Rogert Wattenhofer

Geometric ad-hoc routing: of theory and practice

All too often a seemingly insurmountable divide between theory and practice can be witnessed. In this paper we try to contribute to narrowing this gap in the field of ad-hoc routing. In particular we consider two aspects: We propose a new geometric routing algorithm which is outstandingly efficient on practical average-case networks, however is also in theory asymptotically worst-case optimal. On the other hand we are able to drop the formerly necessary assumption that the distance between network nodes may not fall below a constant value, an assumption that cannot be maintained for practical networks. Abandoning this assumption we identify from a theoretical point of view two fundamentamentally different classes of cost metrics for routing in ad-hoc networks.

Worst-Case optimal and average-case efficient geometric ad-hoc routing

In this paper we present GOAFR, a new geometric ad-hoc routing algorithm combining greedy and face routing. We evaluate this algorithm by both rigorous analysis and comprehensive simulation. GOAFR is the first ad-hoc algorithm to be both asymptotically optimal and average-case efficient. For our simulations we identify a network density range critical for any routing algorithm. We study a dozen of routing algorithms and show that GOAFR outperforms other prominent algorithms, such as GPSR or AFR.

Does topology control reduce interference

Topology control in ad-hoc networks tries to lower node energy consumption by reducing transmission power and by confining interference, collisions and consequently retransmissions. Commonly low interference is claimed to be a consequence to sparseness of the resulting topology. In this paper we disprove this implication. In contrast to most of the related work claiming to solve the interference issue by graph sparseness without providing clear argumentation or proofs, we provide a concise and intuitive definition of interference. Based on this definition we show that most currently proposed topology control algorithms do not effectively constrain interference. Furthermore we propose connectivity-preserving an spanner constructions that are interference-minimal.

Analysis of a cone-based distributed topology control algorithm for wireless multi-hop networks

The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology control algorithm. This algorithm, introduced in [16], does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node <i>u</i> transmits with the minimum power <i>p<subscrpt>u, α</subscrpt></i> required to ensure that in every cone of degree α around <i>u</i>, there is some node that <i>u</i> can reach with power <i>p<subscrpt>u, α</subscrpt></i>. We show that taking α = 5π/6 is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from <i>s</i> to <i>t</i> when every node communicates at maximum power then, if α ⪇ 5π/6, there is still a path in the smallest symmetric graph <i>G</i><subscrpt>α</subscrpt> containing all edges (<i>u, v</i>) such that <i>u</i> can communicate with <i>v</i> using power <i>p<subscrpt>u, α</subscrpt></i>. On the other hand, if α > 5π/6, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.

Complexity in geometric SINR

In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NP-completeness proofs in such a model. In particular, we prove two problems to be NP-complete: Scheduling and One-Shot Scheduling. The first problem consists in finding a minimum-length schedule for a given set of links. The second problem receives a weighted set of links as input and consists in finding a maximum-weight subset of links to be scheduled simultaneously in one shot. In addition to the complexity proofs, we devise an approximation algorithm for each problem.

A cone-based distributed topology-control algorithm for wireless multi-hop networks

The topology of a wireless multi-hop network can be controlled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based distributed topology-control (CBTC) algorithm. This algorithm does not assume that nodes have GPS information available; rather it depends only on directional information. Roughly speaking, the basic idea of the algorithm is that a node u transmits with the minimum power p/sub u,/spl alpha// required to ensure that in every cone of degree /spl alpha/ around u, there is some node that u can reach with power p/sub u,/spl alpha//. We show that taking /spl alpha/=5/spl pi//6 is a necessary and sufficient condition to guarantee that network connectivity is preserved. More precisely, if there is a path from s to t when every node communicates at maximum power then, if /spl alpha//spl les/5/spl pi//6, there is still a path in the smallest symmetric graph G/sub /spl alpha// containing all edges (u,v) such that u can communicate with v using power p/sub u,/spl alpha//. On the other hand, if /spl alpha/>5/spl pi//6, connectivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic reconfiguration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations.

Dozer: ultra-low power data gathering in sensor networks

Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this longevity in unattended operation it is crucial to minimize energy consumption of the battery-powered sensor nodes. This paper proposes Dozer, a data gathering protocol meeting the requirements of periodic data collection and ultra-low power consumption. The protocol comprises MAC-layer, topology control, and routing all coordinated to reduce energy wastage of the communication subsystem. Using a tree-based network structure, packets are reliably routed towards the data sink. Parents thereby schedule precise rendezvous times for all communication with their children. In a deployed network consisting of 40 TinyOS- enabled sensor nodes, Dozer achieves radio duty cycles in the magnitude of 0.2%.

Constant-time distributed dominating set approximation

Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree Δ, our algorithm computes a dominating set of expected size O(kΔ2/k log Δ|DSOPT|) in O(k2) rounds where each node has to send O(k2Δ) messages of size O(logΔ). This is the first algorithm which achieves a non-trivial approximation ratio in a constant number of rounds.

Asymptotically optimal geometric mobile ad-hoc routing

In this paper we present AFR, a new geometric mobile ad-hoc routing algorithm. The algorithm is completely distributed; nodes need to communicate only with direct neighbors in their transmission range. We show that if a best route has cost c, AFR finds a route and terminates with cost &Ogr;(c2) in the worst case. AFR is the first algorithm with cost bounded by a function of the optimal route. We also give a tight lower bound by showing that any geometric routing algorithm has worst-case cost

Topology control meets SINR: the scheduling complexity of arbitrary topologies

Ogr;(c2). Thus AFR is asymptotically optimal. We give a non-geometric algorithm that also matches the lower bound, but needs some memory at each node. This establishes an intriguing trade-off between geometry and memory.