# Reliable Internet of Things: Challenges and Future Trends
## 5 Review of Recent Work in Literature on Reliable Data Transmission in IoT
### Reliability Metrics for IoT  
In this subsection, we discuss different reliability-related metrics used to evaluate IoT applications' performance. We present a list of these metrics in Figure 6, and provide their key idea and uses in Table 7.  
Figure 6: Different Reliability Metrics used in IoT. Packet Delivery Ratio (PDR) is a commonly used reliability metric that measures the ratio of the total number of packets received at a receiver and the total number of packets transmitted by the transmitter [132]. Most IoT applications require a very high PDR value as it ensures that the data between the nodes is shared without any error. With 6G communications, many novel technologies and techniques are used that enhance the PDR to a value of 99.99999%. A low PDR value implies that the channel conditions between the transmitter and receiver are not good due to factors such as multi-path fading. Thus PDR value can provide important feedback on allocating resources such as transmission power of the nodes. A high transmit power may be used for scenarios that suffer from low PDR. Outage probability is another useful metric for resource allocation as it indicates the distance at which the transmitter and receiver become out of range [133; 134; 135; 136]. As a result, the node can select the optimal transmit power and modulation scheme based on IoT application requirements. A higher transmit power will increase the transmission distance, thus mitigating the channel impairments. On the other hand, a lower modulation scheme reduces the data rate but increases the transmission distance. Another important reliability metric for IoT is the channel busy ratio, which indicates the total data load on the network [137]. This metric can be measured by the IoT radio based on the percentage of time the radio senses the channel as free. Resource allocation can be optimized based on channel busy ratio measurements, such as data generation rate, transmit power, and modulation scheme. Packet inter-arrival time is another metric that computes the time difference between two consecutive packets at the receiver [138]. Most IoT applications require a certain packet inter-arrival time threshold, so that information shared by IoT sensors does not expire. A variable packet inter-arrival time can result in unwanted delays in sensor measurement dissemination to the server, reducing the application data analysis accuracy. The end-to-end delay of the packet is a crucial metric that provides information about how much delay is required to transmit the packet [138]. As discussed before, a higher end-to-end delay can be caused by poor channel conditions, network congestion or inefficient multiple access. Latency management techniques can be evaluated based on the end-to-end delay metric. Another useful metric for the Internet of Vehicles (IoVs) is the safety awareness range [139]. This metric uses information accuracy of the messages shared between vehicles to assess how far vehicles can connect reliably. Information shared between vehicles includes their position, acceleration, speed, etc. As other reliability metrics do not focus on information accuracy, this metric provides a better measure of reliability, especially for vehicular networks. Signature verification time is another important metric that measures how much time it takes to process the message in terms of security at the receiver [140]. As many packets