# Approximate Sensory Data Collection: A Survey
## 6 Further Works  
As shown in our Introduction section, approximate data collection is one of the most important issues for WSNs, and many efficient sensory data collection methods have proposed in recent years. However, with the appearance of IoT systems, there still exist some unsolved problems that need to be investigated in the future. Firstly, the appearance of IoT systems make the amount of sensory data manifest explosive growth. As shown in Section 1, the number of connected \"things\" will reach 20.8 billion by 2020, and such a huge number of the intelligent \"things\" would generate a great number of sensory values, which make the amount of sensory data to exceed TB, PB even EB easily. In a word, the era of big sensory data is coming. The big sensory data bring us much information from monitoring physical world; however, the huge amount is also largely beyond the processing ability of most existing data collection techniques. Therefore, a series of new algorithms are eagerly required to collect big sensory data efficiently. Considering the existing techniques, the sampling is one of the feasible ways to design the lightweight and effective algorithms for dealing with big sensory data. However, most of the existing sampling based methods are designed for specific kinds of simple queries and do not support data recovery accurately. Thus, a group of new sampling based data collection algorithms are still worth being studied in the future. Secondly, different from the traditional WSN, an IoT system has various types of sensor nodes. For example, an intelligent traffic monitoring system could include many monitoring sensors, e.g., electronic eyes, GPS devices, intelligent traffic lights, etc. Such sensors will sample multiple types of sensory data from the monitoring environment. A crowd sourcing application based on smart phones might utilize the sensors embodied in it, including accelerometer, digital compass, gyroscope, GPS, microphone, camera and so on. Therefore, sensory data sampled by these sensor nodes also consist of various modality, that is, the scalar data, the vector data, the multimedia data, etc. are always involved in the same sensory dataset, and the variety of sensory data bring many challenges for data collection. However, the current works on sensory data collection only focus on single modular data and rarely consider the problem of how to collect multi-modal sensory data efficiently. To solve the above problem and make multi-modal sensory data be collected cooperatively, the correlation among different types of sensory data should be analyzed thoroughly. Then, new frameworks, which can make multiple types of sensory data become integrated, are going to be constructed. Finally, new distributed and energy-efficient data collection algorithms need to be designed according the above correlations and frameworks. In order to achieve the three steps above, the stochastic process based methods and the time series analysis, etc. are suitable to be adopted. Thirdly, although the amount of raw big sensory data sets is quite huge, there exists massive redundant information in them since the sensory data are strongly correlated with each other. Considering that the network's energy and resource are quite limited for WSNs and IoT systems, we only want to transmit and collect the useful sensory dataset instead of the raw ones. We call such datasets kernel datasets, and the method of retrieving the kernel dataset from big sensory data is rarely taken into account by the existing works, although it is significant for dealing with big sensory data. Therefore, a series of new techniques to draw kernel datasets in WSNs and IoT systems need to be explored in the future. To achieve such an aim, the _global information loss rate_, which is an important metric to evaluate the quality of kernel dataset, should be firstly defined. To the best of our knowledge, the work in [56] is the unique literature to give the formal definition of the _global information loss rate_ based on statistics.