Reputation Based Tracking in Sensor Networks

Tanya Roosta, Marci Meingast, Shankar Sastry

TRUST, Washington, D.C. Meeting January 9–10, 2006

Motivation for Reputation Based Tracking

•Reputation systems have proven useful as a self-policing mechanism to address the threat of compromised entities

•Reputation Systems have been used in online transaction systems, such as Ebay rating system

•CORE and CONFIDANT are two protocols in wireless ad hoc networks that use reputation systems for data transmission

•Reputation system framework can be applied to sensor network applications, such as multi-object tracking

TRUST, Washington, D.C. Meeting January 9–10, 2006

Data Fusion (cont.)

•The node with the highest signal strength reading declares itself the leader in its neighborhood

•The leader locally fuses its neighbors’ observations using the following equation:

•Each leader then sends the fused observation from its neighborhood back to the super-node closest to itself

TRUST, Washington, D.C. Meeting January 9–10, 2006

Hierarchical Multi-object Tracking

•Assumptions:

•There are regular sensor nodes scattered throughout the deployment area

•There are a few super-nodes that are computationally more powerful than regular nodes

•Both types of nodes are static

•The number of objects moving in the network is not known apriory

TRUST, Washington, D.C. Meeting January 9–10, 2006

Hierarchical Multi-object Tracking

•The algorithm has two phases:

•Data Fusion (Local)

•Data Association (global)

•Data Fusion component:

•Takes care of aggregating the sensor observations

•The sensor observation is modeled as:

TRUST, Washington, D.C. Meeting January 9–10, 2006

Data Association Phase

•Once the super-node has received the fused observations from each local area in the region it governs, data association needs to be performed

•This is accomplished by applying Markov Chain Monte Carlo to the fused observations and linking the data points together to form tracks

•Black circles are the fused observations

•Figure (b) shows the data and track association

TRUST, Washington, D.C. Meeting January 9–10, 2006

Attack Model

•Sensor nodes are usually deployed in hostile environments and are unattended

•They can be physically captured and compromised

•The adversary can use the compromised nodes to inject faulty data into the network to throw off the tracking algorithm

•The faulty readings can affect both the number of formed tracks and the accuracy of each track

•We propose using a reputation system at the data fusion portion of the tracking to take care of malicious nodes who do not claim to be the leader

TRUST, Washington, D.C. Meeting January 9–10, 2006

Reputation System (cont.)

•Each node has instantaneous positive and negative reputations that gets updated when it sends in an observation:

•These values are used to update to overall positive and negative ratings:

TRUST, Washington, D.C. Meeting January 9–10, 2006

Reputation System

•The reputation of each node is a continuous value in the interval [0,1]

•Every node has a reputation table for its neighbors which is initialized to 0.5

•Every time a node becomes the leader it updates the reputations of its neighbors that send him an observation

•The leader updates the reputations by running a RANSAC-like procedure on the observations and finding the median of the calculated values

TRUST, Washington, D.C. Meeting January 9–10, 2006

Simulation Results

•Estimated object track compared to ground truth without the reputation system

•Estimated object track compared to ground truth with the reputation system