Future Multi-access edge computing (MEC) systems are required to execute applications composed of dependent tasks under end-to-end delay requirements. Further, they are likely to rely on renewable (or green) energy sources that are limited and fluctuate over time. These conditions give rise to a fundamental optimization problem that involves determining how tasks should be offloaded across edge servers so that dependency and delay constraints are satisfied while maximizing green energy usage or reducing an operator’s reliance on brown energy. In addition, the problem involves how green energy is shared among servers. Henceforth, we address a novel problem, called Green Dependent Task Offloading (G-DTO), that aims to maximize green energy usage, subject to the following constraints: (i) each edge server has limited green energy and computational capacity, and (ii) each task of an application, with a specified size, energy, and computational requirement, must be executed by a given deadline. To determine the optimal solution, we outline a Mixed-Integer Linear Programming (MILP) model. We also outline a genetic-algorithm-based approach, called G-DTO/GA. The simulation results on 18 synthetic network scenarios with small problem instances show that G-DTO/GA achieves an average of 96.83% green energy usage, closely matching the optimal MILP solution while requiring only 9.93% of the MILP runtime. Further experiments on the same 18 synthetic network scenarios, but with large problem instances, show that G-DTO/GA sustains high green energy usage, averaging 90.10%.

Future Internet of things (IoT) networks will host applications that involve data collection and computation tasks on one or more servers. To this end, this paper proposes the first mixed integer linear program (MILP) to schedule and embed applications on energy harvesting nodes, where it optimizes (i) the sampling time of devices, (ii) whether to run an application, and (iii) the energy usage of devices, gateways and servers. To ensure applications are run often, we adopt the maximum age of service (AoS) metric, and set the MILP’s objective to minimize the maximum AoS or min-max AoS of applications. This paper also proposes two novel solutions: (i) a receding horizon control (RHC) based method, and (ii) a solution that greedily embeds applications according to their AoS. The results show that the min-max AoS of RHC and greedy approach is respectively 1.07x and 1.13x higher than MILP.

A key issue when operating an Internet of things (IoT) network is that devices may be infected by malware. Consequently, when collecting data from these devices, e.g., using an uncrewed aerial vehicle (UAV), malware data may be transmitted to a gateway that is then used to attack servers. To address this issue, we equip a UAV with a virtual network function, and study a novel problem involving the allocation of computation and communication resource to identify malware traffic from solar-powered devices. To solve this problem, we first outline a mixed integer linear program (MILP) that jointly optimizes i) UAV placement, ii) UAV trajectory, iii) data processing, iv) channel allocation, and v) energy usage of devices. To facilitate online decision making, we design a neural network-based solution called neural network mapping (NNM) to store integer valued decision variables offline. During flight, the UAV then uses the neural network to retrieve the corresponding integer values for a given scenario, which allows the UAV to solve the said MILP as a linear program that can be computed quickly. Our simulation results show that NNM is able to effectively reduce the amount of data from malware that arrives at a gateway.

This article studies a novel network or topology construction problem using uncrewed aerial vehicles (UAVs). Unlike prior works, it aims to construct a topology thatjointlyoptimizes communications between ground users operating in an Industrial Internet of things network as well as provide localization services to users. It first outlines a mixed integer linear program (MILP) that maximizes the minimum flow-rate of ground users subject to localization accuracy. Its key decision variables relate to, first, the position of UAVs, second, routing of traffic between users, and third, link schedule. It then presents a heuristic called localization-aware UAV deployment and communication-aware relay deployment (LAUD-CARD) to position UAVs. The results show that LAUD-CARD is able to achieve 90% of the optimal or MILP result.

