Low overhead scheduling of LoRa transmissions for improved scalability

Recently, LoRaWAN has attracted much attention for the realization of many Internet of Things applications because it offers low-power, long-distance, and low-cost wireless communication. Recent works have shown that the LoRaWAN specification for class A devices comes with scalability limitations due to the ALOHA-like nature of the MAC layer. In this paper, we propose a synchronization and scheduling mechanism for LoRaWAN networks consisting of class A devices. The mechanism runs on top of the LoRaWAN MAC layer. A central network synchronization and scheduling entity will schedule uplink and downlink transmissions. In order to reduce the synchronization packet length, all time slots that are being assigned to an end node are encoded in a probabilistic space-efficient data structure. An end node will check if a time slot is part of the received data structure in order to determine when to transmit. Time slots are assigned based on the traffic needs of the end nodes. We show that in case of a nonsaturated multichannel LoRaWAN network with synchronization being done in a separate channel, the packet delivery ratio (PDR) is easily 7% (for SF7) to 30% (for SF12) higher than in an unsynchronized LoRaWAN network. For saturated networks, the differences in PDR become more profound as nodes are only scheduled as long as they can be accommodated given the remaining capacity of the network. The synchronization process will use less than 3-mAh extra battery capacity per end node during a one year period, for synchronization periods longer than three days. This is less than the battery capacity used to transmit packets that are going to be lost in an unsynchronized network due to collisions

Fair allocation for transmission parameters to achieve scalability in LoRaWAN

LoRa and LoRaWAN are promising solutions for the upcoming challenges that the Internet of Things (IoT) presents. But even with the recent developments in LoRaWAN, there are still problems to overcome due to the scale of applications required for IoTs. LoRa and LoRaWAN provide many of the desired characteristics for IoT such as long-range transmissions, low power use, and low device cost, but also have an issue with node capacity. Scaling LoRaWAN networks for the capacity that the IoTs desires is the current challenge, so therefore, I aim to improve current solutions to meet this challenge. Since LoRaWAN is a new technology, research in the area is in early development, with some contributions existing but no complete solution that can be applied to a diverse range of LoRaWAN applications. My research extended upon previous research focused on the fairness of collision probabilities across nodes in a network, and thus, I created a solution that improved the scalability of LoRaWAN. Overall, I contributed a parameter allocation algorithm, a transmission power assignment scheme, and a path loss rule for node placement for LoRaWAN networks that, in simulation, achieved better performance than current solutions. This was possible due to lack of consideration regarding the importance of LoRa spreading factor parameters by current solutions and the realistic implications of path loss. By factoring for these two aspects, a more adaptable solution can be created. This solution can effectively maximise LoRaWAN's most important capability and also consider the physical limitations of the real-world like path loss. The proposed solution, Fair Allocation for Transmission Parameters (FATP) for LoRaWAN, adapts the idea of fair device performance in a network to the realistic limitations a real-world application would introduce, such as node distribution and path loss. Firstly, the parameter allocation algorithm is responsible for understanding the path loss of every device and the ideal method of leveraging the spreading factor parameter to achieve a fair network. A fair network, in this instance, is one where every device has the same probability for collision when transmitting. This algorithm then adapts the ideal method of assigning the spreading factor parameter to devices based on their path loss so that the network remains as near to optimal as possible, while respecting the limitations of the real world. Secondly, the transmission power scheme is responsible for minimising the power usage of devices by controlling how much power is used to transmit with. This power scheme functions by minimising every device's transmission power to the minimum amount that will still guarantee a viable transmission. Finally, the path loss rule is a guideline to follow when placing devices in a network. This guideline describes the minimum path loss a device should be allowed to have before it becomes impossible to mitigate its interference with other devices. In the simulation, FATP achieved better performance in terms of data extraction rate (DER) and reducing the variance of performance across nodes in a network for a range of node distributions when compared with current solutions. FATP provided up to a 9% improvement for network DER with an average of a 4% network DER improvement when compared to the nearest best performing current solution, an RSSI-based solution. Most notably, FATP, on average, reduced the variance of individual node performance by a factor of approximately 59. FATP achieved this while consuming approximately the same amount of energy as the RSSI-based solution. Overall, FATP achieved its goals of improving network DER to increase scalability while also ensuring that nodes operate more fairly with more consistent DER performance

A Survey on Long-Range Wide-Area Network Technology Optimizations

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance.We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in massive IoT, due to the increased number of devices and their Adhoc transmissions, collision becomes and concern. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices need to be powered using a battery and cannot be attached to an energy grid. LoRaWAN originally has a star network wherein IoT devices communicated to a single gateway. Massive IoT causes increased traffic at a single gateway. To address Massive IoT issues of collision and gateway load handling, we have designed a reinforcement learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any algorithms with channel sensing. We compare energy efficiency, packet delivery ratio, latency, and signal strength with existing state of art algorithms and prove that our proposed solution is efficient for massive IoT LoRaWAN with star topology

Sub-GHz LPWAN network coexistence, management and virtualization : an overview and open research challenges

The IoT domain is characterized by many applications that require low-bandwidth communications over a long range, at a low cost and at low power. Low power wide area networks (LPWANs) fulfill these requirements by using sub-GHz radio frequencies (typically 433 or 868 MHz) with typical transmission ranges in the order of 1 up to 50 km. As a result, a single base station can cover large areas and can support high numbers of connected devices (> 1000 per base station). Notorious initiatives in this domain are LoRa, Sigfox and the upcoming IEEE 802.11ah (or "HaLow") standard. Although these new technologies have the potential to significantly impact many IoT deployments, the current market is very fragmented and many challenges exists related to deployment, scalability, management and coexistence aspects, making adoption of these technologies difficult for many companies. To remedy this, this paper proposes a conceptual framework to improve the performance of LPWAN networks through in-network optimization, cross-technology coexistence and cooperation and virtualization of management functions. In addition, the paper gives an overview of state of the art solutions and identifies open challenges for each of these aspects

A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN

The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication performance. Selecting appropriate transmission parameters, such as channel and spreading factor (SF), can effectively reduce the collisions between long-range (LoRa) devices. However, most of the schemes proposed in the current literature are not easy to implement on an IoT device with limited computational complexity and memory. To solve this issue, we propose a lightweight transmission-parameter selection scheme, i.e., a joint channel and SF selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN). In the proposed scheme, appropriate transmission parameters can be selected by simple four arithmetic operations using only Acknowledge (ACK) information. Additionally, we theoretically analyze the computational complexity and memory requirement of our proposed scheme, which verified that our proposed scheme could select transmission parameters with extremely low computational complexity and memory requirement. Moreover, a large number of experiments were implemented on the LoRa devices in the real world to evaluate the effectiveness of our proposed scheme. The experimental results demonstrate the following main phenomena. (1) Compared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels. (2) The frame success rate (FSR) can be improved by selecting access channels and using SFs as opposed to only selecting access channels. (3) Since interference exists between adjacent channels, FSR and fairness can be improved by increasing the interval of adjacent available channels.Comment: 14 pages, 12 figures, 8 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl