We formulate the problem of robots ferrying messages between statically-placed source and sink pairs that they can communicate with wirelessly. We first analyze the capacity region for this problem under ideal conditions. We indicate how robots could be scheduled optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival rate. We then consider the setting where the arrival rate is unknown and present a coarse-grained backpressure message ferrying algorithm (CBMF) for it. In CBMF, the robots are matched to sources and sinks once every epoch to maximize a queue-differential-based weight. The matching controls both motion and transmission for each robot. We show through analysis and simulations the conditions under which CBMF can stabilize the network, and its corresponding delay performance. From a practical point of view, we propose a heuristic approach to adapt the epoch duration according to network conditions that can improve the end-to-end delay while guaranteeing the network stability at the same time. We also study the structural properties with its explicit delay performance of the CBMF algorithm in a homogeneous network.

We consider the problem of deploying a team of robots in an unknown, obstructed environment to form a multi-hop communication network. As a solution, we present a unified framework, onLinE rObotic Network formAtion (LEONA), that is general enough to permit optimizing the communication network for different utility functions in non-convex environments. LEONA adopts the principle of “optimism in the face of uncertainty” to allow the team of robots to form optimal network configurations efficiently and rapidly without having to map link qualities in the entire area. We demonstrate and evaluate this framework on two specific scenarios concerning the formation of a multi-hop communication path between fixed end-points: one minimizing the total path cost, and another maximizing the bottleneck communication rate. Our simulation-based evaluation shows that the use of the optimism principle can significantly reduce resources spent in exploring and mapping the entire region prior to network optimization. We also present a mathematical modeling of how the searched area scales with various relevant parameters in each case.

In this paper, we propose a novel multi-lane model in Delay Tolerant Network (DTN). Different from traditional ad hoc network, DTN allows nodes to forward information in a store-and-carry manner so as to deal with the absence of end-to-end connectivity. Vehicles are classified into different categories according to their everyday routine, so called multi-lane. The main purpose is to decide a reliable next-hop forwarding procedure based on incomplete surrounding information. Furthermore, considering the searching interval during which nodes carry the information, we aim to explore the VANETs system performance under different network constraints like node density, velocity.

With the increasing demand of communication, it is of great necessity to maximize the utility of vehicular network. VANETs (Vehicular Ad-hoc Networks) especially in the context of high mobility attract more attention all over the world. Pure vehicle-to-vehicle(V2V) networks may relatively have long delay and worse performance compared to vehicle-to-infrastructure(V2I) networks, while V2V networks have larger communication area. Consequently, vehicular network integrated with 3G network is of significance. In these networks, gateways are generally used as interfaces between hybrid networks, thus gateway selection becomes the primary challenge. The most important thing considered in our work is that whenever infrastructures and 3G networks are considered, price is a core problem. The justice of caring the interests of all the vehicle nodes is going to be considered. In this paper, we consider the VANET and 3G connected model under cognitive situations, and then a price based on hierarchical strategy on the cooperative sensing and cluster head selection is proposed to ensure the maximum benefit of both the cluster heads and the cluster members.

This paper investigates the capacity, delay and energy consumption for MotionCast (a multicast mechanism for MANETs) with general Markovian mobility. We consider MotionCast in an extended cell partitioned network under a Markovian node mobility model and exactly compute the pernode throughput capacity. A Two hop relay algorithm is proposed to guarantee such capacity, which also achieves a better delay-capacity tradeoff, i.e., Θ(N log k). Considering that redundancy can significantly improve network delay, we present a Two hop relay algorithm with redundancy and study the general influence of redundancy on the capacity and delay of MotionCast network. Moreover, we use the minimum energy function to characterize the energy consumption for the MotionCast network. An accurate piecewise minimum energy function to keep network stable is derived. Furthermore, a simple Minimum energy algorithm is designed, which reduces actual energy consumption arbitrarily close to the minimum energy function at the cost of increasing delay. Our result shows that the proposed algorithm achieves the optimal energy-delay tradeoff.

In this paper, we define multicast for an ad hoc network through nodes' mobility as MotionCast and study the delay and capacity tradeoffs for it. Assuming nodes move according to an independently and identically distributed (i.i.d.) pattern and each desires to send packets to$k$distinctive destinations, we compare the delay and capacity in two transmission protocols: one uses 2-hop relay algorithm without redundancy; the other adopts the scheme of redundant packets transmissions to improve delay while at the expense of the capacity. In addition, we obtain the maximum capacity and the minimum delay under certain constraints. We find that the per-node delay and capacity for the 2-hop algorithm without redundancy are$\Theta(1/k)$and$\Theta(n\log k)$, respectively; for the 2-hop algorithm with redundancy, they are$\Omega(1/k\sqrt{n\log k})$and$\Theta(\sqrt{n\log k})$, respectively. The capacity of the 2-hop relay algorithm without redundancy is better than the multicast capacity of static networks developed by Li [IEEE/ACM Trans. Netw., vol. 17, no. 3, pp. 950–961, Jun. 2009] as long as$k$is strictly less than$n$in an order sense, while when$k=\Theta(n)$, mobility does not increase capacity anymore. The ratio between delay and capacity satisfies delay/rate$~~\geq O(nk\log k)$for these two protocols, which are both smaller than that of directly extending the fundamental tradeoff for unicast established by Neely and Modiano [IEEE Trans. Inf. Theory, vol. 51, no. 6, pp. 1917–1937, Jun. 2005] to multicast, i.e., delay/rate$~~\geq O(n k^2)$. More importantly, we have proved that the fundamental delay–capacity tradeoff ratio for multicast is delay/rate$~~\geq O(n\log k)$, which would guide us to design better routing schemes for multicast.