# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 2 Related Work  
According to [1], the default channel access mechanism is the pure ALOHA technique, a random access protocol where data is transmitted randomly from various end devices across a single channel in accordance with their communication requirements. Its lightweight nature as well as its simplicity and the absence of synchronization between the devices renders it quite suitable for sensor networks with low-traffic applications. The behavior of ALOHA-based protocol in terms of reliability, network latency, throughput, and energy efficiency has come under scrutiny from researchers who argue that it suffers from low scalability while at the same time its performance significantly deteriorates in cases of heavy traffic as the number of network devices increases [2; 3; 4; 5]. In order to improve the abovementioned metrics, several extensions or modifications to the LoRaWAN MAC layer protocol have been proposed in the literature. Slotted ALOHA, introduced as an improvement over ALOHA protocol, allows the data transmission from each station only at the beginning of the time slot, achieving better performance metrics as it doubles the capacity of the wireless channel compared to pure-ALOHA. In [2], [PERSON] et al. concluded that by using the slotted ALOHA protocol over LoRaWAN, instead of the ALOHA approach, a 5.8\(\times\) increase in network throughput and a 26% reduction of packet collisions is obtained in high-traffic load conditions. In addition to the random access protocols which are generally used in LoRaWANs, channelization protocols are also of particular interest. Time-division multiple access (TDMA) is a collision free protocol which provides multiple access to end devices by dividing the available bandwidth in time. In [6; 7], [PERSON] et al. proposed a receiver-initiated version of the aforementioned protocol which was tested and evaluated using a real deployment of eleven wireless sensor nodes. Ultra-low-power wake-up-radios which offer continuous listening of the wireless communication mean that extremely low power-consumption is introduced in this work to boost the overall network performance.The suggested approach was compared to the listen before talk (LBT) protocol in terms of network reliability, latency, and energy efficiency, achieving superior performance. In [8], the authors suggested an enhanced version of [6], also employing the WURs' low-power capacities. More precisely, using the TDMA principles, this work composes a lightweight form of prediction in an effort to prevent an end device that has no information to relay to reserve a whole timeslot in the current transmission cycle. This way notable outcomes in terms of mean data latency and times of node polling are brought about. [PERSON] et al. in [PERSON] et al. (2018), proposes an AI-based TDMA mechanism in an effort to extend the network's longevity by balancing the energy consumption of the network devices. In detail, a learning automaton tool is used in order to determine, depending on the residual battery of each device, which one of the available end nodes would serve as a cluster head during the next cycle of the TDMA. The suggested mechanism attains improvements in terms of network's lifespan of up to 1.4\(\times\) and 6.7\(\times\) when compared to the round-robin and static TDMA approaches, respectively. While the transmission settings play a pivotal role in the performance of a LoRa network, as they affect the energy consumption, the data rate, and the coverage area, the optimal configuration should be a continuous process in case of the presence of moving nodes. That being said, in [PERSON] et al. (2018) the authors proposed a dynamic selection of optimal transmission settings based on estimated path loss of each node and the nearest gateway. The authors in [PERSON] et al.