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Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods. [ABSTRACT FROM AUTHOR]

Energy saving and effective utilization are an essential issue for wireless sensor network. Most previous cluster based routing protocols only care the relationship of cluster heads and sensor nodes but ignore the huge difference costs between them. In this paper, we present a routing protocol based on genetic algorithm for a middle layer oriented network in which the network consists of several stations that are responsible for receiving data and forwarding the data to the sink. The amount of stations should be not too many and not too few. Both cases will cause either too much construction cost or extra transmission energy consumption. We implement five methods to compare the performance and test the stability of our presented methods. Experimental results demonstrate that our proposed scheme reduces the amount of stations by 36.8 and 20% compared with FF and HL in 100-node network. Furthermore, three methods are introduced to improve our proposed scheme for effective cope with the expansion of network scale problem. [ABSTRACT FROM AUTHOR]

In this paper, a soft computing method, a probability theory, and a graph theoretic approach are utilized to describe this phenomenon. Uncertain random spectra, namely, natural tenacity, are established in this paper for assessing the survivability of MWSNs. We propose an elaborate mathematical formulation that relates the survivability to the natural tenacity. In addition, then the probability of link connectivity is proposed in order to connect the natural tenacity to the connectivity of the network, which reflects the most important indicators of network survivability. The natural tenacity of MWSNs is explored by both analytical and numerical ways. Moreover, we also compare it with other survivability measures. Under the smooth Gauss-semi-Markov mobility model, the effectiveness of the measure is verified through numerical analysis. The result indicates that the natural tenacity has a strong analytical ability to measure the survivability of MWSNs objectively. [ABSTRACT FROM AUTHOR]

The deployment problem (DP) of a wireless sensor network (WSN) is no doubt a critical issue because the strategies it takes will not only strongly impact the overall performance but also the power consumed by the sensors in such a system. This means that a good solution to the deployment problem (DP) cannot just “enhance its performance,” it can also “save the energy” to prolong the lifetime of a wireless sensor network (WSN). However, finding an optimum solution for most DPs with limited computation resources is still a challenging research problem, especially for the NP-hard optimization problems. Compared to exhaustive search and deterministic algorithms, metaheuristics provides an alternative way to solve these optimization problems, by looking for a near optimal solution using limited computation resources within a reasonable time. Aimed at providing not only a systematic survey of metaheuristics for solving the deployment problem but also a roadmap for researchers working on WSN and metaheuristics to further develop and improve a wireless sensor network system, this paper begins with an overview of WSN and the deployment problem, followed by discussions on metaheuristics and how to use them to solve the DP. Then, a comprehensive comparison between metaheuristics for the DP is given. Finally, open issues and future trends of this field are addressed. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is composed of a set of sensor nodes. It is deemed suitable for deploying with large-scale in the environment for variety of applications. Recent advances in WSN have led to many new protocols specifically for reducing the power consumption of sensor nodes. A new scheme for predetermining the optimized routing path is proposed based on the enhanced parallel cat swarm optimization (EPCSO) in this paper. This is the first leading precedent that the EPCSO is employed to provide the routing scheme for the WSN. The experimental result indicates that the EPCSO is capable of generating a set of the predetermined paths and of smelting the balanced path for every sensor node to forward the interested packages. In addition, a scheme for deploying the sensor nodes based on their payload and the distance to the sink node is presented to extend the life cycle of the WSN. A simulation is given and the results obtained by the EPCSO are compared with the AODV, the LD method based on ACO, and the LD method based on CSO. The simulation results indicate that our proposed method reduces more than 35% power consumption on average. [ABSTRACT FROM AUTHOR]

• Maximizing network lifetime will be cope with by resolving energy hole problem and preserving energy. • The energy hole problem is relieved by gradient deployment algorithm. • Based on the Choquet integral and Entropy, entropy-driven abnormal event monitoring algorithm is proposed. • Entropy-driven aggregation tree-based routing algorithm is designed for energy consumption. Data aggregation is one of the essential and fundamental processes in Wireless Sensor Networks (WSNs). When and how to gather data from the sensors to the sink has a direct impact on the lifetime of the WSNs because the energy consumption is proportion to the frequency of data transmission. In general, sensors in a WSN are randomly distributed for creating a massive coverage WSN environment within a short period. Because the sensors nearby the sink are responsible for more data forwarding tasks, they normally have much shorter lifetime than those located away from the sink. Once all sensors nearby the sink run out of energy, the data collected from the terminal sensors can not reach the sink because all established connections to the sink are broken, which is called the termination of a WSN lifetime. This paper proposes a strategy with multiple algorithms for deploying sensors aiming at maximizing a WSN lifetime. The proposed Entropy-driven Data Aggregation with Gradient Distribution (EDAGD) deployment strategy contains three algorithms: (1) multihop tree-based data aggregation, (2) entropy-driven aggregation tree-based routing algorithm and (3) gradient deployment algorithm. The numerical and experimental results indicating that the proposed EDAGD method outperforms the conventional algorithm with the random deployment strategy. [ABSTRACT FROM AUTHOR]