Harshavardhan Chenji

DistressNet: a wireless ad hoc and sensor network architecture for situation management in disaster response

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.

Cut Detection in Wireless Sensor Networks

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.

Secure neighbor discovery and wormhole localization in mobile ad hoc networks

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 communications. In this article 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, and a wormhole localization protocol, which allows nodes that detected the presence of a wormhole to determine wormholes location. To the best of our knowledge, this work is the first to secure neighbor discovery in mobile adhoc networks and to localize a wormhole. MSND leverages concepts of graph rigidity for wormhole detection. We prove security properties of our protocols, and demonstrate their effectiveness through extensive simulations and a real system evaluation employing Epic motes and iRobot robots.

Optimizing quality-of-information in cost-sensitive sensor data fusion

This paper investigates maximizing quality of information subject to cost constraints in data fusion systems. We consider data fusion applications that try to estimate or predict some current or future state of a complex physical world. Examples include target tracking, path planning, and sensor node localization. Rather than optimizing generic network-level metrics such as latency or throughput, we achieve more resource-efficient sensor network operation by directly optimizing an application-level notion of quality, namely prediction error. This is done while accommodating cost constraints. Unlike prior cost-sensitive prediction/regression schemes, our solution considers more complex prediction problems that arise in sensor networks where phenomena behave differently under different conditions, and where both ordered and unordered prediction attributes are used. The scheme is evaluated through real sensor network applications in localization and path planning. Experimental results show that non-trivial cost savings can be achieved by our scheme compared to popular cost-insensitive schemes, and a significantly better prediction error can be achieved compared to the cost-sensitive linear regression schemes.1

A wireless system for reducing response time in Urban Search & Rescue

Time is a critical factor in the Urban Search & Rescue operations immediately following natural and man-made disasters. Building on our collaboration with first responders we identify a set of areas for improving response times: victim detection in collapsed buildings, information storage and collection about buildings (collapsed or not), detection of first responder team separation and lost tools, and throughput and latency of data delivered to first responders. In this paper, we present the design (i.e., software/hardware architectures, and the guiding design principles), implementation and realistic evaluation of DistressNet, a system that targets the aforementioned areas for reducing the Urban Search & Rescue response time. DistressNet, built on COTS hardware and on open standards and protocols, pushes complexity that the very diverse Urban Search & Rescue scenarios pose, to user level applications (apps). Apps in DistressNet run on unmodified hardware ranging from smartphones, to motes and wireless routers. For the benefit of the research community, we also share some lessons learned during our experiences in the design, building and evaluation of DistressNet.

Secure Neighbor Discovery in Mobile Ad Hoc Networks

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 communications. IIn 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.

Mobile sensor network localization in harsh environments

The node localization problem in mobile sensor networks has recently received significant attention. Particle filters, adapted from robotics, have produced good localization accuracies in conventional settings, but suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio irregularity. We propose FuzLoc, a fuzzy logic-based approach for mobile node localization in challenging environments and formulate the localization problem as a fuzzy multilateration problem, with a fuzzy grid-prediction scheme for sparse networks. We demonstrate the performance and feasibility of our localization scheme through extensive simulations and a proof-of-concept implementation on hardware, respectively. Simulation results augmented by data gathered from our 42 node indoor testbed demonstrate improvements in the localization accuracy from 20%-40% when the radio irregularity is high.