# Reliable Internet of Things: Challenges and Future Trends
## 5 Review of Recent Work in Literature on Reliable Data Transmission in IoT
### Latency Management  
Latency is a vital QoS indicator for IoT applications. Most applications are sensitive to latency and need a lower latency value within a threshold to ensure reliable communication. As shown in Figure 4, techniques such as smart retransmissions, resource allocation, multiple access and physical layer techniques, traffic prediction, and cooperation from other networks is used for improving the latency of IoT. This subsection provides an overview of work done in latency management in IoT as shown in Table 5.  
The authors in [120] considered a narrowband IoT (NB-IoT) scenario where an increased number of retransmissions in the standard improves the packet delivery ratio but increases the energy consumption and latency. The proposed technique computes optimal durations of the control channel and data channel. Moreover, the authors also find out the optimal coverage class for each user where coverage class defines the number of retransmissions allowed. Simulation results highlight reduced energy consumption and reduced latency achieved by the proposed protocol. Figure 4: Different categories of recent work in Latency management in IoT. The work in [121] considers a scenario where IoT devices use multiple Radio Access Technologies (RATs) to transmit data to the Mobile Edge Computing (MEC) server. The proposed protocol computes optimal task offloading decisions, including the number of tasks locally computed at IoT device, amount of task computed at MEC server, and task splitting between different MEC servers. Besides, optimal transmit power and RAT selection for task offloading are also proposed. The developed optimization problem is solved using the Alternate Convex Search (ACS) algorithm. The results highlight reduced energy consumption as well as reduced latency when the proposed protocol is used. In [122], the authors consider a cloud scenario with several Virtual Network Functions (VNFs). VNFs are mapped to different applications such as delay-sensitive, mission-critical, and latency tolerant. The goal of the proposed technique is the efficient allocation of VNFs to reduce latency. The authors use a regression algorithm to predict the demand of each application type and make allocations accordingly. The proposed algorithm works initially on offline training. This is followed by online training afterward. The work in [123] considers a terrestrial network scenario composed of IoT-enabled satellites and a MEC server. IoT nodes collect data and send it to satellites or satellite gateways for further processing. As satellites have energy constraints, they offload the IoT data to the gateways for further processing. The proposed work finds an optimal association between a satellite and a gateway using Lagrange multiplier and Deep Reinforcement Learning (DRL) techniquesThe results highlight reduced latency of IoT data at a reduced energy cost. In [124], a UAV-assisted MEC-based IoT network scenario is considered. UAV facilitates computing in terms of storage and computation service. UAV can also improve network coverage by providing relaying services to overcome poor channel conditions. The problem is divided into three sub-problems. The first one deals with IoT nodes and UAV association, the second one handles scheduling of communication resources, and the last one manages UAV placement. The proposed algorithm shows low latency as compared to other related techniques.