Guiran Chang

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.

Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius

In this paper, the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and a small amount of energy consumption in a wireless sensor network is studied. As opposed to the uniform sensing model previously, we consider a large number of sensors with adjustable sensing radius that are randomly deployed to monitor a target area. A novel coverage control scheme based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed in a heterogeneous sensor network. By devising a cluster-based architecture, the algorithm is applied in a distributed way. Furthermore, an ameliorated binary coding is addressed to represent both sensing radius adjustment and sensor selection. Numerical and simulation results validate that the procedure to find the optimal balance point among the maximum coverage rate, the least energy consumption, as well as the minimum number of active nodes is fast and effective.

Modeling and evaluation of trust in cloud computing environments

Today, one of the most important factors for the success of cloud computing is to create trust and security. Cloud computing will face a lot of challenges when the key element trust is absent. There are no special trust evaluation models for cloud computing environment. In this paper, the definition of trust in cloud systems is introduced and the properties of trust are analyzed. Based on the properties and semantics of trust, an extensible trust evaluation model named ETEC is proposed, which includes a time-variant comprehensive evaluation method for expressing direct trust and a space-variant evaluation method for calculating recommendation trust. To compute trust in cloud systems, an algorithm based on the ETEC model is given. Simulation and analysis shows that this model can calculate the trust degree effectively and reasonably in cloud computing environments.

Efficient Nash equilibrium based cloud resource allocation by using a continuous double auction

To allocate cloud resources efficiently and obtain the maximum economic benefit are the major goals of the cloud resource providers and users. The objective of this paper is to present a novel cloud resource allocation algorithm named NECDA to overcome some of the shortcomings of the current mechanisms. A cloud resource allocation model of m*n type based on M/M/1 queuing system is established first. Then the Nash equilibrium mode is applied in the cloud computing among m*n allocation environment, the optimization objective of each allocation is performance-QoS and economic-QoS, and the Nash equilibrium is achieved by taking advantage of the continuous double auction in each step, each provider agent determines its requested value based on its workload, and each user agent determines its bid value based on the remaining time and resources. By simulations, we conclude that the NECDA is more suited to cloud computing environments and can better meet the QoS requirements of users.

Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments

Failures are normal rather than exceptional in cloud computing environments, high fault tolerance issue is one of the major obstacles for opening up a new era of high serviceability cloud computing as fault tolerance plays a key role in ensuring cloud serviceability. Fault tolerant service is an essential part of Service Level Objectives (SLOs) in clouds. To achieve high level of cloud serviceability and to meet high level of cloud SLOs, a foolproof fault tolerance strategy is needed. In this paper, the definitions of fault, error, and failure in a cloud are given, and the principles for high fault tolerance objectives are systematically analyzed by referring to the fault tolerance theories suitable for large-scale distributed computing environments. Based on the principles and semantics of cloud fault tolerance, a dynamic adaptive fault tolerance strategy DAFT is put forward. It includes: (i) analyzing the mathematical relationship between different failure rates and two different fault tolerance strategies, which are checkpointing fault tolerance strategy and data replication fault tolerance strategy; (ii) building a dynamic adaptive checkpointing fault tolerance model and a dynamic adaptive replication fault tolerance model by combining the two fault tolerance models together to maximize the serviceability and meet the SLOs; and (iii) evaluating the dynamic adaptive fault tolerance strategy under various conditions in large-scale cloud data centers and consider different system centric parameters, such as fault tolerance degree, fault tolerance overhead, response time, etc. Theoretical as well as experimental results conclusively demonstrate that the dynamic adaptive fault tolerance strategy DAFT has high potential as it provides efficient fault tolerance enhancements, significant cloud serviceability improvement, and great SLOs satisfaction. It efficiently and effectively achieves a trade-off for fault tolerance objectives in cloud computing environments.

