A two-stage game theoretical approach for interference mitigation in Body-to-Body Networks

International audienceIn this paper, we identify and exploit opportunities for cooperation between a group of mobile Wireless Body Area Networks (WBANs), forming a Body-to-Body Network (BBN), through inter-body interference detection and subsequent mitigation. Thus, we consider a dynamic system composed of several BBNs and we analyze the joint mutual and cross-technology interference problem due to the utilization of a limited number of channels by different transmission technologies (i.e., ZigBee and WiFi) sharing the same radio spectrum. To this end, we propose a game theoretical approach to address the problem of Socially-aware Interference Mitigation (SIM) in BBNs, where WBANs are " social " and interact with each other. Our approach considers a two-stage channel allocation scheme: a BBN-stage for inter-WBANs' communications and a WBAN-stage for intra-WBAN communications. We demonstrate that the proposed BBN-stage and WBAN-stage games admit exact potential functions, and we develop a Best-Response (BR-SIM) algorithm that converges to Nash equilibrium points. A second algorithm, named Sub-Optimal Randomized Trials (SORT-SIM), is then proposed and compared to BR-SIM in terms of efficiency and computation time. We further compare the BR-SIM and SORT-SIM algorithms to two power control algorithms in terms of signal-to-interference ratio and aggregate interference, and show that they outperform the power control schemes in several cases. Numerical results, obtained in several realistic mobile scenarios, show that the proposed schemes are indeed efficient in optimizing the channel allocation in medium-to-large-scale BBNs

Socially-Aware Interference Mitigation Game in Body-to-Body Networks

International audience—Wireless wearable devices have recently gained increasing attention from industry in fields such as health, fitness, and entertainment. In this paper, we consider a dynamic system composed of several Body-to-Body Networks (BBNs) based on wearable technology, and we analyze the joint mutual and cross-technology interference problem due to the utilization of a limited number of channels by different transmission technologies (i.e., ZigBee and WiFi) sharing the same radio spectrum. To this end, we propose a game theoretical approach to address the problem of Socially-aware Interference Mitigation (SIM) in BBNs. Our approach considers a two-stage channel allocation scheme: a BBN-stage for inter-WBANs' communications and a WBAN-stage for intra-WBAN communications, involving mutual and cross-technology considerations at each stage. We develop best response algorithms that converge fast to Nash equilibrium points. Simulation results show the efficiency of SIM game in optimizing the channel allocation in BBNs. Index Terms—Body-to-Body Networks, 2.4 GHz ISM band, Cross-Technology Interference, Channel Allocation, Game Theory, Nash Equilibrium

A dataset for mobile edge computing network topologies

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. Specifically, based on the experience we have gathered with our experiments Xiang et al. [1], we provide data related to 3 randomly generated topologies, with increasing network size (from 25 to 100 nodes). Moreover, we propose a MEC topology generated from OpenCellID [2] real data and concerning the Base Stations’ location of 234 LTE cells owned by a mobile operator (Vodafone) in the center of Milan. We also provide realistic reference parameters (link bandwidth, computation and storage capacity, offered traffic), derived from real services provided by MEC in the deployment of 5G networks

Resource Calendaring for Mobile Edge Computing in 5G Networks

Mobile Edge Computing (MEC) is a key technology for the deployment of next generation (5G and beyond) mobile networks, specifically for reducing the latency experienced by mobile users which require ultra-low latency, high bandwidth, as well as real-time access to the radio network. In this paper, we propose an optimization framework that considers several key aspects of the resource allocation problem for MEC, by carefully modeling and optimizing the allocation of network resources including computation and storage capacity available on network nodes as well as link capacity. Specifically, both an exact optimization model and an effective heuristic are provided, jointly optimizing (1) the connections admission decision (2) their scheduling, also called calendaring (3) and routing as well as (4) the decision of which nodes will serve such connections and (5) the amount of processing and storage capacity reserved on the chosen nodes. Numerical experiments are conducted in several real-size network scenarios, which demonstrate that the heuristic performs close to the optimum in all the considered network scenarios, while exhibiting a low computing time

