International Journal of Sensors Wireless Communications and Control - Volume 8, Issue 3, 2018

Background and Objective: Internet of things, being a technological advancement in collaboration with traditional wireless sensor networks, are characterized by a highly complex, largescale, dynamically changing, heterogeneous and asymmetric wireless network. These constraints make routing in such wireless sensor networks a challenging task. In this paper, fuzzy link cost estimation based Adaptive Tree routing algorithm (Fuzzy AT) has been introduced which uses the concept of fuzzy logic based link cost estimation, obtained from physical layer and mac layer parameters such as residual energy, packet drop rate, RSSI and total packet receiving time from source node to sink node. The performance of this algorithm has been evaluated with traditional reinforcement learning based algorithms such as real-time search, adaptive tree, ant-based flooded forward routing and Constrained Flooding algorithms on the basis of performance metrics like throughput, latency, energy consumption, energy efficiency and network lifetime. Results and Conclusion: The simulation results reveal that Fuzzy AT algorithm is most appropriate reinforcement learning based routing algorithm among all five algorithms for ensuring energy efficient, reliable, error-free, real-time compatible and QoS aware routing in dynamically changing, asymmetric and unreliable wireless environment of Wireless Sensor Networks.

Background and Objective: Video-based human activity recognition is a prominent area of research due to a wide range of applications from intelligent video surveillance to human-computer interaction. Recent work on video analysis is focused on applying deep learning approach to accomplish the task of activity recognition. Conclusion: Deep networks can dramatically improve the recognition performance because of its hierarchical nature to exploit the video frame structure in reducing the search space of the learning model. This motivated us to provide a comprehensive survey of the state-of-art deep models for recognizing human actions/activities.

Background & Objective: 5G Millimeter Wave (mmWave) Communication System is emerging as an upcoming commercial version of wireless communication for worldwide users. 5G mmWave Communication System can provide high data rates in the range of Gbps for many users simultaneously. This research work presents vegetation attenuation control in 5G mmWave communication system using software defined radio (SDR). In the SDR based 5G transmitter, the vegetation attenuation is calculated for the FCC recommended frequencies by using machine learning (ML). The proposed 5G ML transmitter system keeps learning mmWave propagation vegetation attenuation values for the mmWave frequencies along with the depth of vegetation by using supervised ML. The ML unit predicts the vegetation attenuation values using a regression model with the algorithms like KNearest Neighbors, Decision Tree and Random Forest. Conclusion: Further, the 5G SDR transmitter calculates the Shannon channel capacity (SCC) for the selected frequencies by having ML unit generated vegetation attenuation values to maintain the desired transmission data rates. Vegetation attenuation and SCC are calculated for Delhi Technological University (DTU), New Delhi, India based location as DTU has high vegetation density.

Background & Objective: In this work, ahydrostatic pressure sensor based on a cavity coupled to a photonic crystal waveguide is proposed. A defect is introducedto createa sharp resonance in the structure which makes it useful for detecting pressure changes. The sensing principle is based on the shift of the resonant wavelength with the change refractive index which arises due to the hydrostatic pressure effect. The proposed structure gives a high sensitivity against wide range of pressures and a good quality factor near 3GPa is achieved. Conclusion: The proposed design also shows separated resonant peaks for different indicesand a perfect linear relation between the cutoff wavelength and the pressure which offer a possibility of highly selective pressure detection.

Background & Objective: In the design of ad-hoc networks, potential challenge is how to route information reliably and efficiently from one node to another in high level of mobility. Moreover, remaining queuing capacity is also affected on the packet loss. Much research efforts have been consistently done to create protocols that consider minimum bandwidth, better throughput and less average delay. It is widely accepted that only quality of service (QoS) fulfillment can guarantee the requirements in ad-hoc networks and improve the overall network performance. Conclusion and Results: This paper proposes a QoS-aware optimized link state routing protocol (QOLSR) to enhance data transmission efficiency in cognitive radio ad-hoc networks (CRAHNs). The proposed algorithm utilizes link quality estimation metric and provides best end-to-end paths based on generalization of Dijkstra's algorithm. We implement two routing algorithms, AODV and OLSR and compare their performance. Simulation is performed using NS-2.35 and the results show that the proposed algorithm outperforms the existing baseline AODV routing algorithm.

Background & Objective: In this paper, we convert the problem of joint mode selection and resource allocation in in-band underlay device-to-device (D2D) communications into two subproblems. First, by using the closed-form expressions for the outage probability of both single-cell direct and relay-aided D2D communications in Rayleigh fading channel, a distance-based procedure is derived which introduces proper mode. For the relay-aided mode, considering the throughput and respected geometry, we find the suitable relay area which supports a D2D pair. Secondly, we optimally allocate the radio resources based on three schemes, minimizing the total outage probability, maximizing the total throughput, and maximizing the total diversity gain. According to the numerical analyses, it is indicated that the Hungarian and Bipartite-matching (BP) algorithms offer the same results higher than the random algorithm. In addition, it is demonstrated that the optimization problem based on the diversity gain have the higher diversity gain compared to the others because it considers the outage probability and throughput in a joint manner. Results & Conclusion: As a final remark, in the view of the diversity gain, the performance of the diversity gain-based scheme as well as the throughput-based scheme for amplify-and-forward (AF) and Decode-and-Forward (DF) scenarios are approximately the same.

Background & Objective: This paper proposes a modified eigenvalue-based Generalized Likelihood Ratio Test (GLRT). The proposed model is appropriate for cognitive wireless radio system under colored noise and unknown primary user's power. Herein, we introduce an optimization problem, which maximizes the detection probability under the constraints of false alarm, sensing time, the weighting of eigenvalues, and Energy Efficiency (EE). In addition, we improve the detection probability by achieving the proper number of cooperative secondary users using the power method. Conclusion: Simulation results show that the computational complexity of the proposed method is lower than that for traditional methods, Energy Detection (ED) and Eigenvalue-Based (EVB).