2193838
Scaling into ambient intelligence
Envision the situation that high quality information and entertainment is easily accessible to anyone, anywhere, at any time, and on any device. How realistic is this vision? And what does it require from the underlying technology? Ambient Intelligence (AmI) integrates concepts ranging from ubiquitous computing to autonomous and intelligent systems. An AmI environment will be highly dynamic in many aspects. Underlying technology must be very flexible to cope with this dynamism. The scalability of technology is only one crucial aspect. This paper explores scalability from the processing, the communication, and the software perspectives.
5682640
A dual-space approach to tracking and sensor management in wireless sensor networks
Wireless ad hoc sensor networks have the advantage of spanning a large geographical region and being able to collaboratively detect and track non-local spatio-temporal events. This paper presents a dual-space approach to event tracking and sensor resource management in sensor networks. The dual-space transformation maps a non-local phenomenon, e.g., the edge of a half-plane shadow, to a single point in the dual space, and maps locations of distributed sensor nodes to a set of lines that partitions the dual space. The detection problem becomes finding and tracking the cell that contains the point in the arrangement defined by these lines. This mechanism can be effectively used for power management of the sensor network - nodes that will not be immediately visited by an event can be turned off to save energy required for sensing, processing, and communication. The approach has been successfully demonstrated on a laboratory testbed built using the UC Berkeley motes sensors. An implemented application of detecting and tracking light shadow edges moving over a sensor field is described.
10851913
Taming heterogeneity - the Ptolemy approach
Modern embedded computing systems tend to be heterogeneous in the sense of being composed of subsystems with very different characteristics, which communicate and interact in a variety of ways-synchronous or asynchronous, buffered or unbuffered, etc. Obviously, when designing such systems, a modeling language needs to reflect this heterogeneity. Today's modeling environments usually offer a variant of what we call amorphous heterogeneity to address this problem. This paper argues that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity, to solve this problem. It proposes a model structure and semantic framework that support this form of heterogeneity, and discusses the issues arising from heterogeneous component interaction and the desire for component reuse. It introduces the notion of domain polymorphism as a way to address these issues.
1787760
Greedy is Good: On Service Tree Placement for In-Network Stream Processing
This paper is concerned with reducing communication costs when executing distributed user tasks in a sensor network. We take a service-oriented abstraction of sensor networks, where a user task is composed of a set of data processing modules (called services) with dependencies. Communications in sensor networks consume significant energy and introduce uncertainty in data fidelity due to high bit error rate. These constraints are abstracted as costs on the communication graph. The goal is to place the services within the sensor network so that the communication cost in performing the task is minimized. In addition, since the lifetime of a node, the quality of network links, and the composition of the service graph may change over time, the quality of the placement must be maintained in the face of these dynamics. In this paper, we take a fresh look at what is generally considered a simple but poor performance approach for service placement, namely the greedy algorithm. We prove that a modified greedy algorithm is guaranteed to have cost at most 8 times the optimum placement. In fact, the guarantee is even stronger if there is a high degree of data reduction in the service graph. The advantage of the greedy placement strategy is that when there are local changes in the service graph or when a hosting node fails, the repair only affects the placement of services that depend on the changes. Simulations suggest that in practice the greedy algorithm finds a low cost placement. Furthermore, the cost of repairing a greedy placement decreases rapidly as a function of the proximity of the services to be aggregated.
640793
A component-based approach to modeling and simulating mixed-signal and hybrid systems
Systems with both continuous and discrete behaviors can be modeled using a mixed-signal style or a hybrid systems style. This article presents a component-based modeling and simulation framework that supports both modeling styles. The component framework, based on an actor metamodel, takes a hierarchical approach to manage heterogeneity in modeling complex systems. We describe how ordinary differential equations, discrete event systems, and finite-state machines can be built under this metamodel. A mixed-signal system is a hierarchical composition of continuous-time and discrete event models, and a hybrid system is a hierarchical composition of continuous-time and finite-state-machine models. Hierarchical composition and information hiding help build clean models and efficient execution engines. Simulation technologies, in particular, the interaction between a continuous-time ODE solving engine and various discrete simulation engines are discussed. A signal type system is introduced to schedule hybrid components inside a continuous-time environment. Breakpoints are used to control the numerical integration step sizes so that discrete events are handled properly. A "refiring" mechanism and a "rollback" mechanism are designed to manage continuous components inside a discrete event environment. The technologies are implemented in the Ptolemy II software environment. Examples are given to show the applications of this framework in mixed-signal and hybrid systems.
