LoRa Meets Artificial Intelligence to Optimize Indoor Networks for Static EDs

The Long Range (LoRa) networks are able to provide both long-range coverage and low power consumption for sensing devices. These features make it one of the most attractive solutions for Internet of Things (IoT) applications. The default channel access mechanism used by the protocol is pure ALOHA, which due to its simplicity and lack of synchronization creates scalability problems when the number of supported end devices increases. The slotted ALOHA channel access mechanism has been proposed as an alternative, which through the synchronization of transmissions achieves the improvement of the efficiency and scalability of LoRa networks. One of the most important operating parameters of slotted ALOHA is the backoff algorithm. In this paper we present a novel backoff algorithm assisted by reinforcement learning mechanism, which regulates the offered load on the network by adjusting the congestion window. Simulation results show that the proposed algorithm improves efficiency and energy consumption for the end devices compared to other backoff algorithms from the literature.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

Successful deployment of Long-Range Wide Area Network (LoRaWAN) technology in several Industrial Internet of Things (IIoT) scenarios, such as Outage Management System (OMS) in smart metering, rely on low energy consumption of the end device. In this work, we conducted an experiment to demonstrate an on-off Denial-of-Service (DoS) attack to analyze the impact on the energy consumption of the LoRaWAN end device. We implemented the attack that manipulates the end device to remain in packet retransmission mode for several seconds. The conducted experiments show that the configurable parameters of LoRaWAN that are required for applications, like OMS, are susceptible to energy consumption attacks. In summary, our results show that when an on-off DoS attack is performed, the end device utilizing the Spreading Factor (SF) 12 consumes 92 times more energy due to packet retransmissions as compared to the end node using SF 7 under no attack.

Multiple packet reception (MPR) is becoming a viable reality for wireless random access protocols thanks to advances in the physical layer and new coding techniques. In the simplest $K$ -MPR model, a receiver can resolve up to $K\geq 1$ parallel transmissions. We extend the classical analysis of pure and slotted ALOHA to $K$ -MPR devices with arbitrary degree $K$ for fixed and variable packet size. Through a parsimonious modeling approach, we derive relatively simple analytical expressions. In this way, we gain insight and quantitative understanding on the fundamental tradeoffs between pure and slotted ALOHA and on the scaling of maximum achievable throughput with $K$ .

LoRaWAN exhibits an essential feature, namely, the adaptive data rate (ADR), which has been recommended for the management of resources (e.g., the spreading factor and transmit power) of static end devices (EDs) based on channel conditions. Blind ADR (BADR) has been introduced for LoRaWAN mobile applications that experience frequent channel attenuation when the ED moves (e.g., pet-tracking). This channel condition leads to massive packet loss and retransmission, which significantly increases energy consumption. In this study, ADR and BADR are investigated in mobility environments, their limitations are highlighted, and a novel ADR “retransmission-assisted resource management (R-ARM)” system is proposed. The proposed R-ARM system operates concurrently on the ED and network server sides. This improves the network performance of the LoRaWAN. When compared to those of typical ADR approaches, the simulation results, in this case, show that R-ARM significantly enhances the packet success ratio and convergence period, and it lowers the energy consumption and packet loss ratio.

Deep neural network (DNN) shows great promise in providing more intelligence to ubiquitous end devices. However, the existing partition-offloading schemes adopt data-parallel or model-parallel collaboration between devices and the cloud, which does not make full use of the resources of end devices for deep-level parallel execution. This article proposes eDDNN (i.e., enabling Distributed DNN), a collaborative inference scheme over heterogeneous end devices using cross-platform Web technology, moving the computation close to ubiquitous end devices, improving resource utilization, and reducing the computing pressure of data centers. eDDNN implements D2D communication and collaborative inference among heterogeneous end devices with WebRTC protocol, divides the data and corresponding DNN model into pieces simultaneously, and then executes inference almost independently by establishing a layer dependency table. Besides, eDDNN provides a dynamic allocation algorithm based on deep reinforcement learning to minimize latency. We conduct experiments on various data sets and DNNs and further employ eDDNN into a mobile Web AR application to illustrate the effectiveness. The results show that eDDNN can achieve the latency decrease by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.98\times $ </tex-math></inline-formula> , reduce mobile energy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> , and relieve the computing pressure of the edge server by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.57\times $ </tex-math></inline-formula> , against a typical partition-offloading approach.

The system-level performance of multi-gateway downlink long-range (LoRa) networks is investigated in the present paper. Specifically, we first compute the active probability of a channel and the selection probability of an active end-device (ED) in the closed-form expressions. We then derive the coverage probability (Pcov) and the area spectral efficiency (ASE) under the impact of the capture effects and different spreading factor (SF) allocation schemes. Our findings show that both the Pcov and the ASE of the considered networks can be enhanced significantly by increasing both the duty cycle and the transmit power. Finally, Monte-Carlo simulations are provided to verify the accuracy of the proposed mathematical frameworks.

In internet of things (IoT) applications, a local connection using a single-hop uplink technique has a wide range of device options. Certain device kinds should be chosen based on the application environment. LoRa is a wireless communication device that currently offers good performance for IoT local connections. Range and data rate, on the other hand, are two transmission factors that are diametrically opposed. Therefore, this research aims to examine the LoRa transmission performance at 915 MHz to find the performance pattern based on the compromise between data rate and coverage that is adjusted spreading factor and coding rate. An experimental method of the LoRa network testbed was used to measure and evaluate its performance through the parameter setting of spreading factor, bandwidth, and coding rate. The results show that the transmission range and data rate are affected by the spreading factor and coding rate factors. To obtain the optimal LoRa transmission performance, the SF and CR parameters must be modified to strike a balance between range and data rate.

