Faro: A Scalable and Reliable Outage Detection Algorithm for IoT Mobile Virtual Network Aggregators

In 3GPP networks, Charging Data Record (CDR) plays a very important role since it maintains the information related to the data usage which are used for billing the subscriber. In Evolved Packet Core (EPC) network, CDR can be generated by both Serving Gateway (SGW) and Packet Data Network Gateway (PGW) where both Control Plane (CP) messages which represents the messages related to the creation of the Packet Data Network (PDN) sessions and the User Plane (UP) messages which represents the actual data (Internet data from device or voice/video calls) are transferred. With Control and User Plane Separation (CUPS) architecture, the CP messages will be handled by the CP node (GW-C) and UP messages will be handled by the UP node (GW-U). Similar architecture enhancement of CP and UP separation is continued towards 5G also. In CUPS system, charging information is calculated at the UP node based on actual usage and will be conveyed to CP node in Sx messages using Packet Forwarding Control Protocol (PFCP) via UDP. Loss of this CDR information in between the CP and UP nodes due to any network issue will directly impact the revenue and reputation of the operator. In the current paper we have discussed some of the scenarios which can result in CDR information loss between CP and UP nodes (observed in the commercial deployed networks) and proposed a novel algorithm to recover the same. The simulation result also represents that with the proposed idea more than 95% of the missing information can be recovered with minimal increase in the network load.

On the competitive effects of mobile virtual network operators

The telecommunications industry in Indonesia has experienced a decline in revenue growth in recent years. This is due to a shift in the trend of digital transformation which has caused a shift in the need from legacy service to digital service. Meanwhile, revenues from digital services currently cannot replace revenue from legacy services. Internet of Things (IoT) is a solution that can be utilized by Mobile Network Operators (MNO) to boost the telecommunications business in Indonesia. Concept of IoT aims to expand the benefits of internet connectivity that is continuously connected. IoT consists of sensors, telecommunications networks, and applications that are able to share data, control remotely from a device or `thing'. IoT is one of the entities in the 5G era, that is also a key technology in the industrial revolution 4.0. This paper identify several types of business models that can be implemented for IoT services for MNO in Indonesia. Based on Strength, Weakness, Opportunity, Threat (SWOT) Analysis, showed that IoT has a great economic value in Indonesia market. MNO have an advantage in the IoT business implementation. The strength of MNO is in infrastructure, the ability to market segmentation and internet penetration in Indonesia.

In the Internet of Things (IoT), a large number of smart mobile devices, sensors, and actuators are deployed at the edge of the network. Processing of this large volume of data is a key factor of future Internet and communication systems such as Fifth Generation (5G) cellular networks, which provide dramatic enhancement in data rates, reliability, and energy consumption as compared to existing mobile communication infrastructure. In the context of service migration (inter-Public Land Mobile Network (PLMN) roaming) between different mobile network operators, end-users experience longer service interruption due to network re-connection specifically in cross-border scenarios. In this paper, we propose Mobile-access edge computing (MEC)-based intelligent service migration architecture to improve the service continuity (SC) in multi-domain Long Term Evolution (LTE) and 5G cellular networks. We introduce the concept of virtual roaming requests processed on edge node networks and predict in advance the development of service in visiting networks. As result, we can reduce the service interruption time during service migration in a multi-domain cellular network. Moreover, this MEC-based prediction also helps to improve message communication delay and to use cellular core network resource efficiently.

Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market

Fifth generation (5G) and Beyond-5G (B5G) will be characterized by highly dense deployments, both on network plane and user plane. Internet of Things, massive sensor deployments and base stations will drive even more energy consumption. User behavior towards mobile service usage is witnessing a paradigm shift with heavy capacity, demanding services resulting in an increase of both screen time and data transfers, which leads to additional power consumption. Mobile network operators will face additional energetic challenges, mainly related to power consumption and network sustainability, starting right in the planning phase with concepts like energy efficiency and greenness by design coming into play. The main contribution of this work is a two-tier method to address such challenges leading to positively-offset carbon dioxide emissions related to mobile networks using a novel approach. The first tier contributes to overall power reduction and optimization based on energy efficient methods applied to 5G and B5G networks. The second tier aims to offset the remaining operational power usage by completely offsetting its carbon footprint through geosequestration. This way, we show that the objective of minimizing overall networks’ carbon footprint is achievable. Conclusions are drawn and it is shown that carbon sequestration initiatives or program adherence represent a negligible cost impact on overall network cost, with the added value of greener and more environmentally friendly network operation. This can also relieve the pressure on mobile network operators in order to maximize compliance with environmentally neutral activity.

Mobile communication network provides the terminals of IOT (Internet of Things) with a convenient way to access the network. However a large number of connected IOT terminals will inevitably produce a significant impact on the existing mobile communication network. After summarizing the characteristics of typical applications of IOT, this review provides analysis of the specific impact of IOT on mobile network, and finally proposes the corresponding solutions.

Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it became increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reactive</i> approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR – an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic.We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.