A Dependability Model to Enhance Security of Cloud Environment Using System-Level Virtualization Techniques

Security of cloud computing is one of the challenges to be addressed before the novel pas-as-you-go business model is widely applied, and dependability is one of the most important means to improve security of current heterogeneous cloud platforms. Previous research on dependability in computing systems only uses qualitative approaches and there are few systematic works on dependability in cloud systems. In this paper, the definition of dependability in cloud systems is given and a series of quantitative indicators are presented to evaluate the dependability. A novel cloud dependability model CDSV is established to enhance the security of heterogeneous cloud environments. System-level virtualization techniques are used to enhance the dependability of cloud environments. Systematic analysis shows that this model can enhance the system dependability and security. Experimental results show that the dependability model CDSV can efficiently and safely construct dependability relationship in heterogeneous cloud environments.

Coverage Optimization based on Improved NSGA-II in Wireless Sensor Network

Wireless sensor networks (WSN) constitute the platform of a wide application related to military, remote monitoring, inhospitable physical environment, and national security. Reducing energy consumption to extend network lifetime is one of the most important requirements in designing wireless sensor networks. Keeping only a minimal number of sensors active and putting others into low-power sleep mode is one promising approach to conserve system energy, in which the active sensors can maintain the communication connectivity and cover the target region completely. However, the problem of sorting such minimal active sensor set is NP-complete. In this paper, elitist non-dominated sorting genetic algorithm (NSGA-II), a new multi-objective genetic algorithm, is applied to coverage problem in wireless sensor networks. The novel scheme maximizes the coverage using a relative small quantity of sensor nodes in a given target area. Simulation results show that the algorithm is fast and effective, which gives strong support to the selection of optimal node set.

Efficient Cover Set Selection in Wireless Sensor Networks

Abstract The effectiveness of a cluster-based distributed sensor network, to a large extent, depends on the coverage provided by the sensor nodes. To activate only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy. However, this is an NP-complete problem because of the high-density deployment of wireless sensor networks. In this paper, a novel searching algorithm based on improved NSGA-II (elitist nondominated sorting genetic algorithm) is proposed to select an optimal cover set. In contrast to the binary detection model used in the previous work, a probabilistic detection model is adopted in combination with the detection error range and coverage threshold. With the full network coverage being guaranteed, a number of nodes are made into dormancy mode to save energy. The circulated combination and delete operators are proposed to enhance the search capability. Extensive simulation results are presented to demonstrate the effectiveness of our approach.

A Cold-Start Recommendation Algorithm Based on New User's Implicit Information and Multi-attribute Rating Matrix

Traditional collaborative filtering recommendation algorithms face the cold-start problem. A collaborative filtering recommendation algorithm based on the implicit information of the new users and multi-attribute rating matrix is proposed to solve the problem. The implicit information of the new users is collected as the first-hand interest information. It is combined with other rating information to create a User-Item Rating Matrix (UIRM). Singular Value Decomposition is used to reduce the dimensionality of the UIRM, resulting in the initial neighbor set for target users and a new user-item rating matrix. The user ratings are mapped to the relevant item attributes and the user attributes respectively to generate a User-Item Attribute Rating Matrix and a User Attribute-Item Attribute Rating Matrix (UAIARM). The attributes of new items and UAIARM are matched to find the N users with the highest match degrees as the target of the new items. The attributes of the new users are matched with UAIARM to find the N items with the highest match degrees as the recommended items. Experiment results validate the feasibility of the algorithm.

A dynamic multi-dimensional trust evaluation model to enhance security of cloud computing environments

High security of cloud computing is one of the most challenges to be addressed before the novel pas-as-you-go business paradigm is widely applied over the internet. Trust brings a novel means to improve the security of cloud computing platforms. However, there are few systematic works on trust in cloud systems. In this paper, the definition of trust in cloud systems is given and the properties of trust are analysed by referring to the fruits from social science. Based on the properties and semantics of trust, a dynamic multi-dimensional trust model named DMTC is proposed, which includes a time-variant comprehensive evaluation multi-dimensional method for expressing direct trust and a space-variant evaluation multi-dimensional method for calculating recommendation trust. To compute trust in cloud systems, an algorithm based on the DMTC model is given. Experiment results show that the proposed model can dynamically and effectively construct the trust relationship in cloud computing environment.