This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTCtime synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99% with an overall system uptime of about 93.8% over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

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Sensor networks are typically sensor or radio event driven. Exploiting this property we propose a novel wake on sensor network design. In this context we have designed a new sensor platform called TelosW. The wake-on sensing capability of TelosW lets designated sensors wake up the microcontroUer(MCU) only on occurrence of some event with preconfigurable threshold. TelosW also includes the CCllO} [3]Wake-On Radio (WOR) hardware that performs low power listening without intervention of MCU. These all lead to a completely event driven wake-on sensor network that reduces energy consumption considerably. TelosW is also equipped with an on-board energy meter that can precisely measure in-situ energy consumption. Using the energy meter it is possible to get the insight of energy states of nodes in a network at any time. This makes it possible to practically analyze energy-efficient protocols. The experiments show that the energy consumption has been significantly reduced comparing to same application without wake-on design.Index Terms-TelosW, energy meter, wake-on radio, wake-on sensor. I. INT RODUCT IONFig. l. TelosW platform S IGNIFICANT advancement in wireless communication and microelectronic technologies have revealed the great potential of Wireless Sensor Networks (WSN). Wireless Sen sor Networks have been used for variety of applications such as scientific exploration [16], infrastructure protection [18], surveillance [10], assisted living [17] etc. Despite its research, development and deployment through years, there are a num ber of open issues towards achieving its full potential. Energy efficiency is a key goal of wireless sensor networks design as it decides its efficiency, lifetime, performance etc. ThereforeManuscript it has been a major goal to minimize the energy consumption of individual node, as well as the network as a whole for collective operations.A typical nature of sensor networks is that the most of the activities are event driven. But in current sensor networks, the sensing and communication components are even powered on during significant portion of idle time. This obviously leads to a large amount of undesirable wastage of energy. These all have motivated us to utilize the event driven properties of sensor networks into sensing and communication, that leads to significant energy savings. In this perspective we propose a novel wake-on wireless sensor network, enabled with a hardware platform which supports wake-on capability in sensing and communication.Proper knowledge of in-situ energy consumption is a crucial factor towards informed decision making in wireless sensor networks. Towards the novel effort to measure detailed energy consumption on sensor node for free (at almost no cost of extra energy), the work by Dutta et al. [7] proposes an energy meter hardware design. This free energy metering can be utilized at scale in a distributed environment of sensor network. This will give the valuable insight into the states of the nodes in a network of any size. In this perspective we have used t...

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Abstract-In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion datastream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity; (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories; FindingHumo applies the following techniques on the collected motion datastream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.

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Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we present a distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs real-time tomography inversion under the constraints of network resources. We implemented and evaluated the system design in the CORE emulator. The experiment results validate that our proposed algorithm not only balances the computation load, but also achieves low communication cost and high data loss-tolerance. 1