An Internet of Things (IoT) non-terrestrial network (NTN) refers to an IoT network that utilizes airborne or spaceborne payload for communication. Using NTNs to serve IoT devices has the potential of further expanding the use cases as well as coverage for IoT. This has motivated the work in 3GPP on evolving Narrowband Internet of Things (NB-IoT) and Long Term Evolution for machine type communications (LTE-M) wireless access technologies to support NTN. While the topic of IoT over NTN has attracted much attention, the performance of IoT NTN has not been well studied. In this paper, we investigate the performance of IoT low Earth orbit (LEO) NTN utilizing LTE-M by evaluating the connection density. The evaluation results show that for a LEO satellite at 600 and 1200 km altitude, LTE-M can support 364 and 78 users per km2for a single narrowband in its coverage area, respectively. This implies that an LTE-M based LEO NTN can potentially serve IoT in remote areas, thereby complementing the coverage of terrestrial networks.

IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception.

We derive a modular fluid-flow network congestion control model based on a law of fundamental nature in networks: the conservation of information. Network elements such as queues, users, and transmission channels and network performance indicators like sending/acknowledgment rates and delays are mathematically modeled by applying this law locally. Our contributions are twofold. First, we introduce a modular metamodel that is sufficiently generic to represent any network topology. The proposed model is composed of building blocks that implement mechanisms ignored by the existing ones, which can be recovered from exact reduction or approximation of this new model. Second, we provide a novel classification of previously proposed models in the literature and show that they are often not capable of capturing the transient behavior of the network precisely. Numerical results obtained from packet-level simulations demonstrate the accuracy of the proposed model.

A major challenge for wireless networks is to minimize the energy consumption in the mobile devices. This work evaluates potential performance gains of a dual-radio architecture in which a low power radio is used to wake up the primary high power radio. Our targeted domain is opportunistic communication directly between devices. We show that it can significantly reduce the energy consumption at a price of slightly decreased goodput. We also examine the effect of the MAC protocol on the performance of the dual-radio system and we point out that in dense scenarios MAC layer protocols, such as of 802.15.4, do not perform well. We observe that information on device density of an environment can be used to address this shortcoming.

A modular fluid-flow model for network congestion analysis and control is proposed. The model is derived from an information conservation law stating that the information is either in transit, lost or received. Mathematical models of network elements such as queues, users, and transmission channels, and network description variables, including sending/ acknowledgement rates and delays, are inferred from this law and obtained by applying this principle locally. The modularity of the devised model makes it sufficiently generic to describe any network topology, and appealing for building simulators. Previous models in the literature are often not capable of capturing the transient behavior of the network precisely, making the resulting analysis inaccurate in practice. Those models can be recovered from exact reduction or approximation of this new model. An important aspect of this particular modeling approach is the introduction of new tight building blocks that implement mechanisms ignored by the existing ones, notably at the queue and user levels. Comparisons with packet-level simulations corroborate the proposed model.

The fast spreading of smart mobile devices has changed the way people create and share multimedia contents. A recently proposed idea is to distribute contents opportunistically when devices are within each other's communication range. In this work we have seized this idea and designed and implemented a real-time music-sharing application that allows people to share a sound system for collective listening. The system utilizes a content-centric opportunistic networking middleware. The application provides an interface for users to play audio files stored in their own mobile devices through a shared jukebox that is within direct communication range. The jukebox plays the audio file that is first in queue in its playlist by streaming it from the publishing user device. All devices synchronize with the jukebox so that the users can observe the contents of the playlist on their displays in real-time. This paper presents the idea and motivation for such a system as well as its design and evaluation.

We present a demonstration scenario to validate a middleware architecture that we designed and implemented for distributing content between mobile users in an opportunistic manner. The peer-to-peer networking architecture allows content dissemination between mobile devices without relying on any infrastructure support. We developed a mobile application utilizing the services provided by the implemented middleware. The application facilitates opportunistic content distribution in both one-to-one and one-to-many dissemination modes.

