Internet of Things (IoT) networks have gained significant attention in recent years as it has the potential to transform various industries. A key concern, however, is that sensing devices have limited operational lifetime. Specifically, they have finite energy, which affects the amount of data they are able to collect and upload. One solution is to power these devices wirelessly, where devices harvest energy from Radio Frequency (RF) signals from transmitters such as a Hybrid Access Point (HAP). A key issue, however, is that energy delivery and data transmissions may be conducted on the same frequency band. This means a HAP has to determine a transmission schedule for energy or/and data transmissions. Another issue is that the channel gain of devices varies over time, which affects the amount of harvested energy and transmitted data. In this respect, a challenging issue is that an HAP has causal channel state information only, meaning it is not aware of energy arrivals or channel gain
In Internet of Things (IoT) networks, Unmanned Aerial Vehicles (UAVs) play a critical role as mobile nodes that can be deployed to carry out data collection and computation. In this respect, this paper considers an operator that deploys UAVs to satisfy requests from IoT applications that require Virtual Network Functions (VNFs) that may communicate with one another to be executed at different geographical locations. To this end, this paper formulates a novel Mixed Integer Linear Program (MILP) to determine the optimal assignments of UAVs and VNFs over a planning horizon that maximizes a given performance metric, e.g., revenue. It also outlines a heuristic method named MPopLoc that chooses requests according to popular requested locations and traveling cost of UAVs. The results show that MPopLoc achieved approximately 95.14% of the optimal result.
This paper considers routing and link scheduling in a two-tier wireless Internet of Things (IoT) network. The first tier consists of routers that communicate via active Radio Frequency (RF) transmissions. The second tier consists of passive tags that backscatter ambient RF signals from routers. Our objective is to maximize the network throughput at both tiers. To this end, we outline a Mixed Integer Linear Program (MILP) that jointly optimizes the active time of RF links and backscatter links, and traffic over links. We also present a heuristic called Algorithm-Transmission-Set-Generator (ALGO-TSG) to compute transmission sets. Moreover, we also outline a heuristic called Centralized Max-Flow (CMF) to maximize network throughput by jointly considering routing and link scheduling. The results show that (i) the network throughput achieved by ALGO-TSG at both tiers is 29.80% higher as compared to the case without backscattering, and, (ii) the throughput of CMF is on average 21.36% lower t
This paper considers energy delivery by a Hybrid Access Point (HAP) to one or more Radio Frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal Channel State Information (CSI), and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a Mixed Integer Linear Program (MILP) coupled with a Sample Average Approximation (SAA) method. The second is a Model Predictive Control (MPC) solution that utilizes Gaussian Mixture Model (GMM) and a so called backoff that is used to tighten probabilistic constraints. The results show that the performance of the MPC based solution is within 8% of the optimal solution with a probability of 90.8%.
Many applications operating in the Internet of Things (IoT) require timely and fair data collection from devices. This has motivated research into a new metric called Age of Information (AoI). This paper contributes to this effort by proposing to minimize the maximum average AoI (min-max AoI) in a multi-hop IoT network comprising of solar-powered Power Beacons (PBs). It outlines a Mixed Integer Linear Program (MILP) that jointly optimizes: (i) the beamforming vector used by PBs to charge devices, and (ii) routing, which determines how samples from devices are forwarded to a sink node, and (iii) the sampling time of sources. It also presents two protocols: Centralized Linear Relaxation (CLR) and Distributed Path Selection (DPS), respectively. CLR is run by the sink to determine the transmit power of PBs and the path of each source using two Linear Programs (LPs). On the other hand, DPS is a distributed approach whereby PBs and sources make their own decisions using local information. Ou