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This paper considers networks with a reconfigurable topology with so called 60 GHz dynamic links that can be activated or disabled over time. A fundamental problem is to jointly determine which 60 GHz dynamic links are active and the route chosen by source nodes over time. To this end, this paper outlines a hierarchical deep reinforcement learning solution that can be used to compute the optimal policy that determines for each time slot (i) active dynamic links, and (ii) the route used by each source–destination pair. The results show that the proposed approach results in a maximum average queue length that is 80% shorter than non-learning methods.

The switches in legacy networks perform both control and data forwarding operations. In contrast, in a software defined network (SDN), a controller is responsible for network control and each SDN-switch ( ${s}$ -switch) only forwards data packets. Advantageously, controllers can be upgraded easily to run state-of-the-art network configuration and management solutions. In this context, this paper addresses the novel problem of upgrading a legacy network into an SDN over multiple stages that span months or years. More specifically, it aims to minimize energy consumption by optimizing switch upgrades, controller placement, and traffic routing subject to an operator’s budget, traffic delay tolerance, and controller capacity. We formulate the problem as a mixed integer program (MIP), and develop a heuristic solution that ensures a single path is used between switches and up to two link-disjoint paths are used between an ${s}$ -switch and its controller. Our simulation results show that increasing an operator’s budget and the number of upgrade stages reduce the energy consumption of tested networks by 68.42%. Further, our heuristic solution yields energy saving that is within 5% away from the optimal value. In addition, deploying controllers at strategic locations saves more energy than placing them at arbitrary locations. Lastly, our heuristic solution guarantees the delay requirement of traffic demands and runs up to 307 times faster than a prior solution.

This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects, and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/discharge cycles by up to 47.41% (45.67)% when nodes have a leak (leak-free) battery. Between leak-free and leak battery scenarios, using the former produces up to 51.46% fewer cycles than the latter.

This paper considers the novel problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace legacy switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.

This paper considers data upload from multiple devices to a rotary-wing Unmanned Aerial Vehicle (UAV) equipped with a Successive Interference Cancellation (SIC) radio. The problem at hand is to optimize the UAV’s data collection points, and the set of transmitting devices at each point. We outline three contributions. The first is an Integer Linear Program (ILP), which can be used to compute the optimal trajectory and data transmission schedule. Second, we propose a novel heuristic called Iteratively Construct Link Schedule and Trajectory (ICLST) that includes a link set/schedule selection policy called Highest Sum-Rate Selection (HSRS). Third, we propose a novel learning-based protocol that enables a UAV to independently learn the optimal trajectory with the highest energy efficiency. Our results show that a SIC radio helps double the amount of data collected by the UAV. Placing devices at different heights helps the UAV collect more data. Moreover, ICLST with HSRS is capable of producing a schedule that is near optimal. Additionally, our learning protocol yields a schedule with the highest energy-efficiency.

We consider a Hybrid Access Point (HAP) that charges one or more energy harvesting devices via Radio Frequency (RF). These devices then transmit their data to the HAP. To date, prior works assume devices use Time Division Multiple Access (TDMA) for channel access, and these devices are able to transmit using any amounts of harvested energy. By contrast, we consider Dynamic Framed Slotted Aloha (DFSA) and devices can only transmit if they have sufficient energy. Moreover, nodes are not aware of each other's energy level, meaning the HAP and devices are unaware of the number of devices that is ready to transmit. In addition, we consider different non-linear energy conversion models. To this end, we propose a two-layer approach. At the first layer, the HAP adjusts its transmission power using a Sequential Monte Carlo (SMC) approach, and the frame size according to the Softmax function. At the second layer, devices use another Softmax function to learn the time slot that yields the highest reward for a given frame size. Our results show that throughput is affected by the minimum energy required for each transmission, the temperature of the Softmax function, transmission power used for charging devices, channel gain and network density. Our results indicate that our two-layer learning approach achieves at least 7%, 19%, 40% higher throughput than TDMA, ε -greedy and Aloha.

This paper considers the problem of generating the shortest time division multiple access (TDMA) schedule for use in rechargeable wireless sensor networks (rWSNs) with heterogeneous energy arrivals rates. This novel problem considers: 1) the time required by nodes to harvest sufficient energy to transmit/receive a packet; 2) harvest-use-store (HUS) energy harvesting and usage models, and; 3) battery imperfections, i.e., leakage, storage efficiency, and capacity. This paper shows the problem at hand, called link scheduling in harvest-use-store (LSHUS), is in general NP-Complete. Furthermore, it presents a greedy heuristic, called LS-rWSN, to solve LSHUS. Our experiments show that a longer energy harvesting time (leakage rate) from 1 to 20 (0% to 4%) increases the schedule length by up to 565.82 (44.54%) slots while reducing storage efficiency from 1.0 to 0.6 lengthens the schedule by up to 62.77%. In contrast, battery capacity has an insignificant effect, i.e., enlarging the capacity by 20 times decreases the schedule length by only 6.5%.

Future devices are likely to have the capability to harvest energy from radio-frequency (RF) signals. In this paper, we consider such energy harvesting (EH) devices operating in a two-tier orthogonal frequency-division multiple access-based heterogeneous network. Critically, we investigate how such EH devices can be supported alongside non-RF harvesting or legacy devices. Our aim is to minimize the downlink sum transmit power of both femto and macro base stations and ensure that legacy and EH devices receive a given data rate and amount of energy, respectively. Critically, we study sub-carrier and power allocation to both types of devices and investigate novel questions related to interference, which reduces network capacity but improves the amount of harvested energy by EH devices. To study these questions, we formulate a mixed-integer non-linear program (MINLP) and propose three linear approximations to the MINLP where devices are either assigned one or multiple sub-carriers. Numerical results show that EH devices will not affect network capacity if they can harvest sufficient energy from data transmissions to legacy devices. In addition, if multiple sub-carriers can be assigned to devices, our results show that the sum transmit power decreases by approximately 15% as compared with assigning a single sub-carrier to these devices.

