Baihai Zhang

An Extended Virtual Force-Based Approach to Distributed Self-Deployment in Mobile Sensor Networks

Virtual physics based approach is one of the major solutions for self-deployment in mobile sensor networks with stochastic distribution of nodes. To overcome the connectivity maintenance and nodes stacking problems in the traditional virtual force algorithm (VFA), an extended virtual force-based approach is investigated to achieve the ideal deployment. In low- R c VFA, the orientation force is proposed to guarantee the continuous connectivity. While in high- R c VFA, a judgment of distance force between node and its faraway nodes is considered for preventing node stacking from nonplanar connectivity. Simulation results show that self-deployment by the proposed extended virtual force approach can effectively reach the ideal deployment in the mobile sensor networks with different ratio of communication range to sensing range. Furthermore, it gets better performance in coverage rate, distance uniformity, and connectivity uniformity than prior VFA.

A low energy intelligent clustering protocol for wireless sensor networks

LEACH (low-energy adaptive clustering hierarchy) is a well-known self-organizing, adaptive clustering protocol of wireless sensor networks. However it has some shortcomings when it faces such problems as the cluster construction and energy management. In this paper, LEICP (low energy intelligent clustering protocol), an improvement of the LEACH protocol is proposed to overcome the shortcomings of LEACH. LEICP aims at balancing the energy consumption in every cluster and prolonging the network lifetime. A fitness function is defined to balance the energy consumption in every cluster according to the residual energy and positions of nodes. In every round the node called auxiliary cluster-head calculates the position of the cluster-head using Bacterial Foraging Optimization Algorithm (BFOA). After aggregating the data received, the cluster-head node decides whether to choose another cluster-head as the next hop for delivering the messages or to send the data to the base station directly, using Dijkstra algorithm to compute an optimal path. The performance of LEICP is compared with that of LEACH. Simulation results demonstrate that LEICP can prolong the lifetime of the sensor network by about 62.28% compared with LEACH and acquire uniform number of cluster-heads and messages in the network.

Sliding mode control for multi-agent systems under a time-varying topology

This paper addresses the tracking problem of a class of multi-agent systems under uncertain communication environments which has been modelled by a finite number of constant Laplacian matrices together with their corresponding scheduling functions. Sliding mode control method is applied to solve this nonlinear tracking problem under a time-varying topology. The controller of each tracking agent has been designed by using only its own and neighbours’ information. Sufficient conditions for the existence of a sliding mode control tracking strategy have been provided by the solvability of linear matrix inequalities. At the end of this work, numerical simulations are employed to demonstrate the effectiveness of the proposed sliding mode control tracking strategy.

Dynamics in small worlds of tree topologies of wireless sensor networks

Tree topologies, which construct spatial graphs with large characteristic path lengths and small clustering coefficients, are ubiquitous in deployments of wireless sensor networks. Small worlds are investigated in tree-based networks. Due to link additions, characteristic path lengths reduce rapidly and clustering coefficients increase greatly. A tree abstract, Cayley tree, is considered for the study of the navigation algorithm, which runs automatically in the small worlds of tree-based networks. In the further study, epidemics in the small worlds of tree-based wireless sensor networks on the large scale are studied, and the percolation threshold is calculated, at which the outbreak of the epidemic takes place. Compared with Cayley tree, there is a smaller percolation threshold suffering from the epidemic.

Energy Balanced Redeployment Algorithm for Heterogeneous Wireless Sensor Networks

Wireless sensor networks (WSNs) have gained worldwide attention in recent years. Since WSNs can be conveniently deployed to monitor a given field of interest, they have been considered as a great long-term economic potential for military, environmental, and scientific applications and so forth. One of the most active areas of research in WSNs is the coverage which is one of the most essential functions to guarantee quality of service (QoS) in WSNs. However, less attention is paid on the heterogeneity of the node and the energy balance of the whole network during the redeployment process. In this work, the energy balanced problems in mobile heterogeneous WSNs redeployment have been analyzed. The virtual force algorithm with extended virtual force model is used to improve the QoS of the deployment. Furthermore energy model is added to enhance or limit the movement of the nodes so that the energy of nodes in the whole WSNs can be balanced and the lifetime of the networks can be prolonged. The simulation results verify the effectiveness of this proposed algorithm.

Epidemics on small worlds of tree-based wireless sensor networks

Due to link additions, small world phenomena exist in tree-based wireless sensor networks. Epidemics on small worlds of tree-based networks are studied, and the epidemic threshold at which the outbreak of the epidemic occurs is calculated. Epidemiological processes are analyzed when the infection probability is larger than the percolation threshold. Although different epidemiological processes occur on the underlying tree topology, the number of infected nodes increases exponentially as the infection spreads. The uniform immunization procedure is conducted in the homogeneous small-world network. The infection still extends exponentially although the immunization effectively reduces the prevalence speed.

Necessary and sufficient conditions for consensus of multi-agent systems with nonlinear dynamics and variable topology

This paper studies some necessary and sufficient conditions for consensus of continuous multi-agent systems with nonlinear node dynamics and variable topology. The multi-agent systems are under variable topology. Basic theoretical analysis is carried out for the case where for each agent the nonlinear dynamics are governed by the position terms of the neighbor nodes. A necessary and sufficient condition associated with eigenvalues is given to ensure consensus of the nonlinear multi-agent system. Based on this result, a simulation example is given to verify the theoretical analysis.

Leak detection and localization of gas pipeline system based on wavelet analysis

Pipelines are one of great important components for gas and petrochemical industries. Normally, they are used to transport gas by hundreds or even thousands of miles. It is a momentous task to keep the gas pipeline under safety operation. Leak detection and localization mechanisms play an important role in the management of a gas pipeline system. In this paper, wavelet transform was used to detect local singularities in the pressure time history due to the presence of a leak. In order to test the performance of the proposed method, a practical test rig has been built. The experiment results showed the accurate and robustness of the proposed leak detection and localization methodology.

Verification and predicting temperature and humidity in a solar greenhouse based on convex bidirectional extreme learning machine algorithm

Predictions regarding the solar greenhouse temperature and humidity are important because they play a critical role in greenhouse cultivation. On account of this, it is important to set up a predictive model of temperature and humidity that would precisely predict the temperature and humidity, reducing potential financial losses. This paper presents a novel temperature and humidity prediction model based on convex bidirectional extreme learning machine (CB-ELM). Simulation results show that the convergence rate of the bidirectional extreme learning machine (B-ELM) can further be improved while retaining the same simplicity, by simply recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. The performance of the CB-ELM model is compared with other modeling approaches by applying it to predict solar greenhouse temperature and humidity. The experiment results show that the CB-ELM model predictions are more accurate than those of the B-ELM, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Radial Basis Function (RBF). Therefore, it can be considered as a suitable and effective method for predicting the solar greenhouse temperature and humidity.

Neighbor Constraint Assisted Distributed Localization for Wireless Sensor Networks

Localization is one of the most significant technologies in wireless sensor networks (WSNs) since it plays a critical role in many applications. The main idea in most localization methods is to estimate the sensor-anchor distances that are used by sensors to locate themselves. However, the distance information is always imprecise due to the measurement or estimation errors. In this work, a novel algorithm called neighbor constraint assisted distributed localization (NCA-DL) is proposed, which introduces the application of geometric constraints to these distances within the algorithm. For example, in the case presented here, the assistance provided by a neighbor will consist in formulating a linear equality constraint. These constraints can be further used to formulate optimization problems for distance estimation. Then through some optimization methods, the imprecise distances can be refined and the localization precision is improved.