# Data Gathering Techniques in WSN: A Cross-Layer View
## 2 WSN Architecture--Arising Platforms and Novel Infrastructure Concepts
### Topology  
Throughout the survey, the interaction of WSN and IoT will arise in multiple contexts. While this survey mainly deals with data gathering by means of wireless units, an IoT unit presumes a more high-level entity for localized data gathering. To assess the connection between these two concepts, the reader is advised to refer to the most recent work by [PERSON] et al. [43], for example, where the authors enumerate the IoT data management frameworks, challenges and issues. The chapter focuses on three layers of data management in IoT networks, communication, storage and processing. In addition, deployment of IoT Data management for _smart home and smart city_ is described. It is essential to distinguish between a _one-directional_ WSN platform, where sensors merely gather the data and activate a specific infrastructure and set of technologies to further send it to a sink, and a _bi-directional_ WSN platform, where the sensors are expected to be able to act according to control messages received from a sink. In the latter case, the sink might be a higher-level entity (e.g., a cloud-based server). While the general data-gathering techniques are usually agnostic of the control direction, additional constraints might be imposed. Delay of the responses, latency, BW usage efficiency, security, and privacy are some of the demands to consider. Another example of a bi-directional platform can be seen in social sensor clouds (SSC), which connect a social network with a sensor network via a cloud infrastructure. See, for example, [PERSON] et al. [44], which presents a scenario of a smart village and provides discussion on various aspects including green planning, energy concerns, and speed of data gathering and sharing. In [PERSON] and [PERSON] [45], an on-demand WSN platform is designed. The authors suggest a data-gathering protocol that addresses bandwidth consumption and delivery latency and minimizes the number of requests to save resources. An infrastructure where sensors form groups belonging to private owners constitutes a special case. This may be the case in a smart city environment; this means that privacy and/or security considerations should be prioritized. This is the topic addressed by [PERSON] et al. [46]. The authors provide a trust-assisted cloud for WSN but have throughput issues in mind. [PERSON] et al. [47] suggest a WSN-based IoT platform that provides a reliable connection between sensors in the field and the database on the Internet. The proposed platform is based on the IEEE 802.15.4e time-slotted channel-hopping protocol with resource-constrained devices supporting heterogeneous applications. The paper suggests a scheme that compensates the clock drift for every timeslot to maintain the clock synchronization required for the time-slotted channel-hopping protocol. Edge computing, as discussed by [PERSON] [48], allows distributing the data gathering burden across multiple cloudlets, which might be highly beneficial for large WSN. This platform paradigm aims to improve many important aspects: reduced latency of data delivery, increased bandwidth, scalability, resilience to possible cloud outages, and privacy control. However, the platform presumes an initial capital investment and later maintenance. A virtual sensor network was proposed by [PERSON] et al. [49]. Once a user-initiated sensing request is dispatched to a cloud, a suitable set of sensors is found for the task. The decision is made according to the cost function, which depends on the specific (e.g., monetary) cost of using sensors from the designated set, the benefit that can be received from using these sensors, and their effectiveness in distances and delays (calculated, e.g., in number of hops from sensor to a sink/gateway), also expressed as virtual links. The cost might be customized, while a general virtualization problem is formulated and the algorithm is provided. Integration of unmanned aerial vehicles (UAVs) and WSN for crop monitoring in precision agriculture is described by [PERSON] et al. [50].