In this work, we consider a random access Internet of Things IoT wireless network assisted by two aggregators collecting information from two disjoint groups of sensors. The nodes and the aggregators are transmitting in a random access manner under slotted time, the aggregators perform network-level cooperation for the data collection. The aggregators are equipped with queues to store data packets that are transmitted by the network nodes and relaying them to the destination node. We characterize the throughput performance of the IoT network and we obtain the stability conditions for the queues at the aggregators. We apply the theory of boundary value problems to analyze the delay performance. Our results show that the presence of the aggregators provides significant gains in the IoT network performance, in addition, we provide useful insights regarding the scalability of the IoT network.

In this work, we consider a relay assisted adaptive queue-aware cooperative random access wireless network with multipacket (MPR) reception capabilities. The network consists of N sources transmitting packets to a common destination node with the aid of two relay nodes equipped with queues. The relays assist the sources by forwarding the packets that failed to reach the destination, by using a queue-based transmission control mechanism. Moreover, the relays have also their own traffic. We investigate the stability conditions and the throughput performance of the network for the full MPR channel model. Moreover, we derive expressions for the average queueing delay with the aid of the theory of boundary value problems for the asymmetric two-user network. We evaluate numerically the presented theoretical analysis.

In this work, we consider an adaptive two-user random access scheme with multipacket reception (MPR) capabilities. A user adapts its transmission characteristics based on the status of the other user. The users have external random traffic stored in their queues. We derive the stable throughput region of the system and the convexity conditions of this region. We derive analytically the queueing delay by formulating and solving a Riemann-Hilbert boundary value problem. Finally, we evaluate numerically the presented theoretical results.

In this paper, we consider a two-user and a three-user slotted ALOHA network with multi-packet reception (MPR) capabilities and a queue-aware transmission control. In this setting, the nodes can adapt their transmission probabilities and their transmission parameters based on the status of the other nodes. Each user has external bursty arrivals that are stored in their infinite capacity queues. We focus on the fundamental problem of characterizing the stable throughput region, as well as of investigating the queueing delay. For the two- and the three-user cases, we obtain the exact stability region, whereas in the former case, we also provide the conditions under which the stability region is a convex set. We perform a detailed mathematical analysis to study the queueing delay in the two-user case by formulating two boundary value problems, the solution of which provides the generating function of the joint stationary probability distribution of the queue size at user nodes. Furthermore, for the two-user symmetric case with MPR, we obtain a lower and an upper bound for the average delay without the need of solving a boundary value problem. In addition, we provide a closed form expression for the gap between the lower and the upper bound. The bounds as it is seen in the numerical results appear to be tight. Explicit expressions for the average delay are obtained for the symmetrical model with capture effect. We also provide a closed form expression for the optimal transmission probability that minimizes the average delay in the symmetric capture case. Finally, we evaluate numerically the presented theoretical results.

Modeling wireless access and spectrum markets is challenging due to a plethora of technological and economic aspects that affect their performance. This work develops a modeling framework for analysing such markets using network economics, game theory, and queueing networks. The framework models the service selection of users as well as the competition and coalition among providers. It also develops tools and algorithms to analytically compute the Nash equilibriums (NEs) under the presence of discontinuities in the derivatives of the utility functions of providers. The analysis of different market scenarios reveals various interesting trends in the offered prices, market share, and revenue of providers depending on the user utility function, traffic demand, and mobility pattern. It also demonstrates the role of the quality of service (QoS) in the user utility function in reducing the intensity of competition and allowing for higher prices and revenue. However, the analysis of large-scale markets exhibits a high computational complexity. To improve the computational efficiency, we developed a network aggregation methodology based on the theorem of Norton. This aggregation allows the construction of equivalent networks for a specific region of interest, omitting the details of the entire networks. We demonstrate the aggregation algorithm in the context of capacity planning.

In this paper, we analyse a priority retrial queue with repeated inhomogeneous vacations. Two types of customers arrive at the system and if they find the server unavailable, the first type join an ordinary queue, while the second have to reattempt after a random period. The server departs for a vacation when the ordinary queue is empty upon a service completion. Under this scheme, when a vacation period expires, the server wakes up. If the priority queue is non-empty, it starts serving it exhaustively. Otherwise, it remains awake for a limited time period, waiting for a possible request. If no customers arrive during this period, it goes for another vacation with a different probability distribution from the previous one. The theoretical model has application to the power-saving mechanism in wireless communication systems in which the sleep duration of the device corresponds to the vacation of the server and the listening duration to the time-limited idle period. Various system performance and energy metrics are derived. Moreover, we compare the proposed policy with other vacation schemes. Optimization problems are formulated for finding best values for the critical parameters of the model which maximize the economy of energy achieved in the power save mode, subject to performance constraints.