# Approximate Sensory Data Collection: A Survey
## 1 Introduction  
From the physical world to the cyber world, the Internet of Things (IoTs) and wireless sensor networks (WSNs) have become necessary connections between them, and make it possible for people to observe the physical world at a low cost. Among all the components of IoT systems and WSNs, the sensory data, as the information carriers, are quite important for both IoT systems and WSNs. Usually, the procedure of dealing with sensory data in IoT systems and WSNs can be divided into three phases as shown in Figure 1.  
1. **Sensory Data Acquisition. ** In the first phase, the sensing equipment samples the sensory data from the monitoring physical world, which can be seemed as a discrete process for a continuously varying physical world. 2. **Sensory Data Collection. ** In the second phase, the raw sensor data sampled by each sensor node are transmitted into the network toward the sink (sinks), where the sink node is a special node to connect an IoT system or WSN to the cyber world, such as clouds, data centers, etc., and transmit data information between them. 3. **Sensory Data Computation. ** When the sensory data have been already delivered to the cyber world in the second phase, the further computations, e.g., numerical analysis, knowledge discovery, etc., will be carried out according to the applications and users' requirements. All of the three phases above are necessary and fundamental for any application of IoT systems and WSNs, and this survey will focus on the second phase. Since the aim of the data collection phase is to transmit the sensory data from the sensors to the sink node, the input of such a phase is the rawsensory values sampled by the sensors in the network, and the output is the sensory dataset received by the sink. The naive method for sensory data collection is to deliver every raw sensor value sampled by the sensors to the sink node, and we call it the exact sensory data collection methods. Such an exact data collection method guarantees that all the sensory data information are transmitted to the cyber world; however, it also increase the network's burden with the growth of sensory data. With the development of the corresponding techniques, including microelectronics, the embedded systems, the wireless communication and sensing techniques, in recent years, the scale of sensory data manifest an explosive growth. According to the annual report of Gartner Inc., (Stamford, CT, USA), which is a famous American research and advisory firm providing information technology related insight for IT and other business leaders, 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015, and will reach 20.8 billion by 2020 [1]. Meanwhile, based on the forecast of Cisco (San Jose, CA, USA), it is estimated that more than 250 things will connect each second by 2020, and it is believed that more than 50 billion things will be connected to the Internet by 2020 [2]. Such a large number of smart \"things\", which have sensing abilities, in IoTs will generate a huge amount of sensory data. Furthermore, the volume of worldwide climate data is about 2.5 Petabytes in 2010 estimated by the World Climate Research Program (WCRP), and it is expected to exceed 100 Petabytes in 2020 [3]. The Large Hadron Collider System in Europe deploys 150 million sensors and generates 500 Exabytes sensory data everyday [4]. The taxi monitoring system in Beijing, China produces 48 Petabytes GPS data and 1440 Petabytes other sensory data annually by monitoring 67,000 taxis. Obviously, such a great amount of sensory data already exceeded the transmission abilities of IoT systems and WSNs; therefore, the exact data collection method is not affordable by many WSN and IoT systems due to the limitations on bandwidth and energy.