One approach to improve network capacity in future wireless networks is to deploy access points (APs) in a dense manner and employ a controller to manage these APs. Another innovation is to equip nodes with an in-band full-duplex (IBFD) radio, which allows them to transmit and receive simultaneously over the same frequency band. A key challenge, however, is interference. To this end, a link scheduler plays a critical role in ensuring the benefits of dense APs deployment and IBFD are not negated by severe interference. Henceforth, this paper proposes three centralized algorithms that aim to drain a given set of packets from links in minimum time. We have compared these algorithms against a schedule, where links transmit independently, and an algorithm that schedules links at slot boundaries. We studied the impact of varying node densities, transmission power, and signal-to-interference-plus-noise ratio thresholds on the link schedule. Our results show the overall completion time can be reduced by 68% as compared to scheduling links individually. Moreover, our algorithms reduce the completion time by 13% as compared to scheduling links on a slot-by-slot basis.

This paper considers the problem of deriving a time-division multiple-access (TDMA) schedule for multi-hop wireless networks that allow nodes to perform multiple transmissions/receptions to/from all of their neighbors simultaneously over the same frequency. To date, there are a number of link schedulers for the networks in question but they do not consider flow rate or any notion of fairness when deriving a TDMA schedule. Henceforth, we address this critical gap by proposing a link scheduler, called Algo-Fair, that, in addition to maximizing the number of links in each slot, also provides fairness among flows. In addition, it uses a novel augmentation step to distribute spare capacity fairly among flows. We believe this step is general and can be applied readily in other forms of wireless networks. Apart from that, Algo-Fair generates a schedule directly while yielding a fair rate allocation. This is different from existing methods that first use a flow contention graph to compute a fair share before deriving the corresponding schedule, which may not exist. Numerical results show Algo-Fair has higher fairness, minimum rate, and average total throughput than competing approaches, and low end-to-end delay.

In rechargeable or energy harvesting wireless sensor networks (WSNs), a key concern is the max flow or data rate at one or more sinks. However, this data rate is constrained by the available energy at each node as well as link capacity. To date, in order to increase the amount of data extracted from a WSN, past works have considered routing approaches or they optimize the location of sinks. In contrast, we take a novel approach whereby we aim to “upgrade” the recharging rate of a finite number of “bottleneck” nodes using the so called auxiliary chargers (ACs) equipped with wireless power transfer capability. We formulate a mixed integer linear program (MILP) for the NP-hard problem at hand and propose three novel solutions to place ACs: 1) Path, which preferentially upgrades nodes on the shortest path among paths from sources to sinks, 2) Tabu, a meta-heuristic that first uses Path as the initial solution. It then searches for a neighboring solution that yields a higher max flow rate, and 3) LagOP, which approximates the said MILP using Lagrangian and sub-gradient optimization. Our results show that Tabu has the best performance, where it is able to achieve 99.40% of the max flow rate derived by MILP in tested scenarios.

A key problem in wireless mesh networks is forwarding packets to/from one or more gateways with connectivity to the Internet. In this respect, a short link schedule, which determines the transmission time of links, is critical. To date, existing link schedulers do not consider routers that incorporate advances in multiple input multiple output communications, i.e., interference cancellation and spatial multiplexing. In particular, these routers are able to transmit or receive distinct packets on all their links concurrently as well as deliver multiple packets to a neighbor simultaneously. To this end, we consider the problem of deriving a time division multiple access schedule that forwards packets to their respective destination quickly. We first consider the personalized broadcast problem, which assumes a single gateway and present Algo-PB, a solution that produces a schedule that is within 34.5% of the lower bound, and is 45.5% shorter than those computed by the state-of-the-art algorithms. We then extend the problem to consider multiple gateways. This so-called forest construction problem is modeled as an integer linear program (ILP). We then outline Algo-FC, a novel heuristic that generates a balanced forest by repeatedly picking a node from the heaviest tree and migrating it to another tree if doing so reduces the overall load. Experiment results show that the resulting forest generated by Algo-FC is within 9.1% of the ILP solution.

Works on pico-satellites have gained momentum recently, especially those that consider pico-satellites as part of a much larger constellation or swarm. This feature allows pico-satellites to provide high temporal resolution of observational data and redundancy. In particular, it reduces the need for satellite-to-ground communications and, hence, helps save energy and allows the execution of distributed processing algorithms on the satellites themselves. Consequently, satellite-to-satellite or cross-link communication is critical. To realize these advantages, the cross-link antenna employed on pico-satellites must meet many criteria, namely, small size, lightweight, low-power consumption, high gain, wide bandwidth, circular polarization, and beam steerability. To date, no works have examined the suitability of existing planar antenna designs for the use on pico-satellites. To this end, this paper contributes to the literature by focusing on microstrip patch and slot antennas that have the ability to achieve high gain, beam steering, and wide bandwidth. This paper reviews 66 planar antenna designs, which includes 38-patch and 28-slot antennas. In addition, we provide an extensive qualitative comparison of these antennas in terms of their mass, size, gain, beam steerability, type of polarization, operating frequency band, and return loss. In addition, we have evaluated three antenna designs that best address the pico-satellite challenges on a common platform. We find that the asymmetric E-shaped patch antenna design is the most suitable for the use on 2U CubeSats. This is because of its small size (34 × 13 mm2) and high gain (7.3 dB). In addition, the E-shaped patch antenna yields a wide -10-dB bandwidth of 2300 MHz and a small return loss of -15.2 dB.