Akshaye Dhawan | Ursinus College (original) (raw)
Papers by Akshaye Dhawan
Communications in Computer and Information Science, 2010
Abstract. In this paper, we present two distributed algorithms to max-imize the lifetime of Wirel... more Abstract. In this paper, we present two distributed algorithms to max-imize the lifetime of Wireless Sensor Networks for target coverage when the sensors have the ability to adjust their sensing and communication ranges. These algorithms are based on the enhancement of ...
This poster examines the problem of generating effective, reusable web interfaces for searching a... more This poster examines the problem of generating effective, reusable web interfaces for searching and browsing neuroscience data represented in the Web Ontology Language (OWL). The goal of this work is to design interfaces that are reusable across different ontologies without the need for extensive customizations. In order to achieve this re-usability, we view the underlying semantic data in the ontology as a graph and are currently exploring the use of different graph properties to infer the structure of the class hierarchy in the ontology.
A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintainin... more A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintaining coverage of a set of targets, a known NP-complete problem. In this paper, we present theoretically-grounded, energy-efficient, distributed algorithms that enable sensors to schedule themselves into sleep-sense cycles. We had earlier introduced a lifetime dependency (LD) graph model that captures the interdependencies between these cover sets by modeling each cover as a node and having the edges represent shared sensors. The key motivation behind our approach in this paper has been to start with the question of what an optimal schedule would do with the lifetime dependency graph. We prove some basic properties of the optimal schedule that relate to the LD graph. Based on these properties, we have designed algorithms which choose the covers that exhibit these optimal schedule like properties. We present three new sophisticated algorithms to prioritize covers in the dependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithms is a lifetime improvement of more than 25-30% over the competing algorithms of other groups, and 10-15% over our own; the 2-hop versions have additional improvements, 30-35% and 20-25%, respectively.
We present a new set of distributed algorithms for scheduling sensors to enhance the total lifeti... more 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.
International Journal of Parallel, Emergent and Distributed Systems, 2009
One of the key challenges in Wireless Sensor Networks (WSNs) is that of extending the lifetime of... more 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. * We will maintain additional information on this framework at
In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor net... more 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 this paper, we present two distributed algorithms to maximize the lifetime of Wireless Sensor ... more In this paper, we present two distributed algorithms to maximize the lifetime of Wireless Sensor Networks for target coverage when the sensors have the ability to adjust their sensing and communication ranges. These algorithms are based on the enhancement of distributed algorithms for fixed range sensors proposed in the literature. We outline the algorithms for the adjustable range model, prove their correctness and analyze the time and message complexities. We also conduct simulations demonstrating 20% improvement in network lifetime when compared with the previous approaches. Thus, in addition to sleep-sense scheduling techniques, further improvements in network lifetime can be derived by designing algorithms that make use of the adjustable range model.
One of the key challenges in Wireless Sensor Networks (WSNs) is that of extending the lifetime of... more 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.
Knowledge of neuronal circuitry is foundational to the neurosciences, but no tools have been deve... more Knowledge of neuronal circuitry is foundational to the neurosciences, but no tools have been developed for cataloguing this knowledge. Part of the problem is that the concepts used to describe neural circuits are rapidly evolving and vary substantially across different species. The NeuronBank project (http://neuronbank.org) is developing an informatics infrastructure for managing the dynamic, domain-specific knowledge of neural circuitry, providing a reference source, an outlet for publishing new knowledge, and a useful research tool. Our solution is a federation of customizable knowledge bases, each adaptable to store knowledge of the neural circuitry of a single species. The federation is united by a common set of web services and a central portal that provides core functionality across various knowledge bases. This service-oriented architecture provides domain-specific representations of specialized scientific knowledge while maintaining interoperability across a broad discipline.
Communications in Computer and Information Science, 2010
Abstract. In this paper, we present two distributed algorithms to max-imize the lifetime of Wirel... more Abstract. In this paper, we present two distributed algorithms to max-imize the lifetime of Wireless Sensor Networks for target coverage when the sensors have the ability to adjust their sensing and communication ranges. These algorithms are based on the enhancement of ...
This poster examines the problem of generating effective, reusable web interfaces for searching a... more This poster examines the problem of generating effective, reusable web interfaces for searching and browsing neuroscience data represented in the Web Ontology Language (OWL). The goal of this work is to design interfaces that are reusable across different ontologies without the need for extensive customizations. In order to achieve this re-usability, we view the underlying semantic data in the ontology as a graph and are currently exploring the use of different graph properties to infer the structure of the class hierarchy in the ontology.
A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintainin... more A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintaining coverage of a set of targets, a known NP-complete problem. In this paper, we present theoretically-grounded, energy-efficient, distributed algorithms that enable sensors to schedule themselves into sleep-sense cycles. We had earlier introduced a lifetime dependency (LD) graph model that captures the interdependencies between these cover sets by modeling each cover as a node and having the edges represent shared sensors. The key motivation behind our approach in this paper has been to start with the question of what an optimal schedule would do with the lifetime dependency graph. We prove some basic properties of the optimal schedule that relate to the LD graph. Based on these properties, we have designed algorithms which choose the covers that exhibit these optimal schedule like properties. We present three new sophisticated algorithms to prioritize covers in the dependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithms is a lifetime improvement of more than 25-30% over the competing algorithms of other groups, and 10-15% over our own; the 2-hop versions have additional improvements, 30-35% and 20-25%, respectively.
We present a new set of distributed algorithms for scheduling sensors to enhance the total lifeti... more 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.
International Journal of Parallel, Emergent and Distributed Systems, 2009
One of the key challenges in Wireless Sensor Networks (WSNs) is that of extending the lifetime of... more 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. * We will maintain additional information on this framework at
In this paper, we consider the problem of maximizing the lifetime of a target-covering sensor net... more 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 this paper, we present two distributed algorithms to maximize the lifetime of Wireless Sensor ... more In this paper, we present two distributed algorithms to maximize the lifetime of Wireless Sensor Networks for target coverage when the sensors have the ability to adjust their sensing and communication ranges. These algorithms are based on the enhancement of distributed algorithms for fixed range sensors proposed in the literature. We outline the algorithms for the adjustable range model, prove their correctness and analyze the time and message complexities. We also conduct simulations demonstrating 20% improvement in network lifetime when compared with the previous approaches. Thus, in addition to sleep-sense scheduling techniques, further improvements in network lifetime can be derived by designing algorithms that make use of the adjustable range model.
One of the key challenges in Wireless Sensor Networks (WSNs) is that of extending the lifetime of... more 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.
Knowledge of neuronal circuitry is foundational to the neurosciences, but no tools have been deve... more Knowledge of neuronal circuitry is foundational to the neurosciences, but no tools have been developed for cataloguing this knowledge. Part of the problem is that the concepts used to describe neural circuits are rapidly evolving and vary substantially across different species. The NeuronBank project (http://neuronbank.org) is developing an informatics infrastructure for managing the dynamic, domain-specific knowledge of neural circuitry, providing a reference source, an outlet for publishing new knowledge, and a useful research tool. Our solution is a federation of customizable knowledge bases, each adaptable to store knowledge of the neural circuitry of a single species. The federation is united by a common set of web services and a central portal that provides core functionality across various knowledge bases. This service-oriented architecture provides domain-specific representations of specialized scientific knowledge while maintaining interoperability across a broad discipline.