This paper introduces a novel network planning problem called Multi-stage Edge Server Deployment (M-ESD). The problem calls for a solution that (i) adds fixed edge servers to an existing Multi-access Edge Computing (MEC) network incrementally over multiple stages, e.g., in years, and (ii) optimizes the offloading of tasks to installed servers. More specifically, when upgrading a network, at each stage, the problem involves the following constraints: (i) budget (in $), (ii) server deployment cost (in $) and cost depreciation rate (in %), (iii) number of tasks and their increase rate (in %), and (iv) server storage capacity . The goal of M-ESD is to ensure the resulting network maximizes the average number of tasks that meet their delay requirement. This paper presents a Mixed Integer Linear Programming (MILP) model and a heuristic approach called M-ESD/H to solve the M-ESD problem. Simulation results on small networks show that M-ESD/H produces results that are within 13.6% of the optimal MILP solution. Further, it significantly reduces runtime and produces results in less than 0.1 s as compared to MILP, which failed to produce results in some networks after running for over 48 h. For large networks, M-ESD/H is compared against two versions of M-ESD that consider arbitrary budget allocation and/or edge server placement, i.e., M-ESD/A1 and M-ESD/A2. The results show that M-ESD/H outperforms both M-ESD/A1 and M-ESD/A2 across various options with varying numbers of stages, budget allocation, and tasks.

This article considers an operator that deploys unmanned aerial vehicles (UAVs) to service requests from users/devices in an Internet of Things (IoT) network; each request requires one or more so-called services, such as a database or an Artificial Intelligence (AI) model. A key problem faced by the operator is to determine which services to place on UAVs and which requests to transport to a base location for execution; note, UAVs have resource constraints and hence, they are unable to carry all services or/and serve all users in one trajectory. Further, they do not have connectivity to a base station during flight. To this end, given a set of requests, this article presents novel solutions that minimize the total completion time of these requests. First, it outlines a novel mixed-integer linear program (MILP) that can be used to calculate the optimal assignment of services to trajectories. MILP also decides which requests are transported to a base location for computation, and schedules the return of result(s) to users. The second solution is a heuristic method named MinTime that chooses UAVs and users according to traveling and task execution time. The results show MinTime achieved 91.15% of the optimal result in terms of request completion time.

This article considers maximizing coverage quality in Internet of Things (IoT) networks using unmanned aerial vehicles (UAVs) to augment the link from solar-powered devices to a sink/gateway. Specifically, it aims to jointly optimize the assignment of UAVs to hovering points or a charging station, time in which devices monitor targets, and the amount of data transmitted by devices. These quantities are optimized over a given planning horizon using a mixed integer linear program (MILP). Further, this article presents a heuristic method named decoupled energy aware algorithm (DEAA) to optimize the said quantities. In addition, it outlines a model predictive control (MPC) approach that only requires current and historical energy arrivals information of devices. The simulation results showed that DEAA and MPC achieved 80.58% and 61.19% of the optimal results computed by MILP.

Complete targets coverage is required by many Internet of Things (IoT) applications. In this respect, an important goal is to maximize the number of time slots with complete targets coverage. Achieving such coverage is challenging when devices experience spatio-temporal energy arrivals. To this end, this article outlines a deep learning assisted approach that has an offline stage whereby it determines and stores an exhaustive collection of optimal activation schedules based the energy levels and arrivals of devices. In addition, it presents a network partitioning and training strategy, and outlines an algorithm to mend coverage holes in its online stage. We have compared the proposed approach with the optimal solution, and also a state-of-the-art heuristic algorithm. The results show that our solution achieves 94% of the optimal coverage lifetime. Moreover, the proposed approach has a 35% smaller optimality gap as compared with the said heuristic algorithm.

This article considers a topology construction problem involving a fixed-wing autonomous aerial vehicle (AAV), a set of quad-rotor AAVs and mobile ground users. The constructed topology must maximize the max-min flow rate of ground users over a given planning horizon. To this end, we outline a mixed integer linear program (MILP) that jointly optimizes the trajectory of the fixed-wing AAV, placement of quad-rotor AAVs, and routing of traffic from each source-destination ground user pair. Solving the MILP is challenging because it requires an exhaustive collection of topologies. To this end, this article outlines a solution called rollout to determine the network topology in each time slot of a given planning horizon iteratively. The main idea of rollout is to generate a sequence of future decisions using a heuristic, where a decision corresponds to the placement of quad-rotor AAVs. Further, each sequence of decisions has a cost-to-go value. Rollout then selects the sequence with the highest cost-to-go value. The results show that the max-min flow rate achieved by rollout is on average 81% that of MILP.

