# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 1 Introduction  
Large-scale Wireless Sensor Networks (WSNs) are the main producers of big data, being an essential element of the Internet of Things (IoT) and presenting several applications, such as Smart Cities, Smart Grids, Smart Agriculture, Smart Transportation, Smart Industry, and Environmental Monitoring [1; 2; 3], as well as other Smart Cyber-Physical Systems [4]. The term smart refers to data collection for information extraction and intelligent decision support, which pushes IoT to one of its main goals: Facilitating human life. Smart Cities can improve the quality of life of citizens in many aspects: Traffic control, air pollution monitoring, safety, and fast emergency response times, among others. In addition, the large amount of data collected by sensor nodes can be used to construct predictive models and to plan specific strategies [5]. Agriculture, for example, can benefit from WSNs to improve productivity, optimize resources (water, energy, fertilizers, pesticides, and so on), control pests, and reduce costs and environmental impact [6; 7]. IoT is formed by domains, represented by distinct WSNs [8]. The huge set of data produced by sensor nodes that constitute IoT domains, which is extremely difficult to collect, format, store, manage, analyze, and visualize using the methodologies of traditional computing approaches, is called big data [9]. Big data gathering is a challenging task [10; 11] due to the volume, velocity, and variety of data [12]. Velocity refers to the rate of data generation from a wide variety of sources. In the IoT environment, data comes especially from sensors, but also from applications and other devices. The volume refers to the large amount of data generated by WSN, where an energy-efficient protocol is required. Furthermore, the particular characteristics of WSNs also represent a challenge for big data gathering. WSNs are considered Low-Power and Lossy Networks (LLNs): Typically, a multi-hop mesh network consisting of a large number of resource-constrained devices (limited energy, storage, and processing power) that communicate over unreliable links with a low data rate. Instability of the wireless links caused by mobility, depletion of energy resources, failures, and wireless channel effects makes packet loss a common event. Therefore, traditional protocols may not be directly applicable to the constrained IoT environment [13]. Currently, data monitoring is a key activity in WSNs. However, the ability to send commands/ queries from a central unit to the network nodes is increasingly becoming fundamental [14]. Conventional network protocols rely on message flooding or demand to send one command/query to every destiny node. These strategies are unfeasible for large-scale networks. On the other hand, Information-Centric Networking (ICN) is a promising paradigm for the Future Internet architecture, which can help overcome some challenges related to big data gathering. ICN represents a revolutionary approach in the network field [15; 16]. The basic idea is to enable data routing based on the information provided by nodes (content), rather than based on the address of the nodes. Data routing based on content is scalable, and facilitates data retrieval as well as application development, since data is requested by name and its specific physical location is transparent. The potential and benefits of the ICN paradigm in WSNs have been extensively investigated. In [17; 18; 19], the authors proposed the use of elements of cognition in information-centric wireless networks to dynamically identify good data delivery paths, extend network lifetimes, improve reliability, and minimize latency. In [20], a distributed algorithm was proposed to provide information-centric data collection to mobile devices. The potentialities of the ICN paradigm applied to WSNs were identified in [21]. The authors also proposed some enhancements for reducing traffic overhead and collisions. Problems created by IP addressing style were discussed in [22].