Biblio

The internet of things (IoT) is the popular wireless network for data collection applications. The IoT networks are deployed in dense or sparse architectures, out of which the dense networks are vastly popular as these are capable of gathering the huge volumes of data. The collected data is analyzed using the historical or continuous analytical systems, which uses the back testing or time-series analytics to observe the desired patterns from the target data. The lost or bad interval data always carries the high probability to misguide the analysis reports. The data is lost due to a variety of reasons, out of which the most popular ones are associated with the node failures and connectivity holes, which occurs due to physical damage, software malfunctioning, blackhole/wormhole attacks, route poisoning, etc. In this paper, the work is carried on the new routing scheme for the IoTs to avoid the connectivity holes, which analyzes the activity of wireless nodes and takes the appropriate actions when required.

A spectral-resource-utilization-efficient and highly resilient coarse granular routing optical network architecture is proposed. The improvement in network resiliency is realized by a novel concept named loop inflation that aims to enhance the geographical diversity of a working path and its redundant path. The trade-off between the inflation and the growth in circumference length of loops is controlled by the Simulated Annealing technique. Coarse granular routing is combined with resilient path design to realize higher spectral resource utilization. The routing scheme defines virtual direct links (VDLs) bridging distant nodes to alleviate the spectrum narrowing effect at the nodes traversed, allowing optical channels to be more densely accommodated by the fibers installed. Numerical experiments elucidate that the proposed networks successfully achieve a 30+0/0 route diversity improvement and a 12% fiber number reduction over conventional networks.