This paper considers a satellite network with free space optical links, where satellites are able to form intra and inter satellite links. Briefly, intra-satellite links connect satellites in the same orbit plane, and inter-satellite links (ISLs) connect satellites on different orbital planes. In this respect, a key problem is to jointly determine when to establish inter-satellite links and the routing between a source-destination pair of satellites or ground users. Critically, unlike prior works, the resulting solution must ensure there is no congestion, which leads to packet loss. Henceforth, this paper proposes the first safe reinforcement learning (RL) approach that allows agents to learn a policy to optimize inter-ISL activations and routing of traffic whilst ensuring zero packet loss. In particular, it incorporates a shield mechanism that ensures agents do not take actions that would lead to packet loss. The results show that, as compared to conventional approaches, the proposed RL approach achieves a 33% improvement in terms of average throughput, and reduces average delay and queue lengths by 45%.

This article considers an unmanned aerial vehicle (UAV) that is used in industrial Internet of things (IIoT) networks to execute one or morepreloadedcomputation tasks. A key novelty is that these tasks support imprecise computation, where each task has a mandatory and optional part. Another novelty is that both parts of a task require data from one or more solar-powered ground devices. The mandatory part of each task must be computed by the UAV before the end of its trajectory. If there are sufficient resources and time, the UAV can download more data from devices and execute the optional part of tasks to improve results quality. To schedule tasks on a UAV, this article outlines a novel mixed integer linear program to optimize the execution of tasks and data collection. Furthermore, it outlines the first model predictive control (MPC)-based solution, called MPC-$S$, for the problem at hand that uses current and past energy arrivals information of devices. Our results show that MPC-$S$achieves approximately 89.9% of the optimal results quality.

In this article, we consider an unmanned aerial vehicle (UAV) with an in-band full-duplex radio that is used to interconnect industrial Internet of things (IIoT) devices and exploit their computation and energy resources to help process data. We formulate a mixed integer linear program to optimize the first sampling rate of each device, second amount of data the UAV transmits and receives to/from a device, third position of the UAV over time, and finally the number of virtual machines used by devices and UAV to compute data. We also propose a distributed protocol to determine quantity using aforementioned points. Our results show that an IBFD-UAV has a higher max–min computed sampling rate as compared to when the UAV uses a half duplex radio. Moreover, the said protocol achieves a max–min rate that is 80% optimal.

In future Internet of Thing (IoT) networks, devices can be leveraged to compute tasks or services. To this end, this article addresses a novel problem that requires devices to collaboratively execute tasks with dependencies. A key consideration is that in order to conserve energy, devices may execute a task in approximate mode, which generate errors. To optimize their operation mode, we outline a novel chance-constrained program that aims to execute as many tasks as possible in approximate mode subject to a probabilistic constraint relating to the said errors. We also outline two novel solutions to determine task execution modes: 1) a sample average approximation (SAA) method and 2) a heuristic solution called minimum communication cost (MinC). We have studied the performance of SAA and MinC with round robin (RR), which assigns tasks to devices in an RR manner. Specifically, we find that the maximum energy consumption of devices when using MinC and RR is, respectively, around 14.2% and 23.1% higher than SAA, which yields the optimal solution. Further, MinC results in approximately 27.9% lower energy consumption as compared to RR.

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 article considers the problem of monitoring one or more mobile targets over a given planning horizon. Unlike previous works, it leverages ambient backscatter communication to reduce the energy expenditure of sensor nodes in order to prolong coverage lifetime. We outline a mixed integer linear program (MILP) that aims to maximize the number of time slots in which all mobile targets are monitored by a sensor node; a.k.a. complete mobile targets coverage. For a given targets trajectory, it outputs the activation time of sensor nodes to ensure all targets are monitored by at least one sensor node. Further, we propose an algorithm called maximum energy opportunity selection (MEOS), which uses the energy level of sensor nodes to set their operation mode. The simulation results show that the complete mobile targets coverage lifetime of MILP and MEOS algorithms improves when nodes employ backscatter communications. Specifically, it leads to a 79% improvement in complete mobile targets coverage lifetime.

