Trac N. Nguyen

Minimum latency data aggregation in the physical interference model

Data aggregation has been the focus of many researchers as one of the most important applications in Wireless Sensor Networks. A main issue of data aggregation is how to construct efficient schedules by which data can be aggregated without any interference. The problem of constructing minimum latency data aggregation schedules (MLAS) has been extensively studied in the literature although most of existing works use the graph-based interference model. In this paper, we study the MLAS problem in the more realistic physical model known as signal-to-interference-noise-ratio (SINR). We first derive an @Wlogn approximation lower bound for the MLAS problem in the metric SINR model. We also prove the NP-hardness of the decision version of MLAS in the geometric SINR model. This is a significant contribution as these results have not been obtained before for the SINR model. In addition, we propose two constant factor approximation algorithms whose latency is bounded by O(@D+R) for the dual power model, where @D is the maximum node degree of a network and R is the network radius. Finally we study the performance of the algorithms through simulation.

Dual power assignment optimization for k-edge connectivity in WSNs

Power consumption is one of the crucial issues in Wireless Sensor Networks (WSNs). It therefore has been the focus of many researchers. An important problem concerning power consumption is how to minimize the number of maximum power nodes while maintaining a desired network topology. As fault tolerance is vitally important in practice, it is desirable that the constructed network topology is k-edge-connected (or k-connected). In this paper, we study the dual power assignment problem for k-edge connectivity (kEDP) in WSNs. While other studies consider only the special case k=2, our goal is to address the general problem. We prove the NP-completeness of kEDP problem in the geometric case and provide a 2-approximation algorithm using linear programming techniques. To our knowledge, this approximation ratio is currently the best one. We also introduce a heuristic whose performance is better compared with an approximation algorithm in [1].

Extending Sensor Networks Lifetime Through Energy Efficient Organization

In most applications involving wireless sensor networks, each sensor collects data in the surrounding area, and sends to a central node for processing. To extend network lifetime in such cases, the sensors could be partitioned into groups which are successively scheduled to be active for sensing and delivering data. Each group covers (almost) the entire area, and only one group is active at a given time. These groups of sensors are known as disjoint dominating sets in network and graph theory where it has been shown that the problem of computing the maximum number of disjoint dominating sets in graphs is NP-complete [6]. In this paper, we strengthen this result [3] by showing that this problem remains NP-complete for planar unit disk graphs. We introduce several heuristics for the disjoint dominating sets problem and discuss their performance through some simulation results.

Scheduling problems in interference-aware wireless sensor networks

In this paper, we study the Minimum Latency Aggregation Scheduling problem in two interference models, the collision-interference-free graph model and the physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), with power control. The main issue is to compute schedules with the minimum number of timeslots such that data can be aggregated without any collision or interference. While existing works studied the problem under the uniform power model or the unlimited power model, we investigate the problem assuming a more realistic non-uniform power assignment where the maximum power level is bounded. We propose a constant factor approximation algorithm with O(R + X) timeslots, where R is the network radius and X is the link length diversity. Under a reasonable assumption about the link length diversity, the number of timeslots is bounded by O(R + log n) which gives a constant approximation ratio since the lower bound is max{ R, log n}, where n is the number of nodes. Along with the problem of constructing minimum latency data aggregation schedules, we also study two other related optimization problems, namely the Scheduling and Weighted One-Shot Scheduling problems with power control in the SINR model, and provide constant approximation algorithms.

Dual power assignment optimization and fault tolerance in WSNs

Because of limited battery equipped on each sensor, power consumption is one of the crucial issues in wireless sensor networks (WSNs). It therefore has been the focus of many researchers. An important problem concerning power consumption is to minimize the number of maximum-power nodes while maintaining a desired network topology. As fault tolerance is vitally important in practice, it is desirable that the constructed network topology is

BROADCAST SCHEDULING PROBLEM IN SINR MODEL

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Energy-efficient connected d-hop dominating sets in wireless sensor networks

k-edge-connected or