Sensor 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 servo-mechanisms 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 next paper in this section uses distributed annealing algorithms to govern the purposeful motion of nodes. Unlike most ad hoc wireless networks (e.g., cellular networks) that must adapt to the mobility needs of their users, mission-oriented sensor networks incorporate purposeful mobility. Adaptation to environmental and operational disturbances requires resource bounded optimal response. The severe power and processing constraints on sensor network operations call for energy-aware task execution and the pragmatic use of mobility to ensure that mission goals are accomplished.

Sensitivity to mobility-induced location errors is evaluated by Son, Helmy, and Krishnamachari. They provide empirical algorithms to mitigate the resulting problems. Again, this addresses the pragmatic use of mobility to reliably accomplish mission objectives.

Continuous self-organization of distributed sensors may be needed for area coverage. Ravelomanana, in his paper, formulates fundamental characteristics of design of ad hoc networks that analyze critical transmitting/sensing ranges for connectivity and coverage in three-dimensionsal networks.

Chen, Hou, and Sha develop dynamic clustering algorithms for target tracking. The large number of nodes deployed precludes manual configuration in an operational field or in buildings and homes. Cerpa and Estrin develop self-configuring algorithms for network topologies. Ma and Ayler address system lifetime optimization for in-home networks through an optimal architecture and a resource bounded protocol.

These papers cover major research issues for sensor networks to provide a perceptive infrastructure for dependable data collection for human interpretation. In harnessing the true potential of networked sensors, this is a necessary first step. In order to autonomously execute complex adaptive missions while comprehending and adapting to the dynamics of harsh and often unknown physical environments, these tiny distributed devices must collectively comprehend the time evolution of physical phenomena and their effect on mission execution to close the distributed feedback control loop. To thus endow esprit de corps on isolated computational electro-mechanical devices, more is needed. For example, in Fig. 1, the acoustic signal emanating from a set of target vehicles in a noisy environment may be denoised and collaboratively processed by a network of acoustic sensors using Dynamic Space-time Clustering and Beamforming techniques [¹], [²]. Signal partitioning may be used to determine and predict the individual random mobility patterns of each targeted vehicle. However, a higher level of comprehension of mission goals is needed for the sensor network to understand and predict coordinated movement in formation, a behavior that may be of significantly greater interest to the execution of the mission. If the sensor network must act as the eyes and ears of humans, allowing them to stay at a safe distance, it must dependably comprehend the criticality of its sensor perceptions and responses to mission execution and convey these proficiently to humans for time critical interaction. There is simply no time for humans to receive and analyze a data sheet plotting locations, speed, and direction of movements of individual vehicles and to infer and deter movement in formation.

The next phase of sensor networks research calls for the confluence of computational sciences with physical sciences and with decision and control sciences. Physical sciences model the nonlinear dynamics of physical phenomena. Sensor networks, as distributed dynamic systems, must comprehend and predict the effects of emerging phenomena on mission execution and actuate control actions to successfully execute mission specifications. Prior to deployment, sensor networks need to be endowed with distributed high-level representations of mission specifications that can be dynamically executed by harnessing the collective powers of distributed sensor/actuator nodes in unknown or uncertain environments. Research challenges abound. Advances in symbolic dynamics are needed to identify atomic physical events in sensor data that capture the causal dynamics of the underlying nonlinear processes and abstract event sequences that associate the time evolution of these processes to mission specifications at various levels of fidelity. Advances in nonlinear dynamic systems modeling and control of distributed multitime scale processes are needed to enable individual sensors to comprehend the higher level dynamics and to respond to global changes. Collaborative intelligent inference is necessary to circumvent limitations of sensor data, communications, and equipment faults. Emergent behaviors and phase transitions need to be modeled, predicted, and controlled. These dynamically self-reconfigurable and introspective networks of mobile sensor nodes must be capable of understanding and interpreting mission objectives and adapting their behaviors. Sensor networking technology as a true extension of ourselves—the eyes and ears in the field—calls for a collective intelligence that comprehends the distributed images and sounds to ascertain executable action and actuation.

Shashi Phoha

Shashi Phoha is a professor of electrical engineering and head of the Information Science and Technology Division of the Applied Research Laboratory at Pennsylvania State University. Her research focuses on information sciences which bring together ideas from logic, operations research, formal languages, computational mechanics, and control theory for the scientific analysis of distributed information generated by interacting processes in complex dynamic systems. Prior to 1991, she held senior technical and management positions in industry. She is the principal investigator of the Multidisciplinary University Research Initiative on Surveillance Sensor Networks funded by DARPA and the project director of the Complex Systems Failures MURI funded by ARO. On these and previous research programs, she has extended hybrid control theory to behavior-based dynamical control of distributed systems that consist of interacting electromechanical and computational devices. She has developed real-time data driven cognitive control-actuation algorithms that adapt to nonlinearities, phase transitions, and chaos characteristics. She pioneered the notion of control languages for specifying behavior-based multitiered decision and control mechanisms for interacting agents. She is an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and an editor of the International Journal of Distributed Sensor Networks. She has acted on various program committees of IEEE conferences and symposia. Since 1998, she has been on the board of directors of Autonomous Undersea Vehicle Technology Consortium for International Cooperation between Research, Technology, Industry and Applications. She chaired the Springer-Verlag Technical Advisory Board for the Dictionary of Internet Security, published in May 2002.