Devices in Internet of Things (IoT) networks are likely to operate over a shared medium, and thus they have to use a channel access protocol to minimize or avoid collision. Further, they may have to harvest radio frequency (RF) energy in order to transmit or/and receive data. To this end, this survey presents the first comprehensive review of prior works that employ contention-based and contention-free protocols in IoT networks with one or more dedicated RF energy sources. Specifically, these protocols work in conjunction with RF-energy sources to deliver energy delivery or/and data. In this respect, this survey covers protocols based on Aloha, carrier sense multiple access (CSMA), polling, and dynamic time division multiple access (TDMA). Further, it covers protocols that assume a receiver with successive interference cancellation capability. It highlights key issues and challenges addressed by prior works, and provides a qualitative comparison of these works. Finally, it identifies gaps in the literature and presents a list of future research directions.

This paper considers targets coverage in energy harvesting Internet of Things (IoT) networks. Specifically, solar-powered sensor devices employ network virtualization technology to partition their resources, such as energy, memory, and computation workload, in order to serve requests with different coverage requirements. Our objective is to maximize the revenue from completing requests. To this end, we outline a mixed integer linear program (MILP) to optimize the start time of each request and the set of nodes that serve a request. We also propose a heuristic, called energy harvesting aware request placement (EHARP), to determine requests to be deployed in each time slot based on energy harvesting conditions and the resource state of sensor nodes. Furthermore, we propose two model predictive control (MPC) approaches, called MPC-MILP and MPC-EHARP, respectively, which deploy requests based on energy arrival at devices over a given time window as predicted by a Gaussian mixture model (GMM). Simulation results show that EHARP, MPC-MILP, and MPC-EHARP are 94.75%, 88.73%, and 87.6% optimal. In addition, the revenue obtained by EHARP is 173.8% higher than a competing approach.

Many industries now rely on drones to monitor infrastructures. In this respect, this article considers maximizing the revenue of an Industrial Internet of Things operator that provides two services: 1) data trading; and 2) drones rental. In service 1), the operator sells data of locations/points it acquired via drones. For service 2), it rents idle drones to users. The problem at hand is to determine the allocation of drones to services 1) and 2) that maximizes the operator's revenue over a given planning horizon. We outline a novel integer linear program (ILP) to solve the said problem, which can be used to determine the optimal number of drones assigned to both services. The ILP, however, requires an exhaustive collection of drone trajectories. We therefore present two heuristics called weighted-based algorithm (WBA) and genetic algorithm (GA) to generate trajectories for data collection. The results show that WBA earns 95.6% of the optimal revenue. GA is able to achieve 99% of the revenue of WBA at best.

In this article, we study an industrial Internet of Things (IIoT) network with a sink/gateway that is capable of decoding multiple transmissions via successive interference cancellation (SIC), and energy harvesting devices tasked with providing complete targets coverage of targets. In particular, we study a novel question: how to schedule the sensing and transmission of these devices to ensure complete target coverage over time? We outline a mixed integer linear program (MILP) and two heuristic solutions that jointly optimize the active time and transmit power of devices. Our simulation results show that the complete targets coverage lifetime of our heuristic solutions is within 80% of the optimal complete targets coverage lifetime, as computed by MILP.

This article studies a coverage problem in Internet of things (IoT) networks using unmanned aerial vehicles (UAVs) supported by solar-powered charging platforms. The problem at hand is to determine an assignment of UAVs to either a charging station or a monitoring point over a planning horizon. A key constraint is$K$-coverage, where given a set of$\mathcal {M}$points,$K$of these points must be monitored by a UAV in each time slot. In this respect, the paper aims to design UAVs assignment solutions that yield the longest$K$-coverage lifetime. We formulate a novel mixed integer linear program (MILP) to jointly optimize UAVs assignments over a given planning horizon. The problem is challenging as the energy level of charging platforms and UAVs are coupled across time slots. Moreover, the formulated MILP requires non-causal energy arrivals information at charging platforms. To this end, we outline a model predictive control (MPC) and a Monte Carlo tree search (MCTS) based solution that use non-causal energy arrivals information. The simulation results show that MPC and MCTS achieve approximately 81.04% and 67.07% of the optimal results computed by MILP.

This paper considers a network design problem using Unmanned Aerial Vehicles (UAVs). It aims to create a network to provide communication and computation service to a set of source-destination ground node pairs. The main performance metric is the minimum amount of computed data among a set of source-destination pairs. To optimize this metric, we outline two mixed Integer Linear Programs (MILPs), namely S-MILP and NS-MILP, which are designed respectively for splittable and non-splittable traffic flow models. They jointly optimize the placement of UAVs, assignment of Virtualized Network Functions (VNFs), and routing of unprocessed and processed flow. Further, NS-MILP optimizes the path selection of each source-destination pair. A key challenge is that these MILPs require an exhaustive collection of network topologies. To this end, this paper outlines two heuristic algorithms, called Resource-Aware Location Selection (RALS) and Resource-Aware Path and Location Selection (RAPLS), respectively for each traffic flow model. The simulation results show that RALS and RAPLS achieve on average 83% and 80% of the amount of computed flow of S-MILP and NS-MILP, respectively. Lastly, RALS and RAPLS require 45% and 53% less computation time as compared to S-MILP and NS-MILP, respectively.

This paper studies virtual network function (VNF) scheduling in energy harvesting virtualized Internet of Things (IoT) networks. Unlike prior works, sensor devices leverage imprecise computation to vary their computational workload to conserve energy at the expense of computation quality. In this respect, an optimization problem of interest is to maximize the minimum VNF computation/execution quality. To this end, this paper presents the first mixed integer linear program (MILP) that optimizes i) the VNFs executed by each sensor device, ii) the computational resources allocated to VNFs, iii) sampling rate or amount of data supplied by sensor devices to VNFs, iv) the routing of samples to VNFs and forwarding of computation results, and v) link scheduling. In addition, this paper also proposes a heuristic, called sampling control and computation scheduling (SCACS), for large-scale networks. The simulation results show that SCACS reaches 81.66% of the optimal quality. In addition, the application completion rate when using SCACS is at most 39% higher than a benchmark that randomly selects nodes to sample targets and execute VNFs.

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49% of nodes are malicious.

This article considers task scheduling in solar-powered Internet of Things (IoT) networks where devices are capable of sharing energy wirelessly. Our aim is to minimize the completion time of all tasks. We outline a novel mixed-integer linear program (MILP) to schedule tasks and determine whether devices share their harvested energy via radio frequency (RF) in each time slot. The MILP considers the coupling between the energy level at devices across time slots. It also considers the dependency of tasks, whereby each task must be executed on a given set of devices in a specific order. Further, we propose a heuristic algorithm called minimum time first with energy sharing (MinTime-ES) for large scale networks. Our results show that with energy sharing, MILP and MinTime-ES achieve 28.86% and 7.83% reduction in task completion time as compared to competing algorithms that do not consider energy sharing between devices.

This article studies targets coverage in an energy harvesting (EH) Internet of Things (IoT) network. Specifically, it addresses the problem of activating subsets of EH sensor nodes to monitor targets, such as valuable assets or the ingresses and egresses of a building. A key requirement, so called complete targets coverage, is that all targets must be monitored by at least one sensor node at all times. To meet this requirement, we need to derive set covers, where each set cover is comprised of one or more sensor nodes that are activated simultaneously in each time slot. To this end, we show for the first time how the belief propagation message-passing framework can be used to derive these set covers. Advantageously, our approach does not require future energy arrivals information at devices. The results show that our message-passing approach is within 90% of the optimal coverage lifetime.

