Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures.

Genes act as the blueprint for regulating all activities of a living system. Genes produce proteins, which in turn, sit on the promoter regions of other genes to regulate their activity. Thus, a gene regulatory network is formed. This network is critical in disclosing the various mysteries in the operations of living systems. Often it is very difficult to find these networks in the Wet Lab. As a result, various computational approaches have been used to reconstruct these networks from gene ex-pression data. The techniques primarily used for this purpose include Bayesian networks, Boolean networks, recurrent neural networks, S-systems, and mutual information based methods. The contemporary literature indicates that these techniques often fail to reliably reconstruct real-life networks. In this paper, we have proposed a new technique based on a modified recurrent neural network strategy that is augmented by mutual information. The proposed methodology has been implemented on an 8-gene network of Escherichia coli and a lO-gene network, which have been extensively used by other researchers. The experimental results indicate that the proposed technique achieves satisfactory results when compared to other such techniques developed by contemporary researchers.

An enhanced data analytic framework supported by a flexible and manageable underlying network infrastructure is vital to maximize the utilization of IoT technology. IoT technology facilitates innumerable applications involving decision makings in real time. Smart Parking involving IoT network is one of the important component of Smart City framework. Proper management of parking spaces and finding an empty parking slot in real time saves drivers time and also causes less traffic congestion. In this paper, a smart parking framework, SDIoTPark, powered by IoT technology, is presented. A sophisticated networking paradigm involving SDN based IoT networking was proposed that displayed many potential benefits like flexible, reliable, robust and automatic configuration of smart parking system. A suitable lightweight CNN based computer vision tool namely SDIoTParkNet was designed for the power and resource constrained IoT setup for its real time applicability. The proposed system provides a web app for the users to view occupancy status in real-time. The system displayed universal applicability. The same was experimented in test bed setup and the results indicate improvement in performance both in terms of network management and data analytic paradigm. It displayed high accuracy and less time requirement with respect to existing tools.

The rapid advancement of the Internet of Things (IoT) in real-world applications has attracted immense research endeavors in the last few years. It has got tremendous potential in industrial automation and various other fields. The use of IoT has changed the perspective of general applications in today’s world. In this article, a next-generation architecture, Nx-IoT, is proposed for conventional 6LoWPAN-based IoT. The proposed Nx-IoT architecture works in two modes: 1) single controller-based (6SSDx) and 2) multicontroller-based (6MSDx). The Nx-IoT architecture uses new algorithms for routing management and load distribution among the SDN controllers. The experimentation is carried out in a prototype testbed environment. The result shows improved performances in terms of round trip and packet drop, compared to the conventional 6LoWPAN and cloud system. The proposed Nx-IoT architecture reduces the latency and shows better throughput performances as compared to the existing state of the art.

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network. In most cases, this phenomenon can have some effect on the functioning of the living organism. In this investigation, we have proposed a methodology to mitigate the effects of external perturbations on a genetic network using a proportional-integral-derivative controller. The proposed controller has been used to perturb one or more of the other unaffected master regulators such that the most affected gene/s of the network revert to their normal state. The only required condition of such type of manoeuvring is that there should be multiple master regulators in a network. The proposed technique has been experimented on a 10-gene DREAM4 benchmark network and also on a larger 20-gene network, where only downregulation has been considered due to data constraints. Simulation results indicate that the most vulnerable genes can be reverted to their normal expression levels in 10 out of the 16 simulations performed.

In this work, a computational approach has been proposed based on the hybridisation of two modelling formalisms, recurrent neural networks and half-systems, for the reconstruction of gene regulatory networks from time-series gene expression datasets. To the best of our knowledge, the proposed hybridisation has not been attempted previously in this domain. Here, recurrent neural networks and half-systems have been hybridised to capture the underlying dynamics present in the temporal gene expression profiles. The motivation behind this work is to integrate the advantages of both the techniques in the proposed model such that the problem of reverse engineering of gene regulatory networks can be resolved more efficiently. Artificial bee colony optimisation has been used for the estimation of the model parameters. We have implemented the proposed hybrid methodology on the real-world experimental datasets (in vivo) of the SOS DNA Repair network of Escherichia coli. The obtained results are comparable to or better than that of other reverse engineering methodologies present in contemporary literature.

Gene regulatory networks are generally robust in nature. However, unwanted perturbations arising out of extreme environmental conditions or external pathogen attacks may lead them to malfunction. Potentially, this can have an adverse effect on the biochemical functions of a living system. In this work, we have proposed a computational model based on negative feedback control to eliminate the effects of such unwanted perturbations. We have implemented the recurrent neural network formalism for modelling the underlying network dynamics from a given time-series gene expression dataset. The artificial bee colony optimisation technique has been employed for model parameter estimation. The controller used in this work is of the proportional-integral-derivative type. To the best of our knowledge, this is one of the first research works in this domain to consider a completely non-linear scenario. A 10-gene DREAM4 benchmark network has been considered in this work. The results obtained herein show that the proposed formalism can mitigate the unwanted effects of external disturbances effectively.

IPv6 over low-power wireless personal area network (6LoWPAN) has been widely used for large-scale sensing and actuating purposes in the Internet of Things (IoT). Though promising, many challenges, such as high latency, heterogeneity, and packet loss persist. To mitigate these challenges, the software-defined network (SDN) technique can be hybridized with existing IoT structures that can address many of them. In this article, we propose an approach-edge-based 6LoWPAN-SDN (6LE-SDN) architecture, which can improve the system limitations mentioned. It uses an edge-based computational capability to improve the network performance over 6LoWPAN. To reduce heterogeneity, we develop a hybrid-edge switch that helps to enable communication among 6LoWPAN and SDN entities. For efficient communication between different devices, a new protocol-edge-based 6LoWPAN-SDN protocol (6LE-SDNP) is proposed, which is capable of ensuring optimal routing of the packet for efficient communication among the devices. We use the SDN-based edge controller for reducing the latency of the network apart from improving the interoperability feature. The testbed evaluation of the proposed solution indicates satisfactory performance in terms of reducing latency by 60% and network overhead by 91%. The 6LE-SDN network also succeeded in reducing the average round trip time (RTT) by 31% and the packet loss by 70% as compared to that of the traditional 6LoWPAN-based IoT.

The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system. Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3. The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.

A gene regulatory network reveals the regulatory relationships among genes at a cellular level. The accurate reconstruction of such networks using computational tools, from time series genetic expression data, is crucial to the understanding of the proper functioning of a living organism. Investigations in this domain focused mainly on the identification of as many true regulations as possible. This has somewhat overshadowed the reduction of false predictions in inferred networks. In the present investigation, we have proposed a novel scheme, based on different swarm intelligence algorithms, to reduce the number of inferred false regulations. We have first applied our proposed methodology on the much studied, benchmark experimental datasets of the DNA SOS repair network of Escherichia Coli. Subsequently, we have experimented upon a larger, in silico network extracted from the GeneNetWeaver database. The obtained results suggest that the proposed methodology can reduce the number of false predictions, significantly, without using any supplementary biological information for larger gene regulatory networks.