Local Communication Networks Research Articles

Legitimacy considerations in regulation for local mobile communication network business in Finland and the UK

The article explores the legitimacy of local 5G and upcoming 6G mobile communication networks in Europe. The paper paves the way for a new perspective for understanding the legitimacy of local 5G/6G mobile communication networks by applying the business model-based ecosystem perspective and legitimacy as a theoretical foundation. As regulations are expanding to consider the novel deployment model of local 5G and 6G mobile communication networks, the legitimacy of such networks is under-explored. Analyzing the regulatory approaches taken by two national regulatory authorities (NRA) in Europe, Finland and the UK, we identify seven legitimacy-related themes including innovation orientation, awareness and knowledge of how to build and design local networks, European harmonized spectrum management, competition, cybersecurity and risk assessment and equipment availability. Our study lays the groundwork for exploring how the application of the business model-based ecosystem perspective can be utilized to advance regulatory initiatives for local mobile communication network businesses. It also identifies which legitimacy-related aspects are involved in setting regulatory priorities.

Heterogeneous integration of spin-photon interfaces with a CMOS platform.

Colour centres in diamond have emerged as a leading solid-state platform for advancing quantum technologies, satisfying the DiVincenzo criteria1 and recently achieving quantum advantage in secret key distribution2. Blueprint studies3-5 indicate that general-purpose quantum computing using local quantum communication networks will require millions of physical qubits to encode thousands of logical qubits, presenting an open scalability challenge. Here we introduce a modular quantum system-on-chip (QSoC) architecture that integrates thousands of individually addressable tin-vacancy spin qubits in two-dimensional arrays of quantum microchiplets into an application-specific integrated circuit designed for cryogenic control. We demonstrate crucial fabrication steps and architectural subcomponents, including QSoC transfer by means of a 'lock-and-release' method for large-scale heterogeneous integration, high-throughput spin-qubit calibration and spectral tuning, and efficient spin state preparation and measurement. This QSoC architecture supports full connectivity for quantum memory arrays by spectral tuning across spin-photon frequency channels. Design studies building on these measurements indicate further scaling potential by means of increased qubit density, larger QSoC active regions and optical networking across QSoC modules.

Agency in business networks: combining IMP research with a relational sociological perspective to challenge views on sustainability and ethics

PurposeThe purpose of this paper is to clarify the ontological assumptions regarding the concept of agency and sociality within business networks in the Industrial Marketing and Purchasing Group (IMP) research by refining these assumptions with a relational sociological (RS) perspective. This paper reinforces the robustness of the actors-resources-activities (ARA) model with an in-depth investigation of the actor dimension, where local interactions between interdependent individuals play a central role in building common futures within business networks through organisational reflexivity.Design/methodology/approachThis conceptual paper investigates the social ontology of research. It challenges the implicit assumptions of IMP research regarding agency and sociality within business networks with a problematisation strategy (Sandberg and Alvesson, 2011). Combining IMP views on agency with the RS perspective, it sets this combined framework as an alternative for the analysis of sustainability and ethics within business networks.FindingsCombining IMP research and an RS perspective allows us to extend the knowledge of sociality within business networks, highlighting the centrality of meaning sharing in the process of network change. By focusing on symbolic interaction processes, an RS perspective contributes to a deeper theoretical understanding of the relationship between local communication and business network patterns. Combined with an IMP perspective on agency, it provides researchers with an alternative conceptual framework for examining sustainability by considering ethics and leadership dialectically.Research limitations/implicationsRS is still an emerging stream within sociology, characterised by diverse views. Not all relational sociologists, as scientists, feel obliged to engage with sustainability research. Thus, the paper is a two-sided invitation to IMP researchers and relational sociologists to delve into the adaptation processes in business networks in highly uncertain environments.Practical implicationsRS focusing on the centrality of communication in local interactions, business network researchers can show that organisational leaders are not the ones with a charismatic vision isolated from any natural and social environment; rather, they are the people with “the capacity to assist the group to continue acting ethically, creatively and courageously in the unknown” (Stacey,2013).Social implicationsAdopting an RS perspective on agency in business networks can help managers and researchers determine how business networks can be managed in a more sustainable way. Combined with a dialectical and processual understanding of ethics, the IMP-RS perspective emphasises day-to-day local communication practices within and between organisations that challenges microeconomic views on nature, strategy, ethics and leadership. This paper thus places the social at the centre of sustainability approaches.Originality/valueFrom an RS perspective, business networks are analysed as patterns of interactions between many organisations and individuals. The value of this conceptual paper is in showing that change within business networks is negotiated through local interactions and symbolic communication between individuals. Thus, it suggests the need to combine the individual and the organisational levels to analyse agency within business networks and to examine the adaptation of business networks to sustainability.

