Wael Guibene

An evaluation of low power wide area network technologies for the Internet of Things

We explore the state of the art in solutions for low power wide area (LPWA) networks and technologies serving the Internet of Things (IoT) and Connectivity for Everything markets. These networks are forecast to capture up to 55% market share using battery-powered devices operating up to 10 years and link distances measured in tens of kilometers. In this paper, we survey two LPWA technologies; ultra-narrow band solutions by SigFox and the LoRa technology by Semtech. Both technologies operate in the licence-exempt industrial, scientific, & medical (ISM) bands (EU 868 MHz / US 915 MHz). We survey both solutions in terms of physical layer (PHY) and associated medium access control (MAC) capabilities from an end-to-end system viewpoint. We then proceed to explore coverage ranges in eastern Ireland.We present results indicating a potential coverage area of 3,800 km2 and from a real-world experimental test case involving the use of SigFoxs technology operating over a 25 km test link between a 25 mW LPWA client test and a basestation. Finally, we provide example results demonstrating a received SNR consistently exceeding 20 dB over this test link distance.

Survey on Clean Slate Cellular-IoT Standard Proposals

In this paper we investigate the proposals made by various industries for the Cellular Internet of Things (C-IoT). We start by introducing the context of C-IoT and demonstrate how this technology is closely linked to the Low Power-Wide Area (LPWA) technologies and networks. An in-depth look and system level evaluation is given for each clean slate technology and a comparison is made based on its specifications.

A neural-network-based realization of in-network computation for the Internet of Things

Ultra-dense Internet of Things (IoT) networks and machine type communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. We explore leveraging the communication within IoT to serve data processing by appropriately shaping the aggregate behavior of a network to parallel more traditional computation methods. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of an IoT network for simultaneous data processing and transfer. That is, we provide a framework to treat a network holistically as an artificial neural network, rather than placing neural networks within the network. The operation of components of a neural network, neurons and connections between neurons, are performed by the various elements of the IoT network, i.e., the devices and their connections. The proposed approach reduces the latency in delivering processed information and supports the locality of information inherent to IoT by removing the need for transfer to remote data processing sites.

Techniques for resilient real-world IoT

In this paper, we focus on two methods that can be used to increase the resilience of remotely-deployed and unattended IoT devices that must operate without direct human intervention. The methods include adaptive messaging rates for power-constrained devices where the most energy intensive process involves the transmission and reception of data via wireless means, and data caching and replay for extended network connectivity outages. In this paper, we discuss how these techniques were applied in a real-world wireless IoT sensor network in Dublin, Ireland. Specifically, we show that: 1) The operating lifetime of a power-constrained device can be extended through the use of adaptive messaging rates 2) Sensor data backfilling following extended periods of network outages is a feasible method of preventing sensor data loss. We provide a example demonstrating that the operating lifetime of a power-constrained IoT device can be extended using the adaptive message rate approach. Additionally, we demonstrate how extended network connectivity outages can be recovered from using an elastic message cache approach. Finally, we conclude that the combination of both of these techniques are feasible methods to increase the resilience of real-world IoT devices operating in volatile power and wireless communications environments.

Evaluation of LPWAN Technologies for Smart Cities: River Monitoring Use-Case

This paper presents the results of the joint Intel/Nimbus Low Power-Wide Area (LPWA) technology PoC deployment in smart cities context. The PoC deployment addresses the problem of river Liffey monitoring in Dublin city center. We deployed a buoy on the Liffey river in Dublin for duration of 8 months. The deployed buoy embeds many sensors inside the hull enclosure and outside. The data captured is, from the water: depth, temperature and velocity; from inside the hull: temperature, humidity and barometric pressure; from the GPS unit: location and timestamp; and from the system: battery voltage. The buoy also embeds a LoRa-based LPWA transceiver and a 3G modem for backup. The paper gives an insight of the results obtained in terms of range and data consistency and gives conclusions on the use of LPWA technologies in the context of smart cities.

Actor-Oriented Design Patterns for Performance Modeling of Wireless Communications in Cyber-physical Systems

The work at hand proposes a set of modeling recipes to address the aggressive development time window of current and future wireless communications in cyber-physical systems. These systems pose a significant challenge in meeting Time-to-Market due to the ever more challenging requirements such as ultra-low latency for mission-critical applications, extremely high throughput demanding tons of communication and computation resources working concurrently, and, at the same time, low power consumption and small chip area for its field deployment. This paper presents three actor-oriented design patterns for a systematic creation of implementation-aware performance models of complex real-time wireless communication systems that can be applied in existing system level frameworks. The main benefits of this modeling approach are: 1) Time semantic model correctness where the effects of a chosen hardware platform can be taken into account. 2) Behavioral modeling completeness by construction, and 3) reduced time-to-market through reduced modeling effort, improved maintainability and testability. Furthermore, by adhering to this modeling paradigm, it is possible to easily integrate the following features for the improvement of functional safety: a) system timing diagnostics, b) an appropriate handling of timing violations, and c) a simulation-based scheduleability analysis. To demonstrate the aforementioned benefits, a model of a real world pre-5G baseband processor for V2X communications is created in Intel CoFluent where our claims are confirmed when assessing real-time deadline compliance of possible HW implementations.