Harshavardhan Chenji's research while affiliated with Texas A&M University and other places

A wireless sensor network can get separated into multiple connected components due to the failure of some of its nodes, which is called a “cut.” In this paper, we consider the problem of detecting cuts by the remaining nodes of a wireless sensor network. We propose an algorithm that allows 1) every node to detect when the connectivity to a specially designated node has been lost, and 2) one or more nodes (that are connected to the special node after the cut) to detect the occurrence of the cut. The algorithm is distributed and asynchronous: every node needs to communicate with only those nodes that are within its communication range. The algorithm is based on the iterative computation of a fictitious “electrical potential” of the nodes. The convergence rate of the underlying iterative scheme is independent of the size and structure of the network. We demonstrate the effectiveness of the proposed algorithm through simulations and a real hardware implementation.

Neighbor discovery is an important part of many protocols for wireless adhoc networks, including localization and routing. When neighbor discovery fails, communications and protocols performance deteriorate. In networks affected by relay attacks, also known as wormholes, the failure may be more subtle. The wormhole may selectively deny or degrade com-munications. In this paper we present Mobile Secure Neighbor Discovery (MSND), which offers a measure of protection against wormholes by allowing participating mobile nodes to securely determine if they are neighbors. To the best of our knowledge, this work is the first to secure neighbor discovery in mobile adhoc networks. MSND leverages concepts of graph rigidity for wormhole detection. We prove security properties of our protocol, and demonstrate its effectiveness through extensive simulations and a real system evaluation employing Epic motes and iRobot robots.

In this paper we consider the problem of monitoring detecting separation of agents from a base station in robotic and sensor networks. Such separation can be caused by mobility and/or failure of the agents. While separation/cut detection may be performed by passing messages between a node and the base in static networks, such a solution is impractical for networks with high mobility, since routes are constantly changing. We propose a distributed algorithm to detect separation from the base station. The algorithm consists of an averaging scheme in which every node updates a scalar state by communicating with its current neighbors. We prove that if a node is permanently disconnected from the base station, its state converges to $0$. If a node is connected to the base station in an average sense, even if not connected in any instant, then we show that the expected value of its state converges to a positive number. Therefore, a node can detect if it has been separated from the base station by monitoring its state. The effectiveness of the proposed algorithm is demonstrated through simulations, a real system implementation and experiments involving both static as well as mobile networks.

Situational awareness in a disaster is critical to effective response. Disaster responders require timely delivery of high volumes of accurate data to make correct decisions. To meet these needs, we present DistressNet, an ad hoc wireless architecture that supports disaster response with distributed collaborative sensing, topology-aware routing using a multichannel protocol, and accurate resource localization. Sensing suites use collaborative and distributed mechanisms to optimize data collection and minimize total energy use. Message delivery is aided by novel topology management, while congestion is minimized through the use of mediated multichannel radio protocols. Estimation techniques improve localization accuracy in difficult environments.

We propose a distributed algorithm to detect ¿cuts¿ in sensor networks, i.e., the failure of a set of nodes that separates the networks into two or more components. The algorithm consists of a simple iterative scheme in which every node updates a scalar state by communicating with its nearest neighbors. In the absence of cuts, the states converge to values that are equal to potentials in a fictitious electrical network. When a set of nodes gets separated from a special node, that we call a ¿source node¿, their states converge to 0 because ¿current is extracted¿ from the component but none is injected. These trends are used by every node to detect if a cut has occurred that has rendered it disconnected from the source. Although the algorithm is iterative and involves only local communication, its convergence rate is quite fast and is independent of the size of the network.