NDR: Noise and Dimensionality Reduction of CSI for indoor positioning using deep learning

International audienceDue to the emerging demand for IoT applications, indoor positioning became an invaluable task. We propose a novel lightweight deep learning solution to the indoor positioning problem based on noise and dimensionality reduction of MIMO Channel State Information (CSI). Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP’s hyperparameters are empirically tuned to achieve the highest accuracy. The proposed solution is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna that is used to generate the dataset. Our method yields a mean error which is 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm

NDR: Noise and Dimensionality Reduction of CSI for indoor positioning using deep learning

International audienceDue to the emerging demand for IoT applications, indoor positioning became an invaluable task. We propose a novel lightweight deep learning solution to the indoor positioning problem based on noise and dimensionality reduction of MIMO Channel State Information (CSI). Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP’s hyperparameters are empirically tuned to achieve the highest accuracy. The proposed solution is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna that is used to generate the dataset. Our method yields a mean error which is 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm

GSAR: Greedy Stand-Alone Position-Based Routing protocol to avoid hole problem occurance in Mobile Ad Hoc Networks

The routing process in a Mobile Ad Hoc Network (MANET) poses critical challenges because of its features such as frequent topology changes and resource limitations. Hence, designing a reliable and dynamic routing protocol that satisfies MANET requirements is highly demanded. The Greedy Forwarding Strategy (GFS) has been the most used strategy in position-based routing protocols. The GFS algorithm was designed as a high-performance protocol that adopts hop count in soliciting shortest path. However, the GFS does not consider MANET needs and is therefore insufficient in computing reliable routes. Hence, this study aims to improve the existing GFS by transforming it into a dynamic stand-alone routing protocol that responds swiftly to MANET needs, and provides reliable routes among the communicating nodes. To achieve the aim, two mechanisms were proposed as extensions to the current GFS, namely the Dynamic Beaconing Updates Mechanism (DBUM) and the Dynamic and Reactive Reliability Estimation with Selective Metrics Mechanism (DRESM). The DBUM algorithm is mainly responsible for providing a node with up-to-date status information about its neighbours. The DRESM algorithm is responsible for making forwarding decisions based on multiple routing metrics. Both mechanisms were integrated into the conventional GFS to form Greedy Stand-Alone Routing (GSAR) protocol. Evaluations of GSAR were performed using network simulator Ns2 based upon a defined set of performance metrics, scenarios and topologies. The results demonstrate that GSAR eliminates recovery mode mechanism in GFS and consequently improve overall network performance. Under various mobility conditions, GSAR avoids hole problem by about 87% and 79% over Greedy Perimeter Stateless Routing and Position-based Opportunistic Routing Protocol respectively. Therefore, the GSAR protocol is a reasonable alternative to position-based unicast routing protocol in MANET

Position-Based Packet Forwarding for Vehicular Ad-Hoc Networks

Mobile Ad-Hoc Networks, or MANETs, are data communication networks between (potentially) mobile computer systems equipped with wireless communication devices and — in their purest form — in complete absence of communication infrastructure. Usage scenarios for these systems include communication during disaster recovery or battlefield communications. One of the great research challenges concerning MANETs is the Packet Forwarding Problem, i.e., the question to which neighbor node a data packet should be handed over to reach non-neighboring nodes. While this problem has been previously solved by the adaption of classic routing algorithms from wired networks, the availability of GPS enables to include information about the geographic position of nodes into the routing decision, by selecting forwarders that are geographically closest to the destination. While these algorithms have been shown to improve communication performance in networks with a high degree of node mobility, they require (a) a beaconing service that allows every node to build a table of its neighbors and (b) a so-called Location Service that allows to acquire the current position of non-neighboring nodes in the network. In this thesis, we propose Contention-Based Forwarding (or CBF), a greedy routing heuristic that is no longer in need of a beaconing service. Moreover, a forwarding node running CBF does not at all select the next forwarder explicitly but broadcasts the packet containing its own position and the position of the destination. The selection of the forwarding is now done in a contention period, where every possible forwarder, i.e., every receiver of the packet, considers its own suitability to forward by calculating the geographical progress for the packet if forwarded by itself. Then it waits for a time reciprocal to this suitability before simply retransmitting. If the retransmission of a packet is overheard, the own postponed retransmission process is canceled. In this thesis, we demonstrate that CBF outperforms beacon and position-based routing by delivering packets with constant overhead, almost ignorant of mobility. Also, we introduce two strategies to cope with the problem of packet duplication. A problem left open by greedy routing heuristics is routing in the presence of local optima, or voids. Voids are node placement situations, where — in spite of an existing route — no neighboring node is geographically closer to the destination than the current forwarder. In these situations, greedy forwarding fails and standard graph-based recovery well known from classical Position-Based Forwarding cannot be applied due to the lack of the beacon-based construction of neighbor tables. As a solution, we propagate Contention-Based Distance Vector Routing, a contention-based adaption of AODV that acquires topology information in the area of the void and does contention on the topological distance to the forwarder. Besides the forwarding algorithms, we extend position-based routing by two location services. The first, the Reactive Location Service or RLS is simple, purely on-demand and very robust to mobility, the second Hierarchical Location Service, is more complex but outperforms RLS in scalability. The second big column in this thesis is ad-hoc multi-hop communication in the context of Vehicular Ad-Hoc Networks , or VANET, i.e., networks where the communication system is carried by vehicles. These systems very elegantly fit into the propositions and requirements for our more general routing approaches since they have (a) easy access to position information an (b) "suffer" from high mobility. For VANETs, we separate the routing problem into highway and city scenarios and study various routing algorithms in both. In the end, we advocate the usage of position-based routing in both scenarios; moreover, the contention-based approaches are most promising. While a lot of ad-hoc research has been deemed to be theoretical, we have also built a multi-car communication system. For this system, we provided the network and system architecture and provided the communication software. In this thesis, we will describe these efforts as a proof-of-concept and provide measurement results