Rapid growth of mobile wireless industry has generated numerous challenges for service operators. The provisioning of increasing demand in capacity and higher quality of service can be met with redimensioning of networks which makes a priori network planning difficult. We propose a network performance monitoring system to which all mobile devices contribute by voluntarily submitting periodical reports on the availability and quality of network access that they experience at their current geographical locations. Network performance measures such as signal strength, coverage, call block/drop statistics and operator service capabilities are reported to a server with a network status information database as experienced by mobile devices. The proposed database will indicate areas with lack of coverage, insufficient capacity, and high interference, which may serve as cues for operators to upgrade/retune their infrastructure equipment and for regulators to take action against operators that do not fulfill commitments made to their subscribers.

Many dynamic call admission control (CAC) schemes have been proposed in the literature for adaptive reservations in cellular networks. Efficient application of these schemes requires reliable and up-to-date feedback of system performance to the CAC mechanism. However, exact analyses of these schemes in real time using multi-dimensional Markov chain models are challenging due to the need to solve large sets of flow equations. One dimensional Markov chain models have been widely used to derive performance metrics such as call blocking probabilities of multiple traffic classes assuming that all classes of calls have equal capacity requirements and exponentially distributed channel holding times with equal mean values. These assumptions need to be relaxed for a more general evaluation of CAC performance in multi-service cellular networks. In this paper we classify CAC schemes according to their Markov chain models into two categories: symmetric and asymmetric, and develop computationally efficient analytical methods to compute call blocking probabilities of various traffic classes for several widely known CAC schemes under relaxed assumptions. We obtain a product form solution to evaluate symmetric schemes and propose a novel performance evaluation approximation method with low computational cost for asymmetric schemes. Numerical results demonstrate the accuracy and efficiency of the proposed method.

Call holding times in telephony networks are commonly approximated by exponential distributions to facilitate traffic engineering. However, for traffic engineering of cellular networks, channel occupancy times need to be modeled instead to facilitate analytical modeling or to feed network simulations. In this paper, we classify channel occupancy times and present an empirical study based on data obtained from a real cellular network to determine which probability distribution functions can approximate them better. The results are environment dependent, but no assumptions that can be influential are made, as opposed to previous analytical and simulation studies which results are highly dependent on the assumptions made by the authors. We show that all types of channel occupancy times can be approximated by lognormal distribution. For stationary users, channel occupancy times are commonly approximated by exponential distribution due to its tractability, assuming that cell residence times are also exponentially distributed. However, we show that lognormal distribution fits much better to both channel occupancy and call holding times regardless of whether users are stationary or mobile.

Several dynamic call admission control (CAC) schemes for cellular networks have been proposed in the literature to reserve resources adaptively to provide the desired quality of service (QoS) to not only high priority calls but also to low priority ones. Efficient adaptive reservations depend on reliable and up-to-date system status feedback provided to the CAC mechanism. However exact analysis of these schemes using multidimensional Markov chain models are intractable in real time due to the need to solve large sets of flow equations. Hence performance metrics such as call blocking probabilities of various QoS classes are generally evaluated using one dimensional Markov chain models assuming that channel occupancy times for all QoS classes have equal mean values and all arriving calls have equal capacity requirements. In this paper we re-evaluate the analytical methods to compute call blocking probabilities of various QoS classes for several widely known CAC schemes by relaxing these assumptions, and propose a novel approximation method for performance evaluation with low computational complexity. Numerical results show that proposed method provides results that match well with the exact solutions.

Providing the desired call blocking probability (CBP) to not only new but also existing calls has been a challenge for wireless mobile network service providers. To satisfy different requirements for new and handoff CBP, several call admission control (CAC) schemes have been proposed in the literature. Exact analysis of these schemes using 2D Markov chains is computationally intensive. Therefore, computationally efficient methods to analyze these systems using 1D Markov chain models have been considered. The "traditional" approach assumes that channel holding time for new and handoff calls have equal mean values. While the "normalized" approach relaxes this assumption, it is accurate only for the new call bounding CAC scheme. In this paper, we reevaluate the analytical methods for CBP probabilities for several widely known CAC schemes under more general assumptions by providing an easy to implement method. The numerical results show that when the average values of channel holding times for new and handoff calls are different the proposed approach gives more accurate results when compared with the traditional and normalized methods based on 1D Markov chain modeling, while keeping the computational complexity low.