Sensor Positionning using Spring Relaxation Algorithm (Oct. 2011)
Experimentations conducted on SensLab platforms. Here, the Spring Relaxation Algorithm (SRA) is based on Received Signal Strength Indicator (RSSI). Using RSSI as a distance metric involves errors in the measured values, resulting path-loss, fading, and shadowing effects. We underline the intrinsic limitations of RSSI as a distance metric, in terms of accuracy and stability. Contrary to what we assumed, collaborative localization protocol based on Spring-Relaxation algorithm can not smooth the distance-estimation errors obtained with RSSI measurements. The animation provided below presents a comparison between SRA based on close-to-real distance (left side) and SRA based on RSSI (right side).Video is available here:
- Sensor Positionning using Spring Relaxation Algorithm (real
distance VS RSSI)
Is RSSI a good metric for Wireless Sensor Networks ? (Oct. 2011)
Numerous localization protocols in Wireless Sensor Networks are based on Received Signal Strength Indicator. Because absolute positioning is not always available, localization based on RSSI is popular. More, no extra hardware is needed unlike solutions based on infra-red or ultrasonic. Moreover, the theory gives a RSSI as a function of distance. However, using RSSI as a distance metric involves errors in the measured values, resulting path-loss, fading, and shadowing effects. We present experimentation results from three large WSNs, each with up to 250 nodes. Based on our findings from the 3 systems, the relation between RSSI and distance is investigated according to the topology properties and the radio environment. We underline the intrinsic limitations of RSSI as a distance metric, in terms of accuracy and stability. Contrary to what we assumed, collaborative localization protocol based on Spring-Relaxation algorithm can not smooth the distance-estimation errors obtained with RSSI measurements.
Self-Stabilizing Small k-Dominating Sets (March. 2011)
A self-stabilizing algorithm, after transient faults hit the system and place it in some arbitrary global state, recovers in finite time without external (e.g., human) intervention. We propose a distributed asynchronous silent self-stabilizing algorithm for finding a minimal k-dominating set of at most ⌈n/k+1⌉ processes in an arbitrary identified network of size n. We propose a transformer that allows our algorithm work under an unfair daemon (the weakest scheduling assumption). The complexity of our solution is in O(n) rounds and O(Dn2) steps using O(log n+ k.log(n/k) ) bits per process where D is the diameter of the network.Qualitative Localization Protocol: QLoP (2008)
The QLoP's main idea is to provide a proximity index between node and
its neighbors based on topological information (1 and 2 hops
neighborhood) and not on physical measures (ToA, TDoA or RSSI).QLoP is used to provide robust routing and GPS-free topology control.
Sources WSNet: QLoP
Dynamic Behavior of Topology Control and Self-Organization Algorithms for Wireless Sensor Networks (2007)
Wireless Sensor Networks (WSN) are not static graphs. In my thesis, I highlight 3 differents phases on WSN's life: birth, working life and death. It's important to consider theses 3 phases when we develop and evaluate communication protocols for WSN.Sources WSNet
Sensors used