Performance evaluation of wireless sensor networks for mobile event and mobile sink

Extending lifetime and energy efficiency are important objectives and challenges in-Wireless Sensor Networks (WSNs). In large scale WSNs, when the nodes are near to the sink they consume much more energy than the nodes far from the sink. In our previous work, we considered that the sink node was stationary and only event node was moving in the observation field. In this work, we consider both cases when the sink node and event node are moving. For the simulations, we use TwoRayGround and Shadowing radio models, lattice topology and AODV protocol. We compare the simulation results for the cases when the sink node and event node are mobile and stationary. The simulation results have shown that the goodput of TwoRayGround is better than Shadowing in case of mobile event, but the depletion of Shadowing is better than TwoRayGround in case of mobile event. The goodput in case of mobile sink is better than stationary sink when the transmission rate is lower than 10pps. For TwoRayGround radio model, the depletion in case of mobile sink is better than stationary sink when the number of nodes is increasedPeer ReviewedPostprint (published version

Routing efficiency in wireless sensor-actor networks considering semi-automated architecture

Wireless networks have become increasingly popular and advances in wireless communications and electronics have enabled the development of different kind of networks such as Mobile Ad-hoc Networks (MANETs), Wireless Sensor Networks (WSNs) and Wireless Sensor-Actor Networks (WSANs). These networks have different kind of characteristics, therefore new protocols that fit their features should be developed. We have developed a simulation system to test MANETs, WSNs and WSANs. In this paper, we consider the performance behavior of two protocols: AODV and DSR using TwoRayGround model and Shadowing model for lattice and random topologies. We study the routing efficiency and compare the performance of two protocols for different scenarios. By computer simulations, we found that for large number of nodes when we used TwoRayGround model and random topology, the DSR protocol has a better performance. However, when the transmission rate is higher, the routing efficiency parameter is unstable.Peer ReviewedPostprint (published version

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Channel Estimation via Loss Field: Accurate Site-Trained Modeling for Shadowing Prediction

Future mobile ad hoc networks will share spectrum between many users. Channels will be assigned on the fly to guarantee signal and interference power requirements for requested links. Channel losses must be re-estimated between many pairs of users as they move and as environmental conditions change. Computational complexity must be low, precluding the use of some accurate but computationally intensive site-specific channel models. Channel model errors must be low, precluding the use of standard statistical channel models. We propose a new channel model, CELF, which uses channel loss measurements from a deployed network in the area and a Bayesian linear regression method to estimate a site-specific loss field for the area. The loss field is explainable as the site's 'shadowing' of the radio propagation across the area of interest, but it requires no site-specific terrain or building information. Then, for any arbitrary pair of transmitter and receiver positions, CELF sums the loss field near the link line to estimate its channel loss. We use extensive measurements to show that CELF lowers the variance of channel estimates by up to 56%. It outperforms 3 popular machine learning methods in variance reduction and training efficiency

CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

Fault Tolerant Coverage and Connectivity in Presence of Channel Randomness

Some applications of wireless sensor network require K-coverage and K-connectivity to ensure the system to be fault tolerance and to make it more reliable. Therefore, it makes coverage and connectivity an important issue in wireless sensor networks. In this paper, we proposed K-coverage and K-connectivity models for wireless sensor networks. In both models, nodes are distributed according to Poisson distribution in the sensor field. To make the proposed model more realistic we used log-normal shadowing path loss model to capture the radio irregularities and studied its impact on K-coverage and K-connectivity. The value of K can be different for different types of applications. Further, we also analyzed the problem of node failure for K-coverage model. In the simulation section, results clearly show that coverage and connectivity of wireless sensor network depend on the node density, shadowing parameters like the path loss exponent, and standard deviation

Radio Irregularity Obstacles-Aware Model for Wireless Sensor Networks

Radio irregularity and signal attenuation are common phenomena in wireless sensor networks (WSNs) caused by many factors, such as the impact of environmental characteristics, the non-isotropic path losses, and especially, the obstacle on the transmission (multi) paths. The diversity of these phenomena make difficulty for accurate evaluation of WSNs’ applications which specifically require high coverage and connectivity. Thus, in this paper, we investigated the radio irregularity and signal power attenuation, primarily due to the obstacle in WSNs. With empirical data obtained from experiments using a well-known sensor node,i.e., MICAz, we found that the signal strength attenuation is different in each case according to obstacle characteristics. Then, we proposed a radio model, called Radio Irregularity Obstacle-Aware Model (RIOAM). The results obtained from real measurements are also supported with regard to those from the simulation. Our model effectiveness is justified against a radio irregularity model (RIM) – higher precision with the existence of obstacles in WSNs