986010
On the Causality of Mixed-Signal and Hybrid Models
This paper extends the application of the Cantor metric as a mathematical tool for defining causalities from pure discrete models to mixed-signal and hybrid models. Using the Cantor metric, which maps timed signals, continuous or discrete, into a metric space, we define causality as contractive properties of processes operating on these signals. Thus, the Banach fixed point theorem can be applies to establish conditions for the existence, uniqueness, and liveness of the behaviors for mixed-signal and hybrid systems. The results also provide theoretical foundations for the simulation technologies for such systems, including the time-marching strategy, evaluation of feedback loops, and the necessity of supporting rollback.
6360856
Lookahead revisited in wireless network simulations
Rapid growth in wireless communication systems motivates the development of technology supporting the simulation of large-scale wireless systems. However, it is widely recognized that wireless communications do not have substantial "lookahead" needed by conservative synchronization protocols. This paper focuses on identifying and exploiting lookahead for such models. We find lookahead in three ways, exploiting characteristics of low power networks, the transceiver logic, and the way in which protocol stacks are typically constructed. We show how these observations allow a variety of conservative synchronization protocols to take advantage of lookahead, describe a synchronization method we use, and empirically examine the performance this method offers on a large-scale simulation of a sensor network intended for homeland defense scenarios
16397904
A suite of web-based programs to search for transcriptional regulatory motifs
The identification of regulatory motifs is important for the study of gene expression. Here we present a suite of programs that we have developed to search for regulatory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory motifs from co-regulated genes in prokaryotes or lower eukaryotes; (ii) CompareProspector, an extension to BioProspector which incorporates comparative genomics features to be used for higher eukaryotes; (iii) MDscan, a program for finding protein-DNA interaction sites from ChIP-on-chip targets. All three programs examine a group of sequences that may share common regulatory motifs and output a list of putative motifs as position-specific probability matrices, the individual sites used to construct the motifs and the location of each site on the input sequences. The web servers and executables can be accessed at http://seqmotifs.stanford.edu.
6152126
QoS-aware dynamic service composition in ambient intelligence environments
Due to the large success of wireless networks and handheld devices, the ambient intelligence (AmI) paradigm is becoming a reality. One of the most challenging objectives to achieve in AmI environments is to enable a user to perform a task by composing on the fly networked services available at a specific time and place. Towards this goal, we propose a solution based on semantic Web services, and we show how service capabilities described as conversations can be integrated to perform a user task that is also described as a conversation, further meeting the QoS requirements of the user task. Experimental results show that the runtime overhead of our algorithm is reasonable, and further, that QoS-awareness improves its performance.
15044551
Web Intelligence (WI): What Makes Wisdom Web?
Web Intelligence (WI) sheds new light on directions for scientific research and development which explores the fundamental roles as well as practical impacts of Artificial Intelligence (AI) and advanced Information Technology (IT) on the next generation of Web-empowered products, systems, services, and activities. This paper gives new perspectives on the future WI research and highlights some of the research challenges and initiatives.
6396851
Multiagent diffusion and distributed optimization
Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degree of planning, coordination and negotiation to achieve the given goal. This paper presents a multiagent framework for tackling global optimization tasks inspired by diffusion in nature. The framework is designed for situations where agent communication must be kept to a minimal. Hence, complicated coordination and negotiation is not possible. Distributed agents in this framework share the common goal of finding the global optimal solution. They cooperate to achieve this common goal by sharing and updating a common belief that captures their estimation of the whereabouts of the optimal solution. To facilitate this, agents are naturally organized in families with a parent and its offsprings as members. This paper also presents an algorithm called Evolutionary Diffusion Optimization, which is implemented base on the proposed agent framework. Experimental results on some benchmark problems are presented together with performance comparison with a simulated annealing algorithm.