Ad hoc networks, due to their improvised nature, are frequently established in insecure environments and hence become susceptible to attacks. These attacks are launched by participating malicious nodes against different network services. Routing protocols, which act as the binding force in these networks, are a common target of these nodes. A number of secure routing protocols have recently been proposed, which make use of cryptographic algorithms to secure the routes. However, in doing so, these protocols entail a number of prerequisites during both the network establishment and operation phases. In contrast, trust-based routing protocols locate trusted rather than secure routes in the network by observing the sincerity in participation by other nodes. These protocols thus permit rapid deployment along with a dynamically adaptive operation, which conforms with the current network situation. In this paper, we evaluate the performance of three trust-based reactive routing protocols in a network with varying number of malicious nodes. With the help of exhaustive simulations, we demonstrate that the performance of the three protocols varies significantly even under similar attack, traffic, and mobility conditions. However, each trust-based routing protocol has its own peculiar advantage making it suitable for application in a particular extemporized environment.

RNA pseudoknots are functional structure elements with key roles in viral and cellular processes. Prediction of a pseudoknotted minimum free energy structure is an NP-complete problem. Practical algorithms for RNA structure prediction including restricted classes of pseudoknots suffer from high runtime and poor accuracy for longer sequences. A heuristic approach is to search for promising pseudoknot candidates in a sequence and verify those. Afterwards, the detected pseudoknots can be further analysed using bioinformatics or laboratory techniques. We present a novel pseudoknot detection method called DotKnot that extracts stem regions from the secondary structure probability dot plot and assembles pseudoknot candidates in a constructive fashion. We evaluate pseudoknot free energies using novel parameters, which have recently become available. We show that the conventional probability dot plot makes a wide class of pseudoknots including those with bulged stems manageable in an explicit fashion. The energy parameters now become the limiting factor in pseudoknot prediction. DotKnot is an efficient method for long sequences, which finds pseudoknots with higher accuracy compared to other known prediction algorithms. DotKnot is accessible as a web server at http://dotknot.csse.uwa.edu.au.

FlexiTP is a novel TDMA protocol that offers a synchronized and loose slot structure. Nodes in the network can build, modify, or extend their scheduled number of slots during execution, based on their local information. Nodes wake up for their scheduled slots; otherwise, they switch into power-saving sleep mode. This flexible schedule allows FlexiTP to be strongly fault tolerant and highly energy efficient. FlexiTP is scalable for a large number of nodes because its depth-first-search schedule minimizes buffering, and it allows communication slots to be reused by nodes outside each other's interference range. Hence, the overall scheme of FlexiTP provides end-to-end guarantees on data delivery (throughput, fair access, and robust self-healing) while also respecting the severe energy and memory constraints of wireless sensor networks. Simulations in ns-2 show that FlexiTP ensures energy efficiency and is robust to network dynamics (faults such as dropped packets and nodes joining or leaving the network) under various network configurations (network topology and network density), providing an efficient solution for data-gathering applications. Furthermore, under high contention, FlexiTP outperforms 2-MAC in terms of energy efficiency and network performance.

Deep learning algorithms, in particular neural networks, have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of deep learning approaches in gravitational wave detection, parameter estimation, and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of the deep artificial neural network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modeled the sky as a sphere and have considered cases in which the sphere is divided into 18, 50, 128, 1024, 2048, and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on its right ascension and declination values for each of these cases. To do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between $30M_{\odot }$ and $80M_{\odot }$ into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences, and amplitude ratios at each of the three detectors Hanford, Livingston, and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (greater than 90%) for coarse angular resolution using 18, 50, and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818, and GW170823 for 1024, 2048, and 4096 sectors and compare the result with that from BAYESTAR and parameter estimation. In addition, we report that the time taken by our model to localize one gravitational wave signal is around 0.018 s on 14 Intel Xeon CPU cores.