# Reliable Internet of Things: Challenges and Future Trends
## 4 Overview of Reliable Data Transmission in IoT Network  
We provide an overview of reliable data transmission in IoT networks in this section. In Figure 2, we present three major components of reliable IoT data dissemination. These include resource allocation, latency management, and security. Efficient resource allocation is important for reliably sharing data between IoT nodes and servers. As the spectrum resource is limited due to large data generated by IoT nodes, it is important to propose intelligent spectrum utilization techniques. Techniques such as cognitive spectrum management could be used for sharing of spectrum bands by several IoT nodes. Transmit power is also an important resource that needs to be allocated carefully. To save energy of IoT nodes so that they can stay active for longer periods without charging, adaptive transmit power techniques are required. Fog computing is a vital part of future IoT networks. Fog nodes located near the IoT devices provide storage and computational capacity to the IoT network. IoT networks can place popular and most useful content in the cache storage of these fog nodes. Hence, cache storage allocation is an important challenge. Moreover, IoT nodes may not perform all tasks locally and, therefore, will offload many tasks to these fog nodes. In this regard,  
Figure 2: Key components of Reliable Data Transmission in IoT. optimal task offloading algorithms are needed to ensure that the computational capacity resource of fog nodes is used efficiently. Latency management is another crucial unit of reliable data transmission in IoT. IoT applications may not work well if data are not shared within the desired latency. Many new applications such as autonomous driving and industrial automation have stringent latency requirements, and hence, latency management is needed. In this regard, smart retransmissions can add diversity and enhance the probability of quick packet reception at the receiver. Moreover, optimal medium access techniques need to be developed to allow IoT nodes quick and fair channel access. Accurate data traffic prediction can support latency management techniques as knowledge of upcoming traffic at an IoT server, and fog node equips it to handle it better. Hence, Artificial Intelligence (AI) based techniques that forecast the frequency and size of data can be very useful. Besides, other network technologies can support IoT networks to transmit their data quickly. In this regard, Unmanned Aerial Vehicle (UAV) and vehicular network has the potential to act as a relay for data traffic generated by IoT nodes [91; 92; 93; 94; 95; 96; 97; 98; 99; 100]. Security is an essential component of reliable data transmission in IoT. Several attacks may be generated in an IoT network that can compromise the privacy and confidentiality of data transmitted. Moreover, malicious nodes can insert fake and wrong data in the network that can affect the decision-making of IoT applications. To tackle this issue, advanced cryptographic techniques are needed that can ensure the security of transmitted data while keeping the required overhead to a minimum. As an increase in the security overhead can result in higher data latency, tradeoff between security robustness and latency needs to be evaluated. Blockchain is an upcoming technology that can provide robust security to IoT devices. Hence, blockchain-based solutions need to be developed in the context of IoT applications. Other security techniques such as physical layer security can also improve the reliability of IoT networks. These techniques can work in collaboration with cryptographic techniques to provide a robust solution. Lastly, data integrity attacks and anomaly detection schemes are also required to ensure that correct data are received based on which decisions can be made. Contributions of the Paper in Comparison with Other Survey Papers  
We present an overview of different recent survey papers related to IoT in Table 3. The survey paper in [93] focuses on security and privacy challenges in edge computing-based IoT.