Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced expanding use in urban environments in recent years. However, the growing density of drones raises significant challenges, such as avoiding collisions and managing air traffic efficiently, especially in congested areas. To address these issues, a structured drone road system (DRS) and an effective guidance algorithm are essential. In this paper, we focus on the guidance aspect and propose a novel short-term decentralized greedy (STDG) algorithm designed to operate within a DRS framework. The algorithm uses only the position and speed information of nearby drones — communicated via periodically transmitted beacons — to make real-time decisions such as stopping, changing lanes, or adjusting speed for the next few seconds. Unlike existing methods that rely on centralized coordination, our algorithm enables drones to operate independently while ensuring safety and efficiency. We present simulation results showing the impact of key wireless and algorithm parameters on performance metrics like the drone collision rate, average speed and throughput of the drone road system.

Recently, swarms or formations of drones have received increased interest both in the literature and in applications. To dynamically adapt to their operating environment, swarm members need to communicate wirelessly for control and coordination tasks. One fundamental communication pattern required for basic safety purposes, such as collision avoidance, is beaconing, where drones frequently transmit information about their position, speed, heading, and other operational data to a local neighbourhood, using a local broadcast service. In this paper, we propose and analyse a protocol stack which allows to use the recurring-beaconing primitive for additional purposes. In particular, we propose the VarDis (Variable Dissemination) protocol, which creates the abstraction of variables to which all members of a drone swarm have (read) access, and which can naturally be used for centralized control of a swarm, amongst other applications. We describe the involved protocols and provide a performance analysis of VarDis.

Deployment of Unmanned Aerial Vehicles (UAVs) in autonomous formations necessitates accurate and timely communication of safety information. A communication protocol that supports timely and successful transfer of safety information between UAVs is therefore needed. This paper presents Distributed Self-allocated Time slot Reuse (D-STR). Our D-STR protocol addresses the essential task of communicating safety information in rigid Unmanned Aerial Vehicle (UAV) formations with different network topologies, enabling collision-free deployment of the formation. This is an important step for improving the safety and practicality of UAV formations in application scenarios that span a range of industries.

The DCP/Vardis protocol stack is designed to support coordination and sensor data exchange tasks in ad-hoc type large-scale and multi-hop networks of drones (or drone swarms), by piggybacking coordination and sensor data onto frequently transmitted beacon packets that are present anyway to help with collision avoidance. The Vardis protocol introduces the concept of a variable, which is available to all nodes, and for which information about any modifying operation (create, update, delete) is disseminated globally using beacons. In this paper we study the sensitivity of Vardis application performance metrics related to reliability and delay to some of its key parameters. This sensitivity analysis allows to assess the relative impact of these parameters and to derive recommendations for setting their values.

The applications of Unmanned Aerial Vehicles (UAVs) in urban environments have been expanding rapidly in recent years. This has led to an increasing number of drones in urban airspace, which could become an even more serious issue in the conceivable future, resulting in significant challenges for safety and traffic management. To address these challenges, one approach is to establish a road system for drones, similar to ground vehicle roads, to restrict their movements. In this paper, we propose a novel Drone Road System (DRS) design through an XML-based machine-readable representation. It provides high flexibility in system design and also enables drones to dynamically obtain updated system information through communication with infrastructure or other drones. We also conduct a simulation study to evaluate the feasibility of applying our DRS design in urban environments. The results indicate strong potential for the proposed DRS to reduce collisions while sustaining efficient throughput in urban drone traffic.

To optimise the use of drones, flocking behaviour enhances the efficiency of task execution. Inter-drone collision avoidance is a critical requirement in any flocking algorithm. Conventional approaches typically adjust individual trajectories to prevent collisions. This paper investigates the feasibility of using a start-delay scheduling approach, in which drones follow straight-line trajectories with optimised velocity profiles. Two major constraints that could result in circular dependencies are identified. The overall system relationships between drones are represented as an adjacency matrix, and the presence of cycles is detected using a topological sorting algorithm. Simulation results across fleet sizes from 25 to 5,000 drones identify the density ranges that support cycle-free scheduling, leading to the derivation of a density function that guarantees at least $95 \%$ of configurations remain cycle-free. The proposed analysis provides a foundation for collision-free flock scheduling and establishes the basis for algorithms that assign individual time delays to drones while maintaining efficient operation.

Drone applications continue to expand across various domains, with flocking offering enhanced cooperative capabilities but introducing significant challenges during initial formation. Existing flocking algorithms often struggle with efficiency and scalability, particularly when potential collisions force drones into suboptimal trajectories. This paper presents a time-efficient prioritised scheduling algorithm that improves the initial formation process of drone flocks. The method assigns each drone a priority based on its number of potential collisions and its likelihood of reaching its target position without permanently obstructing other drones. Using this hierarchy, each drone computes an appropriate delay to ensure a collision-free path. Simulation results show that the proposed algorithm successfully generates collision-free trajectories for flocks of up to 5000 drones and outperforms the coupling-degree-based heuristic prioritised planning method (CDH-PP) in both performance and computational efficiency.

Abstract Infrastructure‐less networks connect communication devices end‐to‐end by managing links and routes independent of fixed networking facilities, relying on dedicated protocols running on end‐user devices. The large variety of infrastructure‐less concepts and related aspects can be confusing both for beginning Ph.D. students as well as experienced researchers who wish to get an overview of neighbouring areas to their own research foci. Frequently discussed topics such as different types of sensor‐, vehicular‐, or opportunistic networks are covered. The authors describe different networking concepts by looking at aspects such as the main properties, common applications, and ongoing research. Furthermore, the concepts by common characteristics such as node mobility, network density, or power consumption are compared. The authors also discuss network performance evaluation by describing commonly used metrics, different evaluation techniques, and software tools for simulation‐based evaluation. The references given in each section help obtain in‐depth information about the presented topics and give hints about open research questions, which can be a starting point for own investigations.

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

Among the various required resources for this civilization, the habitat is one of the crucial resources to live on Mars. Such an extraterrestrial habitat is designed to provide a safe place to live during the initial missions. It is equipped with monitoring and life support systems to ensure the astronauts' safety. In this work, we present a robust monitoring system with a use case for extraterrestrial habitats. Similar to a typical monitoring system, it consists of sensor nodes and a gateway connected through a wireless communication channel. In our system, we introduce robustness to various failures that can occur after deployment, namely, board, sensor and gateway failure, in the form of redundancy. For each failure, the problem is tackled differently. For the first two types, we use additional hardware as backup, while for the last type, we use neighbouring devices as backups. The backup devices function as replacements for the failed component, which helps the system to collect the data which would otherwise be lost. We evaluate how much the performance of the system improves by using backup devices. We use a Continuous-Time Markov chain for the theoretical evaluation and an experimental setup that includes the hardware prototype for the empirical evaluation. We also analyze the effect of a simple medium access mechanism on the system's performance in the presence of heavy noise on the channel. Based on our requirements, we use a simple custom medium access control (MAC) algorithm called Slotted-ALOHA with Random Back-off (SARB) to make communication reliable. We demonstrate that around $30-34\%$ of the packets are recovered, with the use of backup devices (redundancy), which would otherwise be lost in case of failures. We also demonstrate that the system's performance improves by $3.8-13.2\%$ with the use of a simple medium access technique (SARB).

Unmanned Aerial Vehicles (UAVs) in multi-UAV systems rely on sensor data to achieve a collective goal, and for command and control. This paper presents Distributed Assignment and Resolution of Time slots (D-ART), a Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocol that supports the dissemination of safety-critical sensor data between UAVs in a formation. D-ART allows for each UAV to self-organize their time slot allocation, and has provisions for UAV-driven changes to the superframe size as necessary. We determine the best settings and evaluate the performance of D-ART using time to convergence as a key performance measure, as time to convergence directly corresponds to the time taken to generate the smallest possible TDMA schedule for a formation of unknown size.

Copter-type UAVs (unmanned aerial vehicles) or drones are expected to become more and more popular for deliveries of small goods in urban areas. One strategy to reduce the risks of drone collisions is to constrain their movements to a drone road system as far as possible. In this paper, for reasons of scalability, we assume that path-planning decisions for drones are not made centrally but rather autonomously by each individual drone, based solely on position/speed/heading information received from other drones through WiFi-based communications. We present a system model for moving drones along a straight road segment or tube, in which the tube is partitioned into lanes. We furthermore present a cost-based algorithm by which drones make lane-switching decisions, and evaluate the performance of differently parameterized versions of this algorithm, highlighting some of the involved tradeoffs. Our algorithm and results can serve as a baseline for more advanced algorithms, for example, including more elaborate sensors.

This paper looks at the case of mitigating col-lisions between Unmanned Aerial Vehicles (UAVs) in a formation through the use of safety beacons to relay information about UAVs that are at risk of collision due to their geographic proximity to one another. The UAVs send these safety beacons using time division multiple access (TDMA). With TDMA, UAVs can achieve collision-free transmission, thereby reducing the uncertainty of the UAVs in the formation receiving the necessary safety information. In this paper, we provide a system model for a specific regular deployment and a spatial reuse scheme for allocating UAVs to a TDMA slot that operates as follows: the regular, two-dimensional UAV deployment is partitioned into a hexagonal tiling, where all UAVs in the same tile are allocated to different TDMA slots and all UAVs in the same position in their respective tiles are allocated to the same TDMA slot. Through spatial reuse of TDMA slots, theoretical results demonstrate that our scheme can support large formations with a bounded transmission period (i.e. a bounded superframe length). We also ascertain a safety margin factor for the transmit power that can be applied to moderate the effects of interference from multiple UAVs transmitting in the same time slot.

