Internet of Things (IoT) is an emergent and evolving technology, interconnecting the cyber and physical worlds. IoT technology finds applications in a broad spectrum of areas such as homes, health, water and sanitation, transportation and environmental monitoring. However, the endless opportunities and benefits of IoT come with many security challenges due to the reduced computation, communication, storage and energy capabilities of the IoT smart devices. Several computationally lightweight cryptographic protocols exist for these resource-constrained IoT smart devices. However, lightweight solutions render the resource-rich ends of the IoT systems (e.g., edge, fog or cloud modes) vulnerable as nodes at those ends have the capacity for computationally heavier cryptographic protocols, and they operate in relatively more malicious environments. This asymmetric computational nature of IoT systems requires security protocols that can adapt to the resource availability at the node that they operate. This survey describes the IoT structure, computational capabilities of the devices at the end, edge, fog and cloud platforms, and classifies existing lightweight cryptographic protocols. The comparative analysis of the existing lightweight cryptographic solutions along with their advantages, drawbacks and vulnerabilities highlights the need for elastic cryptographic protocols which are capable of adapting to the asymmetric capabilities of the different nodes in IoT systems.

Elementary properties of the trace map are used to construct a family of two-weight, irreducible cyclic codes. These codes are neither subfield codes nor are they semiprimitive.

This paper introduces a computationally inexpensive method for extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in the one-mode projections are distributed according to a Poisson binomial distribution and that finding the expected weight distribution of a one-mode network projected from a random bipartite network only requires knowledge of the bipartite degree distributions. Being able to extract the backbone of a projection is highly beneficial in filtering out redundant information in large complex networks and narrowing down the information in the one-mode projection to the most relevant. We demonstrate that the backbone of a one-mode projection aids in the detection of communities.