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In wireless sensor network, data loss is inevitable due to its inherent characteristics. This phenomenon is even serious in some situation which brings a big challenge to the applications of sensor data. However, the traditional data estimation methods can not be directly used in wireless sensor network and existing estimation algorithms fail to provide a satisfactory accuracy or have high complexity. To address this problem, Temporal and Spatial Correlation Algorithm (TSCA) is proposed to estimate missing data as accurately as possible in this paper. Firstly, it saves all the data sensed at the same time as a time series, and the most relevant series are selected as the analysis sample, which improves efficiency and accuracy of the algorithm significantly. Secondly, it estimates missing values from temporal and spatial dimensions. Different weights are assigned to these two dimensions. Thirdly, there are two strategies to deal with severe data loss, which improves the applicability of the algorithm. Simulation results on different sensor datasets verify that the proposed approach outperforms existing solutions in terms of estimation accuracy. [ABSTRACT FROM AUTHOR]

Wireless sensor network based on service-oriented architecture has provided important support for Internet of Things in smart city; therefore, multi-requests can be satisfied through service composition. However, as the number and complexity of requests increase, the dynamic and low energy of sensors in wireless sensor network gradually limit the development of the service-oriented architecture in Internet of Things; therefore, optimizing energy under the condition that multi-requests can be satisfied in the dynamic wireless sensor network becomes an important problem. To address this challenge, first, this paper analyzes the mobility of sensors to predict the path stability and manage the information and capabilities on them so that we can find the satisfactory service provider candidates. Then, this article proposes a service mapping pre-process to achieve the work flow reduction in a service layer to save energy in advance and a service mapping process to find the optimal sensors in a network layer that can satisfy the requests. Simulation results show that our strategies can achieve the goal of energy conservation, load balancing, and network lifetime extension under the premise of guaranteeing requests function and non-function attributes and have better performance than traditional ones. [ABSTRACT FROM AUTHOR]