Akshaye Dhawan

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)

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.

Energy efficient distributed algorithms for sensor target coverage based on properties of an optimal schedule

A major challenge in Wireless Sensor Networks is that ofmaximizing 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 toschedule themselves into sleep-sense cycles. We had earlier introduceda lifetime dependency (LD) graph model that captures the interdependenciesbetween these cover sets by modeling each cover as a node andhaving the edges represent shared sensors. The key motivation behindour approach in this paper has been to start with the question of whatan optimal schedule would do with the lifetime dependency graph. Weprove some basic properties of the optimal schedule that relate to theLD graph. Based on these properties, we have designed algorithms whichchoose the covers that exhibit these optimal schedule like properties. Wepresent three new sophisticated algorithms to prioritize covers in thedependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithmsis a lifetime improvement of more than 25-30% over the competing algorithmsof other groups, and 10-15% over our own; the 2-hop versionshave additional improvements, 30-35% and 20-25%, respectively.

Distributed Scheduling of a Network of Adjustable Range Sensors for Coverage Problems

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.

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.

NeuronBank: A Tool for Cataloging Neuronal Circuitry

The basic unit of any nervous system is the neuron. Therefore, understanding the operation of nervous systems ultimately requires an inventory of their constituent neurons and synaptic connectivity, which form neural circuits. The presence of uniquely identifiable neurons or classes of neurons in many invertebrates has facilitated the construction of cellular-level connectivity diagrams that can be generalized across individuals within a species. Homologous neurons can also be recognized across species. Here we describe NeuronBank.org, a web-based tool that we are developing for cataloging, searching, and analyzing neuronal circuitry within and across species. Information from a single species is represented in an individual branch of NeuronBank. Users can search within a branch or perform queries across branches to look for similarities in neuronal circuits across species. The branches allow for an extensible ontology so that additional characteristics can be added as knowledge grows. Each entry in NeuronBank generates a unique accession ID, allowing it to be easily cited. There is also an automatic link to a Wiki page allowing an encyclopedic explanation of the entry. All of the 44 previously published neurons plus one previously unpublished neuron from the mollusc, Tritonia diomedea, have been entered into a branch of NeuronBank as have 4 previously published neurons from the mollusc, Melibe leonina. The ability to organize information about neuronal circuits will make this information more accessible, ultimately aiding research on these important models.

Maximum Lifetime Scheduling in Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a lot of recent research interest due to their applicability in security, monitoring, disaster relief and environmental applications. WSNs consist of a number of low-cost sensors scattered in a geographical area of interest and connected by a wireless RF interface. Sensors gather information about the monitored area and send this information to gateway nodes. The radio on board these sensor nodes has limited range and allows the node to transmit over short distances. In most deployment scenarios, it is not possible for each node to communicate directly to the sink and hence, the model of communication is to transmit over short distances to other peers in the direction of the sink nodes.

Development of NeuronBank: A Federation of Customizable Knowledge Bases of Neuronal Circuitry

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.

A Distributed Greedy Algorithm for Constructing Connected Dominating Sets in Wireless Sensor Networks

A Connected Dominating Set (CDS) of the graph representing a Wireless Sensor Network can be used as a virtual backbone for routing in the network. Since sensor nodes are constrained by limited on-board batteries, it is desirable to have a small CDS for the network. However, constructing a minimum size CDS has been shown to be a NP-hard problem. In this paper we present a distributed greedy algorithm for constructing a CDS that we call Greedy Connect. Our algorithm operates in two phases, first constructing a dominating set and then connecting the nodes in this set. We evaluate our algorithm using simulations and compare it to the two-hop K2 algorithm in the literature. Depending on the network topology, our algorithm generally constructs a CDS that is up to 30% smaller in size than K2.