# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 1 Introduction  
This work builds upon a previously presented paper at the Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN) [1]. This paper discusses the potential of LoRa as a relay technology and describes a novel approach to dealing with channel contention, where multiple relays' transmission time overlaps. It expands upon that paper by presenting additional results and showing how gossip routing can be used to build robust linear LoRa networks. Because of the rapid growth of the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are becoming increasingly important in a number of scenarios. They are of particular importance in areas where traditional cellular networks have limited coverage or deployment costs make them unviable [2]. LPWANs are ideal for situations where low bit rates and long ranges are needed such as smart farming, electric metering, building monitoring, and logistic tracking applications [3]. Many LPWAN technologies have been proposed that operate in both licensed and unlicensed frequency bands. LoRa, Sigfox, and NB-IOT are leading LPWAN technologies that are commercially available. According to [3; 4], LoRa outperforms Sigfox and NB-IoT in many areas. In a real-world scenario, LoRa, despite its relatively high packet loss ratio (up to 15%), holds significant potential, particularly in the field of environmental monitoring [5]. While LoRa is typically deployed with a gateway device, one important use case is to consider LoRa technology as a linear or near-linear network [6]. This important use case provides an opportunity to adopt LoRa technology for various monitoring applications (e.g., pipelines, roads, tunnels, borders, railways), where end devices (sensor nodes) are connected in such a way as to form Linear Wireless Sensor Networks (LWSNs), alternatively known as chain-type wireless sensor networks [7; 8]. In this type of network, multiple nodes are connected in such a way that the network forms a long and thin topology, and data packets are transmitted by using a series of nodes that are connected in a multi-hop fashion using a relaying mechanism. In a linear network, sensor nodes strictly follow a straight-line pattern, such as roads or railway tracks. In near-linear networks, sensor nodes can be deployed in a more loosely linear pattern, such as tunnels or pipelines. Nodes within the network may have sensors or actuators connected to them and, so, generate or act on the data received or they may simply pass the data on to the next node, in which case they are called \"relay\" nodes. This type of topology is used to extend the network coverage over long distances [9]. This paper explored the practicalities of using LoRa as a linear network. This topology finds applications in a range of IoT scenarios, including road monitoring, traffic surveillance, and gas pipeline oversight, as illustrated in Figure 1. Within these network setups, data collection occurs through a series of sensor nodes arranged in a linear or near-linear manner. These nodes facilitate the transmission of the data to a central node. Furthermore, Linear Wireless Sensor Networks (LWSNs) from the topological point of view can be deployed as linear parallel wireless sensors. These networks, also referred to as chain-type wireless sensor networks, involve deploying sensor nodes in two parallel lines following a near-linear pattern. This strategic arrangement significantly extends the network coverage over long distances. Notably, these types of networks are considered well-suited for meeting specific design requirements, such as monitoring railway tracks [6]. The feasibility of LoRa-based networks has been evaluated in various monitoring applications [10; 11]. However, for them to be widely deployed, a number of challenges need to be addressed, in particular link coordination, reliable transmission, and resource allocation. Network reliability and the optimisation of deterministic delay in LoRa-based linear networks are the main challenges [12].