Position-based services that deliver networked amenities based on roaming users positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in Position-based services (PBS). For effective PBS, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques, however only few deliberate the progress of position-based real-time applications considering significant tasks of PBS. In this paper, a method for conserving position confidentiality of roaming PBS users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor, and estimates user destination along with position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm which will assure the timely delivery of position-based services at the network edge. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBS.

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication and robustness; blockchain technology comes out as a sustainable solution. A blockchain enabled distributed security framework using edge-cloud and software defined networking (SDN) is presented in this paper. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The software defined networking (SDN)-enabled gateway offers dynamic network traffic flow management, that contributes in the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that, the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge-cloud and SDN paradigm.

Adversary models have been fundamental to the various cryptographic protocols and methods. However, their use in most of the branches of research in computer science is comparatively restricted, primarily in case of the research in cyber physical security (e.g., vulnerability studies, position confidentiality). In this paper, we propose an energy-aware green adversary model for its use in smart industrial environment through achieving confidentiality. Even though, mutually the hardware and, the software parts of cyber-physical systems can be improved to decrease its energy consumption, this paper focuses on aspects of conserving position and information confidentiality. On the basis of our findings (assumptions, adversary goals and capabilities) from the literature, we give some testimonials to help practitioners and researchers working in cyber physical security. The proposed model that runs on real time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query thus facilitating energy consumption minimization. Moreover, we calculate the transferring/acceptance slots required for each query to avoid deteriorating slots. The experimental results confirms that, the proposed approach can diminish energy consumption up to 5 times in comparison to existing approaches.

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology due to the mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.

The Industrial Internet of Things (IIoT) is widely used in smart factories, enabling smart manufacturing and improving productivity. Although the application of IIoT has significantly altered a number of industries, increased connectivity has also given rise to security concerns. For example, information is collected from different domains in smart industrial environments and transmitted through public IoT channels. However, this phase can lead to communication security and privacy leakage issues when applied to resource-constrained smart industrial devices. This work proposes a novel collaborative authentication and key exchange protocol based on physically unclonable functions (PUFs) to enable safe and efficient communication amongst smart industrial devices in an IIoT context. The proposed protocol uses PUF to enhance the security of smart industrial devices. Additionally, to mitigate difficult situations, such as device loss, the proposed protocol combines an elliptic curve Diffie-Hellman key exchange scheme to achieve forward security and collaborative authentication between the domain server and smart industrial devices during the key exchange phase. We analyze and provide security proofs for the proposed protocol. Furthermore, performance analysis is conducted to evaluate the computational costs of the protocol. The results of this work contribute to the development of secure and reliable authentication and key exchange protocols within the context of IIoT, promoting the adoption of IIoT technology in smart factories while reducing potential security threats.