SIAM Journal on Computing
Mobility increases the capacity of ad hoc wireless networks
IEEE/ACM Transactions on Networking (TON)
The Critical Transmitting Range for Connectivity in Sparse Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
A message ferrying approach for data delivery in sparse mobile ad hoc networks
Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing
Exploiting mobility for energy efficient data collection in wireless sensor networks
Mobile Networks and Applications
The random trip model: stability, stationary regime, and perfect simulation
IEEE/ACM Transactions on Networking (TON)
Understanding the simulation of mobility models with Palm calculus
Performance Evaluation
On the connectivity of dynamic random geometric graphs
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Flooding time in edge-Markovian dynamic graphs
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
How to Explore a Fast-Changing World (Cover Time of a Simple Random Walk on Evolving Graphs)
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Adaptive redundancy for data propagation exploiting dynamic sensory mobility
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
Parsimonious flooding in dynamic graphs
Proceedings of the 28th ACM symposium on Principles of distributed computing
Information spreading in stationary Markovian evolving graphs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Modelling mobility: a discrete revolution
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Spatial node distribution of manhattan path based random waypoint mobility models with applications
SIROCCO'09 Proceedings of the 16th international conference on Structural Information and Communication Complexity
Modelling mobility: a discrete revolution
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Smooth movement and Manhattan path based Random Waypoint mobility
Information Processing Letters
Dynamic networks: models and algorithms
ACM SIGACT News
Modelling mobility: A discrete revolution
Ad Hoc Networks
Parsimonious flooding in geometric random-walks
DISC'11 Proceedings of the 25th international conference on Distributed computing
Information dissemination via random walks in d-dimensional space
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Information spreading in dynamic graphs
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
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We consider a Mobile Ad-hoc NETwork (MANET) formed by n agents that move at speed V according to the Manhattan Random-Way Point model over a square region of side length L. The resulting stationary (agent) spatial probability distribution is far to be uniform: the average density over the "central zone" is asymptotically higher than that over the "suburb". Agents exchange data iff they are at distance at most R within each other. We study the flooding time of this MANET: the number of time steps required to broadcast a message from one source agent to all agents of the network in the stationary phase. We prove the first asymptotical upper bound on the flooding time. This bound holds with high probability, it is a decreasing function of R and V, and it is tight for a wide and relevant range of the network parameters (i.e. L, R and V). A consequence of our result is that flooding over the sparse and highly-disconnected suburb can be as fast as flooding over the dense and connected central zone. Rather surprisingly, this property holds even when R is exponentially below the connectivity threshold of the MANET and the speed V is very low.