Olivier Powell

Energy optimal data propagation in wireless sensor networks

We propose an algorithm to compute the optimal parameters of a probabilistic data propagation algorithm for wireless sensor networks (WSN). The probabilistic data propagation algorithm we consider was introduced in previous work, and it is known that this algorithm, when used with adequate parameters, balances the energy consumption and increases the lifespan of the WSN. However, we show that in the general case achieving energy balance may not be possible. We propose a centralized algorithm to compute the optimal parameters of the probabilistic data propagation algorithm, and prove that these parameters maximize the lifespan of the network even when it is not possible to achieve energy balance. Compared to previous work, our contribution is the following: (a) we give a formal definition of an optimal data propagation algorithm: an algorithm maximizing the lifespan of the network. (b) We find a simple necessary and sufficient condition for the data propagation algorithm to be optimal. (c) We constructively prove that there exists a choice of parameters optimizing the probabilistic data propagation algorithm. (d) We provide a centralized algorithm to compute these optimal parameters, thus enabling their use in a WSN. (e) We extend previous work by considering the energy consumption per sensor, instead of the consumption per slice, and propose a spreading technique to balance the energy among sensors of a same slice. The technique is numerically validated by simulating a WSN accomplishing a data monitoring task and propagating data using the probabilistic data propagation algorithm with optimal parameters.

Simple and efficient geographic routing around obstacles for wireless sensor networks

Geographic routing is becoming the protocol of choice for many sensor network applications. The current state of the art is unsatisfactory: some algorithms are very efficient, however they require a preliminary planarization of the communication graph. Planarization induces overhead and is thus not realistic for some scenarios such as the case of highly dynamic network topologies. On the other hand, georouting algorithms which do not rely on planarization have fairly low success rates and fail to route messages around all but the simplest obstacles. To overcome these limitations, we propose the GRIC geographic routing algorithm. It has absolutely no topology maintenance overhead, almost 100% delivery rates (when no obstacles are added), bypasses large convex obstacles, finds short paths to the destination, resists link failure and is fairly simple to implement. The case of hard concave obstacles is also studied; such obstacles are hard instances for which performance diminishes.

Localization algorithm for wireless ad-hoc sensor networks with traffic overhead minimization by emission inhibition

Widely used positioning systems like GPS are not a valid solution in large networks with small size, low cost sensors, due both to their size and their cost. Thus, new solutions for localization awareness are emerging, commonly based on the existence of a few references spread into the network. We propose a localization algorithm to reduce the number of transmitting nodes. The algorithm relies on self selecting nodes for location information disclosure. Each node makes a decision based on its proximity to the nodes in the area covered only by two of the references used for its own localization. We analyze different aspects of the location awareness propagation problem: communication overhead, redundant transmissions, network coverage.

Efficient Tracking of Moving Targets by Passively Handling Traces in Sensor Networks

We study the important problem of tracking moving targets in wireless sensor networks. We try to overcome the limitations of standard state of the art tracking methods based on continuous location tracking, i.e. the high energy dissipation and communication overhead imposed by the active participation of sensors in the tracking process and the low scalability, especially in sparse networks. Instead, our approach uses sensors in a passive way: they only record and judiciously spread information about observed target presence in their vicinity; this information is then used by the (powerful) tracking agent to locate the target by just following the traces left at sensors. Our protocol is greedy, local, distributed, energy efficient and very successful, in the sense that (as shown by extensive simulations) the tracking agent manages to quickly locate and follow the target; also, we achieve good trade-offs between the energy dissipation and latency.

Trustworthily Forwarding Sensor Networks Information to the Internet

The Internet is soon going to be extended with the information collected from sensor networks deployed in wild remote regions of the world. For example, sensors may be dispersed in the jungle and forward information about the sensed states of the natural ecosystem, such as, humidity, fire detection... However, it is still quite easy for attackers to disconnect the sensors network from the Internet. For example, the sensors usually forward their messages to a base station, the Internet gateway, in a hop-by-hop fashion because they are resource-constrained in terms of energy, the spending of energy dramatically increases with the range of transmission and the attackers may capture intermediate sensors and drop messages rather than forwarding them. In this paper we study how computational trust can be used to mitigate the issue of sinkhole attacks and evaluate our approach on top of the MIX protocol.

A note on measuring in P

We revisit the problem of generalising Lutzs resource-bounded measure (RBM) to small complexity classes, and propose a definition of a random-based RBM on P=∪k∈N DTIME (O(nk)), which we argue as being a good generalisation to P of Lutzs RBM. We cannot unconditionally prove the existence of such a measure, but we give sufficient and necessary conditions for its existence. We also revisit µτ, an RBM for P defined by Strauss [Inform. Comput. 136(1) (1997) 1], and correct an erroneous claim concerning the relations between µτ and random sets. A correction to this mistake is then proposed, which is a less powerful but accurate relation between µτ and random sets.In order to obtain these results, we introduce a mathematical structure called a measuring system, which is a general setting that can be used to compare different RBMs on any fixed complexity class through a partial ordering relation.