Chi-Tsun Cheng

A Delay-Aware Data Collection Network Structure for Wireless Sensor Networks

Wireless sensor networks utilize large numbers of wireless sensor nodes to collect information from their sensing terrain. Wireless sensor nodes are battery-powered devices. Energy saving is always crucial to the lifetime of a wireless sensor network. Recently, many algorithms are proposed to tackle the energy saving problem in wireless sensor networks. In these algorithms, however, data collection efficiency is usually compromised in return for gaining longer network lifetime. There are strong needs to develop wireless sensor networks algorithms with optimization priorities biased to aspects besides energy saving. In this paper, a delay-aware data collection network structure for wireless sensor networks is proposed. The objective of the proposed network structure is to minimize delays in the data collection processes of wireless sensor networks. Two network formation algorithms are designed to construct the proposed network structure in a centralized and a decentralized approach. Performances of the proposed network structure are evaluated using computer simulations. Simulation results show that, when comparing with other common network structures in wireless sensor networks, the proposed network structure is able to shorten the delays in the data collection process significantly.

A Clustering Algorithm for Wireless Sensor Networks Based on Social Insect Colonies

A wireless sensor network comprises a number of inexpensive power constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the remote base station in a coordinated way. Employing techniques of clustering can reduce energy consumption of wireless sensor nodes and prolong the network lifetime. This paper proposes a decentralized clustering algorithm for wireless sensor networks based on the structure of social insect colonies. The clustering algorithm is evaluated assuming a first-order radio model. Simulation results show that the proposed algorithm brings a consistent improvement over other decentralized and centralized clustering algorithms in terms of network lifetime and sensing coverage. Simulation results also show that the proposed algorithm can reduce delays in data collection processes.

A Delay-Aware Network Structure for Wireless Sensor Networks With In-Network Data Fusion

A wireless sensor network (WSN) comprises a large number of wireless sensor nodes. Wireless sensor nodes are battery-powered devices with limited processing and transmission power. Therefore, energy consumption is a critical issue in system designs of WSNs. In-network data fusion and clustering have been shown to be effective techniques in reducing energy consumption in WSNs. However, clustering can introduce bottlenecks to a network, which causes extra delays in a data aggregation process. The problem will be more severe when in-network data fusion does not yield any size reduction in outgoing data. Such problems can be greatly alleviated by modifying the network structure. In this paper, a delay-aware network structure for WSNs with in-network data fusion is proposed. The proposed structure organizes sensor nodes into clusters of different sizes so that each cluster can communicate with the fusion center in an interleaved manner. An optimization process is proposed to optimize intra-cluster communication distance. Simulation results show that, when compared with other existing aggregation structures, the proposed network structure can reduce delays in data aggregation processes and keep the total energy consumption at low levels provided that data are only partially fusible.

An Energy-Aware Scheduling Scheme for Wireless Sensor Networks

Wireless sensor networks are widely adopted in target tracking applications. Wireless sensor nodes are battery-powered devices. These low-cost communication devices have high failure rates and are vulnerable to attacks. To maintain network connectivity and to ensure large network coverage, excessive wireless sensor nodes are usually deployed to provide redundancy. However, these extra wireless sensor nodes would dissipate more power. This problem can be relieved by proper scheduling and putting unnecessary sensor nodes into sleep mode. In this paper, an energy-aware scheduling scheme for wireless sensor networks is proposed. The proposed scheme is a kind of an adaptive/periodic on-off scheduling scheme in which sensor nodes use only local information to make scheduling decisions. The scheme is evaluated in terms of target hit rate, detection delay, and energy consumption per successful target detection. Simulation results show that when comparing with other generic scheduling schemes, the proposed scheme can significantly reduce energy consumption.

A Genetic Algorithm-Inspired UUV Path Planner Based on Dynamic Programming

Path planning can be viewed as an optimization process in which an optimum path between two points is to be found under some predefined constraints. Some typical constraints are path length, fuel consumption, and path safety factor. Exact algorithms such as linear programming (LP) and dynamic programming (DP) are widely adopted in vehicle maneuvering systems. However, as the problem domain scales up, exact algorithms suffer from high computational complexity. In contrast, metaheuristic algorithms such as evolutionary algorithms (EA) and genetic algorithms (GA) can provide suboptimum solutions without the full understanding of the problem domain. Metaheuristic algorithms are capable of providing decent solutions within a finite period of time, even for large-scaled problems. In this paper, a GA-inspired unmanned underwater vehicle (UUV) path planner based on DP is proposed. Simulation results show that the proposed algorithm can outperform a GA-based UUV path planner in terms of speed and solution quality.

