Coverage problem with uncertain properties in wireless sensor networks: A survey

In the application environment having dense distribution of marginal wireless sensor network (WSN), the data transmission process will generate a large number of conflicts, which will result in loss of transmission data and increase of transmission delay. The multi-path data transmission method can effectively solve the problem of large data loss and transmission delay caused by collisions. A new approach of multi-path reliable transmission for application of marginal WSN (named RCB-MRT) is proposed in this paper. It adopts redundancy mechanism to realize the reliability of data transmission, and uses concurrent woven multi-path technology to improve the transmission efficiency of data packets. Firstly, it divides the data packets that the sensor node needs to transmit into several sub-packets with data redundancy, and then forwards the sub-packets to the aggregation node through multi-path by the intermediate nodes of marginal environment. The results of our experimental tests show that the proposed multi-path reliable transmission method can effectively reduce data packet loss rate, reduce transmission delay and increase network lifetime. The method is very useful for the applications of marginal wireless sensor network.

New approach of multi-path reliable transmission for marginal wireless sensor network

To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.

A hybrid-strategy-improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks.

Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases

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.

/pdf/neighbor-constraint-assisted-distributed-localization-for-12tjulatpy.pdf

Neighbor Constraint Assisted Distributed Localization for Wireless Sensor Networks

This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targets -covered.

/pdf/distributed-multitarget-probabilistic-coverage-control-iew0y0xrnl.pdf

Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

https://downloads.hindawi.com/journals/aaa/2014/278694.pdf

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

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Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm