Tajudeen Olawale Olasupo

Empirical Path Loss Models for Wireless Sensor Network Deployments in Short and Tall Natural Grass Environments

Extensive research has not been done on propagation modeling for natural short- and tall-grassy environments for the purpose of wireless sensor deployment. This study is essential for efficiently deploying wireless sensors in different applications such as tracking the grazing habits of cows on the grass or monitoring sporting activities. This study proposes empirical path loss models for wireless sensor deployments in grassy environments. The proposed models are compared with the theoretical models to demonstrate their inaccuracy in predicting the path loss between sensor nodes deployed in natural grassy environments. The results show that the theoretical model values deviate from the proposed model values by 12%-42%. In addition, the results of the proposed models are compared with those of the experimental results obtained from similar natural grassy terrains at different locations resulting in similar outcomes. Finally, the results of the proposed models are compared with those of the previous studies and other terrain models such as those in dense tree environments. These comparisons show that there is a significant difference in path loss and empirical model parameters. The proposed models as well as the measured data can be used for efficient planning and future deployments of wireless sensor networks in similar grass terrains.

Effects of terrain variations in Wireless Sensor Network deployments

This research highlights the need for more accurate wireless sensor network deployments (WSN) models to support decision-making. Specifically, it presents the effects of terrain variations on network connectivity in Wireless Sensor Networks (WSN) deployments. Through use of statistical hypothesis testing, visualization, and Analysis of Variance (ANOVA), results support significant difference in network connectivity when comparing deployment performance under ideal versus more practical or irregular terrains. These results point to the inadequacy of current simulation environments for supporting deployment decision-making, and point to the need for future research that that improves WSN performance via better deployment techniques.

The Impacts of Node Orientation on Radio Propagation Models for Airborne-Deployed Sensor Networks in Large-Scale Tree Vegetation Terrains

In deterministic deployment of wireless sensor networks (WSNs), nodes are carefully placed at desired locations, with careful planning for separation distances, heights, and node orientations. This deployment strategy ensures good radio communication and sensing coverage. However, deterministic deployments are impractical for harsh large tree vegetation environments that span hundreds of kilometers. In these cases, stochastic deployments may enable effective deployment strategies. When relying on stochastic deployments, nodes are likely to be positioned at un-desired locations with unknown node orientations, which can lead to suboptimal radio connectivity and network performance. This paper examines the effects of factors affecting path loss under conditions resembling stochastic aerial deployments of WSN. Unlike previous work in the existing literature, this paper takes into account important deployment factors that affect network performance, such as node orientation and positioning, energy-to-bit-rate ratio, packet loss rate, and the composition of environment. Based on experimentation, a set of path loss models are proposed to model radio signal propagation in jungle-like environments under side-effects encountered in aerial deployments. The proposed models are compared with theoretical models and with other proposed models in the literature. Results show a significant difference with improved network performance that exhibits enhanced energy-to-bit-rate ratio, reduced packet loss rate, and increased node lifetime.

Investigation of the impact of heterogeneous traffic on performance of LTE-based mission critical communication networks

The existing Private/Professional Mobile Radio (PMR) technologies designed specifically for Mission Critical Communication (MCC) systems are narrowband and wideband devices, with limited network data capacity in emergency scenarios. They are majorly used to support MCC voice communications and low data rate applications during mission critical operations. However, the need for broadband systems that would support high radio data capacity keep increasing during major incidents and accident scenarios. Because of this, the MCC agencies were attracted by the broadband capabilities of Long Term Evolution (LTE) technology. But, the capacity of LTE-based MCC systems is still a concern. Therefore, this study describes simulation approach to model heterogeneous data applications over the LTE-based MCC network. The relation between the traffic load (video, data, and voice, short messaging) and waiting time is presented. ARENA simulation tool is used to show the throughput, waiting time, and resource utilization to be expected when using LTE-based MCC networks. The simulation results are compared with the analytical and 3GPP models. Results show that up to ten users each with traffic less than 3.6 Mbps can simultaneously upload data on the uplink of the LTE-based MCC network. The results from this study can help the network designers in the implementation of equipment and devices that could support MCC services over LTE networks.

Empirical path loss model for vehicle-to-vehicle IoT device communication in fleet management

Vehicle fleet management systems can monitor and provide accurate vehicle management information, such as location, idle time, speed, and mileage (among others). This information can be transmitted using direct communication between cars and base stations. However, this concept assumes that vehicles are served by a cellular base station at all times, which is not always the case. In order to fulfil the vision of Internet-of-Things (IoT), where “things” self-manage themselves, there needs to be a mechanism for vehicles to transmit important information in cases where base stations are not available. In these cases, IoT sensing devices can be used to establish device-to-device mesh communication networks between vehicles where no cellular service is available and act as routers to deliver information to destination nodes available within cellular coverage. In these cases, design and deployment of such systems rely on proper modeling and characterization of signal propagation between the in-vehicle-to-in-vehicle communication of IoT devices. This study proposes models that can be used for such design and in similar environments with the end goal of improving the quality of service of these systems and get them closer to the vision of selfmanagement. The proposed models are compared with theoretical models which deviate by 6 to 23%.

Link performance modeling of wireless sensor network deployment for mission-critical applications (underground deployment)

Reliable communication of information from the incident scene to nearest responder (point-to-point communication) is vital to mission-critical operations. Sensor nodes such as mobile robots are deployed into some unfriendly environments to sense the environments and send information to the responders. However, the heterogeneous environments limit navigation and reliable transfer of critical data to the responders. This study proposes a communication model that supports operation of static sensor nodes and maneuvering of mobile sensor nodes in mission-critical tasks. It also shows semiempirical energy per bit to noise spectral density, empirical radio propagation models and parameters for some “harsh environments – underground-to-underground communications.” These values and models are obtained from combination of experimental approach and analytical approach of additive white Gaussian noise channel. They are used to ensure a reliable communication of wireless sensor nodes deployed in the environments for the purpose of mission-critical services. Also, the values and models are validated in a theoretical and semianalytical simulation scenarios. Result shows that both techniques are nearly identical. The semi-empirical approach, the proposed models, and values, can be used for efficient planning and future deployments of wireless sensor networks for mission-critical services.

Automatic detection of radio signal obstruction in wireless sensor networks' on-demand deployment

This paper presents an application of machine learning approach for automatic terrain classification suitable for optimal wireless sensor network performance in on-demand deployment. The work entails practical terrain image processing using supervised SVM kernel algorithm moving from gray scale level to color and covering every aspect of a typical terrain image. This paper showcases the integral part of proposed intelligent decision making framework that will be used in wireless sensor network deployment process. The proposed system will automatically identify the areas with potential obstructions to radio frequency signal in a pre-deployment procedure or simulation. This research work presents the performance of the approach which is consistence with practical deployment behavior.