An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.

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Integrated coverage and connectivity configuration in wireless sensor networks

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multi-agent coordination, estimation in sensor networks, and large-scale machine learning. We develop and analyze distributed algorithms based on dual subgradient averaging, and we provide sharp bounds on their convergence rates as a function of the network size and topology. Our analysis allows us to clearly separate the convergence of the optimization algorithm itself and the effects of communication dependent on the network structure. We show that the number of iterations required by our algorithm scales inversely in the spectral gap of the network, and confirm this prediction's sharpness both by theoretical lower bounds and simulations for various networks. Our approach includes the cases of deterministic optimization and communication, as well as problems with stochastic optimization and/or communication.

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual nodes are equipped with local learning abilities. They derive local estimates for the parameter of interest and share information with their neighbors only, giving rise to peer-to-peer protocols. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. It improves performance in terms of transient and steady-state mean-square error, as compared with traditional noncooperative schemes. Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived, presenting a very good match with simulations.

Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

The engineering of genetic circuits with predictive functionality in living cells represents a defining focus of the expanding field of synthetic biology. This focus was elegantly set in motion a decade ago with the design and construction of a genetic toggle switch and an oscillator, with subsequent highlights that have included circuits capable of pattern generation, noise shaping, edge detection and event counting. Here we describe an engineered gene network with global intercellular coupling that is capable of generating synchronized oscillations in a growing population of cells. Using microfluidic devices tailored for cellular populations at differing length scales, we investigate the collective synchronization properties along with spatiotemporal waves occurring at millimetre scales. We use computational modelling to describe quantitatively the observed dependence of the period and amplitude of the bulk oscillations on the flow rate. The synchronized genetic clock sets the stage for the use of microbes in the creation of a macroscopic biosensor with an oscillatory output. Furthermore, it provides a specific model system for the generation of a mechanistic description of emergent coordinated behaviour at the colony level.

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A synchronized quorum of genetic clocks

https://people.eecs.berkeley.edu/~prabal/pubs/papers/OSU-CISRC-1203-TR71.pdf

A line in the sand: a wireless sensor network for target detection, classification, and tracking

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality.

/pdf/detection-classification-and-tracking-of-targets-1zzfzsdctr.pdf

Detection, classification, and tracking of targets

We outline a framework for collaborative signal processing in distributed sensor networks. The ideas are presented in the context of tracking multiple moving objects in a sensor field. The key steps involved in the tracking procedure include event detection, target classification, and estimation and prediction of target location. Algorithms for various tasks are discussed with an emphasis on classification. Results based on experiments with real data are reported which provide useful insights into the essential nature of the problems. Issues, challenges and directions for future research are identified.

/pdf/detection-classification-and-tracking-of-targets-in-4ve69xfjxg.pdf

Detection, Classification and Tracking of Targets in Distributed Sensor Networks

A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays inversely with respect to the square of distance from the source. By comparing energy readings measured at surrounding acoustic sensors, the source location during that time interval can be accurately estimated as the intersection of multiple hyperspheres. Theoretical bounds on the number of sensors required to yield unique solution are derived. Extensive simulations have been conducted to characterize the performance of this method under various parameter perturbations and noise conditions. Potential advantages of this approach include low intersensor communication requirement, robustness with respect to parameter perturbations and measurement noise, and low-complexity implementation.

/pdf/energy-based-collaborative-source-localization-using-47egg7y8zt.pdf

Energy-based collaborative source localization using acoustic microsensor array

A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays exponentially with respect to the distance from an omni-directional acoustic source. By comparing energy readings measured at surrounding acoustic sensors during the same time interval can be accurately estimated. We show that the potential target location is restricted to a hyper-sphere in the sensor field given the acoustic energy reading at a pair of sensors. Given multiple sensor acoustic energy readings, the target location is solved as the location that is closest (in the least square sense) to all the corresponding hyper-spheres. We further simplified this nonlinear least square problem to an unconstrained linear least square problem that yields a closed form solution. Experiment results using military vehicle acoustic data show great promise of this novel approach.

/pdf/energy-based-collaborative-source-localization-using-40t73d6n7n.pdf

Energy based collaborative source localization using acoustic micro-sensor array

In this paper, a context-based human target detection and position estimation algorithm, as well as a position calibration algorithm based on radar irradiation angle are proposed to improve the positioning accuracy, which is limited by the sparse and easily submerged characteristics of the point cloud generated by millimeter-wave radar, which leads to difficulty in achieving high-precision positioning. Furthermore, an indoor target positioning and tracking system is built using 77 GHz millimeter-wave radar to verify the proposed algorithms. The experimental results indicate that the proposed algorithms can improve the positioning accuracy both in single-person and multi-person positioning scenarios, with median positioning errors 8.7 cm (36.7% decrease) and 12.95 cm (average) respectively. Therefore, the proposed sensing method is considered as a very promising technique for designing a high precision human trajectory tracking and positioning radar system.

Dan Li

Papers

Detection, classification, and tracking of targets

Detection, Classification and Tracking of Targets in Distributed Sensor Networks

Energy-based collaborative source localization using acoustic microsensor array

Energy based collaborative source localization using acoustic micro-sensor array

Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar