# Recent Trends in AI-Based Intelligent Sensing
## 5 Challenges and Future Research Directions
### Challenges  
1. Data Security and Privacy: Despite the success of AI and ML models, they face the major challenging issue of data security. ML models extract features by learning patterns that contain information, which can be vulnerable to real-world attacks [168]. One of the legitimate concerns in any real-time environment is data integrity and also it affects the quality of datasets and overall performance of the system. For example, the UAV-enabled intelligent transport system in a smart city where information about vehicle location and speed can be leaked by malicious entities [169; 170]. The sensors must gather and share only essential information that is required to execute any operation. Standard rules and procedures must be applied to maintain data integrity. The presentation of information has to go through several stages in machines. Most of the machines are connected, resource-constrained devices and also available as standalone computational units. The first step in intelligent sensing is to gather the collected data from the sensors, which are then merged with information sources to identify and process accordingly. If information can be analyzed locally, further steps need to be taken. However, in most of the devices, the next step is to transfer the information to database storage or cloud services. Such a collection of information is then processed for data analysis and presentation through queries specific to user requirements. In the process from data collection to presentation, several types of security threats need to be taken care of. As illustrated in Figure 10, the process of intelligent sensing consists of multiple stages from sensing to data analysis and security needs to be handled in each of the stages.
2. Data Storage and Management: The storage of an enormous amount of data in the form of audio, video, images, smart device data, and social media has become the main hurdle for several applications that need to be addressed. Mismanagement of data will make it difficult to analyze the quality of data collected by sensors and further affect the decision-making process [171]. The availability of a large amount of data motivates us to accept the ML and AI methods to enhance the overall performance of the sensor-based system. Therefore, to avoid redundancy, more advanced AI algorithms will be needed to extract meaningful data.
3. Power Consumption : Nowadays, the use of wearable flexible sensors has gained significant attention in medical applications [172; 173; 174]. These sensors are placed in contact with the clothes worn by a person to measure physiological parameters like temperature, ECG, EMG, muscle activity, and cardiovascular problems. The power consumption of these devices is an important issue that needs attention. In addition to this, the production cost of a flexible sensor is also a challenging issue that needs to be addressed [175]. Low power consumption sensors such as Shimmer and Telos should be used for monitoring the health to reduce the power consumption of wearable flexible sensors.
4. Hardware Deployment: Despite the benefits of AI, designing algorithms on hardware requires sufficient computing resources, power consumption, high computational complexity, which is a very challenging task [176]. Hence, the collaboration between AI and hardware components needs serious efforts to enhance intelligent communication. The large memory footprint of the trained model and the enormous amount of sensor data affect the training accuracy and computational speed on hardware. Moreover, due to lack of specific libraries for hardware, the trained model is not properly deployed from a specific framework to low-power devices (i.e., edge or mobile) and FPGAs. These may delay the product delivery for a couple of weeks. Many researchers are focusing on reducing the complexity of AI and ML algorithms from hardware perspective and thus enhance the overall performance of the real-time inference model to make it memory-efficient [177; 178].