A neural network propagation model for LoRaWAN and critical analysis with real-world measurements

Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in similar scenarios. This could facilitate network planning for outdoor–indoor environments

Game theoretic approach in routing protocols for wireless mobile ad hoc networks

Mobile Ad hoc Networks (MANETs) are becoming popular as a means of providing communication among a group of people. Because of self-configuring and self-organizing characteristics, MANETs can be deployed quickly. There is no infrastructure defined in the network, therefore all of the participating nodes relay packets for other nodes and perform routing if necessary. Because of the limitations in wireless transmission range, communication links could be multi-hop. Routing protocol is the most important element of MANET. Routing protocols for MANET can broadly be classified as proactive routing protocol and reactive routing protocol. In proactive routing protocols like Destination Sequence Distance Vector (DSDV), mobile nodes periodically exchange routing information among themselves. Hence proactive routing protocols generate high overhead messages in the network. On the other hand, reactive routing protocols like Ad hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) work on-demand. Hence reactive routing protocols generate fewer number of overhead messages in the network compared to proactive routing protocols. But reactive routing protocols use a global search mechanism called flooding during the route discovery process. By flooding mechanism a source node can discover multiple routes to a destination. Flooding generates a large number of overhead packets in the network and is the root cause of scaling problem of reactive routing protocols. Hierarchical Dynamic Source Routing (HDSR) protocol has been proposed in this dissertation to solve that scaling problem. The DSR protocol has been modified and optimized to implement HDSR protocol. HDSR protocol reduces the flooding problem of reactive routing protocols by introducing hierarchy among nodes. Two game theoretic models, Forwarding Dilemma Game (FDG) and Forwarding Game Routing Protocol (FGRP), is proposed to minimize the \u27flooding\u27 effect by restricting nodes that should participate in route discovery process based on their status. Both FDG and FGRP protocols reduce overhead packet and improve network performances in terms of delay packet delivery ratio and throughput. Both protocols were implemented in AODV and the resulting protocol outperformed AODV in our NS-2 simulations. A thorough connectivity analysis was also performed for FDG and FGRP to ensure that these protocols do not introduce disconnectivity. Surprisingly, both FDG and FGRP showed better connectivity compared to AODV in moderate to high node density networks

Towards an intelligent and supportive environment for people with physical or cognitive restrictions

AmbienNet environment has been developed with the aim of demonstrating the feasibility of accessible intelligent environments designed to support people with disabilities and older persons living independently. Its main purpose is to examine in depth the advantages and disadvantages of pervasive supporting systems based on the paradigm of Ambient Intelligence for people with sensory, physical or cognitive limitations. Hence diverse supporting technologies and applications have been designed in order to test their accessibility, ease of use and validity. This paper presents the architecture of AmbienNet intelligent environment and an intelligent application to support indoors navigation for smart wheelchairs designed for validation purposes.Ministerio de Educación y Ciencia TIN2006-15617-C[01,02,03

Factors affecting the performance of ad hoc on-demand distance vector protocol in simulated mobile ad hoc network scenarios using Taguchi approach

The performance of ad hoc on demand vector (AODV) protocol is affected hugely by some common major factors. These factors are terrain, network size, node velocity, pause time, transmission range, traffic load, and packet rates. The main purpose of this study is to analyse the effects of those factors and some selected two-way interactions on the performance measure of drop rates and average end-to-end delay. Taguchi approach was used in this study. Initially, L16 orthogonal array was used to determine the effects of the seven main factors and eight others two-way interactions between selected factors. The final results revealed that terrain, network size, transmission range, and traffic load have significant effects on drop rates. On the other hand, we discovered that terrain, transmission range, traffic load and interaction between node velocity and pause time have significant effects on average end-to-end. Interaction plot for L16 singled out strong interaction between node velocity and pause time for the effect on average end-to-end delay. Furthermore, L8 orthogonal array was applied to analyse the seven main factors only since most of the interactions effects from L16 were largely insignificant to the response. The most influential factors affecting the drop rates (in descending order) were terrain, transmission range, pause time, network size, packet rates, node velocity, and traffic load. For average end-to-end delay, the most influential factors (in descending order) were transmission range, pause time, terrain, network size, traffic load, packet rates, and node velocity. ANOVA results for L8 shows that terrain and transmission range have significant effects on drop rates. For average end-to-end delay, terrain, pause time and transmission range have significant effects on the response

TCP Performance in Mobile Ad hoc Networks

In this paper, we present a survey of TCP (Transmission Control Protocol) protocol for better performance in the MANET (Mobile Ad Hoc Network). After a short presentation of the main features of TCP, we give the most important problems from which TCP suffer in MANET. We present after that some approaches proposed in the literature in order to improve its performance. Our paper contains also a performance evaluation of TCP NewReno and TCP Vegas transport protocols under AODV and DSR routing protocols. The simulations are conducted under varying conditions of number of TCP connections, number of nodes and mobility.Hamrioui, S.; Lloret, J.; Lorenz, P.; Lalam, M. (2013). TCP Performance in Mobile Ad hoc Networks. Network Protocols and Algorithms. 5(4):117-142. doi:10.5296/npa.v5i4.4773S1171425

Video Frame Differentiation for Streamed Multimedia over Heavilty Loaded IEEE 802.11e WLAN using TXOP

In this paper we perform an experimental investigation of using video frame differentiation in conjunction with the TXOP facility to enhance the transmission of parallel multimedia streaming sessions in IEEE 802.11e. The delay constraints associated with the audio and video streams that comprise a multimedia session pose the greatest challenge since real-time multimedia is particularly sensitive to delay as the packets require a strict bounded end-to-end delay. Video streaming applications are considered to be bursty. This burstiness is due to the frame rate of vide., the intrinsic hierarchical structure of the constituent video frame types. The TXOP facility is particularly suited to efficiently deal with this burstiness since it can be used to reserve bandwidth for the duration of the packet burst associated with a packetised video frame. Through experimental investigation, we show that there is a significant performance improvement for video streaming applications under heavily loaded conditions by differentiating between the constituent video frame types. The results shoe that video frame differentiation reduces the mean loss rate by 12% and increases the mean PSNR by 13.1 dB

An Experimental Investigation of Real-time Video Transmission over IEEE 802.11e WLAN Networks using TXOP

Real-time multimedia streaming applications require a strict bounded end-to-end delay and are considered to be bursty as each video frame is typically transmitted as a burst of packets. In this paper we show how the distribution of video frame sizes can be used to efficiently dimension the IEEE 802.11e TXOP limit parameter to efficiently deal with this burstiness in order to enhance the transmission of real-time video streaming services. Through experimental investigation, we show that by using the mean video frame size to dimension the TXOP limit parameter, the transmission delay for the video frame is reduced by 67% under heavily loaded conditions. Other techniques investigated in this paper include applying the TXOP facility separately to each of the constituent I, P, and B video frame types