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to, but it may be the only choice in the foreseeable future. In such a case, node A has to decide whether to forward the data or bet its luck and wait for the next opportunity. Optimistic forwarding proposed in reference 31 (discussed later) is used in IBRR. To select the next hop, a node needs to know not only the present and future connectivity relation with its current time, but also the same information of its current neighbors. This one-hop look-ahead is necessary for making routing decisions in the relay based routing framework. Look-ahead beyond one hop can prove to be time-consuming and counterproductive. To select the next hop, a node needs to evaluate the potential candidates and select the most promising forwarding node and forward the data to that node. The selection is based on the following metrics: r r r r r r Spatial location and orbital information of the candidate nodes Bandwidth of the intersatellite link to the candidate nodes Relative velocity/mobility between two nodes Vicinity of this candidate to other satellites and ground stations Capability of the candidate satellites Data transmission time
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The authors propose to use interrogation where the nodes do not transmit any hello messages except to initialize a session between two neighboring nodes. After the initialization, the IBRR protocol proceeds to exchange orbital and routing information between nodes in the form of queries and responses and may or may not continue with actual data transmission. Instead of discovering the entire path to the destination, a node puts more effort into acquiring information about the immediate neighbors and that of the neighbors neighbors (one-hop look-ahead). The best next-hop candidates for node A are nodes that have sent out the most number of replies to node A s beacons. In the simulations, the authors choose the number of best next hops to forward for each node to be 3 without giving any reason. In ZebraNet [11], wireless sensor nodes, namely collars (attached to Zebras), collect location data and opportunistically report their histories when they come in radio range of base stations, or the researchers or data collection objects, which periodically drive through (or y-over) with receivers to collect data. Collars operate on batteries with/without solar recharge. The goal is to study the animal behaviors through designing a collar and communication protocols that works on Zebras (high data collection rate). They study two routing protocols: ood-based routing protocols and history-based protocols. In the ood-based routing protocol, data are ooded to their neighbors whenever they meet. It is expected that as nodes move extensively and meet a number of neighbors, given enough time, data will eventually reach to the base station. In the history-based routing protocol, each node is assigned a likelihood of transferring data to the base station based on its past success. A higher value corresponds to a higher probability of eventually being within the range of the base station. Data are forwarded to its neighbor with the highest transferring probability. Experimental results indicate that the ood-based protocol yields higher
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system throughput if the buffer capacity at each node is large enough, but the energy consumed by the ood-based protocol can be eight times that of the historybased protocol. There is a tradeoff between throughput and energy consumption. Their conclusion is that while ooding makes sense at low-radio-range and lowconnectivity points in the design space, it is not a good choice in a high-connectivity regime. Per Contact Routing Based on Average End-to-End Performance Metrics. In the protocols reviewed in the previous section, decisions about forwarding packets (when and where) are based on the likelihood of the delivery of each neighbor. No end-to-end performance is considered. In this section, we review three protocols in which decisions are based on end-to-end performance: one on the probability to deliver to the destination, one on the expected shortest path to the destination, and one on the average end-to-end delay. Extending the previous work in reference 25, the meets and visits (MV) protocol [33] uses the same exchange scheme as in reference 25, but presents a new way to estimate the likelihood of forwarding. MV learns the frequency of meetings between nodes and visits to certain regions. The past frequencies are used to rank each bundle according to the likelihood of delivering a bundle through a speci ed path. MV k determines a probability, Pn (i), that the current node, k, can successfully deliver a bundle to a region i within n transfers. The probability is estimated by the following formula, assuming an in nite buffer at each node and N being the number of nodes in the network,
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