Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human‐in‐the‐loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber‐Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi‐robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi‐staged mission within an inspection scenario for the heterogeneous Symbiotic Multi‐Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query‐based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

This paper describes the simulation results and hardware implementation of an inexpensive, low-complexity LED based indoor positioning system. Localization by multiple LEDs estimation model (MLEM) approximates position of a mobile receiver by the acquisition of positional information from LED transmitters. Multiple LED orientation can either be with or without overlap. Receivers in a no-overlap LED orientation experience only single access while multiple access receivers are designed for orientations with overlaps. Single and multiple access systems were developed and implemented by the use of low cost ATMEG 328 microcontroller. Since multiple LEDs transmit data at the same wavelength and are asynchronous, overlap in multiple access system causes interference. The possibility of this interference is reduced by packet based pulse duration multiplexing (PDM) and a low duty cycle transmission protocol. By the use of MLEM, root mean square error in position estimation is reduced to about 1 percent of the length an indoor location. Experimental results show that overlap increases positional accuracy over a wider coverage region and that the multiple access system allows for a more reliable positioning.

In this work, we develop a low complexity indoor positioning system (IPS) and design a lightweight, low-cost and wearable receiver for it. The accuracy of proximity-based LED IPS has been improved using overlap between LED beams but LED packets in the overlap region are subject to collisions. In this work, we design collision handling algorithms for the IPS that considers building and lighting infrastructures. Mathematical analyses of the proposed algorithms are done and models for the probability of collisions are developed. The models, which are verified using simulations, are used to calculate the time required for position update called positioning time. Analysis of the positioning time is done for single and multiple receivers systems and validated with experimental measurements. Results show positioning error as low as 56 cm with a positioning time of about 300 ms for slotted unsynchronized systems and 500 ms for unslotted unsynchronized systems which makes the developed system pragmatic and appropriate for human positioning.

Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading.Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.