Theodore Brown

Assigning sensors to missions with demands

We introduce Semi-Matching with Demands (SMD), which models a certain problem in sensor networks of assigning individual sensors to sensing tasks. If there are multiple sensing tasks or missions to be accomplished simultaneously, and if sensor assignment must be exclusive, then this is a bipartite semi-matching problem. Each mission is associated with a demand value and a profit value; each sensormission pair is associated with a utility offer (possibly 0). The goal is a sensor assignment that maximizes the profits of the satisfied missions (with no credit for partially satisfied missions). SMD is NP-hard and as hard to approximate as MAXIMUM INDEPENDENT SET. Therefore we investigate less difficult constrained versions of the problem. We give a simple greedy Δ-approximation algorithm for a degree-constrained version (Δ-SMD), in which each mission receives positive utility offers from at most Δ sensors. For small Δ, we show that Δ-SMD is equivalent to k-SET PACKING (with k = Δ), which yields a polynomial-time (Δ+1)/2- approximation. For Δ = 2, we solve the problem optimally by reduction to maximum matching. Finally, we introduce a geometric version which remains strongly NP-hard but has a PTAS.

Sensor-mission assignment in wireless sensor networks

When a sensor network is deployed, it is typically required to support multiple simultaneous missions. Schemes that assign sensing resources to missions thus become necessary. In this article, we formally define the sensor-mission assignment problem and discuss some of its variants. In its most general form, this problem is NP-hard. We propose algorithms for the different variants, some of which include approximation guarantees. We also propose distributed algorithms to assign sensors to missions which we adapt to include energy-awareness to extend network lifetime. Finally, we show comprehensive simulation results comparing these solutions to an upper bound on the optimal solution.

Assigning Sensors to Competing Missions

When a sensor network is deployed in the field, it is typically required to support multiple simultaneous missions, which may start and finish at different times. Schemes that match sensor resources to mission demands thus become necessary. In this paper, we propose centralized and distributed schemes to assign sensors to missions. We also adapt our distributed scheme to make it energy-aware to extend network lifetime. Finally, we show simulation results comparing these solutions. We find that our greedy algorithm frequently performs near-optimally and that the distributed schemes usually perform nearly as well.

Cheap or Flexible Sensor Coverage

We consider dual classes of geometric coverage problems, in which disks, corresponding to coverage regions of sensors, are used to cover a region or set of points in the plane. The first class of problems involve assigning radii to already-positioned sensors (being cheap ). The second class of problems are motivated by the fact that the sensors may, because of practical difficulties, be positioned with only approximate accuracy (being flexible ). This changes the character of some coverage problems that solve for optimal disk positions or disk sizes, ordinarily assuming the disks can be placed precisely in their chosen positions, and motivates new problems. Given a set of disk sensor locations, we show for most settings how to assign either (near-)optimal radius values or allowable amounts of placement error. Our primary results are 1) in the 1-d setting we give a faster dynamic programming algorithm for the (linear) sensor radius problem; and 2) we find a max-min fair set of radii for the 2-d continuous problems in polynomial time. We also give results for other settings, including fast approximation algorithms for the 1-d continuous case.

Should I Stay or Should I Go? Maximizing Lifetime with Relays

As sensor mobility becomes more and more universal, Wireless Sensor Network (WSN) configurations that utilize such mobility will become the norm. We consider the problem of maximizing the lifetime of a wireless connection between a transmitter and a receiver using mobile relays. Initially, all relays are positioned arbitrarily on the line between the transmitter and the receiver and have arbitrary battery capacities. Energy is consumed in proportion to the distance traveled for mobility and in proportion to an exponential function of the distance over which information is sent for communication. Relays can move to different locations as long as they have the energy to do so. The objective is to find positions and thus transmission ranges for the nodes that maximize the lifetime of the network. We study two models. The first is more restrictive, and corresponds to the case where relays are allowed to be set once at time zero (single deployment), while the second model corresponds to the case where relays can be adjusted multiple times (multiple deployments). We show how to compute an optimal solution for the case of no movement cost for both models. We consider a discrete version of the single deployment model, in which relays must be deployed on grid points. We provide two algorithms for this case: a dynamic programming algorithm and a binary search algorithm on potential lifetimes. We prove that both algorithms are FPTASs for the non-discrete problem, if batteries are not too small. Based on these algorithms and on additional ideas we develop a number of heuristics for the multiple deployments model. We evaluate them using simulations and compare them with the lower bound of relays not moving at all and the upper bound of cost-free movement. Our simulations - across a range of mobility and transmission costs, sensible starting locations and battery capacities - demonstrate the benefit of moving over remaining at initial locations even for single deployment.

Evaluation of network trust using provenance based on distributed local intelligence

Provenance can play a significant role in a military information system for supporting the calculation of information trust. A nodes trust can change over time after its initial deployment due to various reasons such as energy loss, environmental conditions or exhausting sources. We introduce a node-level trust-enhancing mechanism for information networks using provenance. A unique characteristic of the proposed trust architecture presented here is the use of provenance through the path of the information from source to destination in determining the information trust. In this proposed architecture each node in the system has a trust and provenance vector. Each information item transmitted over the network has a trust value associated with it. Nodes reexamine and update the trust value associated with the information, creating a distributed system that is more flexible and more responsive. As our system allows reconfigurations, initiatives taken by the intermediate nodes such as replacement of untrusted nodes will enhance the network trust in mission critical situations faster than a centralized approach.

A parallel quicksort algorithm

Abstract An optimal parallel version of quicksort, pquicksort, is presented and analyzed. The algorithm is designed for a p-processor MIMD (multiple instruction, multiple data) machine with shared memory and is an exclusive read, exclusive write algorithm. It is shown that in almost all permutations of N values (with probability 1 - o(1/N)) pquicksort will sort in O(N lg N/p) for p ≤ N/lg N.

Location Dependent Heuristics for Sensor Coverage Planning

The ability of a sensor device is affected significantly by the surroundings and environment in which it is placed. In almost all sensor modalities, some directions are better observed by a sensor than others. Furthermore, the exact impact on the sensing ability of the device is dependent on the position assigned to the sensor. While the problem of determining good coverage schemes for sensors of a field have many good solutions, not many approaches are known to address the challenges arising due to location specific distortion. In this paper, we look at the problem of incorporating terrain specific challenges in sensor coverage, and propose a geometric solution to address them.

Parallel median splitting and k-splitting with application to merging and sorting

Multiple-instruction multiple-data (MIMD) algorithms that use multiple processors to do median splitting, k-splitting and parallel splitting into t equal sections are presented. Both concurrent read, exclusive write (CREW) and exclusive read, exclusive write (EREW) versions of the algorithms are given. It is shown that a k-splitting problem can be easily converted into a median-splitting problem. Methods for finding multiple split points quickly and application of k-splitting to merging and sorting are discussed. >

Leveraging provenance to improve data fusion in sensor networks

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations.