LoRaWAN promises to provide wide-area network access to low-cost devices that can operate for up to 10 years on a single 1000mAh battery. This makes LoRaWAN particularly suited to data collection applications (e.g. monitoring applications), where device lifetime is a key performance metric. However, when supporting a large number of devices, LoRaWAN suffers from a scalability issue due to the high collision probability of its Aloha-based MAC layer. The performance worsens further when using acknowledged transmissions due to the duty cycle restriction at the gateway. For this, we propose FREE, a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN. FREE takes advantage of applications that do not have hard delay requirements on data delivery by supporting synchronized bulk data transmission. This means data is buffered for transmission in scheduled time slots instead of transmitted straight away. FREE allocates spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRaWAN end-devices. As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments. We evaluate the performance of FREE versus different legacy LoRaWAN configurations. The numerical results show that FREE scales well and achieves almost 100% data delivery and the device lifetime is estimated to over 10 years independent of traffic type and network size. Comparing to poor scalability, low data delivery and device lifetime of fewer than 2 years for acknowledged data traffic in the standard LoRaWAN configurations.

In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa/LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa/LoRaWan, like other frequency modulation based radio interfaces, exhibits the capture effect in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.

LoRa is a low-power and long range radio communication technology designed for low-power Internet of Things devices. These devices are often deployed in remote areas where the end-to-end connectivity provided through one or more gateways may be limited. In this paper, we examine the case where the gateway is not available at all times. As a consequence, the sensing data need to be buffered locally and transmitted as soon as a gateway becomes available. However, due to the Aloha-style transmission policy of current LoRa-based standards, such as the LoRaWAN, delivering a large number of packets in a short period of time by a large number of nodes becomes impossible. To avoid bursts of collisions and expedite data collection, we propose a time-slotted transmission scheduling mechanism. We formulate the data scheduling optimisation problem, taking into account LoRa characteristics, and compare its performance to low complexity heuristics. Moreover, we conduct a set of simulations to show the benefits of synchronous communications on the data collection time and the network performance. The results show that the data collection can reliably be achieved at least 10 times faster compared to an Aloha-based approach for networks with 100 or more nodes. We also develop a proof-of-concept to assess the overhead cost of communicating the schedule to the nodes and we present experimental results.

LoRa-based transmissions suffer from extensive collisions due to the ALOHA-style transmission policy. As a consequence, delivering a high number of packets in a short amount of time becomes an unfeasible task. To tackle this problem we propose to schedule node transmissions in slots of different size depending on the Spreading Factor (SF). Transmissions with the same SF are scheduled in different slots to avoid collisions while those with different SF can occur in parallel. In this paper, we propose two algorithms executed in an offline manner to allocate SFs to nodes aimed at minimising the total data collection time while respecting the radio duty cycle restriction. The examined simulation scenarios and our comparison with LoRaWAN show an up to 101% improvement in terms of data collection time and an up to 250% improvement in terms of energy consumption combined with a nearly 100% packet delivery ratio.

In this paper, we present the multicast-forwarding scheme called, Enhanced Stateless Multicast RPL Forwarding (ESMRF). ESMRF is a new scheme allow nodes be able to send multicast traffic up and down the Routing Protocol for Low-power and Lossy networks (RPL) tree. In contrast Stateless Multicast RPL Forwarding (SMRF) scheme has a limitation on its sending operation where only multicast can be sent down the tree. ESMRF main idea is for sources of multicast traffic to encapsulate their multicast packet in an ICMPv6delegation packet and send it to the root of RPL tree that forwards the multicast packet on behalf of the original source. ESMRF shows a promising packet delivery ratio and end-to-end delay in multi-hop RPL trees when compared with Trickle Multicast (TM) and SMRF. ESMRF shows the same packet delivery ratio and end-to-end delay as SMRF in multi-hop forward straight-line topology in which the RPL root is the source of multicast traffic while having low memory overhead. In a backward straight-line topology in which the highest rank node is the source of multicast traffic, ESMRF exhibits similar performance to its performance in the forward straight-line topology. In this case, SMRF totally fails to deliver. It also outperforms the TM scheme in the two topologies in overall performance. In a random topology, ESMRF clearly outperforms both the TM and SMRF schemes in terms of delivery ratio and end-to-end delay.