic overhead and energy consumption. However, the buffer size of sensor is usually limited, which results in many messages being dropped and frequent need for retransmission. The Shared Wireless Info-Station (SWIM) system is proposed in [9] for gathering information from radio-tagged whales, assuming that randomly moving sensors have the same probability of meeting the sink, and thus a sensor node needs only to distribute a number of copies of a packet to other nodes so as to attain the desired data delivery ratio. By considering the characteristics of DTMSNs, the ZebraNet project [10] proposes a history-based approach for routing. The routing decision here is made according to a sensor node's past success rate in transmitting data packets to the sink nodes directly. However, the data delivery ratio here is very low. The Data MULE project uses mobile nodes to collect data from sensors which are then delivered to a base station [11]. A mobile entity called data mule receives data from the nearby static sensors, temporarily store them, and drops off the data at the access points. However, they do not consider opportunistic forwarding between the mobile nodes. [PERSON] _et al._[12] used a combination of random walk and utility-based forwarding. Random walk is used until a node with a sufficiently high utility metric is found after which the utility metric is used to route to the destination node. [PERSON] _et al._[13] presented a virtual coordinate system where the node coordinates are composed of a set of probabilities, each representing the chance that a node will be found in a specific location. This information is then used to compute the best available route. Other endeavors aiming to enhance the performance of DTMSN routing include [14, 15]. In [14], a replication-based efficient data delivery (RED) protocol is presented. RED uses a history-based method like ZebraNet to calculate the delivery probabilities of sensor nodes. When message transmission occurs, the delivery probability of the source sensor increases; contrarily, if there is no transmission during a given interval, the delivery probability would decrease. However, in DTMSN scenarios where nodes are sparse and intermittently connected, it often takes a comparatively long time for a node to meet another which has a higher delivery probability. Therefore the updating frequency of the delivery probability in RED is usually low, and there may exist sensors which are far away from the sink but still have high delivery probabilities. Thus the history-based method is not effective and cannot denote the actual ability that a node delivers data to sink nodes. In addition, the message management of propagating many small messages in the network may incur further processing overhead and inefficiency of bandwidth utilization. In [15], the authors propose a FAD protocol to increase the data delivery ratio in DTMSN.