Long-Range Wide Area Network (LoRaWAN), as Low Power Wide Area Network that connects battery-powered devices, has gained much attention lately. The LoRaWAN technology uses ALOHA as the medium access control where the End Devices (EDs) transmit data randomly and re-transmit up to eight times if collisions occur, which is not feasible in large networks. Several techniques, including synchronization and scheduling schemes, to address the challenge have been reported. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different Spreading Factors (SFs). This phenomenon can result in collisions, idle slots, and inefficient energy use, hence limiting the LoRaWAN network scalability. To alleviate the aforementioned problems, this work proposes a dynamic Multi-Frame Multi-Spreading Factor (MFMSF) scheduling algorithm with slotted synchronization approach. In the proposed algorithm, the time slots are assigned dynamically to EDs on first to synchronize first to be assigned basis. It was revealed that the proposed dynamic MFMSF TDMA algorithm is more energy-efficient than the existing algorithm in a denser network with devices arranged in a circular disk with different radii. The packet delivery rate for the dynamic MFMSF TDMA is about 99% signifying reduced collisions during data transmission. This implies that the use of the proposed dynamic MFMSF scheduling algorithm in LoRaWAN allows large number of EDs to communicate with reduced collisions and enhanced energy efficiency of the battery-powered devices.

RFID technology has been widely utilized for supply chain management, objection location, vehicle control, and electronic toll collection (ETC). One of the most challenging RFID applications has to do with the improvement in reliability, scheduling and efficiency of large-scale RFID tags scenario. ALOHA algorithm is one of the key technologies which have been widely used in different RFID applications. ALOHA algorithm can be divided into the following subgroups: Pure ALOHA, Slotted ALOHA, Framed Slotted ALOHA and Dynamic Framed Slotted ALOHA. The performance of these ALOHA algorithms are analyzed in this paper. The simulation results show that the system achieves the optimal performance if the number of slots is equal to the RFID tags.

This paper studies the effect of multipath interference on the performance of random access techniques in which the power level of a packet depends on its time of arrival. The protocols considered are pure ALOHA and slotted ALOHA. Key performance measures such as channel throughout and mean packet delay are obtained. These results are derived partially using minislot approximation and are verified via computer simulations. For practical purposes in evaluating the successful packet reception probability the signal capture effect is also taken into consideration. How seriously the multipath interference affects the two protocols are witnessed through numerical examples. Copyright © 1999 John Wiley & Sons, Ltd.

The key contributions of this paper are the proposed Pure Collective Aloha (PCA) medium access control (MAC) protocol with multilevel powers and its closed-form theoretical analysis. The PCA MAC protocol collects a batch of packets before transmitting these packets sequentially in the channel. The key idea in the design of this PCA MAC protocol with multilevel powers is to spread out the arrivals of each batch of packets in a channel and the multilevel powers of the packets received at the receiver allow capture for the case of the strongest signal power packet among the overlapping lower signal power packets to be successfully received at the receiver. If it is the only received packet, it is successfully received, regardless of its received power level. The PCA MAC protocol with multilevel powers is analyzed theoretically. The explicit closed-form expressions for the optimal offered loads and the maximum throughputs of the Pure Aloha, the Slotted Aloha and the PCA MAC protocols with multilevel powers are presented. The numerical results show that the throughput of the asynchronous PCA MAC protocol with multilevel powers is about 90.5% better than that of the asynchronous Pure Aloha MAC protocol with multilevel powers and can approach the maximum throughput bound of the synchronous Slotted Aloha MAC protocol with multilevel powers, without the need of any synchronization mechanism to slot boundaries.

Markov chain analysis of collaborative codes in random multi access communication systems

Massive Machine-Type Communication (MTC) is one of the main challenges for current and future cellular mobile communication systems as 5G. To overcome the controlling overhead issue of Machine-Type Devices (MTD) with low data rates and long duty cycles, we propose a feedback-less communication scheme with a pure ALOHA channel access scheme. Theoretical analysis of the user capacity of pure ALOHA have been conducted under the assumption that each collision of two or more packets renders all packets undecodable. In this contribution, we investigate the accuracy of this assumption for a Filter Bank Multi Carrier with Offset-QAM subcarrier modulation (FBMC-OQAM) system. Therefore, the effects of a 2 packet collision are measured and the influence of different Signal-to-Interference Ratio (SIR) and packet length values are analyzed. Based on these results, a model for an N-user scenario is developed and validated by measurements. For the investigated scenario the measurement results show, that the capacity of a practical pure ALOHA channel access scheme is up to ≈ 75% higher than theoretically predicted, which renders this scheme a promising approach for massive MTC in 5G.

The possibility of utilizing different frequencies for the LoRaWAN is a key component which, together with the other parameters, i.e., (i) bandwidth, (ii) spreading factor, (iii) coding rate, and (iv) transmission power, defines the communication performance. In this paper, we present the substantive im-provements for both the end devices and the radio access network (gateways) to enhance the data rates and decrease the communication latency. The implementation changes were made for the publicly available LoRa Wan module “signetlabdei” for Network Simulator 3. Utilizing the frequency 2.4 GHz, the transmission time in the LoRaWAN network has improved by 80 % decreasing from 75 ms to 14 ms. The frequency 2.4 GHz for the EU region also showed the best performance due to the extended bandwidth (transmission success above 90 %) in the case of the mMTC scenario with thousands of devices deployed. Together with the updated LoRaWAN module, the reported results are expected to serve as a building block for mMTC-oriented simulation scenarios.