Varun Subramanian

Low-cost sound-based localization using programmable mixed-signal systems-on-chip

There have been extensive theoretical studies on sound-based localization using both, a pair of microphones and microphone arrays. In contrast, there has been much less work on implementing and experimenting sound-based localization realized as customized electronic designs. This paper presents a low-cost implementation of the sound-based localization method proposed in Halupka et al. 11]. A new method called wave counting is proposed in this paper, as an alternative to the Maximum Likelihood procedure used in 11]. The implementation uses PSoC programmable mixed-signal embedded system-on-chip, which incorporates microcontroller, on-chip SRAM and flash memory, programmable digital blocks, and programmable analog blocks, all integrated on the same chip. The paper presents an extensive set of experiments to characterize the quality of localization using the proposed low-cost design.

Online adaptation policy design for grid sensor networks with reconfigurable embedded nodes

This paper presents a systematic methodology for designing the adaptation policies of reconfigurable sensor networks. The work is motivated by the need to provide efficient sensing, processing, and networking capabilities under tight hardware, bandwidth, and energy constraints. The design flow includes two main steps: generation of alternative design points representing different performance-cost trade-offs, and finding the switching rates between the points to achieve effective adaptation. Experiments studied the scaling of the methods with the size of the networks, and the effectiveness of the produced policies with respect to data loss, latency, power consumption, and buffer space.

Towards a model and specification for visual programming of massively distributed embedded systems

Massively distributed embedded systems are rapidly emerging as a key concept for many modern applications. However, providing efficient and scalable decision making capabilities to such systems is currently a significant challenge. This paper proposes a model and a specification language to allow automated synthesis of distributed controllers, which implement and interact through formalisms of different semantics. The paper refers to a case study to illustrate the main capabilities of the proposed concept.

A goal-oriented programming framework for grid sensor networks with reconfigurable embedded nodes

Cyber-physical systems (CPS) are large, distributed embedded systems integrated with various sensors and actuators. CPS are rapidly emerging as an important computing paradigm in many modern applications. Developing CPS applications is currently challenging due to the sheer complexity of the related functionality as well as the broad set of constraints and unknowns that must be tackled during operation. This article presents a novel high-level programming model and the supporting optimization and middleware routines for executing applications on physically-distributed networks of reconfigurable embedded systems. The proposed model describes the optimization goals, sensing inputs, actuation outputs, events, and constraints of an application, while leaving to the compiler and execution environment the task of optimally implementing the derived functionality. Experimental results discuss the additional performance optimizations enabled by the proposed model, and the timing and power consumption of the middleware routines, and present a temperature monitoring application implemented on a network of reconfigurable, embedded processors.

Improved sound-based localization through a network of reconfigurable mixed-signal nodes

This paper presents a low cost implementation of the phase-based sound localization method proposed in Halupka et al [2]. The implementation uses PSoC programmable mixed-signal embedded System on Chip, which incorporates microcontroller, on-chip SRAM and flash memory, programmable digital blocks and programmable analog blocks, all integrated on the same chip. In order to improve the localization accuracy, filter corner frequency reconfiguration and gain reconfiguration is implemented. A wireless sensor network implementation is also presented. An extensive set of experiments are provided to explore the advantages of dynamic reconfigurability as well as the network implementation.

PNet: A Grid Type Sensor Network of Reconfigurable Nodes

This paper discusses a grid-type sensor network with reconfigurable PSoCs as sensor nodes, and details the middleware routines that support the high-level model for distributed programming. The advantages of using reconfigurable PSoCs over other architectures, like MICA2 motes, are also discussed. The paper refers to a case study to illustrate the capabilities of the proposed network concept.

Maximizing the accuracy of sound based tracking via a low-cost network of reconfigurable embedded nodes

This paper presents an approach for optimizing the accuracy of data models produced based on data sampled through a network of embedded sensors. The method considers three orthogonal facets defining model precision: minimizing the sampling error of the individual embedded nodes, sampling sufficient data from distributed areas to correctly represent the phenomenon of interest, and meeting the timing delays that guarantee the timeliness of data. The three objectives are achieved by dynamically reconfiguring the architecture of the embedded nodes, and dynamically selecting the data transfer paths to the decision making nodes. Sound based trajectory tracking is used as a case study for the proposed approach.

Decentralized detection and tracking of emergent kinetic data for wireless grids of embedded sensors

The paper proposes methods to detect and track emergent kinetic data representing clouds of physical entities, e.g., clouds of polluting gas, or clusters of autonomous agents, like robots or vehicles. Kinetic data are important entities in Cyber-Physical Systems as they express phenomena that are distributed in space and time, and are dynamic with respect to their characteristic attributes and lifetime. The main attributes of kinetic data are topography (position, boundary, and area), nature (signature), and dynamics (up to n-th order gradients). The related operators define the distribution (discretization) and composition (aggregation) of the attributes. The paper presents fully decentralized methods for optimized implementation of kinetic data on a grid network of wireless embedded sensing nodes. The efficiency of the algorithms is compared with similar methods proposed in the literature.

Linear programming approach for performance-driven data aggregation in networks of embedded sensors

Cyber Physical Systems are distributed systems-of-systems that integrate sensing, processing, networking and actuation. Aggregating physical data over space and in time emerges as an intrinsic part of data acquisition, and is critical for dependable decision making under performance and resource constraints. This paper presents a Linear Programming-based method for optimizing the aggregation of data sampled from geographically-distributed areas while satisfy timing, precision, and resource constraints. The paper presents experimental results for data aggregation, including a case study on gas detection using a network of sensors.

Linear Programming-Based Optimization for Robust Data Modeling in a Distributed Sensing Platform

Creating accurate data models describing the dynamics of physical phenomena in time and space is important in optimized control and decision making. Models highlight various trends and patterns. However, producing accurate models is challenging as different errors are introduced by sampling platforms with limited resources, e.g., insufficient sampling rates, data loss due to buffer overwriting, reduced communication bandwidth, and long communication delays. Furthermore, the dynamics of the environment, like mobile energy sources and sinks, might further increase errors as resources must be shared between the sampling and communication activities. This paper presents a procedure to systematically construct robust data models using samples acquired through a grid network of embedded sensing devices with limited resources, like bandwidth and buffer memory. Models are in the form of ordinary differential equations. The procedure constructs local data models by lumping state variables. Local models are then collected centrally to produce global models. The proposed modeling scheme uses a linear programming formulation to compute the lumping level at each node, and the parameters of the networked sensing platform, i.e., best data communication paths and bandwidths. Two algorithms are described to predict the trajectories of mobile energy sources/sinks as predictions can further reduce data loss and delays during communication. The computed parameters and trajectory predictions are used to configure the local decision making routines of the networked sampling nodes. Even though the procedure can be used to model a broader set of phenomena, experiments discuss the effectiveness of the method for thermal modeling of ULTRASPARC Niagara T1 architecture. Experiments show that the presented method reduces the overall error between 58.29% and 76.91% with an average of 68.87%, and communication delay between -11.49% and 57.62% with an average of 21.85%.