Routing in Industrial Wireless Sensor Networks: A Survey

Recently, industrial wireless sensor networks have attracted more attention, due to their large benefits in terms of faster installation, cost savings, and flexibility. Nevertheless, the acceptance of wireless sensor networks by the industrial community is not without its difficulties. In fact, several research efforts have been made in this field and a number of state-of-the-art reviews exist, presenting the current standards, the challenges, and the design principles. However, a comprehensive review on routing protocols appears to be missing. In this paper, we give a survey on routing protocols for industrial monitoring applications of the wireless sensor networks technology, and we present their limitations and weaknesses according to the industrial requirements

Starvation avoidance-based dynamic multichannel access for low priority traffics in 802.11ac communication systems

International audienceUsing optimally wide channels in 802.11ac networks has been the topic of various research work. Nevertheless, the efficiency of both Static and Dynamic Multichannel Access methods (SMA and DMA) is a challenging issue, since they employ the Enhanced Distributed Channel Access (EDCA) to provide the Quality of Service (QoS). Indeed, EDCA enables QoS through service differentiation between priority (Voice and Video) and non-priority (Besteffort and Background) traffics. Consequently, the concept of priority may severely starve non-priority traffics from channel access, and lead to an unfair bandwidth allocation. In this paper, we highlight the starvation issue of non-priority traffics in 802.11ac networks. Thereby, we propose a Starvation avoidance DMA (SDMA) method by means of 80 and 160 MHz channel bonding. The individual throughput is measured for comparing SDMA and DMA methods. The obtained results show a significant improvement in throughput of non-priority traffics, while maintaining the throughput level of priority traffics

M/g/c/c State Dependent Queueing Model for Road Traffic Simulation

In this paper, we present a stochastic queuing model for the road traffic, which captures the stationary density-flow relationships in both uncongested and congestion conditions. The proposed model is based on the M/g/c/c state dependent queuing model of Jain and Smith, and is inspired from the deterministic Godunov scheme for the road traffic simulation. We first propose a reformulation of the M/g/c/c state dependent model that works with density-flow fundamental diagrams rather than density-speed relationships. We then extend this model in order to consider upstream traffic demand as well as downstream traffic supply. Finally we calculate the speed and travel time distributions for the M/g/c/c state dependent queuing model and for the proposed model and we derive stationary performance measures (expected number of cars, blocking probability, expected travel time and throughput). A comparison with results predicted by the M/g/c/c state dependent queuing model shows that the proposed model correctly represents the dynamic of traffic and gives a good performances measures. The results illustrate the good accuracy of the proposed model

M/G/c/c state dependent queuing model for a road traffic system of two sections in tandem

We propose in this article a M/G/c/c state dependent queuing model for road traffic flow. The model is based on finite capacity queuing theory which captures the stationary density-flow relationships. It is also inspired from the deterministic Godunov scheme for the road traffic simulation. We first present a reformulation of the existing linear case of M/G/c/c state dependent model, in order to use flow rather than speed variables. We then extend this model in order to consider upstream traffic demand and downstream traffic supply. After that, we propose the model for two road sections in tandem whereboth sections influence each other. In order to deal with this mutual dependence, we solve an implicit system given by an algebraic equation. Finally, we derive some performance measures (throughput and expected travel time). A comparison with results predicted by the M/G/c /c state dependent queuing networks shows that the model we propose here captures really the dynamics of the road traffic

M/g/c/c State Dependent Queueing Model for Road Traffic Simulation

In this paper, we present a stochastic queuing model for the road traffic, which captures the stationary density-flow relationships in both uncongested and congestion conditions. The proposed model is based on the M/g/c/c state dependent queuing model of Jain and Smith, and is inspired from the deterministic Godunov scheme for the road traffic simulation. We first propose a reformulation of the M/g/c/c state dependent model that works with density-flow fundamental diagrams rather than density-speed relationships. We then extend this model in order to consider upstream traffic demand as well as downstream traffic supply. Finally we calculate the speed and travel time distributions for the M/g/c/c state dependent queuing model and for the proposed model and we derive stationary performance measures (expected number of cars, blocking probability, expected travel time and throughput). A comparison with results predicted by the M/g/c/c state dependent queuing model shows that the proposed model correctly represents the dynamic of traffic and gives a good performances measures. The results illustrate the good accuracy of the proposed model