Thanks to their arising abilities to influence the human lifestyle, along with reducing the healthcare systems’ cost, wireless body area networks (WBANs) still form a strongly growing research field. Recent advances focus on the opportunities of coexistence and communication between a group of WBANs, that will forward the sensing data, using persons as network relays, until reaching a remote analysis server or cloud servers via the Internet, forming thus a body-to-body network (BBN). Such new-style networks support a range of innovative and promising applications, including ubiquitous healthcare (U-health), interactive games, and military, to cite a few. In this paper, we first present the evolution of the single WBAN concept to the cooperative network of multiple WBANs, giving rise to the BBN concept. A synopsis of the WBAN and BBN respective standards and applications is given, and the emerging BBN challenges are highlighted. Then, we present and discuss the existing WBAN proposals, especially the candidate WBAN protocols that could be adapted and used in BBNs, focusing on four intrinsically related axes of great importance for BBN design: energy efficiency, mobility prediction, quality of service (QoS) and security. Further BBN open issues are also investigated, namely, the wireless propagation between humans carrying wearable devices, the interference, storage and privacy issues as well as the heterogeneity of BBN devices and traffic.

Mobile Edge Computing (MEC) is vital to support the numerous, future applications that are envisioned in the 5G and beyond mobile networks. Since computation capabilities are available at the edge of the network, applications that need ultra low-latency, high bandwidth and reliability can be deployed more easily. This opens up the possibility of developing smart resource allocation approaches that can exploit the MEC infrastructure in an optimized way and, at the same time, fulfill the requirements of applications. However, up to date, the progress of research in this area is limited by the unavailability of publicly available true MEC topologies that could be used to run extensive experiments and to compare the performance on different solutions concerning planning, scheduling, routing etc. For this reason, we decided to infer and make publicly available several synthetic MEC topologies and scenarios.

Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. Our paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

In recent years, Body-to-Body Networks (BBNs) have gained momentum as a means to monitor people behavior and simplify their interaction with the surrounding environment; thus representing a key element of the Internet of Things (IoT) networking paradigm. Within BBNs, several transmission technologies sharing the same unlicensed band (namely the ISM band) coexist, increasing dramatically the level of interference, which in turn negatively affects the network performance. In this paper, we consider an IoT system composed of several BBNs and we analyze the Cross Technology Interference (CTI) problem caused by the utilization of different transmission technologies that share the same radio spectrum. We formulate an optimization model considering both the Mutual and Cross Technology Interference in order to mitigate the overall level of interference within the IoT system, taking explicitly into account the node mobility. We further develop two heuristic approaches to solve efficiently the interference mitigation problem in large scale network scenarios. Numerical results show that the proposed heuristics represent two efficient and practical alternatives to the optimal solution for solving the CTI mitigation problem in large scale IoT scenarios.

We investigate the distributed spectrum management problem in opportunistic TV white space (TVWS) systems using a game theoretical approach that accounts for adjacent-channel interference and spatial reuse. TV band devices (TVBDs) compete to access idle TV channels and select channel “blocks” that optimize an objective function. This function provides a tradeoff between the achieved rate and a cost factor that depends on the interference between TVBDs. We consider practical cases where contiguous or noncontiguous channels can be accessed by TVBDs, imposing realistic constraints on the maximum frequency span between the aggregated/bonded channels. We show that under general conditions, the proposed TVWS management games admit a potential function. Accordingly, a “best response” strategy allows us to determine the spectrum assignment of all players. This algorithm is shown to converge in a few iterations to a Nash equilibrium. Furthermore, we propose an effective algorithm based on Imitation dynamics, where a TVBD probabilistically imitates successful selection strategies of other TVBDs in order to improve its objective function. Numerical results show that our game theoretical framework provides a very effective tradeoff (close to optimal, centralized spectrum allocations) between efficient TV spectrum use and reduction of interference between TVBDs.