Chor Ping Low

Efficient Load-Balanced Clustering Algorithms for wireless sensor networks

Wireless sensor networks have been receiving increasing attention in recent years due to their potential applications in the establishment of dynamic communications for emergency/rescue operations, disaster relief efforts, and military networks. In this paper, we investigate the problem of grouping the sensor nodes into clusters to enhance the overall scalability of the network. A selected set of nodes, known as gateway nodes, will act as cluster-heads for each cluster and the objective is to balance the load among these gateways. Load-Balanced Clustering increases system stability and improves the communication between the various nodes in the network. We call the problem addressed in this paper as the Load-Balanced Clustering Problem (LBCP). We first show that a special case of LBCP (whereby the traffic load contributed by all sensor nodes are the same) is optimally solvable in polynomial time. We next prove that the general case of LBCP is NP-hard. We then proposed an efficient 32-approximation algorithm for the problem.

Preserving and Exploiting Genetic Diversity in Evolutionary Programming Algorithms

Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the Cauchy distribution instead of the commonly used Gaussian distribution. In this paper, we seek to improve the performance of EP via exploring another important factor of EP, namely, the selection strategy. Three selection rules R1-R3 have been presented to encourage both fitness diversity and solution diversity. Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity. Simple theoretical analysis suggests that through the proper use of R1-R5, EP is more likely to find high-fitness solutions quickly. Our claim has been examined on 25 benchmark functions. Empirical evidence shows that our solution selection and exchange rules can significantly enhance the performance of EP.

An efficient reconfiguration algorithm for degradable VLSI/WSI arrays

This paper considers the problem of reconfiguring two-dimensional degradable VLSI/WSI arrays under the constraint of row and column rerouting. The goal of the reconfiguration problem is to derive a fault-free subarray T from the defective host array such that the dimensions of T are larger than some specified minimum. This problem has been shown to be NP-complete under various switching and routing constraints. However, we show that a special case of the reconfiguration problem is optimally solvable in linear time. Using this result, a new fast and efficient reconfiguration algorithm is proposed. Empirical study shows that the new algorithm indeed produces good results in terms of the percentages of harvest and degradation of VLSI/WSI arrays.

A distributed group mobility adaptive clustering algorithm for mobile ad hoc networks

This paper proposes a distributed group mobility adaptive (DGMA) clustering algorithm for mobile ad hoc networks (MANETs) on the basis of a revised group mobility metric, linear distance based spatial dependency (LDSD), which is derived from the linear distance of a nodes movement instead of its instantaneous speed and direction. In particular, it is suitable for group mobility pattern where group partitions and mergence are prevalent behaviors of mobile groups. The proposed clustering scheme aims to form more stable clusters by prolonging cluster lifetime and reducing the clustering iterations even in highly dynamic environment. Simulation results show that the performance of the proposed framework is superior to two widely referenced clustering approaches, the Lowest-ID clustering scheme and the mobility based clustering algorithm MOBIC, in terms of average clusterhead lifetime, average resident time, average number of clusterhead changes, and average number of cluster reaffiliations.

On finding feasible solutions for the delay constrained group multicast routing problem

Group multicasting is a generalization of multicasting whereby every member of a group is allowed to multicast messages to other members that belong to the same group. In this paper, we study the problem of finding feasible solutions for the delay constrained group multicast routing problem (DCGMRP). The routing problem in this case involves the construction of a set of delay bounded multicast trees with bandwidth requirements, one for each member of the group, for multicasting messages to other members of the group. We first show that the problem is NP-complete. Next, we propose a heuristic algorithm to find feasible solutions for this problem. Simulation results show that our proposed algorithm is able to achieve a high probability of finding feasible solutions for DCGMRP, whenever one exists.

Distributed multicast routing, with end-to-end delay and delay variation constraints

We study the problem of constructing multicast trees for high-bandwidth delay-sensitive applications in a point-to-point communication network. This problem arise in real time multimedia applications, which often requires bounded end-to-end delay along paths from the source to each destination and bounded variations among the delays along these paths to ensure that data packets reaches the destinations without exceeding certain delay, failing which the user will experience jitters effect. This problem can be formulated as that of finding a minimum cost Steiner tree which satisfy the above-mentioned constraints and is known to be computationally intractable, being NP-complete. In this paper, we propose a new distributed algorithm, called the Distributed Delay and Delay Variation Bounded Multicast (DDVBM) algorithm for the problem. Empirical studies shows that our proposed algorithm performs very well in terms of cost and inter-destination delay variations of the solutions that it generates as compared to some existing algorithms.

Load-Balanced Clustering Algorithms for Wireless Sensor Networks

In this paper, we investigate the problem of grouping the sensor nodes into clusters to enhance the overall scalability of the network. A selected set of nodes, known as gateway nodes, will act as cluster-heads for each cluster and the objective is to balance the load among these gateways. Load balanced clustering increases system stability and improves the communication between the various nodes in the network. We call the problem addressed in this paper as the load-balanced clustering problem (LBCP). We first show that a special case of LBCP (whereby the traffic load contributed by all sensor nodes are the same) is optimally solvable in polynomial time. We next prove that the general case of LBCP is NP-hard. We then proposed an efficient 2/3-approximation algorithm for the problem.

Energy neutral clustering for energy harvesting wireless sensors networks

For wireless sensors with energy harvesting capability, energy neutral operation can be achieved by ensuring that the amount of energy consumed is no more that the amount of energy harvested in certain period of time. As a result, an energy neutral sensor will be able to sense and relay useful information to the destination perpetually. In this paper, we propose a hierarchical routing protocol that can cluster sensors in the network in a way that all sensors will be able to maintain energy neutral status. Based on a novel Cluster Head Group mechanism, our proposed routing protocol can provide consistent data delivery with a low control message overhead. This in turn helps to improve the total amount of information bits that can be relayed to destinations as compared with other traditional clustering protocols. Simulations confirm that our proposed routing protocol can provide such improvement while ensuring the energy neutral status of the whole network.

Prediction free energy neutral power management for energy harvesting wireless sensor nodes

Current power management mechanisms for energy harvesting wireless sensors typically rely on predicted information about the amount of energy that can be harvested in the future. However, such mechanisms suffer from inevitable prediction errors, which in turn degrade the overall performance in real implementations. To circumvent such problems, we propose a fundamental framework to efficiently manage the harvested energy in a prediction free manner. In particular, we theoretically derive a set of Budget Assigning Principles (BAPs) to maximize the amount of harvested energy that can be utilized by a sensor in the presence of battery energy storage inefficiencies, which in turn maximize the sensors performance level in terms of the sensors average duty cycle. A Prediction FREE Energy Neutral (P-FREEN) power management mechanism is then proposed to implement the BAPs based solely on current observed energy harvesting rate and battery residual energy level. The performance of P-FREEN is verified via theoretical analysis and extensive computer simulations using real life energy harvesting data sets.

Throughput optimal energy neutral management for energy harvesting wireless sensor networks

Energy harvesting techniques have enabled the provisioning of alternative energy sources beside the conventional one which is typically provided by the batteries. Using such harvesting techniques together with proper energy management mechanisms, a Energy Neutral state can be achieved so that desired performance level can be supported perpetually. Existing energy neutral management mechanisms rely on the prediction of the amount of energy that can be harvested in the future. Such mechanisms suffer from the time consuming prediction processes and great fluctuations in the actual implementations. Hence, in this paper we propose real time adaptive energy management policies that enable energy neutral management based solely on observed information in the past instead of predicting the amount of energy that can be harvested in the future. We will show that these policies are throughput optimal in the sense that we are able to ensure that the data queue of each sensor node remain stable when the largest possible data rate is applied under different channel capacity scenarios. We also propose an energy harvest-store(use) method to reduce the energy losses caused by storing energy in non ideal energy buffers. Simulation studies show that our proposed throughput optimal policies and harvest-store(use) method are indeed able to improve the overall throughput as compared to existing schemes.