Overestimating Wireless Demand: Policy and Investment Implications of Upward Bias in Mobile Data Forecasts

As the growth of the Internet of Things (IoT) devices continues, more and more wireless technologies are getting introduced into the market. NarrowBand-IoT and Long-Term Evaluation Cat M1 (LTE-M) are two Low-Powered Wide Area Network (LPWAN) communication technologies introduced by 3GPP in release 13 of LTE. Some of the features supported by these Cellular-IoT (C-IoT) technologies include low power consumption, extended coverage, low cost of deployment, roaming between Mobile Network Operator’s (MNO) networks, etc. This paper focuses on the experimental evaluation of NB-IoT and LTE-M networks in an international roaming scenario. The goal behind the experiments is to test and benchmark the performance of NB-IoT and LTE-M by calculating network Key Performance Indicators (KPIs) such as bitrate, end-to-end latency, and packet drop in a home and in roaming network scenarios. In order to perform the experiments, a Danish operator is chosen with nationwide coverage in Denmark (DK), Sweden (SE), and Norway (NO). A total of 6000 packets with varying payload sizes are sent using both the C-IoT technologies in all three countries combined. The idea behind the experiments is to test whether or not C-IoT technologies are reliable for supporting roaming applications in the IoT paradigm.

As the Internet of Things (IoT) continues to grow, efficient and reliable communication methods are essential for enabling widespread connectivity, particularly in remote and resource-constrained environments. LoRa, with its low power consumption and long-range capabilities, has become a cornerstone of IoT communication. The LoRaWAN protocol extends this functionality by enabling devices to transmit data globally through internet-connected gateways. These gateways aggregate data packets from nearby devices and forward them to centralized servers.However, traditional LoRaWAN gateways face significant limitations. They are often cost-prohibitive, require constant internet connectivity, and lack advanced data processing capabilities, making them unsuitable for deployment in areas with unreliable or no cellular coverage. These limitations hinder the adoption of LoRaWAN in scenarios such as environmental monitoring and rural IoT networks.To address these challenges, we developed a low-cost, energy-efficient edge-computing LoRaWAN gateway using embedded systems such as Raspberry Pi 0. Unlike conventional gateways, these edge-computing gateways locally process incoming data, enabling intelligent features such as optimized transmission, data buffering during network outages, and adaptive communication strategies. Additionally, these gateways can be configured to operate independently of cellular networks by utilizing satellite connectivity, further enhancing their usability in remote or off-grid applications.Preliminary testing demonstrates that these gateways consume up to one-third of the power required by traditional gateways while maintaining reliable data transmission. This substantial reduction in power consumption extends operational lifespans and reduces deployment costs. The ability to process and optimize data locally also improves network efficiency, ensuring timely and reliable communication even under challenging conditions.This innovation provides a scalable, cost-effective solution for IoT connectivity in remote and underserved regions. By addressing the limitations of conventional gateways, these edge-computing gateways enhance the feasibility of deploying IoT networks in scenarios where traditional infrastructure is impractical or unavailable. The broader implications of this work include improved access to IoT technologies for applications such as environmental monitoring and agriculture, ultimately expanding the reach and impact of IoT systems worldwide.

The Internet of Things (IoT) is a vision of a future society where an ever-increasing number of heterogeneous physical devices (things) obtain Internet connectivity, thus enabling a large number of applications for a broad range of industries and society at large. Mobile network operators, expected to provide the network infrastructure for many of these applications, face an unprecedented level of complexity. This complexity not only relates to the number of applications that share the network infrastructure, but also to the different network Quality of Service (QoS) requirements these applications have. To achieve economies of scale, automation in management of those applications throughout their lifecycle is essential. In this paper, we propose an architecture that automates allocation, monitoring and deallocation of both cloud and cellular network resources to ensure QoS for applications that involve connected devices communicating with cloud-hosted software. We describe an implementation of this architecture using a combination of open source, commercial, and custom components and evaluate it through a series of measurements. Results show that our implementation can simultaneously support mobile broadband and low latency, high availability mission-critical applications.

The paper analyzes gross upward pricing pressure indices—texttt {iGUPPI}—to assess the anti-competitive effects of mergers between vertically integrated firms where independent rivals are active in the downstream market. Such indices could be used, for example, to screen mergers between mobile network operators that compete with mobile virtual network operators in the downstream retail market. It is shown that the texttt {iGUPPI} for the downstream divisions of the merging firms corresponds to the sum of two well-known upward pricing pressure indices: the texttt {GUPPI} concept of Salop and Moresi (Georget Law J, 2009), and the texttt {vGUPPI} concept of Moresi and Salop (Antitrust Law J 79(1):185–214, 2013). However, this simple decomposition does not hold for their upstream divisions a priori. Here, additional effects that are not included in the two concepts arise. Further assumptions with respect to the price reactions of the downstream divisions to increases in the input prices are imposed so that the texttt {iGUPPI} for the upstream divisions is decomposable into an upstream market version of the texttt {GUPPI} and the texttt {vGUPPI}.

Competition in Wireless Telecommunications: The Role of MVNOs and Cable’s Entry into Wireless

This paper analyzes the spectrum trading problem in virtualized fifth generation networks to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a many‐to‐many matching game with utility‐based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual, and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.