# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 2 Related Work  
### Lightweight Cryptographic Applications  
The world is witnessing the unprecedented growth of interconnected devices in almost every aspect of life. IoT devices and applications are now proliferating in every industry, including automobiles, health, and appliances, in addition to smart homes and smart cities, and monitoring systems for power, electricity, water, and gas [15]. IoT services and applications that could operate on billions of IoT nodes increasingly demand more robust lightweight cryptographic algorithms to secure their operations. These futuristic IoT services and applications include social IoT, and IoT search engines, services computing, recommendation systems, analytics, and experimental platforms [16; 17]. Since the IoT trend first began, many researchers and institutions have introduced lighter-weight encryption algorithms to enhance security in a variety of IoT applications, including health applications, smart homes and cities, monitoring, tracking and surveillance, and location-based services (LBSs) [18; 19].  
The authors in [20] proposed a novel cryptographic technique named KE-IBE, which relies on identity-based cryptography (IBC) and overcomes its key escrow issue. The researchers presented a privacy-preserving eHealth solution to ensure patient privacy and better fulfill IoT environment requirements. The technique provides anonymity while running at a reasonable speed. In the same context of medical applications for IoT, the authors in [21] improved an intrusion detection approach for the Internet of Medical Things (IoMT). The authors highlighted that sensitive patient data transmitted over IoMT can be vulnerable to different types of attacks, such as man-in-the-middle, remote hijacking, password guessing, DoS attacks, and many others. The authors utilized the grey wolf technique to optimize the feature extraction process. The proposed approach resolved the problem of local minimum optimization and obtained faster convergence compared to counterpart optimizers. The proposed approach provided an enhancement of 15% for accuracy and reduced time complexity by 32%, based on comprehensive experiments and benchmarking using an intrusion detection dataset.  
Data visualization was used by [22] to propose a technique for zero-day malware detection using a similarity matrix. This approach is useful for IoT environments where big data are generated and collected from different sources, such as servers, network sensors, and mobile devices. The authors of [23] addressed the challenge raised by the rapid growth of multimedia texts, audio, images, and videos that are continuously generated and must be transmitted securely every second. These researchers proposed a secure surveillance framework for healthcare applications using intelligent integration of the efficient chaotic map to extract data in a monitored scene using a visual sensor (camera). Their results showed that the proposed framework fulfilled the security requirements for surveillance applications.  
The authors in [24] showed an augmented reality (AR) application in a smart city. Using smartphones and augmented reality technologies, it enables citizens to access crucial information such as bus routes, transportation arrival times, and tourism attractions inan easy and effective manner. Images and geo-location markers activate AR information, which is delivered via a secure IoT infrastructure. The IoT infrastructure is built on bus-mounted IoT devices that move information to linked cloud servers via a secure platform. System security is ensured by the use of lightweight cryptography in conjunction with low-power IoT devices. As explained in [25], the system is built on an attribute-based encryption (ABE) scheme for fine-grained access control without a lengthy user permission process.  
Table 1 compares the lightweight approaches discussed in this section.