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

Exosomes define a local and systemic communication network in healthy pancreas and pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), a lethal disease, requires a grasp of its biology for effective therapies. Exosomes, implicated in cancer, are poorly understood in living systems. Here we use the genetically engineered mouse model (ExoBow) to map the spatiotemporal distribution of exosomes from healthy and PDAC pancreas in vivo to determine their biological significance. We show that, within the PDAC microenvironment, cancer cells establish preferential communication routes through exosomes with cancer associated fibroblasts and endothelial cells. The latter being a conserved event in the healthy pancreas. Inhibiting exosomes secretion in both scenarios enhances angiogenesis, underscoring their contribution to vascularization and to cancer. Inter-organ communication is significantly increased in PDAC with specific organs as most frequent targets of exosomes communication occurring in health with the thymus, bone-marrow, brain, and intestines, and in PDAC with the kidneys, lungs and thymus. In sum, we find that exosomes mediate an organized intra- and inter- pancreas communication network with modulatory effects in vivo.

Regulatory challenges and implications of the European electronic communications code (EECC) for local mobile communication network business

Regulatory provisions pose legal constraints on deploying mobile communication networks and related services. Local 5G and upcoming 6G networks, particularly those that are independent of the big mobile network operators (MNOs), face new challenges due to the incoherent harmonization and implementation of the regulatory provisions and the standby approach undertaken by many EU member countries. This paper analyses the European Electronic Communications Code (EECC) Directive from the perspective of local mobile communication networks by combining business model innovation and legitimacy approaches in an ecosystemic context. Based on the analysis, we recommend regulators focus on terminology, spectrum management, access and interconnection, security and privacy, and competition when enabling local mobile communications business.

Telecommunications localism: the fight for control over local communication networks in the United States

While called the “bedrock” and “cornerstone” of US media policy, the localism principle has been chipped away over 40 years by the Federal Communications Commission's (FCC) full throated embrace of market-based practices. Recently, however, localism has emerged as a pivotal battleground in telecommunications. Through case studies of three recent regulatory issues—local cable franchising, municipal broadband, and small cell deployment—this paper examines the role of localism in US-based cable and telecommunications policy. It coins the term “telecommunications localism” to capture the changing technological focus of media regulation, from content to infrastructure. In regulatory actions over the past decade the FCC has simultaneously acknowledged local municipalities’ role in telecommunications deployment and actively worked to curtail this power through deregulation. These deregulatory actions strip municipalities of their autonomy over license negotiations and local rights of way and stymies the development and deployment of local communication systems.

Spectrum Management for Local Mobile Communication Networks

Spectrum management is a complex topic that divides stakeholders' perspectives. Recently, a major paradigm shift has taken place with the emergence of local 4G and 5G networks via local spectrum licensing models in several countries, allowing different stakeholders to establish their own mobile communication networks within specific facilities, such as a factories and ports. These local mobile communication networks can serve mobile network operators' (MNOs) end users, closed user groups, or a mix, resulting in operations under different regulatory conditions. Their emergence relies on spectrum availability in the given location, which is managed by national regulators. Local spectrum availability can be increased with spectrum sharing where two or more radio systems operate in the same band, but stakeholder views differ - some promote it while others object it. This paper analyzes the most recent spectrum management developments from the viewpoint of emerging local mobile communication networks considering the role of spectrum sharing. The analysis reveals how differently the countries have prepared for the new business opportunity arising from local mobile communication networks in their spectrum awarding decisions and quantifies current spectrum availability for establishing local 5G networks. The results show varying spectrum management approaches taken by the regulators. Especially, local spectrum licenses appear in the most recent spectrum awards. To realize a large number of local 4G/5G and future 6G networks, spectrum sharing will become critical and needs to be clearly defined and understood to mitigate currently strong stakeholders' objection.

