This paper presents a sequential learning framework for sensors in a network, where a few sensors assume the role of an instructor to train other sensors in the network. The instructors provide estimated labels for measurements of new sensors. These labels are possibly noisy, because a classifier of the instructor may not be perfect. A recursive density estimator is proposed to obtain the true measurement model (i.e., the observation density conditioned on the label) in spite of the training with noisy labels. Specifically, this paper answers the question "Can a sensor train other sensors?", provides necessary conditions for sensors to act as instructors, presents a sequential learning framework using recursive nonparametric kernel density estimation, and provides a convergence rate for the expected error in an observation density. The underlying concepts are illustrated and validated with simulation results.

In this paper, we propose a dynamically run-time reconfigurable power aware cryptographic processor for secure autonomous encryption. The design proposes the implementation of a dynamically reconfigurable AES cryptography process on an FPGA. The proposed design encompasses a microarchitecture which is variously power, latency, and throughput optimized via hardware acceleration and partial reconfiguration by a multi-level autonomic controller and a data router to enable tradeoffs under changing operational requirements within resource constraints. The multi-level controller decides on the appropriate configuration based on varying operational workloads to characterize the effect that time-varying task parameters have on the hardware architecture, to enable a run-time tradeoff of performance and resources usage (Key length, computational efficiency, latency and throughput).

Mobility management protocols allow wireless devices to move between networks. These protocols have traditionally supported the mobility of individual nodes and are therefore not optimized to support the migration of groups. Accordingly, the time required to re-establish connectivity, frequency of dropped packets and contention for the air interface increase significantly for mobile groups. We propose a protocol for mobile groups that reduces all of the above by allowing a single node to perform handoffs on behalf of all group members. This "gateway" node eliminates the need for multiple handoff messages by obscuring group membership to external parties. Through extensive simulation and implementation, we show significant reduction in handoff times, message complexity and packet loss for groups of heterogeneous, mobile sensors running AODV and DSDV. By leveraging the naturally occurring hierarchy, we demonstrate that it is possible for groups to efficiently use traditional mobility protocols to support their collective movements.

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course

We describe a distributed sensor network test-bed and a surveillance experiment to demonstrate the integration of distributed tracking algorithms with strategies for location estimation, energy management and mobility management of sensor nodes. The test-bed consists of real sensor nodes augmented with a simulated environment. Data from real world tracking is provided to the simulated environment, where it is used to self-organize the sensor network in an energy-efficient way. Results from the simulation are then fed back to the real world to enable the sensor network to reorganize for reinforced tracking.

We present a new paradigm to control software systems based on the supervisory control theory (SCT). Our method uses the SCT to model the execution of a software application by restricting the actions of the OS with little or no modifications in the underlying OS. Our approach can be generalized to any software application as the interactions of the application with the OS are modeled at a process level as a deterministic finite state automaton (DFSA) termed as a "plant." A "supervisor" that controls the plant is a DFSA synthesized from a set of control specifications. The supervisor operates synchronously with the plant to restrict the language accepted by the plant to satisfy the control specifications. Using the above method of control to mitigate faults, as a proof-of-concept, we implement two supervisors under the Redhat Linux 7.2 OS to mitigate overflow and segmentation faults in five different programs. We quantify the performance of the unsupervised and supervised plant by using a language measure and give methods to compute the measure using state transition cost matrix and characteristic vector.

