2D Time-frequency interference modelling using stochastic geometry for performance evaluation in Low-Power Wide-Area Networks

In wireless networks, interferences between trans- missions are modelled either in time or frequency domain. In this article, we jointly analyze interferences in the time- frequency domain using a stochastic geometry model assuming the total time-frequency resources to be a two-dimensional plane and transmissions from Internet of Things (IoT) devices time- frequency patterns on this plane. To evaluate the interference, we quantify the overlap between the information packets: provided that the overlap is not too strong, the packets are not necessarily lost due to capture effect. This flexible model can be used for multiple medium access scenarios and is especially adapted to the random time-frequency access schemes used in Low-Power Wide-Area Networks (LPWANs). By characterizing the outage probability and throughput, our approach permits to evaluate the performance of two representative LPWA technologies Sigfox{\textsuperscript \textregistered} and LoRaWA{\textsuperscript \textregistered}

Channel-Envelope Differencing Eliminates Secret Key Correlation: LoRa-Based Key Generation in Low Power Wide Area Networks

This paper presents automatic key generation for long-range wireless communications in low power wide area networks (LPWANs), employing LoRa as a case study. Differential quantization is adopted to extract a high level of randomness. Experiments conducted both in an outdoor urban environment and in an indoor environment demonstrate that this key generation technique is applicable for LPWANs, and shows that it is able to reliably generate secure keys

Long-Range Communications in Unlicensed Bands: the Rising Stars in the IoT and Smart City Scenarios

Connectivity is probably the most basic building block of the Internet of Things (IoT) paradigm. Up to know, the two main approaches to provide data access to the \emph{things} have been based either on multi-hop mesh networks using short-range communication technologies in the unlicensed spectrum, or on long-range, legacy cellular technologies, mainly 2G/GSM, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-GHz frequency bands, used to realize access networks with star topology which are referred to a \emph{Low-Power Wide Area Networks} (LPWANs). In this paper, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, in particular for the typical Smart Cities applications

Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes

Both physical and MAC-layer need to be jointly optimized to maximize the autonomy of IoT devices. Therefore, a cross-layer design is imperative to effectively realize Low Power Wide Area networks (LPWANs). In the present paper, a cross-layer assessment framework including power modeling is proposed. Through this simulation framework, the energy consumption of IoT devices, currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a cross-layer approach significantly improves energy efficiency and overall throughput. Two major contributions are made. First, an open-source LPWAN assessment framework has been conceived. It allows testing and evaluating hypotheses and schemes. Secondly, as a representative case, the LoRaWAN protocol is assessed. The findings indicate how a cross-layer approach can optimize LPWANs in terms of energy efficiency and throughput. For instance, it is shown that the use of larger payloads can reduce up to three times the energy consumption on quasi-static channels yet may bring an energy penalty under adverse dynamic conditions

Analysis of LoRaWAN Uplink with Multiple Demodulating Paths and Capture Effect

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used {SX1301 gateway chipset}, which is capable of demodulating {up to} eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated {three} policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage {probability}, {FSP}, and {throughput}. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes

IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN

Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks (LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of IoT networks in general

Aggressive Fragmentation Strategy for Enhanced Network Performance in Dense LPWANs

Low Power Wide Area Networks (LPWANs) are gaining ground in the IoT landscape and, in particular, for Industrial IoT applications. However, given the strict duty cycle restrictions (e.g. 1% in SubGHz bands) and the limited power supply of devices, requirements of some applications can not always be met. This paper analyzes the potential of the combination of packet fragmentation -in the direction of the IETF LPWAN working group- and negative group acknowledgement (NACK) in LoRaWAN networks, a widespread LPWAN technology. Results show that the proposed strategy can lead to significant gains in terms of goodput and energy efficiency under congested situations.Comment: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC

Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks

The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications

Computational Methods for Network-Aware and Network-Agnostic IoT Low Power Wide Area Networks (LPWANs)

In this paper, we tackle the design issue of optimal deployment of low power wide area network (LPWAN) Internet of Things (IoT) gateways (GWs). We classify GW deployment problem into two different categories, i.e., network-aware and network-agnostic. In network-aware GW deployment, precise location of IoT end devices (EDs) is known and thus the design questions are: 1) where to place GWs, i.e., to maximize received signal strength and 2) given received signal strength which GW should the ED be associated with to balance the network load. For, Network-agnostic GW deployment, same questions are answered in the absence of precise knowledge for the locations of EDs. For the network-aware deployment we borrow tools from machine-learning such as ${K}$ -means clustering for determination of optimal GW location. Subsequently, the link assignment problem is presented as an integer linear programming optimization. We prove that the network-agnostic GW deployment principle of placement of GWs at highest altitudes, if applied automatically, may lead to very deteriorated network performance increasing the network operational costs. Consequently, we introduce the concept of network-agnostic GW placement algorithm whereby the location of GWs can be estimated without prior knowledge of specific locations of EDs and we use it as a guiding principle to design spatial algorithm for finding GW locations. We show that spatial algorithm can, in principle, provide effective GW placement suggestions compared to a network-aware method such as ${K}$ -means clustering. We show that using a computational method for GW placement like ${K}$ -means or spatial algorithm, has a potential of creating competitive network performance using just the same number of GWs, thus cutting down the financial costs of the network and increasing its sustainability