Ensuring strong data guarantees in highly mobile ad hoc networks via quorum systems

Abstract

Ensuring the consistency and the availability of replicated data in highly mobile ad hoc networks is a challenging task because of the lack of a backbone infrastructure. Previous work provides strong data guarantees by limiting the motion and the speed of the mobile nodes during the entire system lifetime, and by relying on assumptions that are not realistic for most mobile applications. We provide a small set of mobility constraints that are sufficient to ensure strong data guarantees and that can be applied when nodes move along unknown paths and speed, and are sparsely distributed.In the second part of the paper we analyze the problem of conserving energy while ensuring strong data guarantees, using quorum system techniques. We devise a condition necessary for a quorum system to guarantee data consistency and data availability under our mobility model. This condition shows the unsuitability of previous quorum systems and is the basis for a novel class of quorum systems suitable for highly mobile networks, called Mobile Dissemination Quorum (MDQ) systems. We also show a MDQ system that is provably optimal in terms of communication cost by proposing an efficient implementation of a read/write atomic shared memory.The suitability of our mobility model and MDQ systems is validated through simulations using the random waypoint model and the restricted random waypoint on a city section. Finally, we apply our results to assist routing and coordinate the low duty cycle of mobile nodes while maintaining network connectivity.

Revising Quorum Systems for Energy Conservation in Sensor Networks

“…Several solutions based on geographic information have been proposed to improve the efficiency of data diffusion [12,7]. Recently, Tulone [9] has proposed a novel class of quorum systems suitable for highly mobile ad hoc networks. However, the focus of that work is on mobility and not on low-power sensor networks.…”

“…For simplicity, we group sensors that are within their radio broadcast via focal points [3], however other cluster strategies can be employed. More specifically, we rely the implementation of focal points proposed in [9], which requires only one sensor node per focal point to transmit messages on behalf of it. Note that this approach not only conserves energy, but also improves the network bandwidth and reduces collisions.…”

“…We address that in our definition of load balance, which does not refer to sensor nodes but to focal points. This improves the load balance of the system since only one sensor per focal point is in charge of transmitting a message [9]. Therefore, the load of each sensor node in a focal point i never exceeds the load of a focal point i.…”

“…Our choice of tolerating up to one third of faulty focal points favors the robustness of the system versus its energy consumption (note that faulty nodes mask both sensor failures and the often not uniform distribution of sensors in a geographic area). However, we can reconcile fault-tolerance and efficiency by choosing a quorum system with high resiliency (e.g., Q d ) and dynamically adapting the estimated number f of maximum failures as discussed in [9]. More precisely, nodes start with a small quorum size and increase it as the number of failures that have been detected or notified by other nodes increases.…”

Geo-registers: An Abstraction for Spatial-Based Distributed Computing

“…In mobile environments, a few solutions have been proposed to implement such shared objects. In [1,3], atomic memory implementations for mobile ad hoc networks are presented. Both approaches differ from the one presented in this paper because their aim is to build a register maintained by a set of geographic regions while our aim is to build a register in a given geographic region.…”