Nanotechnology is enabling the development of miniature devices able to perform simple tasks at the nanoscale. The interconnection of such nano-devices with traditional wireless networks and ultimately the Internet enables a new networking paradigm known as the Internet of Nano-Things (IoNT). Despite their promising applications, nano-devices have constrained power, energy, and computation capabilities along with very limited memory on board, which may only be able to hold one packet at once and, thus, requires packets to be delivered before certain hard deadlines. Toward this goal, a fully-distributed computation-light provably-correct scheduling/MAC protocol is introduced for bufferless nano-devices, which can maximize the network throughput, while achieving perpetual operation. More specifically, the proposed scheduling algorithm allows every nano-device to make optimal transmission decisions locally based on its incoming traffic rate, virtual debts, and channel sensing results. It is proven that the proposed algorithm is timely throughput optimal in the sense that it can guarantee reliable data delivery before deadlines as long as the incoming traffic rates are within the derived maximum network capacity region. This feature not only can lead to high network throughput for the IoNT, but also guarantees that the memory of each device is empty before the next packet arrives, thus addressing the fundamental challenge imposed by the extremely limited memory of nano-devices. In addition, the optimal deadline is derived, which guarantees that all the nano-devices can achieve perpetual communications by jointly considering the energy consumption of communications over the terahertz channel and energy harvesting based on piezoelectric nano-generators.

Cognitive radio sensor networks (CRSNs) are event-based systems such that sensor nodes detect events and the event readings of the sensors are collaboratively conveyed in a multi-hop manner through vacant channels from event regions to a sink. Hence, the event-to-sink communication and the dynamic radio environment require a coordination scheme in CRSNs. In this paper, we propose a spectrum-aware clustering protocol to address the event-to-sink communication coordination issue in mobile CRSNs. Our clustering scheme consists of two phases. The first phase is the determination of nodes eligible for clustering, and the second phase is to form clusters among those nodes according to vacant spectrum bands. Clusters are temporary and they are not preserved after the end of events. Furthermore, we find average re-clustering probability, expected cluster coverage area, and find maximum event generation frequency for energy-efficient operation of our protocol. We study performance of our protocol in terms of control and data packet exchange, time steps required for clustering, connectivity of clusters, energy consumed for clustering, and re-clustering ratio due to the mobility. Performance comparison simulations show that our algorithm has better performance in terms of connectivity and energy consumption.

Multi-stream carrier aggregation (MSCA) has been recently proposed as a mechanism to increase the amount of bandwidth available to users for heterogeneous networks (HetNets) in 5G wireless systems. Previous studies have focused only on maximizing the network capacity and fairness, without considering the energy efficiency of the MSCA. In this paper, the use of MSCA to minimize the energy consumption in a multi-layer HetNet is studied. The convexity of the energy minimization problem is examined, leading to the need of a quasi-convex relaxation. With this approximation, an algorithm bisection method for energy minimization is designed to solve the energy minimization problem and obtains an optimum cell-association policy. Since the operators are generally interested in a balance between the energy minimization and capacity maximization, such multi-objective optimization is needed, and we studied it in this paper. The two aforementioned conflicting objectives can be jointly analyzed and solved through scalarization, even though the energy minimization has a quasi-convex objective function, and not a convex one. Performance evaluation is provided to identify the achievable energy savings of our proposed algorithm and to characterize the tradeoffs between the energy minimization and capacity maximization in a multi-layer HetNet in 5G systems that support MSCA.

Cellular network technologies have traditionally evolved to meet the ever-increasing need for capacity and coverage. Particularly, there has been a significant focus on exploiting the use of small cells and heterogeneous networks (HetNets). In the latter, the economic and environmental impact of the energy consumption is a key concern. Although much research has been done to address the energy consumption in HetNets, existing approaches have failed to capture the key factors affecting it. In this paper, the energy consumption in HetNets is analyzed with a focus on their multi-layer nature and the dependence of the energy consumption on the spatio-temporal traffic demands and the internal base station hardware components. The problem of minimizing the energy consumption is then studied and characterized in terms of a 0-1 Knapsack-like problem. Due to the differences with the classical 0-1 Knapsack problem, an efficient algorithm is introduced to minimize the energy consumption by adjusting the cell-association and the base stations on-off policies. Such algorithm is shown to be applicable to the twoand m-layer HetNet cases. Performance evaluation is provided to identify the achievable energy savings of our algorithm and its effect on the energy consumption, activity, and load across multiple layers.