Recently there has been interest in using drones/unmanned aerial vehicles in search-and-rescue applications. Here we apply a formation of drones equipped with sectorised antennae to navigate to a transmitter using Direction of Arrival (DoA) estimation to navigate. We present results indicating that the error of the DoA estimate is dependent on the DoA and evaluate a mitigation technique, finding that incrementally changing the drone orientation across the formation reduces the DoA estimation error. Further, we investigate a "dumbbell" formation in which the two "weights" generate independent DoA estimates, the difference between which are used to broadly classify the distance to the transmitter. We found that the choice of distance thresholds and relative direction of the transmitter substantially changes the performance of this distance heuristic.

Unmanned Aerial Vehicles (UAVs) show promise in a variety of applications and recently were explored in the area of Search and Rescue (SAR) for finding victims. In this paper we consider the problem of finding multiple unknown stationary transmitters in a discrete simulated unknown environment, where the goal is to locate all transmitters in as short a time as possible. Existing solutions in the UAV search space typically search for a single target, assume a simple environment, assume target properties are known or have other unrealistic assumptions. We simulate large, complex environments with limited a priori information about the environment and transmitter properties. We propose a Bayesian search algorithm, Information Exploration Behaviour (IEB), that maximizes predicted information gain at each search step, incorporating information from multiple sensors whilst making minimal assumptions about the scenario. This search method is inspired by the information theory concept of empowerment. Our algorithm shows significant speed-up compared to baseline algorithms, being orders of magnitude faster than a random agent and 10 times faster than a lawnmower strategy, even in complex scenarios. The IEB agent is able to make use of received transmitter signals from unknown sources and incorporate both an exploration and search strategy.

This paper evaluates Cellular-V2X (C-V2X) and IEEE 802.11p (802.11p) in the logistics warehouse environment. Unlike for traditional road networks, the performance of these technologies has never been compared in the warehouse environment, despite the potential benefits to safety, efficiency, and automation they may bring. To perform this evaluation, we use the simulated failure rate of an aisle-end collision avoidance application as a metric. As part of this work a simulation scenario and simulator including a custom aisle-end warehouse channel model was developed based upon path loss measurements taken in a representative warehouse. The performance of each technology was simulated as vehicle density, transmit power, and modulation and coding scheme (MCS) changed. For 802.11p, density, transmit power, and MCS had no significant effect on the technology’s performance within the resolution of the evaluation scenario. C-V2X performed worse than 802.11p for all densities, MCS values, and transmit powers. Based on this evaluation, 802.11p is more appropriate than C-V2X for the warehouse environment.

In this paper we model a situation where several wireless body sensor networks (WBSN) compete for occupation of a number of frequency channels. Each channel can host at most one WBSN with satisfactory performance and WBSNs have the ability to change their operating channel, subject to the constraint that they can only monitor the performance or occupancy of their current channel but not of any other channel. We consider a number of randomized schemes for changing the frequency channels and present and evaluate Markov chain models for these, building on a “balls-in-bins” approach.

There are two kinds of network data: Network telemetry (e.g. packet counters) and business data (e.g. user roles). Existing approaches to querying network data keep these separate, increasing the number and complexity of queries users must write to answer questions about networks. We present Scout, a framework for creating tools which combine these two types of data. It is comprised of: An information model which can represent both network telemetry and business-domain data in use-case-specific schemas; a nascent query language for this information model; and an algorithm for executing queries on schemas. A preliminary evaluation showed that a Scout-based tool can answer questions pertaining to both network telemetry and business data, and reduces the knowledge and number of queries needed to answer realistic questions about networks.

Over the years researchers have used Weibull distribution to model packet level Internet traffic behaviour without any physical or analytical justification other than its parameter flexibility and heavy-tailed behaviour. In this article we present an extensive data analysis of two-way traffic in various Internet access and backbone core links and show analytically why Internet traffic converges to Weibull distribution as traffic moves from access to core links. In addition we show the flexibility of Weibull distribution in capturing stochastic properties of Internet traffic at packet, flow and session levels in various access and backbone core links. An extensive literature survey with new developments in Internet traffic count data modelling has been presented. The contributions in this article establish the notion of the "Renewal of Renewal Theory in Internet Traffic Modelling". This is the first study which presents a duplex analysis of all structural components of Internet traffic (packets, flows and sessions) at access and backbone core tiers of Internet. The results has been validated by using real traffic data fitness tests and trace driven queueing performance evaluation. The results of this article will help researchers use simple renewal processes as a better alternate to complex self-similar or modulated stochastic processes for modelling all structural components of Internet traffic at any time scale with physical justifications.