A reliable design of Wireless Body Area Networks

International audienceIn this paper, we propose a reliable topology design and provisioning approach for Wireless Body Area Networks (named RTDP-WBAN) that takes into account the mobility of the patient while guaranteeing a reliable data delivery required to support healthcare applications' needs. To do so, we first propose a 3D coordinate system able to calculate the coordinates of relay-sensor nodes in different body postures and movements. This system uses a 3D-model of a standard human body and a specific set of node positions with stable communication links, forming a virtual backbone. Next, we investigate the optimal relay nodes positioning jointly with the reliable and cost-effective data routing for different body postures and movements. Therefore, we use an Integer Linear Programming (ILP) model, that is able to find the optimal number and locations of relay nodes and calculate the optimal data routing from sensors and relays towards the sink, minimizing both the network setup cost and the energy consumption. We solve the model in dynamic WBAN (Stand, Sit and Walk) scenarios, and compare its performance to other relaying approaches. Experiment results showed that our realistic and dynamic WBAN design approach significantly improves results obtained in the literature, in terms of reliability, energy-consumption and number of relays deployed on the body

Semi-distributed Traffic Engineering for Elastic Flows in Software Defined Networks

Software-Defined Networking (SDN) is becoming the reference paradigm to provide advanced Traffic Engineering (TE) solutions for future networks. However, taking all TE decisions at the controller, in a centralized fashion, may require long delays to react to network changes. With the most recent advancements in SDN programmability some decisions can (and should indeed) be offloaded to switches. In this paper we present a model to route elastic demands in a general network topology adopting a semi-distributed approach of the control plane to deal with path congestion. Specifically, we envision a Stackelberg approach where the SDN controller takes the role of Leader, choosing the most appropriate subset of routing paths for the selfish users (network switches), which behave as Followers, making local routing decisions based on path congestion. To overcome the complexity of the problem and meet the time requirements of real-life settings, we propose effective heuristic procedures which take into accurate account traffic dynamics, considering a stochastic scenario where both the number and size of flows change over time. We test our framework with a custom-developed simulator in different network topologies and instance sizes. Numerical results show how our model and heuristics achieve the desired balance between making global decisions and reacting rapidly to congestion events

Socially-Aware Network Design Games

In many scenarios network design is not enforced by a central authority, but arises from the interactions of several self-interested agents. This is the case of the Internet, where connectivity is due to Autonomous Systems' choices, but also of overlay networks, where each user client can decide the set of connections to establish. Recent works have used game theory, and in particular the concept of Nash Equilibrium, to characterize stable networks created by a set of selfish agents. The majority of these works assume that users are completely non-cooperative, leading, in most cases, to inefficient equilibria. To improve efficiency, in this paper we propose two novel socially-aware network design games. In the first game we incorporate a socially-aware component in the users' utility functions, while in the second game we use additionally a Stackelberg (leader-follower) approach, where a leader (e.g., the network administrator) architects the desired network buying an appropriate subset of network's links, driving in this way the users to overall efficient Nash equilibria. We provide bounds on the Price of Anarchy and other efficiency measures, and study the performance of the proposed schemes in several network scenarios, including realistic topologies where players build an overlay on top of real Internet Service Provider networks. Numerical results demonstrate that (1) introducing some incentives to make users more socially-aware is an effective solution to achieve stable and efficient networks in a distributed way, and (2) the proposed Stackelberg approach permits to achieve dramatic performance improvements, designing almost always the socially optimal network

Optimal Control of Storage under Time Varying Electricity Prices

International audienceEnd users equipped with storage may exploit time variations in electricity prices to earn profit by doing energy arbitrage, i.e., buying energy when it is cheap and selling it when it is expensive. We propose an algorithm to find an optimal solution of the energy arbitrage problem under given time varying electricity prices. Our algorithm is based on discretization of optimal Lagrange multipliers of a convex problem and has a structure in which the optimal control decisions are independent of past or future prices beyond a certain time horizon. The proposed algorithm has a run time complexity of O(N 2) in the worst case, where N denotes the time horizon. To show the efficacy of the proposed algorithm, we compare its run-time performance with other algorithms used in MATLAB's constrained optimization solvers. Our algorithm is found to be at least ten times faster, and hence has the potential to be used for in real-time. Using the proposed algorithm, we also evaluate the benefits of doing energy arbitrage over an extended period of time for which price signals are available from some ISO's in USA and Europe