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The following topics are dealt with: information processing in sensor networks; sensor network hardware and software systems; wireless sensor networks; advances in distributed consensus and inference; spatial summarization and tracking; secure networking; acoustic localization, underground structure monitoring, and bridge monitoring.
The key application scenario of wireless sensor networks is data gathering: sensor nodes transmit data, possibly in a multi-hop fashion, to an information sink. The performance of sensor networks is thus characterized by the rate at which information can be aggregated to the sink. In this paper, we derive the first scaling laws describing the achievable rate in worst-case, i.e. arbitrarily deployed,...
Desynchronization is a novel primitive for sensor networks: it implies that nodes perfectly interleave periodic events to occur in a round-robin schedule. This primitive can be used to evenly distribute sampling burden in a group of nodes, schedule sleep cycles, or organize a collision-free TDMA schedule for transmitting wireless messages. Here we present Desync, a biologically-inspired self-maintaining...
We propose modeling environmental noise in order to efficiently and accurately simulate wireless packet delivery. We measure noise traces in many different environments and propose three algorithms to simulate noise from these traces. We evaluate applying these algorithms to signal-to-noise curves in comparison to existing simulation approaches used in EmStar, TOSSIM, and ns2. We measure simulation...
Most currently deployed sensor networks use the same channel to communicate information among nodes. This is a source of great inefficiency as it poorly utilizes the available wireless spectrum. This paper takes advantage of radio capabilities of MicaZ motes that can communicate on multiple frequencies as specified in the 802.15.4 standard. We consider the case of a data collection sensor network...
Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio- acoustic data has traditionally been a difficult and time- consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deploy- able acoustic sensor platforms presents opportunities...
A fundamental problem in wireless sensor networks is localization - the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or...
Sensor location information is a prerequisite to the utility of most sensor networks. In this paper we present a robust and low-complexity algorithm to self-localize and orient sensors in a network based on angle-of-arrival (AOA) information. The proposed non-iterative subspace-based method is robust to missing and noisy measurements and works for cases when sensor orientations are either known or...
Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a structure-aware self-adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground...
This paper considers the problem of blindly calibrating sensor response using routine sensor network measurements. We show that as long as the sensors slightly oversample the signals of interest, then unknown sensor gains can be perfectly recovered. Remarkably, neither a controlled stimulus nor a dense deployment is required. We also characterize necessary and sufficient conditions for the identification...
In this paper we consider the problem of transmitting quantized data while performing an average consensus algorithm. Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. The main motivation for our work is the observation that consensus algorithms offer the perfect example of network communications where there is an...
We study the problem of performing sensor fusion and distributed consensus in networks, where the objective is to calculate some linear function of the initial sensor values at some or all of the sensors. We utilize a linear iteration where, at each time-step, each sensor updates its value to be a weighted average of its own previous value and those of its neighbors. We show that this approach can...
Large-scale sensor network applications require in-network processing and data fusion to compute statistically relevant summaries of the sensed measurements. This paper studies distributed message-passing algorithms, in which neighboring nodes in the network pass local information relevant to a global computation, for performing statistical inference. We focus on the class of reweighted belief propagation...
In this paper, we introduce the "Plug" sensor network, a ubiquitous networked sensing platform ideally suited to broad deployment in environments where people work and live. The backbone of the Plug sensor network is a set of 35 sensor-, radio-, and computation-enabled power strips distributed throughout the third floor of the MIT Media Lab. A single Plug device fulfills all the functional...
We present mPlatform, a new reconfigurable modular sensornet platform that enables real-time processing on multiple heterogeneous processors. At the heart of the mPlatform is a scalable high- performance communication bus connecting the different modules of a node, allowing time-critical data to be shared without delay and supporting reconfigurability at the hardware level. Furthermore, the bus allows...
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