Complexity Measures for IoT Network Traffic

The coming era of widespread integration of Internet of Things (IoT) devices to all areas of society has facilitated a fundamental transformation of local and global communication networks, giving rise to novel issues relating to capacity planning, network administration, and cybersecurity. Accurate network traffic prediction is one of the key enablers for addressing these challenges. While there are methods for quantifying the complexity (predictability in timing, shape, and volume) of wide-scale aggregate traffic, they cannot be directly applied to IoT traffic as they do not account for the heterogeneity of IoT devices. Lacking an effective complexity characterization for IoT traffic, network traffic administrators are under-informed on the impacts of IoT device-type traffic on their networks. In this work, the complexity of IoT traffic is examined from two novel perspectives, the information-theoretic approach of Lempel–Ziv, a foundational algorithm in lossless data compression, and in the distribution of spectral components of the Fourier transform. Based on these perspectives, two new measures of IoT network traffic complexity are proposed. Furthermore, we introduce a novel mathematical framework to permit a formal comparison of new and existing methods. The new framework additionally verifies that the new metrics satisfy desirable properties for a measure of complexity. In a comprehensive empirical study, our results, when compared with existing approaches, exceed all others in behavioral resolution, convergence rate, physical interpretability, and algorithmic stability, under severely heterogeneous conditions. Benchmark experiments demonstrate substantial run-time improvements over existing approaches, creating a strong case for their use in online, real-time settings.

Autonomous Cooperative Search Model for Multi-UAV With Limited Communication Network

With the rapid development of artificial intelligence technology, the multi-UAV cooperative search has wide applications in the field of Internet of Things, such as resource exploration, emergency rescue, intelligent transportation, etc. However, the communication network in an unknown environment may be inaccessible, and the real-time information sharing among UAVs cannot be guaranteed, resulting in the failure of cooperative search. Aiming at this issue, this article is devoted to the design of the multi-UAV flight strategy to improve the cooperative search capability in an uncertain communication environment. Specifically, a new cooperative architecture oriented to a local communication network is devised to control the observation locations of multiple UAVs in the search process, and some local communication networks are established based on the distance among UAVs to meet the requirements of the search task. On this foundation, we develop a multi-UAV cooperative search model (MCSM) with communication cost and formation benefit as an optimization function to ensure the effectiveness of multi-UAV search. Moreover, in the process of model solving, an improved sparrow search algorithm (ISSA) is presented with some different search strategies to enhance the optimization capability. To verify the superiority of the proposed method, we designed several groups of simulation experiments to analyze the performance of MCSM. Experimental results illustrate that our method can not only maintain high cooperative search accuracy but also has high stability and convergence speed.

Protecting endangered megafauna through AI analysis of drone images in a low-connectivity setting: a case study from Namibia.

