Politecast - a new communication primitive for wireless sensor networks

Wireless sensor networks have the potential for becoming a huge market. Ericsson predicts 50 billion devices interconnected to the Internet by the year 2020. Before that, the devices must be made to be able to withstand years of usage without having to change power source as that would be too costly. These devices are typically small, inexpensive and severally resource constrained. Communication is mainly wireless, and the wireless transceiver on the node is typically the most power hungry component. Therefore, reducing the usage of radio is key to long lifetime. In this thesis I identify four problems with the conventional broadcast primitive. Based on those problems, I implement a new communication primitive. This primitive is called Politecast. I evaluate politecast in three case studies: the Steal the Light toy example, a Neighbor Discovery simulation and a full two-month deployment of the Lega system in the art gallery Liljevalchs. With the evaluations, Politecast is shown to be able to massively reduce the amount of traffic being transmitted and thus reducing congestion and increasing application performance. It also prolongs node lifetime by reducing the overhearing by waking up neighbors

The cross layer RMPA handover: a reliable mobility pattern aware handover strategy for broadband wireless communication in a high-speed railway domain

Enhancing the handover process in broadband wireless communication deployment has traditionally motivated many research initiatives. In a high-speed railway domain, the challenge is even greater. Owing to the long distances covered, the mobile node gets involved in a compulsory sequence of handover processes. Consequently, poor performance during the execution of these handover processes significantly degrades the global end-to-end performance. This article proposes a new handover strategy for the railway domain: the RMPA handover, a Reliable Mobility Pattern Aware IEEE 802.16 handover strategy "customized" for a high-speed mobility scenario. The stringent high mobility feature is balanced with three other positive features in a high-speed context: mobility pattern awareness, different sources for location discovery techniques, and a previously known traffic data profile. To the best of the authors' knowledge, there is no IEEE 802.16 handover scheme that simultaneously covers the optimization of the handover process itself and the efficient timing of the handover process. Our strategy covers both areas of research while providing a cost-effective and standards-based solution. To schedule the handover process efficiently, the RMPA strategy makes use of a context aware handover policy; that is, a handover policy based on the mobile node mobility pattern, the time required to perform the handover, the neighboring network conditions, the data traffic profile, the received power signal, and current location and speed information of the train. Our proposal merges all these variables in a cross layer interaction in the handover policy engine. It also enhances the handover process itself by establishing the values for the set of handover configuration parameters and mechanisms of the handover process. RMPA is a cost-effective strategy because compatibility with standards-based equipment is guaranteed. The major contributions of the RMPA handover are in areas that have been left open to the handover designer's discretion. Our simulation analysis validates the RMPA handover decision rules and design choices. Our results supporting a high-demand video application in the uplink stream show a significant improvement in the end-to-end quality of service parameters, including end-to-end delay (22%) and jitter (80%), when compared with a policy based on signal-to-noise-ratio information.The research described in this article was undertaken at the Training/Education and Research Unit UFI11/16 funded by the UPV/EHU

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

IMPLEMENTATION OF A CHANNEL-AWARE ROUTING PROTOCOL IN THE NETWORK SIMULATOR FOR UNDERWATER ACOUSTIC COMMUNICATION NETWORKING

Over two-thirds of the earth surface are occupied by water, however the level of research in the field of Underwater Wireless Sensor Networking (UWSN) does not necessarily commensurate with the size and potentials. Importantly, the underwater acoustic channel is susceptible to signal degradation as a result of the dynamic and harsh state of the environment such as high propagation delay and limited channel bandwidth. The above challenges are the motivation behind the Implementation of The Channel-aware Routing Protocol (CARP) for the routing performance optimization in UWSN. CARP [1] is a cross-layer routing paradigm which collaboratively leverages the link quality estimation between neighboring sensor nodes and the hop counts from the sink to determine the next relay node for packet forwarding. In this work, CARP was implemented in Aqua-Sim-NG, an ns-3 based underwater wireless sensor network simulator that simulates underwater acoustic channels with high fidelity

Impact of Random Deployment on Operation and Data Quality of Sensor Networks

Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, and precision agriculture. Among the desirable features of wireless sensor networks, one is the ease of deployment. Since the nodes are capable of self-organization, they can be placed easily in areas that are otherwise inaccessible to or impractical for other types of sensing systems. In fact, some have proposed the deployment of wireless sensor networks by dropping nodes from a plane, delivering them in an artillery shell, or launching them via a catapult from onboard a ship. There are also reports of actual aerial deployments, for example the one carried out using an unmanned aerial vehicle (UAV) at a Marine Corps combat centre in California -- the nodes were able to establish a time-synchronized, multi-hop communication network for tracking vehicles that passed along a dirt road. While this has a practical relevance for some civil applications (such as rescue operations), a more realistic deployment involves the careful planning and placement of sensors. Even then, nodes may not be placed optimally to ensure that the network is fully connected and high-quality data pertaining to the phenomena being monitored can be extracted from the network. This work aims to address the problem of random deployment through two complementary approaches: The first approach aims to address the problem of random deployment from a communication perspective. It begins by establishing a comprehensive mathematical model to quantify the energy cost of various concerns of a fully operational wireless sensor network. Based on the analytic model, an energy-efficient topology control protocol is developed. The protocol sets eligibility metric to establish and maintain a multi-hop communication path and to ensure that all nodes exhaust their energy in a uniform manner. The second approach focuses on addressing the problem of imperfect sensing from a signal processing perspective. It investigates the impact of deployment errors (calibration, placement, and orientation errors) on the quality of the sensed data and attempts to identify robust and error-agnostic features. If random placement is unavoidable and dense deployment cannot be supported, robust and error-agnostic features enable one to recognize interesting events from erroneous or imperfect data

An Adaptive Multimedia-Oriented Handoff Scheme for IEEE 802.11 WLANs

Previous studies have shown that the actual handoff schemes employed in the IEEE 802.11 Wireless LANs (WLANs) do not meet the strict delay constraints placed by many multimedia applications like Voice over IP. Both the active and the passive supported scan modes in the standard handoff procedure have important delay that affects the Quality of Service (QoS) required by the real-time communications over 802.11 networks. In addition, the problem is further compounded by the fact that limited coverage areas of Access Points (APs) occupied in 802.11 infrastructure WLANs create frequent handoffs. We propose a new optimized and fast handoff scheme that decrease both handoff latency and occurrence by performing a seamless prevent scan process and an effective next-AP selection. Through simulations and performance evaluation, we show the effectiveness of the new adaptive handoff that reduces the process latency and adds new context-based parameters. The Results illustrate a QoS delay-respect required by applications and an optimized AP-choice that eliminates handoff events that are not beneficial.Comment: 20 pages, 14 figures, 4 table

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented