## 2 Related Work  
The protocol suggested in this paper can apply to various setups and traffic patterns, yet it is mostly suited to the setup in which a sink (or a relay) collects data from multiple devices, and the traffic pattern is such that multiple devices have data to report at the same time; hence, it is susceptible to contention and collisions. Such a setup, in which a sink or multiple sinks collect information, such as sensed data or reports from a large population of sensors and devices, is one of the most essential tasks of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). Data collection is essential for a diverse range of tasks, such as tracking, monitoring, and surveillance, where different applications have different characteristics, specifications, and requirements. Different approaches were considered to tackle different aspects in data collection. For example, Compressive Sensing (CS) is considered to be an effective method for several aspects of data collection, such as reducing the report payload and, hence, the transmission time, or reducing the energy consumption of the sensing operation (e.g., [10; 11; 12; 13]). Additional overviews and surveys on compressive sensing in Wireless Sensor Networks (WSNs) can be found in [14; 15]. Additional means of compressions are summarized in [16; 17]. In multi-hop topologies, in which the data need to travel for multiple hops before reaching the sink, attention was drawn toward load balancing and routing, as well as scheduling transmission opportunities between the relays (e.g., [18; 19; 20; 21; 22; 23; 24]). Under similar multi-hop-setup data aggregation protocols that fuse data arriving at a relay from multiple entities on their way to the sink, routing the data to the sink(s) has been considered (e.g., [25; 26; 27; 28]), and a survey that compares various data aggregation techniques has been presented in [29]. Different approaches, such as compromising on the quality of the collected data, have also been considered; a survey that reviews approximate data collection algorithms can be found in [30]. One of the most important and challenging aspects of wireless communication in general and data collection in particular is air-time utilization. Since all devices communicating with a sink share the same channel (or channels), it is highly important to utilize it (or them) efficiently, all the more so because many of the devices are simple and cannot run sophisticated procedures and algorithms. Different approaches have been adopted to improve channel utilization and reduce air-time usage. Obviously, methods that compress the data--thus shortening the transmission time--such as some of the methods described above, also improve air-time utilization. In this paper, we focus on another important means to improve channel utilization, which coincides with the schemes mentioned above: the MAC mechanism, which controls the usage of the physical transmission medium. The MAC protocol utilized for data collection is highly important and affects the performance in many aspects (e.g., energy consumption, delay, reliability), especially when the network is dense and the data need to be collected from a large device population. Numerous MAC protocols have been suggested for duty-cycle-based wireless sensor networks (e.g., [5; 31]). Even though many of these inclusive MAC protocols, which were designed to handle diverse traffic patterns, can comply with report acquisition applications, their inept adaptation can result in poor performance (e.g., high latency, low channel utilization, stall reports, etc.) when the network is dense or when the traffic pattern triggers high loads in specific regions or neighborhoods. For example, adopting a synchronous approach in which neighboring devices synchronize their duty cycles such that they are all awake at the same time intervals can be a good match, as in any case, all devices in a neighborhood need to communicate with the same sink or relay (e.g., [32; 33; 34]).