Sushil K. Prasad

Maximum Lifetime of Sensor Networks with Adjustable Sensing Range

In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor network in which each sensor can adjust its sensing range. The network model consists of a large number of sensors with adjustable sensing ranges being deployed to monitor a set of targets. Since more than one sensor can cover a target, in order to be energy efficient, one can activate successive subsets of sensors that cover all targets. This paper addresses the problem of maximizing the total lifetime of such an activation schedule. In contrast to the approach taken by Cardei et al. (2005), our formulation directly maximizes the network lifetime rather than maximizing the number of sensor covers. We give a mathematical model of this problem using a linear program with exponential number of variables and solve this linear program using the approximation algorithm of Garg-Konemann (1998). Our experimental results on simulated data show a 4times increase in lifetime when compared with the previous approach taken by Cardei et al. (2005)

Power efficient range assignment for symmetric connectivity in static ad hoc wireless networks

In this paper we study the problem of assigning transmission ranges to the nodes of a static ad hoc wireless network so as to minimize the total power consumed under the constraint that enough power is provided to the nodes to ensure that the network is connected. We focus on the Min-Power Symmetric Connectivity problem, in which the bidirectional links established by the transmission ranges are required to form a connected graph.Implicit in previous work on transmission range assignment under asymmetric connectivity requirements is the proof that Min-Power Symmetric Connectivity is NP-hard and that the MST algorithm has a performance ratio of 2. In this paper we make the following contributions: (1) we show that the related Min-Power Symmetric Unicast problem can be solved efficiently by a shortest-path computation in an appropriately constructed auxiliary graph; (2) we give an exact branch and cut algorithm based on a new integer linear program formulation solving instances with up to 35–40 nodes in 1 hour; (3) we establish the similarity between Min-Power Symmetric Connectivity and the classic Steiner Tree problem in graphs, and use this similarity to give a polynomial-time approximation scheme with performance ratio approaching 5/3 as well as a more practical approximation algorithm with approximation factor 11/6; and (4) we give the results of a comprehensive experimental study comparing new and previously proposed heuristics with the above exact and approximation algorithms.

An Energy-Efficient Distributed Algorithm for Minimum-Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is an essential yet time-consuming task in wireless sensor networks (WSNs). This paper studies the well-known Minimum-Latency Aggregation Schedule (MLAS) problem and proposes an energy-efficient distributed scheduling algorithm named Clu-DDAS based on a novel cluster-based aggregation tree. Our approach differs from all the previous schemes where Connected Dominating Sets or Maximal Independent Sets are employed. We prove that Clu-DDAS has a latency bound of 4R′ + 2Delta − 2, where Delta is the maximum degree and R′ is the inferior network radius which is smaller than the network radius R. Clu-DDAS has comparable latency as the previously best centralized algorithm E-PAS, while Clu-DDAS consumes 78% less energy as shown by the simulation results. Clu-DDAS outperforms the previously best distributed algorithm DAS whose latency bound is 16R′ + Delta − 14 on both latency and energy consumption. On average, Clu-DDAS transmits 67% fewer total messages than DAS does. We also propose an adaptive strategy for updating the schedule to accommodate dynamic network topology.

Parallel heap: an optimal parallel priority queue

We describe a new parallel data structure, namely parallel heap, for exclusive-read exclusive-write parallel random access machines. To our knowledge, it is the first such data structure to efficiently implement a truly parallel priority queue based on a heap structure. Employing p processors, the parallel heap allows deletions of θ(p) highest priority items and insertions of θ(p) new items, each in O(log n) time, where n is the size of the parallel heap. Furthermore, it can efficiently utilize processors in the range 1 through n.

NSF/IEEE-TCPP curriculum initiative on parallel and distributed computing: core topics for undergraduates

Many personal computers and workstations have two or four cores (that is, CPUs) that enable multiple threads to be executed simultaneously. Computers in the near future are expected to have significantly more cores. To take advantage of the hardware of today and tomorrow, you can parallelize your code to distribute work across multiple processors. In the past, parallelization required low-level manipulation of threads and locks. Visual Studio 2010 and the .NET Framework 4 enhance support for parallel programming by providing a new runtime, new class library types, and new diagnostic tools. These features simplify parallel development so that you can write efficient, fine-grained, and scalable parallel code in a natural idiom without having to work directly with threads or the thread pool. The following illustration provides a high-level overview of the parallel programming architecture in the .NET Framework 4.

