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Future Internet of Things (IoTs) networks will consist of a Hybrid Access Point (HAP) that powers devices via Radio Frequency (RF) signals. These devices harvest RF-energy and are tasked with monitoring target(s) such as vehicles at key locations. In order to maximize their expected targets monitoring time, this paper considers the problem of optimizing the HAP's transmit power, and the active time of devices. We formulate a Stochastic Program (SP) and solve it using the Sample Average Approximation method. We also present a sequential Monte-Carlo based reinforcement learning method or SMC-L to learn sensor activation time. Our results show that (i) the confidence interval upper bound of the solution derived by the SP is affected by the number of samples that represent targets appearance time, (ii) increasing the sensing range of devices affects the total expected target monitoring time, (iii) the learning speed of SMC-L is affected by its exploration rate, and (iv) SMC-L does not utilize the energy of devices fully under poor channel conditions.

The Access Points (APs) in a Wireless Local Area Network (WLAN) must be assigned one or more channels to meet traffic demands from users. To date, prior works on channel assignment assume APs have a fixed traffic demand, meaning they do not consider or adapt to spatio-temporal changes in traffic demands. To this end, we leverage Deep Reinforcement Learning (DRL), where we equip APs with a DRL-based channel assignment solution, to maximize the average number of slots in which an AP has sufficient bandwidth to meet user demands. Our APs learn from their historical traffic loads and assign themselves partially overlapping channels with minimal interference. Simulation results show that our DRL solution leads to APs satisfying 60% more user demands as compared to fixed and greedy channel bonding algorithms.

Future Internet of Things (IoTs) networks will have Radio Frequency (RF)-energy harvesting devices powered by a Power Beacon (PB). These devices will be tasked with collection and transmission of data over multiple hops to a fusion center. In this paper, our aim is to maximize the minimum data gathering rate of devices acting as sources. We formulate a Mixed Integer Linear Program (MILP) to compute the optimal max-min rate of sources by deciding for each slot the devices to be charged, a PB's transmission power, the amount of data routed over each link, and the active time of transmitting links. We also present a novel distributed protocol called Distributed Charging and Fairness Gathering (D-CFG) that is used by IoT devices to request charging from a PB and to ensure they forward data in a fair manner. Our results show that D-CFG achieves 57.1% of the theoretical max-min rate computed by the MILP.

In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal.

We consider a Hybrid Access Point (HAP) that is equipped with a Successive Interference Cancellation (SIC) radio, and it is responsible for charging Radio Frequency (RF) energy harvesting devices. In addition, the HAP assigns one or more uplinks data transmission slots to these devices. In this work, we address an important and challenging problem: determine an uplink transmission schedule that maximizes the throughput at the HAP. Unlike past works, we do not assume the HAP has perfect Channel State Information (CSI). We outline a discrete optimization approach that allows the HAP to learn the best transmission schedule. We also present a heuristic approach that allows the HAP to iteratively construct a schedule. Our results show that the HAP is able to learn a transmission schedule that has higher throughput than Time Division Multiple Access (TDMA) where each slot has one device.

Energy harvesting wireless sensor networks (EH-WSNs) form the foundation of Internet of Things (IoTs) systems. Energy harvesting nodes can be deployed strategically to monitor one or more targets such as a valuable asset. However, as these nodes rely on ambient energy sources such as solar, they experience random energy arrivals. Consequently, they may exhaust their harvested energy while monitoring a target. Therefore, network operators require a robust solution that ensures all targets are monitored continuously over some time period with a given probability. In this paper, we consider three novel robust coverage requirements; each must hold with probability ( $1-\epsilon$ ), where $\epsilon$ is the probability of failures. First, sensor nodes must not expend more than their total harvested energy over $T$ time slots. Second, the energy expenditure of each sensor node must not exceed the energy harvested in each slot . Third, the energy expenditure of sensor nodes must not exceed the energy accumulated up to the current slot. We formulate chance-constrained stochastic programs that incorporate these requirements and solve them using the sample average approximation method. We confirm via extensive simulation studies that our programs are capable of computing sensor nodes activation times that meet a given coverage failure probability.

This paper considers a novel problem in rechargeable wireless sensor networks (WSNs), given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an integer linear program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: greedy round node placement, target protection node placement, and energy efficient node placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution.

A key approach to improve the capacity of wireless mesh networks (WMNs) is to equip routers with multiple transmissions or receptions (MTR) capability. Thus, the resulting MTR WMN has significantly higher network capacity because routers can activate multiple links simultaneously. This, however, requires an MTR link scheduler that maximizes network capacity or equivalently, one that is capable of deriving a short schedule. Henceforth, we propose period controlled pseudo time-division multiple access (PCP-TDMA), a link scheduler that allows nodes to cooperatively reduce an initial link schedule or superframe over time in a distributed manner. Routers are able to adapt the superframe size iteratively using only local information to accommodate any topological changes. This means PCP-TDMA is particularly suited for use in large-scale MTR WMNs. We have evaluated PCP-TDMA in various network topologies, and compared it against ALGO-2, a centralized algorithm that uses global topological information to derive a schedule and thus serves as a benchmark. We also compare PCP-TDMA against two distributed approaches: JazzyMAC and ROMA. The results show that PCP-TDMA achieves similar performance with the centralized algorithm in all scenarios, and outperforms the distributed approaches significantly. Specifically, in a fully connected network, the resulting superframe length of PCP-TDMA is less than $1/3$ and $1/2$ of JazzyMAC and ROMA, respectively.

Several studies have demonstrated the benefits of using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks (WSNs). However, these benefits are dependent on the path taken by the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multihopping to the nearest RP. The fundamental problem then becomes computing a tour that visits all these RPs within a given delay bound. Identifying the optimal tour, however, is an NP-hard problem. To address this problem, a heuristic called weighted rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP. WRP is validated via extensive computer simulation, and our results demonstrate that WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption by 22% and increases network lifetime by 44%, as compared with existing algorithms.