Assessing the numbers and distribution of at-risk megafauna such as the black rhino (Diceros bicornis) is key to effective conservation, yet such data are difficult to obtain. Many current monitoring technologies are invasive to the target animals and expensive. Satellite monitoring is emerging as a potential tool for very large animals (e.g., elephant) but detecting smaller species requires higher resolution imaging. Drones can deliver the required resolution and speed of monitoring, but challenges remain in delivering automated monitoring systems where internet connectivity is unreliable or absent. This study describes a model built to run on a drone to identify in situ images of megafauna. Compared with previously reported studies, this automated detection framework has a lower hardware cost and can function with a reduced internet bandwidth requirement for local network communication. It proposes the use of a Jetson Xavier NX, onboard a Parrot Anafi drone, connected to the internet throughout the flight to deliver a lightweight web-based notification system upon detection of the target species. The GPS location with the detected target species images is sent using MQ Telemetry Transport (MQTT), a lightweight messaging protocol using a publisher/subscriber architecture for IoT devices. It provides reliable message delivery when internet connection is sporadic. We used a YOLOv5l6 object detection architecture trained to identify a bounding box for one of five objects of interest in a frame of video. At an intersection over union (IoU) threshold of 0.5, our model achieved an average precision (AP) of 0.81 for black rhino (our primary target) and 0.83 for giraffe (Giraffa giraffa). The model was less successful at identifying the other smaller objects which were not our primary targets: 0.34, 0.25, and 0.42 for ostrich (Struthio camelus australis), springbok (Antidorcas marsupialis) and human respectively. We used several techniques to optimize performance and overcome the inherent challenge of small objects (animals) in the data. Although our primary focus for the development of the model was rhino, we included other species classes to emulate field conditions where many animal species are encountered, and thus reduce the false positive occurrence rate for rhino detections. To constrain model overfitting, we trained the model on a dataset with varied terrain, angle and lighting conditions and used data augmentation techniques (i.e., GANs). We used image tiling and a relatively larger (i.e., higher resolution) image input size to compensate for the difficulty faced in detecting small objects when using YOLO. In this study, we demonstrated the potential of a drone-based AI pipeline model to automate the detection of free-ranging megafauna detection in a remote setting and create alerts to a wildlife manager in a relatively poorly connected field environment.

Wave-Type Interaction within a Robotic Swarm System for Decentralized Estimation of Global Geometric States

For a robotic swarm system composed of autonomous mobile robots, controlling and using asymmetric global geometric states promotes the task performance of the swarm. This paper presents a systematic method for estimating asymmetric global geometric states over a swarm system. To overcome the limitations of local observation or communication ability, we propose a wave-type interaction among neighboring robots. We assume that each robot has a scalar state variable called a phase, which is manipulated through interactions. Through the analysis of eigenvalues of a graph Laplacian matrix corresponding to a local communication network of robots, we show that a robot can estimate global states, such as the size of an entire swarm, by frequency analysis of its phase. We also analyzed the stability of the wave-type interaction based on von-Neumann stability. We verified the proposed method by computer simulations, in which robots in a swarm detected the deformation in the shape of the swarm when the swarm was passing through a narrow area. The result will contribute to building a control system for swarms that can manipulate their shape or characteristics to adapt themselves based on tasks or environmental requirements.

Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions

The promotion of carbon neutrality targets will significantly influence the energy management of multi micro energy grid (MMEG) systems that can supply flexible local loads using various distributed energy resources. So far, few works incorporate the carbon emissions management with the fully-distributed coordination of MMEG systems to protect the environment and individual privacy and improve the energy efficiency. To fill this gap, in this paper, a hierarchical optimization framework is proposed for the MMEG system to solve an environmental economic dispatch problem, such that the energy coordination strategy could be obtained in a fully-distributed way. At the upper level, under a local and time-varying communication network, we develop an aggregate model based on a real-time price policy for the MMEG system. On this basis, a fully distributed consensus-based gradient projection method is proposed to minimize the total system generation cost with the coupled emission constraint satisfied, wherein a piecewise linearization technique is applied to handle the non-convexity and non-smoothness of the cost function caused by valve-point loading effects (VPLE) of thermal units. At the lower level, we apply an accelerated distributed augmented Lagrangian (ADAL) method to deal with a cost minimization optimization problem for users, such that each user could implement its own optimal strategy simultaneously based on the obtained aggregated strategy from the upper level. The effectiveness of the proposed approach is demonstrated by numerical simulations on an MMEG test system.

Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

UAVs are widely employed in military and civilian fields because of their inherent advantages. The cooperative task allocation of multi-UAV is more in conformity with the requirements of UAV application scenarios, which has become a hot research topic. However, resource consumption allocation and the application scenarios of communication constraints are often ignored. This paper proposes a distributed multi-UAV task allocation method based on improved CNP to solve the local cooperative task allocation problem of heterogeneous multi-UAV in the communication-constrained environment. The improved CNP-based method can be divided into four stages: task release, bid application, coalition formation, and signing contracts. In the task release stage, we proposed the adaptive maximum number setting method of information transfer times and the information consistency method to solve conflicts in the local communication network. In the process of coalition formation, the resource consumption allocation algorithm based on the Gini coefficient is proposed to keep the resource difference between UAVs in the coalition within a reasonable range. The simulation results demonstrate that improved CNP-method-based cooperative task allocation can handle the local real-time task allocation problem of heterogeneous multi-UAV under communication constraints; it obtains greater task rewards and spends less time on task completion than the resource-welfare-based method, PTCFA. Simultaneously, the resource consumption algorithm makes the UAV swarm maintain a more reasonable resource difference to maximize the number of missions completed.

Local competitive environment and male condition influence within-bout calling patterns in túngara frogs

ABSTRACT Flexible signalling behaviour is widespread, with adjustments often enhancing gains or reducing costs of signalling based on the current state of the signaller’s local communication network. Male túngara frogs call within multispecies communication networks containing conspecifics (both target receivers and rivals) and eavesdropping predators. These diverse players all exert an influence on male calling strategies. We investigated the degree to which patterns of changes in call characteristics across individual túngara frog calling bouts were influenced by callers’ social environment, body condition, and ambient temperature. Most call bouts exhibited two distinct phases, an initial steep increase in call amplitude (the rise) followed by a longer period of more gradual amplitude increase (the plateau). Rises were completed more quickly when males called in denser choruses, while call amplitude increases during plateau phases were greater for males in better body condition. Males also produced more complex calls and increased complexity sooner when calling in denser choruses. Our results suggest that the social environment is the main driver of within-bout calling patterns. This could be due to (i) increased call effort required when competing in denser choruses, (ii) dilution effects provided by nearby rivals releasing callers from eavesdropping risk or, likely, (iii) a combination of both.

Multi-Agent Reinforcement Learning for Decentralized Resilient Secondary Control of Energy Storage Systems Against DoS Attacks

While distributed secondary controllers have been studied for multiple energy storage systems in islanded microgrids, information infrastructure has to be added for the extensive information transmission among these secondary controllers and the additional communication among distributed controllers is costly and increases the vulnerability surface to cyberattacks. In this work, a data-driven decentralized secondary control scheme is proposed for multiple heterogeneous battery energy storage systems (BESSs). The proposed secondary control scheme can achieve frequency regulation and the state-of-charge (SoC) balancing simultaneously for BESSs without requiring accurate BESS models. This scheme leverages an asynchronous advantage actor-critic (A3C) based multi-agent deep reinforcement learning (MA-DRL) algorithm where the centralized off-line learning with shared convolutional neural networks (CNN) is designed to maximize global rewards and ensure the performance of the entire system and a decentralized online execution mechanism is applied to each BESS. Furthermore, in view of possible denial-of-service (DoS) attack on local communication networks used for signal transfer between secondary controllers and remote sensors, a signal-to-interference-plus-noise ratio (SINR)-based dynamic and proactive event-triggered communication mechanism is proposed to alleviate the impact of DoS attacks and reduce the occupation of communication resources. Simulation results on a four-bus multiple BESS system show that the proposed decentralized secondary controller can achieve simultaneous frequency regulation and SoC balancing. Comparison results with other event-triggered mechanisms and MA-DRL algorithms show the A3C based MA-DRL algorithm with CNN can obtain a comparatively optimal policy through training and the designed event-triggered strategy can dynamically adapt the release frequency based on real-time SINR and significantly reduce the occupied network bandwidth and packet loss rate (PER) induced by DoS attacks.