ENSOR Networks represent a new frontier in technology that promises to push traditional computation beyond the digital abstractions of cyberspace to interact with the real world in human timeframes. Miniature computational devices, often embedded in mobile wireless platforms, interact directly with the physical world, cognizant of a common mission, spanning time and space to monitor changes in the operational environment, and collaborating to actuate distributed tasks in dynamic and uncertain environments. While once the fascinating stuff of science fiction, sensor networks are rapidly becoming the reality that captivates the imagination of researchers and practitioners to enable inexpensive devices to act as numerous eyes and ears of soldiers in surveying a hostile battlefield from a safe distance or to track bio/chemical plumes in the environment for homeland security. Mobile robots with embedded sensor systems explore the surface of Mars and integrated systems of undersea robots are being designed to develop high fidelity nowcasts and forecasts of the ocean through time-space coordinated sampling or to hunt for mines or handle hazardous materials. In general, the next phase of automation calls on networks of sensors to take on the dull, dirty, and dangerous functions of human interest, and to accomplish them with the perception and adaptation of humans and in collaboration with humans. Sensors of physical phenomena with integrated servomechanisms have been commonplace throughout the latter half of the 20th century, controlling thermostats and valves, monitoring flow or adapting to changes in pressure or stress, and providing alarms for fire or flooding. They have been expected to perform these and many other localized isolated tasks with precision and reliability. The distinction of present day demands on sensor networks is in the comprehensive perception of locally sensed changes in the physics of the environment and adaptive time-space coordinated activity of individual servo-mechanisms in support of a common mission. This special section deals with recent advances in the study of Sensor Networks as interacting autonomous mobile sensor nodes. The objective is to address the engineering design issues for achieving dependable performance through dynamic distributed collaboration of many inexpensive, low reliability sensors with limited sensing and communication ranges. Advances in integrated wireless communications, fast servo-controlled sensors/actuators, and micro and nano technologies have together enabled inexpensive devices, often on mobile platforms, to be air dropped or deployed in unknown or dynamic environments. These devices are expected to self-organize and form ad hoc networks to continuously survey a battlefield for enemy targets over long periods of time, conserving precious resources unless some enemy activity is detected. Upon detection, the nodes form dynamic clusters to localize and track enemy targets. Traditional programming, computation, communication, and control techniques must all advance to comprehend the distributed dynamics of the environment and actuate a timely response. Research papers in this section address design trade offs for situation awareness, adaptive and dependable infrastructure, and coordinated inference in mission-oriented mobile sensor networks. The first paper by Bergamo, Asgari, Wang, Maniezzo, Yip, Hudson, Yao, and Estrin solves the far field acoustic source localization problem through beamforming. Waveforms originating at a given source are used by a set of spatially separated acoustic sensors to localize the source through time synchronized estimates of direction of arrival. Experiments in free space and reverberant scenarios demonstrate the power of very low cost devices to achieve sophisticated space-time operation in real-time.

The severe power, time and processing constraints on ad hoc wireless sensor networks for area surveillance require in-situ adaptations to conserve resources and optimize performance. In particular, it may be necessary to make dynamic tradeoffs between centralized processing algorithms, like beamforming, and knowledge based distributed processing algorithms like dynamic space-time clustering (DSTC) that rely on local processing of raw sensor data. Beamforming methods can achieve high levels of accuracy in estimating direction of arrival with a sound wave even when the source is in the far field. Hence accurate localization can be achieved with a relatively sparse sensor network. However, beamforming has severe limitations when the number of nodes increases. It requires orders of magnitude higher energy for transporting the entire time series over the network. DSTC methods, on the other hand, work well when the number of nodes is large because clusters can be formed within a smaller space-time window. This work examines the operational domains of the two centralized and distributed algorithms by analyzing sources of error, dependence on sensor density, sensor geometries, energy usage, control logic for data processing and the effects of network topology on the two algorithms. Based on this analysis, we develop hybrid algorithms that take advantage of the operational characteristics of each one in designing a high performance sensor network.

This paper presents a system architecture for behavior-based collaborative multi-agent systems in the discrete event setting following the Ramadge and Wonham framework. It addresses the issues of robustness to component failures, reliability of wireless communications, scalability to increase in the number of agents, and quantitative analysis of system performance at different levels of control hierarchy. The mission objectives are achieved by hierarchically structured supervisory control. The standard supervisory control theory is extended in the sense that the event alphabet is made a function of the plant parameters. The supervisory control system allows interactions with external agents including human operators. A proof-of-the-concept control architecture is experimentally validated by a wireless mobile robotic system consisting of three pioneer 2 AT mobile robots. This concept could be extended to other distributed systems provided that the continuous-varying dynamics of the underlying physical process is decoupled with the discrete state space of the supervisory control system.

The Applied Research Laboratory Penn State University "Ocean SAmpling MObile network" (SAMON) Project is developing the simulation testbed for the oceanographic communities interactions through the Web interface and the simulation based design of Autonomous Ocean Sampling Program missions. In this paper, a current implementation of the SAMON is presented, and a formal model based on interactive automata is described. The basic model is extended by process algebra constructs to handle mobility, evolution and learning. To allow cooperation of heterogeneous vehicles a generic behavior message-passing language is presented.