Urban Bilstrup

Evaluation of the IEEE 802.11p MAC Method for Vehicle-to-Vehicle Communication

In this paper the medium access control (MAC) method of the upcoming vehicular communication standard IEEE 802.11p has been simulated in a highway scenario with periodic broadcast of time-critical packets (so-called heartbeat messages) in a vehicle-to-vehicle situation. The 802.11p MAC method is based on carrier sense multiple access (CSMA) where nodes listen to the wireless channel before sending. If the channel is busy, the node must defer its access and during high utilization periods this could lead to unbounded delays. This well-known property of CSMA is undesirable for time-critical communications. The simulation results reveal that a specific node/vehicle is forced to drop over 80% of its heartbeat messages because no channel access was possible before the next message was generated. To overcome this problem, we propose to use self-organizing time division multiple access (STDMA) for real-time data traffic between vehicles. This MAC method is already successfully applied in commercial surveillance applications for ships (AIS) and airplanes (VDL mode 4). Our initial results indicate that STDMA outperforms CSMA for time-critical traffic safety applications in ad hoc vehicular networks.

Bluetooth in industrial environment

An initial study of the use of Bluetooth in an industrial environment is presented. The tests have been performed at a paper mill, and in an office environment at Halmstad University. It shows the possibility to use Bluetooth for wireless short range communication in an industrial environment.

Wireless technology in industry-applications and user scenarios

We draw a map of the wireless technology landscape, and place different industrial applications in this context. It is clear that in order to implement wireless communication in safety critical applications more research is needed. We describe one approach aiming at the very low bit error rates of these applications.

An architecture comparison between a wireless sensor network and an active RFID system

In This work a new hardware platform for active RFID and wireless sensor network is presented. Furthermore a comparison of these two architectures is performed, i.e., the singlehop and the multihop architecture. The comparison reveals important issues regarding the utilization and energy consumption for the singlehop as well as for the multihop architecture.

Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy.

Capacity limitations in wireless sensor networks

It is expected that wireless sensor network will be used in home automation and industrial manufacturing in the future. The main driving forces for wireless sensor networks are fault tolerance, energy gain and spatial capacity gain. Unfortunately, an often forgotten issue is the capacity limits that the network topology of a wireless sensor network represents. In this paper we identify gains, losses and limitations in a wireless sensor network, using a simplified theoretical network model. Especially, we want to point out the stringent capacity limitations that this simplified network model provide. Where a comparison between the locality of the performed information exchange and the average capacity available for each node is the main contribution.

Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting

This paper presents a new architecture based on a brain emotional learning model that can be used in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional models with fuzzy inference systems. The main aim of this model is presenting a desirable learning model for chaotic system prediction imitating the brain emotional network. In this research work, the model is used for predicting the solar activity, since it has been recognized as a threat to critical infrastructures in modern society. Specifically sunspot numbers are predicted by applying the proposed brain emotional learning model. The prediction results are compared with the outcomes of using other previous models like the locally linear model tree (LOLIMOT) and radial bias function (RBF) and adaptive neuro-fuzzy inference system (ANFIS).

Deterministic real-time medium access for cognitive industrial radio networks

Industrial communication often has to work in an environment where other networks or radiation create different levels of interference for the data traffic. Additionally, industrial applications often demand predictable real-time performance of the network. One way of trying to utilise the available frequencies in an effective manner is to include cognitive functionality in the network. We present a medium access control protocol for a cognitive radio network, providing deterministic medium access for heterogeneous traffic and dynamic spectrum allocation. Spectrum sensing abilities in the nodes open up for the possibility of increasing successful data transmissions, and a real-time analysis framework provides upper-bounded medium access delay in order to guarantee timely treatment of hard real-time traffic.

Ant Colony Optimization for Channel Assignment Problem in a Clustered Mobile Ad Hoc Network

This paper presents an ant colony optimization (ACO) method as a method for channel assignment in a mobile ad hoc network (MANET), where achieving high spectral efficiency necessitates an efficient channel assignment. The suggested algorithm is intended for graph-coloring problems and it is specifically tweaked to the channel assignment problem in MANET with a clustered network topology. A multi-objective function is designed to make a tradeoff between maximizing spectral utilization and minimizing interference. We compare the convergence behavior and performance of ACO-based method with obtained results from a grouping genetic algorithm (GGA).

Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series

This paper suggests a novel learning model for prediction of chaotic time series, brain emotional learning-based recurrent fuzzy system (BELRFS). The prediction model is inspired by the emotional learning system of the mammal brain. BELRFS is applied for predicting Lorenz and Ikeda time series and the results are compared with the results from a prediction model based on local linear neuro-fuzzy models with linear model tree algorithm (LoLiMoT).