8891616
Distributed state representation for tracking problems in sensor networks
This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena such as those arising from tracking multiple interacting targets, while commonly used in signal processing and control, suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion problem by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information ow within and across subspaces of the state-space model, which can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.
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.
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.
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.
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.
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.
12387425
Self-Protection for Wireless Sensor Networks
Wireless sensor networks have recently been suggested for many surveillance applications such as object monitoring, path protection, or area coverage. Since the sensors themselves are important and critical objects in the network, a natural question is whether they need certain level of protection, so as to resist the attacks targeting on them directly. If this is necessary, then who should provide this protection, and how it can be achieved? We refer to the above problem as self-protection, as we believe the sensors themselves are the best (and often the only) candidate to provide such protection. In this papel; we for the jirst time present a formal study on the selfprotection problem in wireless sensor networks. We show that, if we simply focus on the quality ofjield or object covering, the sensors might not necessarily be self-protected, which in turn makes the system vulnerable. We then investigate dzreerent forms of self-pmtections, and show that the problems are generally NP-complete. We develop eficient approximation algorithms for centrally-controlled sensors. We then extend the algorithms to filly distributed implementation, and introduce a smart sleep-scheduling algorithm that minimize the energy consumption.
8844798
Learning not to share
Strong reasons exist for executing a large-scale discrete-event simulation on a cluster of processor nodes (each of which may be a shared-memory multiprocessor or a uniprocessor). This is the architecture of the largest scale parallel machines, and so the largest simulation problems can only be solved this way. It is a common architecture even in less esoteric settings, and is suitable for memory-bound simulations. This paper describes our approach to porting the SSF simulation kernel to this architecture, using the Message Passing Interface (MPI) system. The notable feature of this transformation is to support an efficient two-level synchronization and communication scheme that addresses cost discrepancies between shared-memory and distributed memory. In the initial implementation, we use a globally synchronous approach between distributed-memory nodes, and an asynchronous shared-memory approach within a SMP cluster. The SSF API reflects inherently shared-memory assumptions; we report therefore on our approach for porting an SSF kernel to a cluster of SMP nodes. Experimental results on two architectures are described, for a model of TCP/IP traffic flows over a hierarchical network. The performance on a distributed network of commodity SMPs connected through ethernet is seen to frequently exceed performance on a Sun shared-memory multiprocessor.
36178651
The vertex linear arboricity of distance graphs
A distance graph is a graph G(R,D) with the set of all points of the real line as vertex set and two vertices u,[email protected]?R are adjacent if and only if |u-v|@?D where the distance set D is a subset of the positive real numbers. Here, the vertex linear arboricity of G(R,D) is determined when D is an interval between 1 and @d. In particular, the vertex linear arboricity of integer distance graphs G(D) is discussed, too.
13217169
New Method for Constructing Isospectral Graphs
There exist several methods available to construct isospectral molecular graphs.l-1° Recently, M. RandiC and his co-workers proposed the ring enlargement process (vide infra) for constructing subspectral graphs.” This method can be also used to construct isospectral graphs; however, it may fail sometimes. For example, the isospectral pair 1, 2 can be enlarged to the isospectral graphs 3,4 shown in Figure 1, where the symbol ‘‘z” signifies an isospectral pair and “-” denotes the ring enlargement procedure. Eigenvalues of 3, 4 are divided into two parts, those in coincidence with that of 1 , 2 are listed on the left, and the remaining ones are put on the right. On the other hand, graph pairs 7, 8 and 9, 10 in Figure 2, yielded from isospectral pair 5, 6, are not isospectral. Noticing the difference of the spectrum of graphs XX, XX’ (by “XX, XX”’ we mean that they are generated from subgraph X in double size by the enlargement procedure, such as 7, 8 from 5; and 9, 10 from a), RandiC suggested that if XX, XX’ are bipartite, they should be isospectral, because all eigenvalues now occur in pairs and the difference between XX and XX’ disappears. Hence, he proposed a conjecture below. CONJECTURE: If graph X yields nonisomorphic bipartite enlargements XX, XX’, etc.; the derived graphs are not only superspectral to the parent graph X but are also mutually cospectral.
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.