Abstract-Wireless Sensor Network is monitored withContikiMAC Cooja flavor to diagnose the energy utilization ratio by nodes and the fault detection process in distributed approach; adopted the Low power Listening (LPL) mechanism with ContikiMAC to prolong the network's lifetime. LPL locate the root cause of communication issue, get rid of the interruption problems, and get back normal communication state. The LPL mechanism reduces the energy utilization in centralized and distribute approaches. Even more, the distributed approach is best suited for network monitoring when energy utilization is main objective in the presence of LPL. It is also important how soon the faulty node can be detected. In this case, latency has vital contributions in monitoring mechanism and latency is achieved by developing the efficient faulty node detection methodology.

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The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively.

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Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

The use of unmanned aerial vehicles (UAVs) is gaining popularity in many applications, i.e. data collection, surveillance, wireless sensor networks (WSNs) etc. In the WSN domain, the UAVs are used to create a more flexible datagathering platform. This integration maximizes the lifetime of a WSN by optimizing the energy budget. In this paper, we have utilized these benefits of UAVs and have proposed an optimum cluster head (CH) selection strategy to maximize the lifetime of WSNs. The proposed method uses the average residual energy, the channel condition and the Euclidean distance of each sensor node (SN) with a UAV to nominate a group of CHs. Based on the initial analytical analysis, the proposed scheme maximizes the lifetime of a WSN by a fair amount in comparison to the state-of-the-art methods.

Abstract-Electronic Design Automation (EDA) industry heavily reuses third party IP cores. These IP cores are vulnerable to insertion of Hardware Trojans (HTs) at design time by third party IP core providers or by malicious insiders in the design team. State of the art research has shown that existing HT detection techniques, which claim to detect all publicly available HT benchmarks, can still be defeated by carefully designing new sophisticated HTs. The reason being that these techniques consider the HT landscape to be limited only to the publicly known HT benchmarks, or other similar (simple) HTs. However the adversary is not limited to these HTs and may devise new HT design principles to bypass these countermeasures. In this paper, we discover certain crucial properties of HTs which lead to the definition of an exponentially large class of Deterministic Hardware Trojans H D that an adversary can (but is not limited to) design. The discovered properties serve as HT design principles, based on which we design a new HT called XOR-LFSR and present it as a 'proof-of-concept' example from the class H D . These design principles help us understand the tremendous ways an adversary has to design a HT, and show that the existing publicly known HT benchmarks are just the tip of the iceberg on this huge landscape. This work, therefore, stresses that instead of guaranteeing a certain (low) false negative rate for a small constant set of publicly known HTs, a rigorous HT detection tool should take into account these newly discovered HT design principles and hence guarantee the detection of an exponentially large class (exponential in number of wires in IP core) of HTs with negligible false negative rate.

In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, and smart Internet-of-Things (IoT). Currently, the use of drone based IoT, also known as Internet-of-Drones (IoD), and their design challenges and techniques are being probed by researchers around the globe. The placement of drones (nodes) is an important consideration in a IoD environment and is closely related to the properties of IoT. Given a base station (BS), sensor nodes (SNs) and IoT devices are designed to capture the signals transmitted by the BS and make use of internet connectivity in a manner to facilitate users. Mutual benefit can be achieved by integrating drones into IoT. The drone based cluster models are not free from challenges. Routing protocols have to be substantiated by key algorithms. Drones are designed to be specific to applications, but the underlying principles are the same. Optimization algorithms are the gateway to better accuracy, performance, and reliability. This article discusses some of these optimization algorithms, include genetic algorithm (GA), bee optimization algorithm, and Chicken Swarm Optimization Clustering Algorithm (CSOCA). Finally, the routing schemes, protocols, and challenges in the context of IoD are discussed.