Kennie H. Jones

Training a wireless sensor network

The networks considered in this paper consist of tiny energy-constrained commodity sensors massively deployed, along with one or more sink nodes providing interface to the outside world. Our contribution is to propose a scalable energy-efficient training protocol for nodes that are initially anonymous, asynchronous and unaware of their location. Our training protocol imposes a flexible and intuitive coordinate system onto the deployment area and partitions the anonymous nodes into clusters where data can be gathered from the environment and synthesized under local control. An important by-product of the training protocol is a simple and natural data fusion protocol as well as an energy-efficient protocol for routing data from clusters to the sink node. Being energy-efficient, our training protocol can be run on either a scheduled or ad-hoc basis to provide robustness and dynamic reconfiguration. We also outline a way of making the training protocol secure by using a parameterized variant of frequency hopping.

On providing anonymity in wireless sensor networks

Securing wireless sensor networks against denial of service attacks that disrupt communications or target nodes serving key roles in the network, e.g. sinks or routers, is instrumental to network availability and performance. Particularly vulnerable to these attacks are the components of any communications or operation infrastructure in the network. In this paper, we address a class of wireless sensor networks where network protocols leverage a dynamic general-purpose virtual infrastructure; the core components of that infrastructure are a coordinate system, a cluster structure, and a routing structure. Since knowledge of this virtual infrastructure enables smart cost-effective DOS attacks on the network, maintaining the anonymity of the virtual infrastructure is a primary security concern. The main contribution of this work is to propose an energy-efficient protocol for maintaining the anonymity of the network virtual infrastructure. Specifically, our solution defines schemes for randomizing communications such that the coordinate system, cluster structure, and routing structure remain invisible to an external observer of network traffic during the setup phase of the network.

Engineering Antifragile Systems: A Change In Design Philosophy

Abstract While technology has made astounding advances in the last century, problems are confronting the engineering community that must be solved. Cost and schedule of producing large systems are increasing at an unsustainable rate and these systems often do not perform as intended. New systems are required that may not be achieved by current methods. To solve these problems, NASA is working to infuse concepts from Complexity Science into the engineering process. Some of these problems may be solved by a change in design philosophy. Instead of designing systems to meet known requirements that will always lead to fragile systems at some degree, systems should be designed wherever possible to be antifragile: designing cognitive cyber-physical systems that can learn from their experience, adapt to unforeseen events they face in their environment, and grow stronger in the face of adversity. Several examples are presented of on ongoing research efforts to employ this philosophy.

Biology Inspired Approach for Communal Behavior in Sensor Networks

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Energy usage in biomimetic models for massively-deployed sensor networks

Promises of ubiquitous control of the physical environment by sensor networks open avenues that will redefine the way we live and work. Due to the small size and low cost of sensors, visionaries promise smart systems enabled by deployment of huge numbers of sensors working in concert. At the moment, sensor network research is concentrating on developing techniques for performing simple tasks with minimal energy expense, assuming some form of centralized control. Centralized control does not scale to large networks and simple tasks in small-scale networks will not lead to the sophisticated applications predicted. Recently, the authors have proposed a new way of looking at sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Here we demonstrate that in such a model, fully distributed data aggregation can be performed efficiently, without synchronization, in a scalable fashion, where individual motes operate autonomously based on local information, cooperating with neighbors to make local decisions that are aggregated across the network achieving globally-meaningful effects.

Communal Cooperation in Sensor Networks for Situation Management

Situation management is a rapidly evolving science where managed sources are processed as realtime streams of events and fused in a way that maximizes comprehension, thus enabling better decisions for action. Sensor networks provide a new technology that promises ubiquitous input and action throughout an environment, which can substantially improve information available to the process. Here we describe a program of NASA that requires improvements in sensor networks and situation management. We present an approach for massively deployed sensor networks that does not rely on centralized control but is founded in lessons learned from the way biological ecosystems are organized. In this approach, fully distributed data aggregation and integration can be performed in a scalable fashion where individual motes operate based on local information, making local decisions that achieve globally-meaningful effects. This exemplifies the robust, fault-tolerant infrastructure required for successful situation management systems

Biology-Inspired distributed consensus in massively-deployed sensor networks

Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks open avenues for new applications that are expected to redefine the way we live and work. Most of recent research has concentrated on developing techniques for performing relatively simple tasks in small-scale sensor networks assuming some form of centralized control. The main contribution of this work is to propose a new way of looking at large-scale sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Indeed, we believe that techniques used in small-scale sensor networks are not likely to scale to large networks; that such large-scale networks must be viewed as an ecosystem in which the sensors/effectors are organisms whose autonomous actions, based on local information, combine in a communal way to produce global results. As an example of a useful function, we demonstrate that fully distributed consensus can be attained in a scalable fashion in massively deployed sensor networks where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects.

Emergent adaptive noise reduction from communal cooperation of sensor grid

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant.

Sensor networks for situation management: a biomimetic model

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications in support of situation management. Recent sensor network research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to situation management as it allows single points of failure and does not scale to massive size networks. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in our model fully distributed data aggregation and integration can be performed in a scalable fashion where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. This exemplifies the robust, fault-tolerant infrastructure required for successful situation management systems

Communal sensor network for adaptive noise reduction in aircraft engine nacelles

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.