# Recent Trends in AI-Based Intelligent Sensing
## 5 Challenges and Future Research Directions
### Future Directions
Network Coding (NC) has been suggested as a promising technique for improving vehicular wireless network throughput by reducing packet loss in transmission. In [198], an adaptive network coding method is proposed with the use of the Hidden Markov Model (HMM) in the network coding scheme to regulate the rate of coding according to the estimated packet loss rate. In the near future, research work combining multipath transmission with hierarchical edge computing in the high-speed cellular-based vehicular network will be a more focused field. Recently, Q-learning (QL), which is an ML algorithm, has shown very promising results in learning problems in energy and computation-constrained sensor devices. The intelligent collision probability inference algorithm based on Q-Learning model was proposed in [199]. It is used to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. Future IoT networks will have an assortment of stimulating features that optimize network performance and communication efficiency. ML techniques allowing machine intelligence to be incorporated in IoT communication technologies are attracting much attention [200]. The MAC layer and network layer capabilities of future IoT networks can be enhanced with ML-based algorithms [199]. 8. Latency Minimization: Latency minimization is a crucial factor in the deployment of real-time applications on energy-constrained platforms such as mobile devices. In the design of AI and computer vision algorithms, latency is considered the primary requirement for resource-intensive tasks. Researchers are exploring ways based on ML and DL methods for reducing latency and energy consumption for future 5G networks [201; 202]. Some of the critical issues in intelligent 5G communication technologies include scheduling medium access control (MAC) layer resources among sensor devices, storing a large amount of data generated at the network edges, and assigning virtual network functions (vNFs) to the hosting devices. These issues can be resolved by reducing the demand on network bandwidth, latency and improving QoS. These 5G networks are capable of implementing critical tasks such as autonomous driving, remote drone control, and real-time AI on handheld devices according to their latency requirements [203]. * Future Citizenship: Due to the government initiative around the world on digitization of identity and social information documents, the resources are accessible to their citizens through various secure online portals. The citizens no longer need to stand in queues to access the resources as all information is available online. In daily life, technology is also involved in the form of smart clothing, smart homes, disease prevention, medicine, etc. It can be said that smart citizenship is the demand of the smart world. Intelligent sensing is all around the technology used by smart citizens. Work presented in [42] discusses contributions of information provided by the local community. One major benefit of such information is to strengthen the quality of government decision making. In the future, citizens will generate valuable data through intelligent sensing on mobile platforms. Thus, the challenges related to theft prevention, forgery, and right to access the information are even more critical for future citizens. * Software Platforms in Intelligent Sensing: The platform on which algorithms can be executed in an intelligent sensing environment requires multiple software applications. The three key steps in the development of such systems are (a) hardware level integration, (b) middleware for feature enhancement, and (c) front end development. For all the three steps, multiple types of software are available which can be integrated with each other to create a single framework. The challenge in this domain is to find one single platform to perform all three steps.