Virtualization of devices operating in Internet of Things (IoTs) networks allows them to host functions or tasks from different users; these devices can thus execute multiple on demand sensing and data processing services concurrently. Devices, however, have limited energy and operational lifetime. To this end, this paper considers supporting Virtual Network Functions (VNFs) in a Radio Frequency (RF)-charging network with a Hybrid Access Point (HAP). Our aim is to minimize the energy used by the HAP to power devices in order to support deployed VNFs. It outlines a Mixed-Integer Linear Program (MILP) to jointly optimize VNFs placement, routing and link scheduling, and also the HAP’s charging duration. Further, it proposes a heuristic, called Decoupled Greedy Algorithm (DGA), that first assigns VNFs onto devices with the highest energy level before optimizing the HAP’s charging, routing and link schedule. Our results show that DGA has a probability higher than 0.95 to successfully se
This letter considers the problem of embedding the maximum number of Virtual Network Requests (VNRs) in an Internet of Things (IoT) network with Power Beacons (PBs). It presents a Mixed Integer Linear Program (MILP) and a heuristic to determine the transmit power allocation of PBs, mappings of virtual nodes and edges onto devices and links, and a link schedule to provision bandwidth to support traffic on virtual edges. Our results show the proposed heuristic attains 90.31% of MILP’s performance.
An Internet of Things (IoT) network or a Wireless Sensor Network (WSN) consists of sensor devices and one or more sinks. In general, these sensor devices monitor or collect samples of targets, e.g., vehicles, or their surrounding environment; e.g., the temperature of a room. They then upload their collected samples to a sink for further analysis. A critical issue when operating sensor devices is their energy limitation. To this end, researchers have considered charging sensor devices using a variety of sources, include solar, wind, and Radio Frequency (RF). Consequently, sensor devices with energy harvesting capability are able to operate perpetually assuming they do not spend more than their harvested energy. Apart from energy harvesting technologies, researchers have recently exploited the negligible energy cost afforded by backscatter communications. Consequently, it allows sensor devices to use more of their harvested energy to collect samples that otherwise would be used for acti
This paper studies a novel wireless powered Internet of Things (IoT) network that consists of (a) a Hybrid Access Point (HAP) that charges devices and also helps facilitate backscattering transmissions, (b) devices that use active Radio Frequency (RF) and backscattering transmissions, and (c) a mobile data collector. Our aim is to maximize the amount of data received by the HAP and data collector. The main problem is to determine the charging duration of the HAP and link activation schedule of devices. We formulate a novel Mixed Integer Linear Program (MILP) and also propose a heuristic algorithm named Reduced-Set Linear Program Approximation (RS-LPA). The results show that (i) throughput increases with the number of backscatter transmission sets, (ii) smaller amount of data is uploaded to the mobile collector when sampling cost is low, and (iii) the throughput of RS-LPA is on average 10.55% lower than MILP.
Devices in Internet of Things (IoT) networks are required to execute tasks such as sensing, computation and communication. These devices, however, have energy limitation, which in turn bounds the number of tasks they can execute and their tasks execution time. To this end, this paper considers energy delivery, tasks assignment and execution in a Radio Frequency (RF) IoT network with a Hybrid Access Point (HAP) and RF-powered devices. We outline a novel Mixed-Integer Linear Program (MILP) to assign tasks to devices, and also to optimize the HAP’s charging duration. We also propose a heuristic algorithm called Energy Saving Task Assignment (ESTA), and two Model Predictive Control (MPC) approaches called MPC-MILP and MPC-ESTA; both of which use channel estimates over a given window or time horizon. Our results show that MPC-MILP and MPC-ESTA respectively consume up to 74.27% and 63.71% less energy as compared to competing approaches. Moreover, MPC-MILP with a small window has better per