We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.
Some effort has also been devoted to characterize forwarding paths. @cite_4 state that the structure of mobility networks is in general characterized by a small diameter, i.e., a device can be reached using a small number of relays. This is shown analytically for random graphs, and empirically based on data from conference deployments. Based on this observation, the authors introduce an efficient algorithm to compute the delay-optimal path between nodes that exploits the small-world character of the underlying mobility network. @cite_28 investigate message forwarding in conference settings and characterize optimal paths in time and space. They find that these paths, while optimal, may take a very long time to reach the destination (thousands of seconds), and report a so-called path explosion phenomenon'', i.e., that shortly after the optimal path reaches the destination, a large number of nearly-optimal paths does the same.
@cite_28 @cite_4
2105273407 2148899275 2145834571 2166406195
A stochastic model is introduced that accurately models the message delay in mobile ad hoc networks where nodes relay messages and the networks are sparsely populated. The model has only two input parameters: the number of nodes and the parameter of an exponential distribution which describes the time until two random mobiles come within communication range of one another. Closed-form expressions are obtained for the Laplace-Stieltjes transform of the message delay, defined as the time needed to transfer a message between a source and a destination. From this we derive both a closed-form expression and an asymptotic approximation (as a function of the number of nodes) of the expected message delay. As an additional result, the probability distribution function is obtained for the number of copies of the message at the time the message is delivered. These calculations are carried out for two protocols: the two-hop multicopy and the unrestricted multicopy protocols. It is shown that despite its simplicity, the model accurately predicts the message delay for both relay strategies for a number of mobility models (the random waypoint, random direction and the random walker mobility models). Forwarding in Delay Tolerant Networks (DTNs) is a challenging problem. We focus on the specific issue of forwarding in an environment where mobile devices are carried by people in a restricted physical space (a conference) and contact patterns are not predictable. We show for the first time a path explosion phenomenon between most pairs of nodes. This means that, once the first path reaches the destination, the number of subsequent paths grows rapidly with time, so there usually exist many near-optimal paths. We study the path explosion phenomenon both analytically and empirically. Our results highlight the importance of unequal contact rates across nodes for understanding the performance of forwarding algorithms. We also find that a variety of well-known forwarding algorithms show surprisingly similar performance in our setting and we interpret this fact in light of the path explosion phenomenon. Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations. Intermittently connected mobile networks are wireless networks where most of the time there does not exist a complete path from source to destination, or such a path is highly unstable and may break soon after it has been discovered. In this context, conventional routing schemes would fail. To deal with such networks we propose the use of an opportunistic hop-by-hop routing model. According to the model, a series of independent, local forwarding decisions are made, based on current connectivity and predictions of future connectivity information diffused through nodes' mobility. The important issue here is how to choose an appropriate next hop. To this end, we propose and analyze via theory and simulations a number of routing algorithms. The champion algorithm turns out to be one that combines the simplicity of a simple random policy, which is efficient in finding good leads towards the destination, with the sophistication of utility-based policies that efficiently follow good leads. We also state and analyze the performance of an oracle-based optimal algorithm, and compare it to the online approaches. The metrics used in the comparison are the average message delivery delay and the number of transmissions per message delivered.