Robust Positioning Systems in the Presence of Outliers Under Weak GPS Signal Conditions

In this paper, two epoch-by-epoch robust positioning techniques for global positioning system (GPS) are proposed to deal with the problem of positioning in weak signal conditions in which the probability of outlier in signal acquisition is larger than zero. We propose to accept outliers into the positioning algorithm, however, in this case either robust estimation or outlier detection must be used to overcome the devastating effect of such outliers on traditional positioning algorithms. In order to improve the sensitivity of a GPS receiver, we propose to use novel methods that are able to deal with the problem of estimating the position of a receiver based on pseudo-ranging measurements that are contaminated by outliers. Simulations are carried out to demonstrate the robustness of the proposed techniques in terms of success rate of the algorithms in finding the correct solution, when there are a different number of outliers in ranging measurements from satellites.

A Constraint-Aware Heuristic Path Planner for Finding Energy-Efficient Paths on Uneven Terrains

Motions of mobile robots need to be optimized to minimize their energy consumption to ensure long periods of continuous operations. Shortest paths do not always guarantee the minimum energy consumption of mobile robots. Moreover, they are not always feasible due to climbing constraints of mobile robots, especially on steep terrains. We utilize a heuristic search algorithm to find energy-optimal paths on hilly terrains using an established energy-cost model for mobile robots. The terrains are represented using grid-based elevation maps. Similar to A*-like heuristic search algorithms, the energy-cost of traversing through a given location of the map depends on a heuristic energy-cost estimation from that particular location to the goal. Using zigzag-like path patterns, the proposed heuristic function can estimate heuristic energy-costs on steep terrains that cannot be estimated using traditional methods. We proved that the proposed heuristic energy-cost function is both admissible and consistent. Therefore, the proposed path planner can always find feasible energy-optimal paths on any given terrain without node revisits, provided that such paths exist. Results of tests on real-world terrain models presented in this paper demonstrate the promising computational performance of the proposed path planner in finding energy-efficient paths.

Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions

Unmanned aerial vehicles (UAVs) are remote controlled or autonomous air vehicles. An UAV can be equipped with various types of sensors to perform life rescue missions or it can be armed with weapons to carry out stealthy attack missions. With the unmanned nature of UAVs, a mission can be taken in any hostile environment without risking the life of pilots. Among life rescue missions, the common objective is often defined as maximizing the total coverage area of the UAVs with the limited resources. When the number of UAVs increases, coordination among these UAVs becomes very complicated even for experienced pilots. In this paper, a cooperative path planner for UAVs is proposed. The path of each UAV is represented by a B-spline curve with a number of control points. The positions of these control points are optimized using an ant colony optimization algorithm (ACO) such that the total coverage of the UAVs is maximized.

Data Clustering with Cluster Size Constraints Using a Modified K-Means Algorithm

Data clustering is a frequently used technique in finance, computer science, and engineering. In most of the applications, cluster sizes are either constrained to particular values or available as prior knowledge. Unfortunately, traditional clustering methods cannot impose constrains on cluster sizes. In this paper, we propose some vital modifications to the standard k-means algorithm such that it can incorporate size constraints for each cluster separately. The modified k-means algorithm can be used to obtain clusters in preferred sizes. A potential application would be obtaining clusters with equal cluster size. Moreover, the modified algorithm makes use of prior knowledge of the given data set for selectively initializing the cluster centroids which helps escaping from local minima. Simulation results on multidimensional data demonstrate that the k-means algorithm with the proposed modifications can fulfill cluster size constraints and lead to more accurate and robust results.

A 2-Dimensional ACO-Based Path Planner for Off-Line Robot Path Planning

Wireless sensor networks are usually deployed in scenarios that are too hostile for human personnel to perform maintenance tasks. Wireless sensor nodes usually exchange information in a multi-hop manner. Connectivity is crucial to the performance of a wireless sensor network. In case a network is partitioned due to node failures, it is possible to re-connect the fragments by setting up bridges using mobile platforms. Given the landscape of a terrain, the mobile platforms should be able reach the target position using a desirable path. In this paper, an off-line robot path planner is proposed to find desirable paths between arbitrary points in a given terrain. The proposed path planner is based on ACO algorithms. Unlike ordinary ACO algorithms, the proposed path planner provides its artificial ants with extra flexibility in making routing decisions. Simulation results show that such enhancement can greatly improve the qualities of the paths obtained. Performances of the proposed path planner can be further optimized by fine-tuning its parameters.