The interest in wireless communication systems for industrial applications has grown significantly over the last years. More flexible, easier to install and maintain, wireless networks present a promising alternative to the currently used wired systems. However, reliability and timeliness requirements at present met by wired networks also need to be fulfilled by wireless solutions. Packet errors introduced when packets travel through wireless channels imply a significant challenge to fulfill these requirements. Relaying has been recognized to improve the reliability in industrial wireless networks without causing additional delay. Furthermore, the recent results have shown that relaying combined with packet aggregation significantly outperforms simple relaying. However, it is not always cost efficient to introduce additional relay nodes into an industrial network and hence, in this paper, we propose using a combination of relaying and packet aggregation at the source nodes. The results show that when relaying and aggregation are used at the source nodes, the transmission schedule plays a crucial role. A schedule adapting to the varying channel conditions improves performance substantially. By carefully choosing which packet to aggregate, even further improvements can be achieved.

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.

We consider the scheduling of retransmissions in the low-latency deterministic network (LLDN) extension to the IEEE 802.15.4 standard. We propose a number of retransmission schemes with varying degrees of required changes to the LLDN specification. In particular, we propose a retransmission scheme that uses cooperative relayers and where the best relayer for a source node is learned using a reinforcement-learning method. The method allows for adapting relayer selections in the face of time-varying channels. Our results show that the relayer-based methods achieve a much better reliability over the other methods, both over static (but unknown) and over time-varying channels.

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.