An improved distance‐based ant colony optimization routing for vehicular ad hoc networks

Vehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.     

Hetero-GCD2RDF: An Interoperable Solution for Geospatial Climatic Data by Deploying Semantic Web Technologies

Wireless Personal Communications - The monumental success over the years, in wireless communication and mobile communication have overcome the challenges such as fading, multipath, interferences...     

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making. In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies. The proposed ontology’s quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy. Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model. The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.     

An Improved Direct Digital Synthesizer Using Hybrid Wave Pipelining and CORDIC algorithm for Software Defined Radio

The improved Direct Digital Synthesizer (DDS) using the Hybrid Wave Pipelining (HWP) technique and COordinate Rotation DIgital Computer (CORDIC) algorithm for Software Defined Radio (SDR) is presented in this paper. In order to achieve high throughput, the hybrid wave pipelining technique is adopted. The HWP can be used to speed up the circuits without insertion of storage elements. The CORDIC algorithm is used for phase-to-amplitude conversion and utilized for dynamic transformation rather than Read Only Memory (ROM) static addressing. The frequency resolution and phase resolution are achieved as 0.023 Hz and 0.088 degree, respectively, at the maximum operating frequency of 199.288 MHz for the proposed DDS architecture. The spectral purity of the proposed design has been improved to 114 dBc with a throughput of 94 %. This paper is focused on the design and implementation of DDS using hybrid wave pipelining with CORDIC approach to target on Xilinx Spartan 3 (XC3S400-5PQ208) Field Programmable Gate Array (FPGA) with a speed grade of ?5. The proposed DDS design reduces the gate count from 49.4 % to 18.2 % as compared to the conventional pipelined Read Only Memory Look Up Table (ROMLUT) DDS method. The throughput of the proposed method has been improved from 78 % to 94 % and 55 % of total power reduction as compared with conventional DDS. The performance of the improved DDS architecture is compared with several existing DDS architectures and it is found that the present design is outperforming and can be used for software defined radios.     

Futuristic speed prediction using auto‐regression and neural networks for mobile ad hoc networks

In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works.     

Dynamic Clustering Mechanism to Avoid Congestion Control in Vehicular Ad Hoc Networks Based on Node Density

Wireless Personal Communications - Due to the rapid growth in wireless communications, vehicular ad-hoc networks (VANETs) face many challenges over wireless communication networks. Nowadays VANETs...     

Container Based Nomadic Vehicular Cloud Using Cell Transmission Model

Nomadic Vehicular Cloud (NVC) is envisaged in this work. The predominant aspects of NVC is, it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model (CTM). Containers are used in the place of Virtual Machines (VM), as containers’ features are very apt to NVC’s dynamic environment. The specifications of 5G NR V2X PC5 interface are applied to NVC, for the feature of not relying on the network coverage. Nowadays, the peak traffic on the road and the bottlenecks due to it are inevitable, which are seen here as the benefits for VC in terms of resource availability and residual in-network time. The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants, that results the container migration failure. As the entire VC participants are on the move, to maintain proximity of the containers hosted by them, estimating their movements plays a vital role. To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity, this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC. The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.