International Journal of Computer and Applications

https://link.springer.com/content/pdf/10.1057%2Fjors.1996.159.pdf

Modeling and Analysis of Stochastic Systems

Advancements in the field of autonomous underwater vehicle

Underwater communication remains a challenging technology via communication cables and the cost of underwater sensor network (UWSN) deployment is still very high. As an alternative, underwater wireless communication has been proposed and have received more attention in the last decade. Preliminary research indicated that the Radio Frequency (RF) and Magneto-Inductive (MI) communication achieve higher data rate in the near field communication. The optical communication achieves good performance when limited to the line-of-sight positioning. The acoustic communication allows long transmission range. However, it suffers from transmission losses and time-varying signal distortion due to its dependency on environmental properties. These latter are salinity, temperature, pressure, depth of transceivers, and the environment geometry. This paper is focused on both the acoustic and magneto-inductive communications, which are the most used technologies for underwater networking. Such as acoustic communication is employed for applications requiring long communication range while the MI is used for real-time communication. Moreover, this paper highlights the trade-off between underwater properties, wireless communication technologies, and communication quality. This can help the researcher community by providing clear insight for further research.

Underwater Wireless Sensor Networks: A Survey on Enabling Technologies, Localization Protocols, and Internet of Underwater Things

Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model

Background/Objectives: Medical science industry has immense measure of information; however a large portion of this information is not mined .Machine Learning takes analytics to the extreme by exploring hidden information in data. Diagnosis of a disease is major objective of medical decision support system which will help the physicians to take effective decision Methods/Statistical analysis: In this research paper Machine Learning techniques, K-Nearest Neighbors (KNN), Decision Tree , Artificial neural networks (ANNs), Radial Basis Function (RBF) neural networks and Support Vector Machine (SVM) are analyzed . Findings: Performance of these techniques is compared through various performance measures such as sensitivity, specificity, accuracy, F measure, Kappa statistics, True Positive Rate, False Positive Rate and ROC on Breast Cancer Wisconsin, Liver Disorder, Hepatitis and cardiovascular Cleveland Heart disease datasets. Research work consists of 10 V-fold cross validation method to measure the fair estimate of these prediction techniques . Application/Improvements: The evaluation of these techniques on diverse medical datasets gave an insight into predictive ability of Machine Learning in medical diagnosis and there is a wide space of improvement.

Evaluation of Predictive Machine Learning Techniques as Expert Systems in Medical Diagnosis

With the huge advantages and increasing growth of unmanned aerial vehicles (UAVs), their popularity expanded and used in every aspect of our lives including government, industry, environment and so...

Trajectory planning and optimization for UAV communication: A review

This paper concentrates on the IEEE 802.11p MAC sublayer to analyze its performance in the presence of hidden terminals. An analytical model for the throughput of the IEEE 802.11p in the presence of hidden terminal is presented in this paper. A 3-D Markov chain is created to model the backoff procedure for each access category. The different contention windows and arbitration interframe space are considered for each access category. The model also takes into consideration control packets and data packets separately. The probabilities of frame blocking, successful transmission, collisions and hidden terminals have been derived and used to calculate the throughput. The simulation results are presented to validate the analytical results of the proposed model. The results of the proposed model are compared with the existing model IEEE 802.11 EDCA (Kosek-Szott et al. in Comput Netw 55(3):622---635, 2011). The results of the proposed model outperform the existing model.

Performance Analysis of IEEE 802.11p in the Presence of Hidden Terminals

At present scenario, Unmanned Aerial Vehicles (UAVs) have been growing in popularity exploiting high mobility, easily deployability and low cost. Sometimes, at remote agricultural sites it become impossible for humans to operate all the time. At such agriculture sites, UAVs can be deployed easily. Planning a path for UAV trajectory is also very difficult task as it involves calculation of infinite number of points. In this article, we have proposed a projectile trajectory for UAV to Ground Terminal (GT) communication without obstacles in between for crop monitoring. Line of site (LoS) and path-loss scenarios are taken into account for UAV-GT communication. For solving higher order integration problem, we have used Simpson’s rule for calculating transmission rate and is maximized between angle of projections 15° and 30° using Genetic Algorithm (GA). It is also observed that gravity plays a major role in the proposed trajectory. Also, energy is a powerful resource and UAV as small in size lacks this resource and must be saved for following trajectory path in fixed time. Therefore, with the help of GA, we have minimized energy for the proposed trajectory.

Trajectory Design for UAV-to-Ground Communication With Energy Optimization Using Genetic Algorithm for Agriculture Application

Radio waves are preferred to acoustic and optical waves for underwater wireless communications for shallow water real-time applications However, for deep water communication, acoustic waves are preferred to electromagnetic waves as acoustic waves are capable of propagating through compressions and rarefactions Moreover, acoustic waves require high power for underwater transmissions Radio waves have a different mechanism of propagation than acoustic waves Also, radio waves have higher data rate underwater than acoustics This leads to low power usage by underwater sensors Therefore, radio frequency electromagnetic waves can be employed to address constrained battery life issue in underwater wireless sensor networks Attenuation loss in seawater depends on temperature and salinity of the wireless medium and frequency of propagating radio signal Temperature and salinity both changes with variation in depth This leads to lower attenuation of radio waves in deep water as compared to shallow water Our research analytically establishes the fact that radio waves are suitable for deep water communications by using acoustics as a complementary technology In this paper, an extensive study of path loss analysis is provided for radio waves in case of shallow and deep water sensor networks

Rishi Pal Singh

Papers

Evaluation of Predictive Machine Learning Techniques as Expert Systems in Medical Diagnosis

Trajectory planning and optimization for UAV communication: A review

Performance Analysis of IEEE 802.11p in the Presence of Hidden Terminals

Trajectory Design for UAV-to-Ground Communication With Energy Optimization Using Genetic Algorithm for Agriculture Application

Path loss analysis of RF waves for underwater wireless sensor networks