Biblio

Internet of Things (IoT) is an advanced automation technology and analytics systems which connected physical objects that have access through the Internet and have their unique flexibility and an ability to be suitable for any environment. There are some critical applications like smart health care system, in which the data collection, sharing and routing through IoT has to be handled in sensitive way. The IPv6 Routing Protocol for LL(Low-power and Lossy) networks (RPL) is the routing protocols to ensure reliable data transfer in 6LOWPAN networks. However, RPL is vulnerable to number of security attacks which creates a major impact on energy consumption and memory requirements which is not suitable for energy constraint networks like IoT. This requires secured RPL protocol to be used for critical data transfer. This paper introduces a novel approach of combining a lightweight LBS (Location Based Service) authentication and Attribute Based Trust Evaluation (ABTE). The algorithm has been implemented for smart health care system and analyzed how its perform in the RPL protocol for IoT constrained environments.

Explosive naval mines pose a threat to ocean and sea faring vessels, both military and civilian. This work applies deep neural network (DNN) methods to the problem of detecting minelike objects (MLO) on the seafloor in side-scan sonar imagery. We explored how the DNN depth, memory requirements, calculation requirements, and training data distribution affect detection efficacy. A visualization technique (class activation map) was incorporated that aids a user in interpreting the model's behavior. We found that modest DNN model sizes yielded better accuracy (98%) than very simple DNN models (93%) and a support vector machine (78%). The largest DNN models achieved textless;1% efficacy increase at a cost of a 17x increase of trainable parameter count and computation requirements. In contrast to DNNs popularized for many-class image recognition tasks, the models for this task require far fewer computational resources (0.3% of parameters), and are suitable for embedded use within an autonomous unmanned underwater vehicle.

This paper describes biometric-based cryptographic techniques for providing confidential communications and strong, mutual and multifactor authentication on the Internet of Things. The described security techniques support the goals of universal access when users are allowed to select from multiple choice alternatives to authenticate their identities. By using a Biometric Authenticated Key Exchange (BAKE) protocol, user credentials are protected against phishing and Man-in-the-Middle attacks. Forward secrecy is achieved using a Diffie-Hellman key establishment scheme with fresh random values each time the BAKE protocol is operated. Confidentiality is achieved using lightweight cryptographic algorithms that are well suited for implementation in resource constrained environments, those limited by processing speed, limited memory and power availability. Lightweight cryptography can offer strong confidentiality solutions that are practical to implement in Internet of Things systems, where efficient execution, and small memory requirements and code size are required.