Guofang Nan

A Network and Device Aware QoS Approach for Cloud-Based Mobile Streaming

Cloud multimedia services provide an efficient, flexible, and scalable data processing method and offer a solution for the user demands of high quality and diversified multimedia. As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering the limited bandwidth available for mobile streaming and different device requirements, this study presented a network and device-aware Quality of Service (QoS) approach that provides multimedia data suitable for a terminal unit environment via interactive mobile streaming services, further considering the overall network environment and adjusting the interactive transmission frequency and the dynamic multimedia transcoding, to avoid the waste of bandwidth and terminal power. Finally, this study realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environments.

Estimation of Node Localization with a Real-Coded Genetic Algorithm in WSNs

Location knowledge of sensor nodes in a network is essential for many tasks such as routing, cooperative sensing, or service delivery in ad hoc, mobile, or sensor networks, and it is hard to get the precision solution by traditional node localization algorithm, while genetic algorithm is an effective methodology for solving combinatorial optimization problems, so, in this paper, a real-coded version of the commonly used genetic algorithm is described in order to evaluate the precision of node localization problem in wireless sensor networks, meanwhile, the corresponding fitness function and genetic operators are designed. The algorithms presented in this paper are validated on a combined Windows XP and MATLAB simulation on a sensor network with fixed number of nodes whose distance measurements are corrupted by Gaussian noise. The results show that the proposed scheme gives accurate location of nodes.

CDSWS: coverage-guaranteed distributed sleep/wake scheduling for wireless sensor networks

Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor network applications, and sleep/wake scheduling mechanism has been proved to an efficient approach to handling this issue. In this article, a coverage-guaranteed distributed sleep/wake scheduling scheme is presented with the purpose of prolonging network lifetime while guaranteeing network coverage. Our scheme divides sensor nodes into clusters based on sensing coverage metrics and allows more than one node in each cluster to keep active simultaneously via a dynamic node selection mechanism. Further, a dynamic refusal scheme is presented to overcome the deadlock problem during cluster merging process, which has not been specially investigated before. The simulation results illustrate that CDSWS outperforms some other existed algorithms in terms of coverage guarantee, algorithm efficiency and energy conservation.

Distributed Resource Allocation in Cloud-Based Wireless Multimedia Social Networks

With the rapid penetration of mobile devices, more users prefer to watch multimedia live-streaming via their mobile terminals. Quality of service provision is normally a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the description of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a gametheoretical framework that is further implemented in a distributed manner. In addition, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share bandwidth. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme.

An Efficient Data Aggregation Algorithm for Cluster-based Sensor Network

Data aggregation in wireless sensor networks eliminates redundancy to improve bandwidth utilization and energyefficiency of sensor nodes. One node, called the cluster leader, collects data from surrounding nodes and then sends the summarized information to upstream nodes. In this paper, we propose an algorithm to select a cluster leader that will perform data aggregation in a partially connected sensor network. The algorithm reduces the traffic flow inside the network by adaptively selecting the shortest route for packet routing to the cluster leader. We also describe a simulation framework for functional analysis of WSN applications taking our proposed algorithm as an example.

An Energy-Efficient and Fault-Tolerant Convergecast Protocol in Wireless Sensor Networks

The simple graph theory is commonly employed in wireless sensor networks topology control. An inherent problem of small-granularity algorithms is the high computing complexity and large solution space when managing large-scale WSNs. Computed transmission paths are of low fault tolerance because of unattended sensor nodes and frail wireless transmitting channels. This paper uses hyper-graph theory to solve these practical problems and proposes a spanning hyper-tree algorithm (SHTa) to compute the minimum transmitting power delivery paths set for WSNs convergecast. There are three main contributions of this paper: (1) we present a novel hyper-graph model to abstract large-scale and high connectivity WSNs into a robust hyper-tree infrastructure; (2) we present a precise mathematical derivation that solves the “hyper-tree existence” problem; (3) SHTa is proposed to compute the delivery paths set, which is the minimum power transmitting convergecast hyper-tree. Variable scale hyper-edges represented as computing units limit solution space and reduce computing complexity. Mutual backup delivery paths in one hyper-edge improve the capability of fault tolerance. With experiment results, SHTa computes short latency paths with low energy consumption, compared with previous algorithms. Furthermore, in dynamic experiments scenes, SHTa retains its robust transmitting quality and presents high fault tolerance.

Pricing and resource allocation for multimedia social network in cloud environments

Distributed resource allocation is critical for efficient sharing multimedia contents in cloud-based wireless multimedia social network environments. In this paper, a cloud-based multimedia service system architecture is proposed to overcome the limited bandwidth allocation problem in the context of social network, in which bandwidth limited mobile users are allowed to directly acquire live multimedia streaming from the desktop users rather than the cloud based on their social relationships. We also present a theoretical framework for bandwidth allocation from desktop users to mobile users by a dynamic resource pricing process in the proposed bandwidth management system, where price-sensitive users and QoS-sensitive users are considered, and all users target at maximizing their total utilities. Finally, an iterative allocation algorithm is designed to simulate the bandwidth allocation process with respect to shared bandwidth and price. Simulation results verify the effectiveness of our proposed pricing model and allocation algorithm in terms of convergence and efficiency.

Two novel encoding strategies based genetic algorithms for circuit partitioning

Circuit partitioning is a key phase in the VLSI design and partitioning algorithm is of great importance. Two styles of genetic algorithms based on different encoding strategies for circuit partitioning are presented. The first adopts the form of 0-1 encoding, and the second uses integer encoding based on modules number. Meanwhile, the corresponding fitness function and genetic operators are designed for each method. Then these two algorithms are implemented to test standard benchmark circuits. Compared with the traditional F-M algorithm, partition results by the two genetic algorithms are markedly improved.

Evolutionary Based Approaches in Wireless Sensor Networks: A Survey

With the broad application and techniques development of wireless sensor networks (WSNs), a series of optimization problems are produced, and most of these problems are NP hard, it is difficult to obtain the high precision of solution by traditional methods. While employing evolutionary algorithms (EAs) in WSNs is emerging as an important field, there is an important need to form a broad review of the current research and its future directions. Thus, this paper discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming in wireless sensor networks. We focus on how to understand of properly using EAs for the real problems and what problems in WSNs can be solved by EAs.

Optimizing Sensor Network Coverage and Regional Connectivity in Industrial IoT Systems

Internet of things (IoT) technologies have been widely used in industrial systems to control the manufacturing environment and monitor production lines. An industrial IoT system can perform data collection and processing and provide services to production decisions. However, the challenge remains for the IoT system to ensure the quality and quantity of data collected from sensor networks. To address the issue, an independent regional connectivity model is presented in the context of sensor networks to guarantee global connectivity with satisfied quality of data service. We also investigate the optimization of sensing coverage and regional connectivity in an industrial IoT system in both deterministic and random deployment. First, a novel optimal network that achieves full sensing coverage and guarantees regional connectivity is presented for deterministic deployment. The optimal pattern is derived, and the advantage of the proposed model is analyzed. Second, based on the assumption that the given sensors are deployed as a Poisson point process, theoretical analysis is presented to determine the minimum number of sensors used for random deployment to achieve certain coverage and connectivity degrees. Numerical results show that our proposed models are efficient for the application of sensor networks in industrial IoT systems.