Cognitive Connectivity Resilience in Multi-layer Remotely Deployed Mobile Internet of Things

Enabling the Internet of things in remote areas without traditional communication infrastructure requires a multi-layer network architecture. The devices in the overlay network are required to provide coverage to the underlay devices as well as to remain connected to other overlay devices. The coordination, planning, and design of such two-layer heterogeneous networks is an important problem to address. Moreover, the mobility of the nodes and their vulnerability to adversaries pose new challenges to the connectivity. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of the underlay devices or the unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Our results show that if sufficient overlay devices are available, the framework leads to a connected configuration that ensures a high coverage of the mobile underlay network. Moreover, the framework can actively reconfigure itself in the event of varying levels of device failure.Comment: To appear in IEEE Global Communications Conference (Globecom 2017

A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized $\alpha$-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.Comment: 6 pages, 4 figure