Performance of symmetric and asymmetric links in wireless networks

Wireless networks are designed to provide the enabling infrastructure for emerging technological advancements. The main characteristics of wireless networks are: Mobility, power constraints, high packet loss, and lower bandwidth. Nodes’ mobility is a crucial consideration for wireless networks, as nodes are moving all the time, and this may result in loss of connectivity in the network. The goal of this work is to explore the effect of replacing the generally held assumption of symmetric radii for wireless networks with asymmetric radii. This replacement may have a direct impact on the connectivity, throughput, and collision avoidance mechanism of mobile networks. The proposed replacement may also impact other mobile protocol’s functionality. In this work, we are mainly concerned with building and maintaining fully connected wireless network with the asymmetric assumption. For this extent, we propose to study the effect of the asymmetric links assumption on the network performance using extensive simulation experiments. Extensive simulation experiments were performed to measure the impact of these parameters. Finally, a resource allocation scheme for wireless networks is proposed for the dual rate scenario. The performance of the proposed framework is evaluated using simulation

Optimal Number of Message Transmissions for Probabilistic Guarantee in the IoT

International audienceThe Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R% of messages generated by sensor nodes are delivered to the sink with a latency ≤ L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. M F air is very easy to compute, whereas M Opt minimizes the total number of transmissions necessary for a message to reach the sink. M F air and M Opt are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions M axT rans is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M axT rans is fixed. For both TSCH networks, we show that M Opt provides a better reliability and a longer lifetime than M F air, which provides a shorter average end-to-end latency. M Opt provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler

Optimal Number of Message Transmissions for Probabilistic Guarantee in the IoT

International audienceThe Internet of Things (IoT) is now experiencing its first phase of industrialization. Industrial companies are completing proofs of concept and many of them plan to invest in automation, flexibility and quality of production in their plants. Their use of a wireless network is conditioned upon its ability to meet three Key Performance Indicators (KPIs), namely a maximum acceptable end-to-end latency L, a targeted end-to-end reliability R and a minimum network lifetime T. The IoT network has to guarantee that at least R% of messages generated by sensor nodes are delivered to the sink with a latency ≤ L, whereas the network lifetime is at least equal to T. In this paper, we show how to provide the targeted end-to-end reliability R by means of retransmissions to cope with the unreliability of wireless links. We present two methods to compute the maximum number of transmissions per message required to achieve R. M F air is very easy to compute, whereas M Opt minimizes the total number of transmissions necessary for a message to reach the sink. M F air and M Opt are then integrated into a TSCH network with a load-based scheduler to evaluate the three KPIs on a generic data-gathering application. We first consider a toy example with eight nodes where the maximum number of transmissions M axT rans is tuned per link and per flow. Finally, a network of 50 nodes, representative of real network deployments, is evaluated assuming M axT rans is fixed. For both TSCH networks, we show that M Opt provides a better reliability and a longer lifetime than M F air, which provides a shorter average end-to-end latency. M Opt provides more predictable end-to-end performances than Kausa, a KPI-aware, state-of-the-art scheduler

Quick and Efficient Link Quality Estimation in Wireless Sensors Networks

International audienceIn wireless sensor networks, link metric estimation at each hop should not require a long history of packet exchanges. In this paper, we explore several approaches to link quality estimation. We report on the results of experiments on the Grenoble testbed of the FIT IoT-lab composed of a set of Cortex M3 nodes with IEEE 802.15.4 radios. Whereas the received signal power is a poor indication of PDR (Packet Delivery Ratio) that one can expect on a given link, LQI (Link Quality Indicator) gives more accurate information. We propose a two stage classification, in which a very large fraction of links are immediately either deemed usable or not, while the remaining ones need a bit more testing before they are advertised by the routing protocol as good or weak links