Connected and Autonomous Vehicles (CAVs) are expected to become a reality on roads in the near future bringing significant social, economic, and environmental benefits. Cooperation and coordination among CAVs will be enabled through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Each vehicle knows its current location and in many cases will have to communicate this information to its associated Roadside Unit (RSU). With the proliferation of CAVs, the RSU is expected to receive and process a large amount of feedback information from its assigned CAVs. Thus, an event-triggered communication scheme is proposed instead of conventional approaches where communication takes place in a periodic manner. Further, the effect of age of information on the resulting accuracy of the vehicle tracking error is taken into account by considering the message queue wait time at the RSU. A family of optimization problems is proposed, used to determine the optimal accuracy threshold for the event-triggered algorithm, so as to minimize the effect of the tracking error caused by the queue wait time.

Due to the fact that Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols are extensively used in commercial wireless networks, the analysis of this family of medium access control protocols has been a topic of great interest for the wireless networking community. In this paper, we present a mean field analysis for the throughput of a slotted CSMA/CA scheme in single-hop networks. The concept of mean field approximation originates in statistical mechanics and it is a widely used approximation method employed to address systems of many interacting particles that cannot be solved exactly. The idea is to simplify the treatment of a many-body system by describing the interaction of one particle with the other interacting particles by an average potential, the so-called mean field. In our analysis, the nodes of a wireless network constitute a set of interacting particles; the interaction of a certain node with the other nodes in the network is reduced to an averaged channel condition. We estimate the amount of busy slots and the network throughput with respect to the offered traffic. Our analysis is of a recursive form, in which at each step the amount of traffic is increased by a specific amount. Also, our model takes into accounts retransmissions of collided packets.

Designing distributed protocols with limited requirements for exchange of feedback information between the nodes is a key element in the development of cognitive and self-organizing ad hoc networks. We argue that the Minority Game (MG) and its variations can constitute useful tools towards this direction. In particular, the MG formulation is well suited for addressing a variety of resource allocation problems in self-organizing wireless networks. In this paper we illustrate the employment of MGs by presenting a paradigm of an MG-based model for dynamic channel allocation.

Like physical systems can exist in different phases, the concept of different phases (or states) can be also attributed to other types of systems, such as wireless networks. In this paper we employ the notion of phase diagrams to characterize a wireless network by means of a reduced number of simulation points. By reducing the amount of simulations - compared to fine-grained simulation-based analyses - we decrease the accuracy of the result, however, not in an arbitrary way. The idea is to derive the so-called phase diagram that fully characterizes the system with respect to the points of state changes. Typically, the different states of a system correspond to distinguishable macroscopic properties, therefore the phase transition points are in a qualitative sense points of particular significance for a system under study. We show how phase diagrams can be estimated from a limited set of simulation points by means of Support Vector Machines.

During the last decade there has been a great interest in applying game theory to several problems of wireless networking. The main focus of the existing research so far has been to ensure and prove that the game shall eventually reach a Nash equilibrium solution. In this paper our aim is to question whether a Nash equilibrium must be used as an allpurpose treatment without taking into consideration additional factors. Specifically, by exploiting the concept of equilibrium phase transitions from statistical mechanics we argue that in the case of a dynamic wireless environment - where the system might be constantly drifting from the Nash equilibrium due to external factors - the Nash equilibrium might be a particularly unstable outcome in terms of the overall performance and hence not a desirable outcome.

In this paper we show how to quantify dependency between the node distributions of wireless networks and the underlying population densities. Furthermore, we show that a quantitative analysis of this relation can be beneficial for understanding and generating realistic network models. Towards this direction we argue that spatial statistics is an appropriate method for this purpose. As a case study we analyze the correlations of GSM-900 and UMTS base stations, with the underlying population densities of Germany. We find that there is a significant statistical similarity between the locations of GSM-900 base stations and population, due to the immense penetration of the network, and a less tight relation in the case of UMTS network. We consider the problem of covering a given population pattern and use the concept of fractal dimension to show how the number of required base stations changes when their maximum coverage range decreases, according to the population pattern.