Sensor Networks

A wireless ad hoc sensor network consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data.  Some examples of wireless ad hoc sensor networks are the following:

1. Military sensor networks to detect and gain as much information as possible about enemy movements, explosions, and other phenomena of interest.
2. Wireless traffic sensor networks to monitor vehicle traffic on highways or in congested parts of a city.
3. Wireless surveillance sensor networks for providing security in shopping malls, parking garages, and other facilities.
4. Wireless parking lot sensor networks to determine which spots are occupied and which are free.

Our Research

Comparing Reputation Schemes for Detecting Malicious Nodes in Sensor Networks

Remotely deployed sensor networks are vulnerable to both physical and electronical security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. We assume the sensor nodes are organized in a hierarchy and use offline neural network based learning technique to predict the sensed data at any node given the data of its siblings. This allows us to detect malicious nodes even when the siblings are not sensing data from the same distribution. The speed of detection of compromised nodes, however, critically depends on the mechanism used to update the reputation of the sensor nodes over time. We compare and contrast the relative strengths of a statistically grounded scheme and a reinforcement learning based scheme both for their robustness to noise and responsiveness to change in sensor behavior. We first extend an existing mechanism to improve detection capability for smaller errors. Next we analyze the influence of different discount factors, including unweighted, exponential, and linear discounts, to study the tradeoff between responsiveness and robustness. We both develop a theoretical analysis to understand the tradeoff and perform experimental verification of our predictions by varying the number of compromised nodes, network size, and patterns in sensed data.

Robust Trust Mechanisms for Monitoring Aggregator Nodes in Sensor Networks

Sensor nodes are often deployed in large numbers to monitor extended sensor fields. In such scenarios data aggregation plays a crucial role on summarizing data forwarded to base stations in sensor networks. As sensor networks are commonly deployed in open and unattended areas, they are vulnerable to physical tampering as well as remote attacks. Existing security techniques focus primarily on methods used by an aggregator node to monitor and detect compromised behavior by nodes that report data to it. We propose a novel mechanism, combining techniques from statistics and artificial intelligence, by which nodes reporting to an aggregator node can monitor whether the latter is reporting incorrect aggregated values. In our framework, nodes are arranged in a hierarchy. We develop a reputation management scheme in which each child node keeps a reputation value of its parent. For each data reporting event, a node is privy only to the data value it reported and the aggregated value forwarded by its parent node. Each node then calculates the probability of the parent reporting correctly aggregated values over an epoch of events by adapting a statistical hypothesis testing scheme. This probability is used to incrementally update the trustworthiness of the parent node using a learning scheme. When the trustworthiness of a node falls below a threshold it can no longer be trusted with the aggregation task and should be reallocated or eliminated from the network. We evaluate the robustness of our adaptation of a couple of statistical hypothesis testing schemes and analyze their applicability for different types of malicious behaviors by compromised sensor nodes.