Efficient collection of sensor data via a new accelerated random walk

Efficient collection of sensor data via a new accelerated random walk

Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on Random Geometric Graphs. Random walks in wireless sensor networks can serve as fully local, lightweight strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. In most cases ...

Decentralized Random Walk-Based Data Collection in Networks

We analyze a decentralized random-walk based algorithm for data collection at the sink in a multi-hop sensor network. Our algorithm, Metropolis-Collect, which involves data packets being passed to random neighbors in the network according to a simple metropolis randomwalk mechanism, requires no configuration and incurs no routing overhead. To analyze this method, we model the data generation pr...

Accelerated collection of sensor data by mobility-enabled topology ranks

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local ...

Energy Efficient Compressed Sensing in Wireless Sensor Networks via Random Walk

Efficient Processing Node Proximity via Random Walk with Restart

Graph is a useful tool to model complicated data structures. One important task in graph analysis is assessing node proximity based on graph topology. Recently, RandomWalk with Restart (RWR) tends to pop up as a promising measure of node proximity, due to its proliferative applications in e.g. recommender systems, and image segmentation. However, the best-known algorithm for computing RWR resor...