Section: New Results

Topology control and neighbor discovery

Participants : Xu Li, Nathalie Mitton, Jovan Radak, David Simplot-Ryl, Isabelle Simplot-Ryl.

Topology control

Topology control is a tool for self-organizing wireless networks locally. It allows a node to consider only a subset of links/neighbors in order to later reduce computing and memory complexity. Topology control in wireless sensor networks is an important issue for scalability and energy efficiency. It is often based on graph reduction performed through the use of Gabriel Graph or Relative Neighborhood Graph. This graph reduction is usually based on geometric values.

In [35] we tackle the problem of possible connectivity loss in the reduced graph by applying a battery level based reduction graph. Experiments are conducted to evaluate our proposition. Results are compared with RNG  [52] reduction which takes into account only the strength of the received signal (RSSI). Results show that our algorithm maintains network connectivity longer than solutions from the literature and balances the energy consumption over nodes.

In [31] , we propose a radically new family of geometric graphs, i.e., Hypocomb, Reduced Hypocomb and Local Hypocomb for topology control. The first two are extracted from a complete graph; the last is extracted from a Unit Disk Graph (UDG). We analytically study their properties including connectivity, planarity and degree bound. All these graphs are connected (provided the original graph is connected) planar. Hypocomb has unbounded degree while Reduced Hypocomb and Local Hypocomb have maximum degree 6 and 8, respectively. To our knowledge, Local Hypocomb is the first strictly-localized, degree-bounded planar graph computed using merely 1-hop neighbor position information. We present a construction algorithm for these graphs and analyze its time complexity. Hypocomb family graphs are promising for wireless ad hoc networking. We report our numerical results on their average degree and their impact on FACE  [49] routing. We discuss their potential applications and some open problems.

Neighbor discovery

To perform topology control, a node needs to discover its neighbors. Hello protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/aliveness by periodic "hello" messages. Central to a hello protocol is the determination of hello message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In [30] , we propose a novel mobility prediction based hello protocol, named ARH (Autoregressive Hello protocol). Each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same model. The node transmits "hello" message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the only competitive protocol TAP (Turnover based Adaptive hello Protocol). With a small model order, ARH achieves the same high neighborhood discovery performance as TAP, with dramatically reduced message overhead (about 50% lower hello rate).

Address allocation

In [9] , we propose a localized address autoconfiguration (LaConf) scheme for wireless ad hoc networks. Address allocation information is maintained on the network border nodes, called addressing agents (AAs), which are locally identified by a geographic routing protocol GFG (Greedy-FACE-Greedy). When a node joins the network, it acquires an address from a neighboring AA (if any exists) by local communication or from the head AA (a geographic extreme AA) by GFG-based multi-hop communication. A Geographic Hash Table (GHT) is adopted for duplicate address detection. Each address is hashed to a unique location in the network field, and the associated assignment information is stored along the face perimeter enclosing that location (in the planar graph). When a node receives an address assignment, it consults with the perimeter nodes around the hash location of the assigned address about any conflicts. AAs detects network partitions and mergers locally according to neighborhood change and triggers AA re-selection and network re-configuration (if necessary). We propose to apply a Connected Dominating Set (CDS) to improve the performance. We also evaluate LaConf through simulation using different planar graphs.