BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

Geothermal and solar energy–based multigeneration system for a district

In this study, parabolic trough collector with an integrated source of geothermal water is used with regenerative Rankine cycle with an open feedwater heater, an electrolyzer, and an absorption cooling system. The absorption fluids used in the solar collectors were Al₂O₃‐ and Fe₂O₃‐based nanofluids. Detailed energetic and exergetic analyses are done for the whole system including all the components. A comparative analysis of both the used working fluids is done and plotted against their different results. The parameters that are varied to change the output of the system are ambient temperature, solar irradiance, the percentage of nanofluids, the mass flow rate of the geothermal well, the temperature gradient of the geothermal well that had an effect on the net power produced, and the outlet temperature of the solar collector overall energetic and exergetic efficiencies. Other useful outputs by this domestic integrated multigeneration system are the heating of domestic water, space heating (maintaining the temperature at 40°C‐50°C), and desalination of seawater (flash distillation). The hydrogen production rate for both the fluids diverges with each other, both producing average from 0.00490 to 0.0567 g/s.     

Silver‐Adapted Diffusive Memristor Based on Organic Nitrogen‐Doped Graphene Oxide Quantum Dots (N‐GOQDs) for Artificial Biosynapse Applications

Carbon‐based electronic devices are suitable candidates for bioinspired electronics due to their low cost, eco‐friendliness, mechanical flexibility, and compatibility with complementary metal‐oxide‐semiconductor technology. New types of materials such as graphene quantum dots (GQDs) have attracted attention in the search for new applications beyond solar cells and energy harvesting due to their superior properties such as elevated photoluminescence, high chemical inertness, and excellent biocompatibility. In this paper, a biocompatible/organic electronic synapse based on nitrogen‐doped graphene oxide quantum dots (N‐GOQDs) is reported, which exhibits threshold resistive switching via silver cation (Ag⁺) migration dynamics. In analogy to the calcium (Ca²⁺) ion dynamics of biological synapses, important biological synapse functions such as short‐term potentiation (STP), paired‐pulse facilitation, and transition from STP to long‐term plasticity behaviors are replicated. Long‐term depression behavior is also evaluated and specific spike‐timing dependent plasticity is assessed. In addition, elaborated switching mechanism of biosimilar Ag⁺ migration dynamics provides the potential for using N‐GOQD‐based artificial synapse in future biocompatible neuromorphic systems.     

Performance enhancement techniques for traffic sign recognition using a deep neural network

Multimedia Tools and Applications - An advanced traffic sign recognition (ATSR) system using novel pre-processing techniques and optimization techniques has been proposed. During the pre-processing...     

An efficient calix[4]arene based silica sorbent for the removal of endosulfan from water

The present work explores sorption behavior of calix[4]arene based silica resin to remove α and β endosulfan isomers from aqueous solution. The efficiency of resin was checked through both batch and column sorption methods. In both methods, the sorption parameters, i.e. pH, equilibrium time, shaking speed and sorbent dosage were optimized as 2, 60 min, 125 rpm and 50 mg, respectively. Freundlich and Langmuir sorption isotherm models were applied to validate the sorption process. The data obtained in both models reveal that the sorption is favorable. Column sorption data were analyzed through Thomas model to calculate kinetic coefficient k(TH) and maximum sorption capacity q(o) of the resin, which were found to be 6.18 and 5.83 cm(3) mg(-1) min(-1) as well as 1.11 and 1.08 mg g(-1) for α and β endosulfan, respectively. Kinetics of sorption shows that it follows pseudo second order rate equation. The optimized method has also been applied to real water samples and the results show that calix[4]arene based silica resin is an effective sorbent to remove endosulfan from waste waters.     

