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

The network model assumed in this paper consists of tiny, energy-constrained, commodity sensors massively deployed alongside with one or more sink nodes that provide the interface to the outside world. The sensors in the network are initially anonymous and unaware of their location. Our main contribution is to propose a new robust and energy-efficient solution for secure operation of wireless sensor networks. The paper motivates a new paradigm where security is based upon using parameterized frequency hopping and cryptographic keys in a unified framework to provide differential security services for wireless sensor networks.

The main contribution of this work is to show that a number of fundamental and seemingly unrelated problems in database design, pattern recognition, robotics, computational geometry, and image processing can be solved simply and elegantly by stating them as instances of a unifying algorithmic framework that we call the multiple query problem. The multiple query problem (MQ, for short) is a 5-tuple (Q, A, D, /spl phi/, /spl oplus/), where Q is a set of queries, A is a set of items, D is a set of solutions, /spl phi/: Q/spl times/A/spl rarr/D is a function, and /spl oplus/ is a commutative and associative binary operator over D. The input to the MQ problem consists of a sequence Q=of m queries from Q and of a sequence A=of n items from A. The goal is to compute, for every query q/sub i/ (1/spl les/i/spl les/m) its solution defined as /spl phi/(q/sub i/,A)=/spl phi/(q/sub i/,a/sub 1/)/spl oplus//spl phi/(q/sub i/,a/sub 2/)/spl oplus//spl middot//spl middot//spl middot//spl oplus//spl phi/(q/sub i/,a/sub n/). We begin by discussing a generic algorithm that solves a large class of MQ problems in O(/spl radic/m+f(n)) time on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n, where f(n) is the time necessary to compute the expression d/sub 1/ /spl oplus/ d/sub 2/ /spl oplus//spl middot//spl middot//spl middot//spl oplus/ d/sub n/ with d/sub i/ /spl isin/ D on such a platform. We then go on to show that the MQ framework affords us an optimal algorithm for the multiple point location problem on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n. Given a set A of n points and a set Q of m (m/spl les/n) points in the plane, our algorithm reports, in O(/spl radic/m+log log n) time, all points of Q that lie inside the convex hull of A. Quite surprisingly, our algorithm solves the multiple point location problem without computing the convex hull of A which, in itself, takes /spl Omega/(/spl radic/n) time on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n. Finally, we prove an /spl Omega/(/spl radic/m+g(n)) time lower bound for nontrivial MQ problems, where g(n) is the lower bound for evaluating the expression d/sub 1/ /spl oplus/ d/sub 2/ /spl oplus//spl middot//spl middot//spl middot//spl oplus/ d/sub n/ with d/sub i/ /spl isin/ D, on a reconfigurable mesh of size /spl radic/n/spl times//spl radic/n.

Sorting is a fundamental problem with applications in all areas of computer science and engineering. In this work, we address the problem of sorting on mesh connected computers enhanced by endowing each row and each column with its own dedicated high-speed bus. This architecture, commonly referred to as a mesh with multiple broadcasting, is commercially available and has been adopted by the DAP family of multiprocessors. Somewhat surprisingly, the problem of sorting m, (m/spl les/n), elements on a mesh with multiple broadcasting of size /spl radic/n/spl times//spl radic/n has been studied, thus far, only in the sparse case, where m/spl isin//spl Theta/(/spl radic/n) and in the dense case, where m/spl isin//spl Theta/O(/spl radic/n). Yet, many applications require using an existing platform of size /spl radic/n/spl times//spl radic/n for sorting m elements, with /spl radic/n<m/spl les/n. Our main contribution is to present the first known adaptive time- and VLSI-optimal sorting algorithm for meshes with multiple broadcasting. Specifically we show that, for every choice of a constant 0

