An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system.

The massive device deployments in the Internet of Things (IoT) generate immense amounts of data that can be leveraged to improve overall network performance. This paper outlines how data gathered from correlated sensor nodes can be used to improve the timeliness of updates of another sensor node in the network. We consider a system of two correlated information sources, i.e., sensor nodes, which periodically send updates to a gateway, regarding the observed physical phenomenon distributed in space and evolving in time. The optimal use of updates in such a system greatly depends on the correlation between the two sources, and to explore this effect we investigate three different models of the covariance between independently obtained observations of the phenomenon of the interest. We extract values for the parameters in the covariance models from data coming from a real sensor network, to provide the reader with a realistic feel for scaling parameters values and the applicability of our analysis in a real scenario. We demonstrate that using correlated information results in a significant increase in device lifetime and compare our approach to others proposed in the literature.

The success of the Internet of Things (IoT) strongly depends on the development of efficient strategies for gathering and processing large amounts of data while saving energy and increasing device lifetime. This work investigates the use of correlated sources of information to improve the timeliness of data collected from sensing devices in the IoT. We consider information sources that transmit periodic updates to a gateway regarding the status of an observed process and determine the optimal update strategy for these sources. We show that there is an optimal waiting time for the first update to be sent by a secondary, correlated source such that estimation error is the lowest.

Ultra-dense Internet of Things (IoT) networks and machine type communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. We explore leveraging the communication within IoT to serve data processing by appropriately shaping the aggregate behavior of a network to parallel more traditional computation methods. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of an IoT network for simultaneous data processing and transfer. That is, we provide a framework to treat a network holistically as an artificial neural network, rather than placing neural networks within the network. The operation of components of a neural network, neurons and connections between neurons, are performed by the various elements of the IoT network, i.e., the devices and their connections. The proposed approach reduces the latency in delivering processed information and supports the locality of information inherent to IoT by removing the need for transfer to remote data processing sites.

Studies of clustering in Wireless Sensor Networks (WSNs) usually tackle the problems of designing new algorithms and compare them based on a set of properties (e.g. energy efficiency, scalability), lacking the understanding of the underlying mechanisms and communication patterns that lead to these properties. Our approach tackles this lack of understanding by applying techniques developed by complex systems scientists. Functional topology graphs, which describe the interactions between system parts, are used to represent different implementations of clustering in WSNs. We employ a complexity metric - functional complexity (CF) - to quantify the potential of the functional topology to transport information. Our analysis highlights the trade-off between scalability and energy efficiency, showing that higher values of CFindicate higher scalability and lower energy efficiency.

Service providers are expected to play an increasingly central role in the mobile market and their relationship with the traditional mobile network operators (MNOs) is starting to change. The dilemma faced by over-the-top service-providers (OTTs) is now whether to enter into a service level agreement with the MNOs (in the same spirit of mobile virtual network operator agreements) or to invest in deploying their own network infrastructure to serve their demand. The purpose of this paper is to study the factors shaping the agreements between OTTs and MNOs and how these factors impact network planning decisions. To this end, we build a synthetic model of cellular network deployment that explores how traditional mobile operators and OTTs compete in deploying new infrastructure. Using our model in conjunction with real-world data, we find that service-driven networks are heavily influenced by regulatory decisions, and that cost structures and demand characteristics play non-marginal roles in the definition of service-driven networks.

Next generation mobile networks will rely ever more heavily on resource sharing. In this paper, we study the sharing of radio access network and spectrum among mobile operators. We assess the impact of sharing these two types of resources on the performance of spatially distributed mobile networks. We apply stochastic geometry to observe the combined effect of, for example, the level of spatial clustering among the deployed base stations, the shared network size, or the coordination in shared spectrum use on network coverage and expected user data rate. We uncover some complex effects of mobile network resource sharing, which involve nonlinearly scaling gains and performance tradeoffs related to the sharing scenario or the spatial clustering level.

In this paper we present an approach towards empowering online telecommunications engineering education by enabling hands-on remote experimentation over Trinity College Dublin's wireless testbed. Moreover, in order to offer a flexible testbed, capable of fulfilling the different and particular requirements of experimenters, we have created a framework that allows the virtualization of our testbed resources to create experimentation units to be used by remote experimenters/learners. Furthermore, we present the FORGEBox framework that offers an environment and resources to create online material capable to access the virtualized and physical testbed resources for incorporating experimentation into HTML-based online educational material.

Cognitive Radios (CRs) and Software Defined Radios (SDRs) have ubiquitous applications ranging from handheld to base station devices. In order to meet the computational requirements of such radios, computing heterogeneity, the mixed usage of General Purpose Processors (GPPs), Digital Signal Processors (DSPs), and Field-Programmable Gate Arrays (FPGAs), is attractive. Developing SDR and CR applications already requires a diverse set of skills, and computing heterogeneity further complicates the process. This paper presents a developmental workflow used successfully by the authors for SDR and CR application running on a platform combining DSP and GPP based processors. The paper discusses tools used to set up the platform, create compilation environment, develop code for GPP/DSP communication, integrate the DSP into GNU Radio, and use the environment to develop SDR/CR applications. It presents a case study showing how computing heterogeneity can be used to address diverse application needs.