Internet of robotic things: Design and develop the quality of service framework for the healthcare sector using CoAP

<p>The number of robotics used globally is gradually growing, according to a variety of research. They are becoming more and more popular in different workplaces, like manufacturing, distribution, medical conditions, military, inaccessible areas, etc. The internet of things (IoT) and robotics groups have until now been guided by a set of, but somewhat compatible, goals, which are mainly to help knowledge systems in the field of general sensing, tracking, and monitoring. Therefore, the development of an internet of robotic things (IoRT), which incorporates the outcome from both cultures, is progressively said to have a significant added benefit. Internet of robotic things, the intersection of the Internet of Things and robotics, is where self-sufficient machines will assemble information from various sensors and speak with one another to perform errands including basic reasoning. As the name suggests, IoRT is the combination of two front-line innovations, the internet of things and robotics. People can manage any electronic device in homes with IoT and can also be used in contactless applications in healthcare. The constrained application protocol (CoAP), for the management and control of a community of homogeneous sensor modules, has recently endorsed multicast communications in IoRT. It will boost connectivity performance, less power consumption due to data aggregation, and enhanced security features with DTLS security features for various applications for the internet of things. This paper presents an implementation of the CoAP framework on IoRT sky motes using the Contiki Cooja Simulator that will be a useful healthcare sector that will confirm their potential and therefore, new research directions are outlined</p>

3S-IoT an Algorithm to make the Network Secure and Smart

The growing and widespread presence of Internet of Things (IoT) has made the lives of all comfortable and handy, but poses various challenges, like efficiency, security, and high energy drain, threatening smart IoT-based applications. Small applications rely on Unicast communication. In a group-oriented communication, multicast is better as transmission takes place using fewer resources. Therefore, many IoT applications rely on multicast transmission. To handle sensitive applications, the multicast traffic requires an actuator control. Securing multicast traffic by itself is cumbersome, as it expects an efficient and flexible Group Key Establishment (GKE) protocol. The paper proposes a three-tier model that can control the IoT and control multicast communications. The first authentication is at network linking where we used a 256-bit keyless encryption technique. Machine learning-based chaotic map key generation authenticates the GKE. Finally, MD5 establishes the system key. 3S-IoT is smart to detect any tempering with the devices. It stores signatures of the connected devices. The algorithm reports any attempt to change or temper a device. 3S-IoT can thwart attacks such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), phishing, and more. We calculated energy consumed, bandwidth, and the time taken to check the robustness of the proposed model. The results establish that 3S-IoT can efficiently deal with the attacks. The paper compares 3S-IoT with Benchmark algorithms.

3S-IoT an Algorithm to make the Network Secured and Smart

Internet of Things (IoT) evolving and widespread presence has made the lives of all comfortable and handy, while on the other hand posing various challenges, i.e. less efficiency, less security, and high energy drain, threatening smart IoT-based applications. Compared to unicast communication, multicast communication is considered more powerful in group-oriented systems, because transmission takes place using less resources. This is why many of the IoT applications rely on multicast in their transmission. This multicast traffic needs to be handled explicitly for sensitive applications requiring actuator control. Securing multicast traffic by itself is cumbersome as it requires an efficient and flexible Group Key Establishment (GKE) protocol. We propose a three-tier model that can, not only be used to control the IoT, but also to control multicast communications. The architecture is built with a 256-bit keyless encryption technique to protect the authentication to create the network link. Machine learning-based chaotic map key generation is used to protect GKE. Finally, using MD5, the system key is authenticated. The algorithm is checked for energy used, bandwidth, and time taken. The proposed model is applied and evaluated against numerous benchmark attacks such as Distributed Denial of Service (DDoS), Man in the Middle and Fishing.