Query processing is a crucial component of various application domains including information retrieval, database design and management, pattern recognition, robotics, and VLSI. Many of these applications involve data stored in a matrix satisfying a number of properties. One property that occurs time and again specifies that the rows and the columns of the matrix are independently sorted. It is customary to refer to such a matrix as sorted. An instance of the batched searching and ranking problem (BSR) involves a sorted matrix A of items from a totally ordered universe, along with a collection Q of queries. Q is an arbitrary mix of the following query types: for a search query q/sub j/, one is interested in an item of A that is closest to q/sub j/; for a rank query q/sub j/ one is interested in the number of items of A that are strictly smaller than q/sub j/. The BSR problem asks for solving all queries in Q. The authors consider the BSR problem in the following context: the matrix A is pretiled, one item per processor, onto an enhanced mesh of size /spl radic/n/spl times//spl radic/n; the m queries are stored, one per processor, in the first m//spl radic/n~ columns of the platform. Their main contribution is twofold. First, they show that any algorithm that solves the BSR problem must take at least /spl Omega/(max{logn, /spl radic/m}) time in the worst case. Second, they show that this time lower bound is tight on meshes of size /spl radic/n/spl times//spl radic/n enhanced with multiple broadcasting, by exhibiting an algorithm solving the BSR problem in /spl Theta/(max{logn, /spl radic/m}) time on such a platform.

Answering rank queries is a recurring operation in various application domains including geographic data processing, information retrieval, database design, information management, and medical image processing. Many of these applications involve data stored in a matrix satisfying a number of properties. One property that occurs time and again in applications specifies that the rows and the columns of the matrix are independently sorted. It is customary to refer to such a matrix as sorted. An instance of the Batched Ranking problem, (BR, for short) involves a sorted matrix A of items from a totally ordered universe, along with a collection Q of queries of the following type: for a query q/sub j/ one is interested in the number of items in A that are smaller than q/sub j/. The BR problem asks for solving all queries in Q. In this work, we consider the BR problem in the following context: the matrix A is pretiled, one item per processor, onto an enhanced mesh of size /spl radic/n/spl times//spl radic/n; the m queries are stored, one per processor, in the first m//spl radic/n columns of the platform. Our main contribution is twofold. First, we show that any algorithm that solves the BR problem must take at least /spl Omega/(log n+/spl radic/m) time in the worst case. Second, we show that this time lower bound is tight on meshes of size /spl radic/n/spl times//spl radic/n enhanced with multiple broadcasting, by exhibiting an algorithm solving the BR problem in O(log n+/spl radic/m) time on such a platform.

Consider arbitrary collections A = a_1,a_2,.. .,a_n of items and Q = q_1,q_2,...,q_m (1 leqslant mn leqslant n) of queries from a totally ordered universe. The multiple rank problem involves computing for every query qi the number of items in A that have a lesser value. Our contribution is to show that the problem at hand can be solved time-optimally on meshes with multiple broadcasting. More specifically, if the collection A is siored in some order one item per processor and if Q is stored one query per processor in the leftmost frac{m} {{sqrt n }} columns of a mesh with multiple broadcasting of size sqrt n x /sqrt n, the corresponding instance of the multiple rank problem can be solved in Theta left( {m^{frac{1} {3}} n^{frac{1} {6}} } right) time. As an application we present a time-optimal algorithm to compute the histogram of a m-level gray image of size sqrt n x sqrt n in Theta left( {m^{frac{1} {3}} n^{frac{1} {6}} } right) time.

In this work, we present a time-and VLSI-optimal sorting algorithm for meshes with multiple broadcasting. Specifically, we show that for every choice of a positive integer constant c, m items \left( {n^{\frac{1} {2} + \frac{1} {{2c}}} \leqslant m \leqslant n} \right) stored in the first \left\lceil {\frac{m} {{\sqrt n }}} \right\rceil columns of a mesh with multiple broadcasting of size \sqrt {n} x \sqrt {n} can be sorted in O({\frac{m} {{\sqrt n }}}) time.