Resource and performance tradeoffs in delay-tolerant wireless networks

Wireless and mobile network technologies often impose severe limitations on the availability of resources, resulting in poor and often unsatisfactory performance of the commonly used wireless networking protocols. For instance, power and memory/storage constraints of miniaturized network nodes reduce the throughput capacity and increase the network latency. Through various approaches and technological advances, researchers attempt to somehow compensate for such hardware limitations. However, this is not always necessary. Sometimes, the required performance of such networks does not need to adhere to the level of services that would be required for performance-critical applications. For example, for some applications of sensor networks, minimal latency is not a critical factor and it could be traded off for a more limited resource, such as energy or throughput. Such networks are termed delay-tolerant networks. Thus, to reduce the energy expenditure, transmission range of such sensor nodes would be quite short, leading to network topologies in which the average number of neighbors of the network nodes is very small. If the sensor nodes are mobile, then most of the time a node has <u>no</u> neighbors; only infrequently another node migrates into its neighborhood. This means that the classical networking approach of store-and-forward would not work well, as there is nearly never an intact path between a source and a destination. Several routing protocols have been proposed for this type of networking environment, one example is the Shared Wireless Infostation Model (SWIM), where a packet propagates through the network by being copied (rather than forwarded) from a node to a node, as links are sporadically created. The goal is that one of the copies of the packet reaches the destination. SWIM is an example of the way that non-critical performance could be traded off for insufficient resources, such as the tradeoffs between energy, delay, storage, capacity, and processing complexity. In this paper, we examine some of these tradeoffs, exposing the ways in which resources could be saved by compromising on the level of performance, as to satisfy the particular limitations of network technologies.