*2.4. Comparison between Wireless Protocols*

The comparison of different wireless technologies to decide which one is the best, has been very attractive for a sector of researchers. For example, the authors in [21], provide a comparative study of Bluetooth, Ultra-wideband (UWB), ZigBee, and WiFi wireless communication standards. They presented an overview evaluating the main features and behaviors of the standards in terms of various metrics: transmission time, data coding efficiency, complexity, and power consumption. The work did not draw any conclusion regarding which one is better because the authors concluded that more factors must be considered such as network reliability, roaming capability, recovery mechanism, chipset price, and installation cost.

Other work focused on finding a solution to the problem related to the selection criteria of a better wireless communication technology face up to the constraints imposed by the intended application and the evaluation of its key features is that of Chakkor Saad et al. [22]. They presented a comparative performance analysis of the following wireless protocols: WiFi, WiMax, UWB, Bluetooth, ZigBee, ZigBeeIP, and GSM/GPRS. They developed a quantitative evaluation with respect to transmission time, the data coding efficiency, the bite error rate, and the power and energy consumption. Their conclusion is that in order to determine which one is the most suitable, other factors must be taken into account such as network reliability, the link capacity between several networks with different protocols, security, the chipset price, the conformity with the application and the cost of installation.

More recently, Naidu and Kumar [23] have carried out a description on the wireless technologies importance, features, and a comparison about Bluetooth, ZigBee, WiFi, and Z-Wave; mainly focused on Self-organizing/Optimization Networks (SONs). The work of these authors was focused on home automation devices, integrating them with a smart hub. The authors describe WiFi SON, they concluded that it is a guaranteed network, offering quality of service and eliminating human interventions.

One of the characteristics of this type of WSN/IoT networks is the sleeping techniques that can be applied to optimize the energy consumption of the sensors. These types of techniques help to reduce the power of each part of the node, a task that consists essentially of turning off or bringing the device to a low-power mode when it is not used, while when in use, it is activated or awake. By reducing the consumption of each part of the node, the overall consumption is reduced and, therefore, the battery life is extended. In this work, sleep techniques are not properly applied; however, these mechanisms can resemble the connections and disconnections of the nodes that we analyze as a performance metric. Similarly, it must be taken into account that when a node falls asleep it is not necessarily disconnected from the network, but in some cases, depending on the applications, the nodes can temporarily turn off their microcontroller unit and thus, optimize their energy to the maximum and the power of the network. A recent approach to reduce the energy consumption in WSNs is through the setting of sleep scheduling. In addition, a relevant proposal is minimizing the number of nodes to cover a constrained area. Good results in terms of complexity, working-node ratio, scalability, and the time of network duration were obtained in [24].

Unlike the previously mentioned works, our research is concentrated in offering a comparison under a collaborative and cooperative scheme between the following wireless technologies: Zigbee, LoRa, Bluetooth Low Energy, and WiFi. These technologies have gained wide acceptation in industrial applications due to their low cost and low power consumption. Our experimental scenery is a campus, since we are looking for emulating a smart campus.
