Zhezhuang Xu

Joint Clustering and Routing Design for Reliable and Efficient Data Collection in Large-Scale Wireless Sensor Networks

For data collection in large-scale wireless sensor networks (WSNs), dynamic clustering provides a scalable and energy-efficient solution, which uses cluster head (CH) rotation and cluster range assignment algorithms to balance the energy consumption. Nevertheless, most existing works consider the clustering and routing as two isolated issues, which is harmful to the connectivity and energy efficiency of the network. In this paper, we provide a detailed analysis on the relations between clustering and routing, and then propose a joint clustering and routing (JCR) protocol for reliable and efficient data collection in large-scale WSN. JCR adopts the backoff timer and gradient routing to generate connected and efficient intercluster topology with the constraint of maximum transmission range. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analyses. The results show that the multihop routing in JCR may lead to the unbalanced CH selection. Then, the solution is provided to optimize the network lifetime by considering the gradient of one-hop neighbor nodes in the setting of backoff timer. Theoretical analysis and simulation results prove the connectivity and efficiency of the network topology generated by JCR.

Hybrid Clustering and Routing Strategy with Low Overhead for Wireless Sensor Networks

Dynamic Clustering is an efficient topology management approach for sensor networks. Observing the fact that the clustering has tight relations with inter-cluster routing problem, we propose a hybrid clustering and routing protocol (HCR) that considers both the cluster head selection and routing discovery problems. Random backoff and gradient routing strategies are used to achieve our design goals with low overhead. Considering the limited transmission range bounded by the hardware, the clustered network generated by HCR is ensured to be connected. Simulation results demonstrate that our proposed strategies ensure the connectivity of the network and improve the energy efficiency for data transmission.

Spatial correlated data collection in wireless sensor networks with multiple sinks

Due to the high density of node deployment in wireless sensor network, the sensing data of nodes in spatially proximate locations are highly correlated. By effectively exploiting this spatial correlation in the data collection process, unnecessary energy costs for redundant data transmission can be largely reduced. In this paper, we focus on collecting spatial correlated data in multi-sink scenario. The main challenge in this scenario is that data collection process should consider how to exploit the spatial correlation and decide which sink the data are transmitted to at the same time. To address this challenge, we propose an algorithm to select a subset of sensor nodes to represent the whole multi-sink sensor network based on the spatial correlated sensing readings. In this algorithm, only these representatives named sources need to upload their data to the chosen sinks. The problem is firstly formulated as a Binary Integer Linear Programming (BILP). Since the problem is proved to be NP-Complete, two heuristic algorithms are designed for approximation. The simulation results show that the proposed algorithms can largely reduce the number of the sources and then significantly improve energy efficiency.

Sharing Mobility Strategy Improves Location Service in Wireless Sensor and Actor Networks

Location service is an efficient solution to handle actor mobility in wireless sensor and actor networks. In previous works, the information that the actor updates to location servers is its latest location. However, in most applications, the actor knows its own mobility strategy, such as its destination, trajectory and speed. If the actor can share its mobility strategy in the update packet, it will be helpful to improve the performance of location service. In this letter, we propose a mobility strategy sharing location service protocol called MLS, to show the description of mobility strategy in the update packet and its utilization for routing. The theoretical analysis and simulation results show that, sharing mobility strategy greatly reduces the update overhead and improves the scalability of MLS.

DGR: dynamic gradient-based routing protocol for unbalanced and persistent data transmission in wireless sensor and actor networks

This paper is concerned with the routing protocol design for large-scale wireless sensor and actor networks (WSANs). The actor-sensor-actor communication (ASAC) strategy is first proposed to guarantee the reliability of persistent actor-actor communication. To keep network connectivity and prolong network lifetime, we propose a dynamic gradient-based routing protocol (DGR) to balance the energy consumption of the network. With the different communication ranges of sensors and actors, the DGR protocol uses a data load expansion strategy to significantly prolong the network lifetime. The balance coefficient and the routing re-establishment threshold are also introduced to make the tradeoff between network lifetime and routing efficiency. Simulation results show the effectiveness of the proposed DGR protocol for unbalanced and persistent data transmission.

Ballooning: An Agent-Based Search Strategy in Wireless Sensor and Actor Networks

Due to the actor mobility and duty cycle of sensor nodes, the data forwarding from sensor nodes to mobile actors is a challenging issue. Motivated by the research results in multi-agent systems, we consider the search packet as an agent moving in the network to search mobile actors. Then a strategy called ballooning is proposed to control the search process. The theoretical and numerical results show that the ballooning strategy can forward the search packet to mobile actors with bounded delay and efficient energy consumption.

Balancing Energy Consumption with Hybrid Clustering and Routing Strategy in Wireless Sensor Networks

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k=1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in gradient k=1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime.

DARC: a distributed and adaptive routing protocol in cluster-based wireless sensor networks

Due to the limited energy of wireless sensor nodes, the energy efficiency of data collection is a key issue in wireless sensor network (WSN). Dynamic clustering is a scalable and energy efficient solution for data collection in WSN. However, it suffers from the imbalanced energy consumption in the intercluster communication. To solve this problem, in this paper, we propose a distributed and adaptive routing protocol for cluster-based wireless sensor networks (DARC). In DARC, an adaptive energy threshold is proposed for the cluster head to control its intercluster routing mode, and a tunable cost function is designed for relay selection. Simulation results prove that DARC effectively balances the energy consumption in the intercluster communication and hence improves the energy efficiency and prolongs the network lifetime.

Wireless sensor networks based localization for audio-source: A GCC-GA method

It is a very important and challenging task to localize an audio-source in wireless sensor networks (WSNs). One of efficient methods for audio-source localization is the so-called time difference of arrival (TDOA) based approach. In this paper, we propose a novel localization method based on Generalized Cross Correlation-Genetic Algorithm (GCC-GA). It utilizes GCC method to calculate the TDOA and GA to improve the localization accuracy. The proposed method is implemented in a real grid wireless sensor network. The experimental results show that the network can estimate the source location with better accuracy and lower complexity.

An adaptive routing strategy for cluster-based wireless sensor networks

Dynamic clustering provides a scalable and energy efficient solution for data transmission in wireless sensor networks. Observing the fact that the cluster reorganization can not solve the problem of imbalanced energy consumption in the inter-cluster communication, we propose an adaptive routing strategy for cluster-based wireless sensor networks (ARC). The goal of ARC is to balance the energy consumption of nodes by adaptively controlling the modes of inter-cluster routing. In ARC, an adaptive energy threshold and a tunable cost function are designed for cluster heads to make routing decisions. Simulation results demonstrate that ARC effectively balances the energy consumption in the inter-cluster communication, and hence improves the energy efficiency and prolongs the network lifetime.