Distributed Remote Estimation Over the Collision Channel With and Without Local Communication

Internet of Things networks are the large-scale distributed systems consisting of a massive number of simple devices communicating, typically, over a shared wireless medium. This new paradigm requires novel ways of coordinating access to limited communication resources without introducing unreasonable delays. Herein, the optimal design of a remote estimation system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> sensors communicating with a fusion center via a collision channel of limited capacity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\leq n$</tex-math></inline-formula> is considered. In particular, for independent and identically distributed observations with a symmetric probability density function, we show that the problem of minimizing the mean-squared error with respect to a threshold strategy is quasi-convex. When coordination among sensors via a local communication network is available, the online learning of possibly unknown parameters of the probabilistic model is possible, enabling each sensor to optimize its own threshold autonomously. We propose two strategies for remote estimation with local communication: 1) one strategy swiftly reaches the performance of the optimal decentralized threshold policy and 2) the second strategy approaches the performance of the optimal centralized scheme with a slower convergence rate. A hybrid scheme that combines the best of both approaches is proposed, offering fast convergence and excellent performance.

Social networks and local communication network patterns following the destructive 2018 Lombok, Indonesia, earthquake sequence

Social communication networks created during the 2018 earthquake sequence in Lombok, Indonesia using social network data from a social media platform, i.e., Facebook, and surveys in local communities were analyzed. The different modes of communication networks used during the disaster event and post-disaster mitigation were examined. Data on social media platform usage were collected from the Facebook page of the Agency for Meteorology, Climatology and Geophysics of Indonesia and the National Board for Disaster Management of Indonesia. The data show large clusters of social relation networks based on dominant issues discussed by the public under several trending hashtags. Meanwhile, the local patterns of social communication networks, particularly the flow of information and communication used by the local communities of the impacted villages in Lombok, differ depending on the culture of communication and time of accessing information. During the 2018 Lombok earthquake sequence, communication was predominantly sought from community leaders, religious leaders, family members, and neighbours. This study highlights the implementation of a decentralization system in Indonesia, which has given the local and regional authorities a significant role in disaster management, response, and emergency command for the local and regional community, instead of relying largely on the response of the central Jakarta government.

Drivers of social acceptability for bivalve aquaculture in Atlantic Canadian communities

Aquaculture is a growing sector because of increased global demands for seafood; bivalve aquaculture production is also increasing in specific regions because of its perceived sustainability and similar environmental interactions across ecosystems. As socioeconomic impacts on prospective sites may differ, this research aimed to perform a high-level scoping of environmental, social, and economic drivers informing social acceptability of bivalve aquaculture in two communities in Nova Scotia and Prince Edward Island, Canada. Communities were surveyed through online questionnaires designed to examine bivalve farming perceptions, information sources, and potential deviations between communities. Results suggested that community perceptions of environmental effects were both positive and negative, social effects were mostly negative, and economic effects were somewhat positive. Results further suggested that insufficient transparency regarding industry practices and the local communication network may have a role in shaping bivalve farming perceptions. Variation between communities regarding perceived social and economic drivers of social acceptability emphasized the importance of community-based research to understand emerging and existing conflicts, including the role information sources may have in driving acceptability. Accordingly, aquaculture regulators and managers should consider community socioeconomic priorities and improved transparency about industry practices when evaluating prospective sites.

Allosteric Effect of Nanobody Binding on Ligand-Specific Active States of the β2 Adrenergic Receptor.

Nanobody binding stabilizes G-protein-coupled receptors (GPCR) in a fully active state and modulates their affinity for bound ligands. However, the atomic-level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like β2 adrenergic receptor (β2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes structural features of the β2AR with an ∼14 Å outward movement of transmembrane helix 6 and a close proximity of transmembrane (TM) helices 5 and 7, and favors the fully active-like conformation of the receptor, independent of ligand binding, in contrast to the conditions under which no intracellular binding partner is bound, in which case the receptor is only stabilized in an intermediate-active state. This activation is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intracellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding triggers tighter and stronger local communication networks between the Nb80 and the ligand-binding sites, primarily involving residues around ICL2 and the intracellular end of TM3, TM5, TM6, as well as ECL2, ECL3, and the extracellular ends of TM6 and TM7. In particular, we identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in conformations modulated by a full agonist, BI167107, and a G-protein-biased partial agonist, salmeterol, involving mainly TM1 and TM2, and TM5, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which should be taken into account in structure-based drug discovery.