Information Systems, Technology and Management

This book constitutes the refereed proceedings of the Third International Conference on Information Systems, Technology and Management, ICISTM 2009, held in Ghaziabad, India, in March 2009 The 30 revised full papers presented together with 4 keynote papers were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on storage and retrieval systems; data mining and classification; managing digital goods and services; scheduling and distributed systems; advances in software engineering; case studies in information management; algorithms and workflows; authentication and detection systems; recommendation and negotiation; secure and multimedia systems; as well as 14 extended poster abstracts.

Implementation of a calendar application based on SyD coordination links

System on devices (SyD) is a specification for a middleware to enable heterogeneous collections of information, databases, or devices (such as hand-held devices) to collaborate with each other. This paper illustrates the advantages of SyD by describing a prototype calendar of meetings application. This application highlights some of the technical merits of SyD by exploiting the use of coordination links. Based on the underlying event-and-trigger mechanism, these links allow automatic updates as well as real-time enforcements of global constraints and interdependencies, not available with existing calendar applications. Additionally, the calendar application illustrates coordination among heterogeneous devices and databases, formation and maintenance of dynamic groups, mobility support through proxies, and performance group transactions across independent data stores.

Distributed algorithms for lifetime of wireless sensor networks based on dependencies among cover sets

We present a new set of distributed algorithms for scheduling sensors to enhance the total lifetime of a wireless sensor network. These algorithms are based on constructing minimal cover sets each consisting of one or more sensors which can collectively cover the local targets. Some of the covers are heuristically better than others for a sensor trying to decide its own sense-sleep status. This leads to various ways to assign priorities to the covers. The algorithms work by having each sensor transition through these possible prioritized cover sets, settling for the best cover it can negotiate with its neighbors. A local lifetime dependency graph consisting of the cover sets as nodes with any two nodes connected if the corresponding covers intersect captures the interdependencies among the covers. We present several variations of the basic algorithmic framework. The priority function of a cover is derived from its degree or connectedness in the dependency graph - usually lower the better. Lifetime improvement is 10% to 20% over the existing algorithms, while maintaining comparable communication overheads. We also show how previous algorithms can be formulated within our framework.

A distributed algorithmic framework for coverage problems in wireless sensor networks

One of the key challenges in Wireless Sensor Networks (WSNs) is that of extending the lifetime of the network while meeting some coverage requirements. In this paper we present a distributed algorithmic framework to enable sensors to determine their sleep-sense cycles based on specific coverage goals. The framework is based on our earlier work on the target coverage problem. We give a general version of the framework that can be used to solve network/graph problems for which melding compatible neighboring local solutions directly yields globally feasible solutions. We also apply this framework to several variations of the coverage problem, namely, target coverage, area coverage and k-coverage problems, to demonstrate its general applicability. Each sensor constructs minimal cover sets for its local coverage objective. The framework entails each sensor prioritizing these local cover sets and then negotiating with its neighbors for satisfying mutual constraints. We introduce a dependency graph model that can capture the interdependencies among the cover sets. Detailed simulations are carried out to further demonstrate the resulting performance improvements and effectiveness of the framework.

A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report

GIS polygon-based (also know as vector-based) spatial data overlay computation is much more complex than raster data computation. Processing of polygonal spatial data files has been a long standing research question in GIS community due to the irregular and data intensive nature of the underlying computation. The state-of-the-art software for overlay computation in GIS community is still desktop-based. We present a cluster-based distributed solution for end-to-end polygon overlay processing, modeled after our Windows Azure cloud-based Crayons system [1]. We present the details of porting Crayons system to MPI-based Linux cluster and show the improvements made by employing efficient data structures such as R-trees. We present performance report and show the scalability of our system, along with the remaining bottlenecks. Our experimental results show an absolute speedup of 15x for end-to-end overlay computation employing up to 80 cores.