Due to the rapid proliferation of Unmanned Aerial Vehicles (UAVs), also termed as drones, the Internet of Drones (IoD) has revolutionized various domains including disaster response, smart surveillance, remote sensing, by facilitating real time collection of aerial data using autonomous coordination. However, the exposed communication landscape of IoD networks render them highly susceptible to known threats including forgery, impersonation and physical capture threats. Most of the conventional key agreement techniques are costly for computations and not suitable for resource constrained IoD system which necessitate low cost yet secure authentication mechanisms. Though, many lightweight drone authentication schemes have been presented, however with security limitations. This study suggested a novel Barrel-Shifter Physically Unclonable Function (BS-PUF) based drone authentication protocol (BSP-IoD) to augment security of IoD network. Leveraging the intrinsic randomness, commutative and invertible properties of BS-PUF, the BSP-IoD ensures construction of mutually agreed session key employing unique challenge-response pairs (CRPs). The scheme could withstand known threats besides supporting perfect forward secrecy and privacy to the user. The BSP-IoD considerably mitigates computational overheads besides ensuring resilience against known attacks including resistance from drone physical capture threat, promoting viability for next generation IoD networks. The formal analysis and performance assessment exhibit that BSP-IoD could address the critical challenges of next generation IoD applications. Furthermore, it supports 56.6% more number of security properties as compared to preceding schemes.

Electronic health record (EHR) provides us medical information about the patient and provides the overall history of the individual. EHR works automatically. It gives access to health records and workflow of physician. Authentication, timeliness and accessibility to information between doctors and patients are also maintained by this healthcare cryptosystem. The strong cryptosystem consists of doctors' and patients' authentic interaction. Hence, we propose trilinear pairing-based cryptosystem security and authentication using elliptic curve cryptography (ECC) and elliptic curve discrete logarithm problem (ECDLP) techniques. Our proposed model authenticates the patients and doctors by key sharing, transmission and authentication to secure the healthcare system. The analysis outcome shows in the performance, accuracy, time and attacks.

The integration of thriving information and communications technology (ICT) and cyber–physical systems (CPSs) has spawned several innovative applications, such as remote healthcare, smart and intelligent transportation, smart logistics, smart grids, and public safety. An emerging software-defined networks (SDNs) technology further enabled to optimize the communication among Industrial IoT (IIoT) and CPS entities. Nonetheless, the communication on public channel among different IIoT entities in an SDN-enabled environment may be exposed to various security threats due to wireless and insecure communication channels. To counter these security challenges in the way of wider CPS or IIoT adoption, we propose a novel three-factor authenticated key exchange mechanism (SUSIC) for SDN-enabled IIoT ecosystem. The SUSIC enables a registered user to access real-time data from physical IIoT environment directly after having mutual authentication performed through SDN-enabled controller node. The scheme is proved to be secure under rigorous formal and informal security analysis. Moreover, the simulation results and performance evaluation signifies toward achieving a better tradeoff between security functionalities and computational overheads comparatively.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.