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Node location is of great significance as a supporting technology of wireless sensor network (WSN). The information without position would be greatly devalued. So this paper is about location algorithm for nodes of ship-borne WSN based on research on structure of ship and work environment of ship-borne WSN. The whole location process consists of two steps: one is location algorithm among cabins (LAAC), and the other one is location algorithm in the cabin (LAIC). LAAC refers to location with the topology of ship-borne WSN. We can learn which cabin the node lies in. LAIC refers to location based on received signal strength indication (RSSI), we can get distance relationship between nodes by RSSI, and then obtain the specific location by solving this distance relationship. In the last part, this paper verifies the designed location algorithm by experimenting on "A" ship. Experiments show that the location algorithm designed by this paper is feasible. [ABSTRACT FROM AUTHOR]

Big data and distributed computing are of great importance in wireless sensor networks (WSNs).They are always bonded together, and the latter one upholds the former one. In distributed computing, mobile agent model is the mainstream technology. With autonomy, communicativeness, mobility, and role, mobile agent model is more suitable for large-scale, resources-restrained WSN to deal with big data. This paper mainly studies migration schemes for mobile agents, determines core factors of migration strategy, and proposes SMLA and DMLA algorithms. And aiming at revealing the characteristics of target tracking, this paper puts forward pid-DMLA algorithm; and considering multiple agents' cooperation, it presents Mpid-DMLA algorithm. Moreover, this paper evaluates and analyzes the advantages and disadvantages of the above-mentioned four algorithms by simulations. [ABSTRACT FROM AUTHOR]

In designing wireless sensor networks, it is important to reduce energy dissipation and prolong network lifetime. This paper presents research on the existing clustering algorithm applied in heterogeneous sensor networks and then puts forward an energy-efficient prediction clustering algorithm, which is adaptive to sensor networks with energy and objects heterogeneous. This algorithm enables the nodes to select the cluster head according to factors such as energy and communication cost, thus the nodes with higher residual energy have higher probability to become a cluster head than those with lower residual energy, so that the network energy can be dissipated uniformly. In order to reduce energy consumption when broadcasting in clustering phase and prolong network lifetime, an energy consumption prediction model is established for regular data acquisition nodes. Simulation results show that compared with current clustering algorithms, this algorithm can achieve longer sensor network lifetime, higher energy efficiency, and superior network monitoring quality. [ABSTRACT FROM AUTHOR]

Optimizing the path planning to reduce the time and cost is an essential consideration in modern society, and existing research has mostly concentrated on static path planning and real-time data information in vehicle navigational applications. Using dynamic path planning to adjust and update the path information in time is a challenging approach to reduce road congestion and traffic accidents. In this paper, we present a data analysis algorithmthat determines an efficient dynamic path for vehicle repair-scrap sites and navigates more flexibly to avoid obstacles, where the key idea is to design the sensor wireless network that helps to obtain data fromdifferent devices. Firstly, the data processing scheme for real-time data with regional cluster and node division can be obtained fromdifferent sensor devices through the wireless senor network. Secondly, the search space and the relevant road information are restricted to a strongly connected graph. The most important strategy for an optimal solution to find the shortest path is the search method. Finally, to validate the performance of our design and algorithm, we have conducted a simulation based on necessary traffic variables. The performance simulation results show that real-time dynamic path planning can be significantly optimized using our data processing scheme. [ABSTRACT FROM AUTHOR]

This paper presents a novel data validation algorithm for wireless sensor network. We applied qualitative methods such as heuristic rule, temporal correlation, spatial correlation, Chauvenet's criterion, and modified z-score as algorithms for validating sensor data samples for faults. Performance of the algorithms is evaluated using real data samples of WSNs prototype for environment monitoring injected with different types of data faults such as out-of-range faults, struck-at faults, and outliers and spike faults. Results show heuristic rule, temporal correlation, spatial correlation, chauvenet's criterion, and modified z-score method sit at different point on accuracy, no single method is perfect in detecting different types of data faults and reports false positives when sensor data samples contain different types of data faults. Selected effective methods such as heuristic rule, temporal correlation, and modified z-score are applied successively to data set for detecting different types of data faults but report false positives due to masking effects and increased fault rate. Finally we propose a novel data validation algorithm that uses novel approach in applying heuristic rule, temporal correlation, and modified z-score to data set for detecting different types of data faults. Compared to other methods, the proposed novel data validation algorithm is effective in detecting different types of data faults and reports high fault detection rate by eliminating false positives. [ABSTRACT FROM AUTHOR]