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Sensor networks are characterized by limited battery supplies. Due to this feature, communication protocols specifically designed for these networks should be aimed a t minimizing energy consumption. To this purpose, the sensor's capability of transmitting with different power levels can be exploited. With this in mind, in this paper an integrated MAC/Routing protocol, called MACRO, which exploits the capability of sensor devices to tune their transmission power is introduced. The proposed protocol requires that each node only knows its own coordinates and the coordinates of the destination, but does not require any exchange of location information. In order to select the next relay node, a competition is triggered at each bop, so that the most energy efficient r e b y node is chosen. This is achieved through maximization of a newly introduced parameter, called weighted progress factor, which represents the progress towards the destination per unit of transmitted power. To this aim, an analytical framework which guarantees that MACRO performs the best choice is derived. MACRO performance is evaluated through n s -2 simulation and compared to other relevant routing schemes. Performance results show that the proposed protocol outperforms other solutions in terms of energy efficiency and boosts data aggregation.

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared to the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner's profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

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