# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 1 Introduction  
Requirements for reducing energy consumption in devices are still increasing due to population growth and industrial development. The problem of the energy consumption is one of the key aspects of Internet applications [1]. Devices with limited power supplies that are part of the Internet-of-Things (IoT) should have optimized interfaces, and optimized communication protocols are expected to reduce energy consumption. This means that when two or more interconnected devices communicate with each other, you will not need to change batteries or charge devices after a short while. Based on several factors (e.g., data characteristics, data transfer mode, network size), there are still lots of possibilities to optimize protocols based on the network needs. Energy efficiency is especially important for end sensory devices with limited energy sources, which are connected by means of so-called wireless sensor networks (WSN). The WSNs, thus, create a periphery of an IoT domain, which interacts with the environment. In WSNs, there is a number of cases in which the device topology contains a central node that collects and evaluates acquired data from sensory devices, such as parking cameras [2] or hospital equipment [3]. There may be a case in which the network traffic and communication are too high and the central node does not manage to process all the data. The reason may be the production of unnecessary communication or periodic transmission of data from sensory IoT devices at very short intervals. As a result, some data may be lost. There are several studies that deal with the reduction (or efficient use) of energy for devices in the WSN or IoT areas. For example, a decent survey on energy efficient techniques in WSNs is providedin [4]. It identifies five main classes of these techniques: data reduction, protocol overhead reduction, energy efficient routing, duty cycling, and topology control. The data reduction usually deals with compression or aggregation of data to minimize the amount of transmitted and processed data. However, there are also approaches to reduce the amount of data produced by sensors by using various sampling techniques [5; 6], such as adaptive, hierarchical, event-triggered, or model-based sampling. The ADAPT (ADaptive Access Parameters Tuning) framework [7] offers optimized data collection in WSNs based on IEEE 802.15.4/ZigBee standards. It enables to adapt media access layer configuration according to the application's reliability requirements and the traffic conditions in order to minimize power consumption. Authors of [8] proposed an event-triggered IoT communication. For saving power, IoT devices often support switching between some active and passive (i.e., sleep) operation modes. However, in many cases, periodical switching between these modes leads to unnecessary activation of the device, which decreases its energy efficiency. IoT communication based on events solves such an issue, since the device is activated only after some event occurs and it, thus, generates less data for transmission. Such an approach is very effective especially for monitoring and controlling purposes. A similar approach was proposed in [9], which utilizes cellular networks for sending a wake-up signal to an IoT device to switch it to an active mode. Event-triggered communication was also used in [10], which proposed two innovative medium access protocols for decentralized control systems. In [6], there are also various media access protocols discussed, such as B-MAC, STEM-T, WiseMAC, S-MAC, or UBMAC. These protocols already target ultra low power operation. In [11], the authors have reduced control overhead of S-MAC protocol and proposed an optimized LO-MAC protocol. Various energy efficient routing techniques and strategies are surveyed in [12]. The authors compared multiple energy-efficient WSN routing protocols, such as LEACH, HEED, DECA, SPIN, and PEGASIS, and identified as their main weakness an assumption that the nodes are static and stationary.