Signal Processing and Deep Learning Techniques for Power Quality Events Monitoring and Classification

Abstract

Power quality disturbances (PQDs) have major challenges in embedded generation systems, renewable energy networks, and HVDC/HVAC electrical power transmission networks. Due to PQDs, electrical power network can have disruption in the protection system, security system, and energy-saving system. PQDs also affect the operation cost and consistency of electrical power systems. This paper presents an innovative method based on compressive sensing (CS), singular spectrum analysis (SSA), wavelet transform (WT) and deep neural network (DNN) for monitoring and classification of PQDs. Feature extraction and selection is an essential part of the classification of PQDs. In this paper, initially, SSA time-series tool and multi-resolution wavelet transform are introduced to extract the features of PQDs, and then CS technique is used to reduce the dimensionality of the extracted features. Finally, DNN-based classifier is used to classify the single-and-combined PQDs. The DNN architecture is constructed utilizing the restricted Boltzmann machine, which is then fine-tuned by back-propagation. The heart of this paper is to enhance the classification and monitoring accuracy and comparison of the results of WT-based classifier with SSA-based classifier. The proposed method is tested using 15 types of single and combined PQDs. These disturbances are transitory in the transmission and distribution networks such as voltage sag, swell, transient, interruption, harmonic, etc. The simulation and experimental results demonstrate that the SSA-based DNN classifier has significantly higher potential than the WT-based classifier to classify the power quality events under noisy and noiseless conditions.     

Machine learning based cluster formation in vehicular communication

Telecommunication Systems - Nowadays vehicular communication has become a widespread phenomenon, which will cause spectrum scarcity. By utilizing the cognitive radio in vehicular communication can...     

Towards engineering in memristors for emerging memory and neuromorphic computing:A review

Resistive random-access memory(RRAM),also known as memristors,having a very simple device structure with two terminals,fulfill almost all of the fundamental requirements of volatile memory,nonvolatile memory,and neuromorphic characteristics.Its memory and neuromorphic behaviors are currently being explored in relation to a range of materials,such as biological materials,perovskites,2D materials,and transition metal oxides.In this review,we discuss the different electrical behaviors exhibited by RRAM devices based on these materials by briefly explaining their corresponding switching mechanisms.We then discuss emergent memory technologies using memristors,together with its potential neuromorphic applications,by elucidating the different material engineering techniques used during device fabrication to improve the memory and neuromorphic performance of devices,in areas such as ION/IOFF ratio,endurance,spike time-dependent plasticity(STDP),and paired-pulse facilitation(PPF),among others.The emulation of essential biological synaptic functions realized in various switching materials,including inorganic metal oxides and new organic materials,as well as diverse device structures such as single-layer and multilayer hetero-structured devices,and crossbar arrays,is analyzed in detail.Finally,we discuss current challenges and future prospects for the development of inorganic and new materials-based memristors.     

MimiBS: Mimicking Base-Station to Provide Location Privacy Protection in Wireless Sensor Networks

In a wireless sensor network (WSN), sink node/base station (BS) gathers data from surrounding nodes and sends them to a remote server via a gateway. BS holds important data. Therefore, it is necessary to hide its location from an inside/outside attacker. Providing BS location anonymity against a local and global adversary, we propose a novel technique called MimiBS “Mimicking Base-Station”. The key idea is the integration of aggregator nodes (ANs) with sensor nodes (SNs), while fine tuning TTL (time to live) value for fake packets, and setting some threshold value for real packet counter rpctr. MimiBS creates multiple traffic-hotspots (zones), which shifts the focus from BS to the newly created ANs hotspots. Multiple traffic-hotspots confuse the adversary while determining the real BS location information. We defend the BS location information anonymity against traffic analysis attack, and traffic tracing attack. MimiBS gives an illusion of having multiple BSs, and thus, if the attacker knows any about AN, he/she will be deceived between the real BS and ANs. MimiBS outperforms BLAST (base-station location anonymity and security technique), RW (random walk), and SP (shortest path), while conducting routing without fake packets, with fake packets, without energy consideration, and with energy consideration respectively.