# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 2 Related Work
### Communication Based on Data Aggregation Mechanism  
In recent years, several works based on data aggregation in wireless sensor networks have been carried out [32]. According studied works in the literature, there are two categories of data aggregation: aggregation structures and aggregation functions.  
On the one hand, many works have focused on aggregation structures such as tree-based, cluster-based and backbone-based structures. The authors in [33] introduced a centralized algorithm and design a distributed protocol for building a tree routing structure with maximum lifetime. [PERSON] et al. [34] proposed a data aggregation tree (MECAT) which can minimize the total energy cost of data forwarding and have introduced a 2-approximation algorithm. They studied two issues: the first one without taking into account relay nodes and the second one with the consider of relay nodes having imperfect link quality. They proved for the first problem that every shortest path tree has an approximation ratio of 2. According to second case, the problem is proven to be NP-complete and a seven-approximation algorithm is proposed. The authors in [35] introduced a spatial clustering algorithm for sensor networks. It can construct a dominating set based on the information description performance of the dominators to perform the data aggregation. In [36], [PERSON] et al. proposed an efficient clustering protocol in wireless sensor network that offers significant energy savings. They performed data aggregation on the basis of entropy of the sensors. The clustering process is distributed and based on the sensed data category, independently of geographic positioning and distance measures. [PERSON] et al. [37] proposed Data Quality Maximization (DQM) protocol based on a backbone that is composed of a set of gateways. The authors investigated a mobile sink moving along a fixed trajectory without stop to collect data. Their proposal protocol is based on predictability of the sink movement and selects the gateways adjacent to the predicted path of the sink. [PERSON] et al. [38] proposed Similar-evolution Based Aggregation (Simba), a raw data-independent aggregation to consider the evolution of data rather than the raw data. The Simba proposal creates a group of isolated nodes which perform data aggregation, therefore reducing the energy consumption in the network.  
On the other hand, several works have focused on aggregation functions. They represent the way to do aggregation. In [39], the authors presented an experimental study that uses the ARIMA model for the design of an energy efficient data collection in wireless sensor networks. Their proposal avoids sensor nodes to deliver redundant data, this can be predicted by the sink node. [PERSON] et al. [40] proposed an A-ARMA method based on the forecasting by means of an ARMA model over moving average windows. The use of the A-ARMA technique reduces the computation in every sensor node and it does not have a pre-computation phases. In [41], the authors proposed Agnostic Aggregation (A2), a dynamic forecasting function. Their proposal can predict values with self-tuned model based on temporal aggregation. The authors in [42] presented the theory of compressive sampling methods.