This article considers the collection and processing of data by solar-powered servers operating in an Internet of Things (IoT) network. Specifically, these servers aim to cooperatively maximize the amount of data collected from devices and computed over multiple time slots. To achieve this aim, they must consider computation deadline, time-varying energy arrivals at sensor devices and other servers. To this end, this article outlines a mixed-integer linear program (MILP), which can be used to optimize the sensing time of sensor devices, offloading decision of each server, and the number of virtual machines (VMs) assigned to each device. Further, this article proposes a multiagent co-operative$Q$-learning approach coupled with the Hungarian algorithm to assign VMs to devices. It allows servers to learn when to share their energy and VMs with neighbor servers using only noncausal energy and channel gain information. The simulation results show that the amount of processed data by co-operative$Q$-learning is 93% that of MILP.

Data collection is a fundamental operation in energy harvesting industrial Internet of Things networks. To this end, we consider a hybrid access point (HAP) or controller that is responsible for charging and collecting$L$bits from sensor devices. The problem at hand is to optimize the transmit power allocation of the HAP over multiple time frames. The main challenge is that the HAP has causal channel state information to devices. In this article, we outline a novel two-step reinforcement learning with Gibbs sampling (TSRL-Gibbs) strategy, where the first step uses Q-learning and an action space comprising transmit power allocation sampled from a multidimensional simplex. The second step applies Gibbs sampling to further refine the action space. Our results show that TSRL-Gibbs requires up to 28.5% fewer frames than competing approaches.

This article studies a novel wireless-powered Internet of Things (IoT) network that consists of: 1) a hybrid access point (HAP) that charges devices and also helps facilitate backscattering transmissions; 2) devices that use active radio frequency (RF) and backscattering transmissions; and 3) 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: 1) throughput increases with the number of backscatter transmission sets; 2) smaller amount of data is uploaded to the mobile collector when sampling cost is low; and 3) the throughput of RS-LPA is on average 10.55% lower than MILP.

This article considers routing and link scheduling in a two-tier wireless backhaul network. The first tier consists of routers and the second tier consists of radio frequency (RF)-energy-harvesting Internet-of-Things (IoT) devices that rely on routers for energy. Our aim is to derive the shortest time division multiple access (TDMA) link schedule that satisfies the traffic demand of routers and energy demand of IoT devices. We formulate a linear program (LP) to jointly derive a routing and link schedule solution. We also propose a heuristic link scheduler called transmission set generation (TSG) to generate transmission sets and to derive the transmit power allocation of routers. In addition, we present a novel routing metric that considers RF-energy-harvesting devices on a given path. TSG on average achieves 31.25% shorter schedules as compared to competing schemes. Finally, our novel routing metric results in link schedules that are at most 24.75% longer than those computed by LP.

This article 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 the 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: 1) the network throughput achieved by ALGO-TSG at both tiers is 29.80% higher as compared to the case without backscattering and 2) the throughput of CMF is on average 21.36% lower than the throughput computed by MILP.

This article considers radio-frequency (RF) energy harvesting devices that use an irregular slotted Aloha (IRSA) channel access protocol to transmit their data to a hybrid access point (HAP). Specifically, it addresses the fundamental problem of optimizing the number of packet replicas transmitted by each device in each time frame. Unlike prior works, it considers a learning approach to optimize the number of replicas according to the energy level of devices. This article first uses a model-based Markov decision process (MDP) to study the problem at hand. Then, it proposes a model-free, centralized, and a distributed$Q$-learning-based solution that aims to maximize the number of successful transmissions in each time frame. Our simulation results show that our centralized and distributed solutions, respectively, achieve up to 38% and 29% more successful transmissions than conventional Aloha.

Recently, many works seek to exploit the negligible transmission cost of backscattering radio frequency (RF) signals, and also demonstrated its feasibility in allowing passive or batteryless tags to communicate over multiple hops. In this context, this article studies data collection in amultihopInternet of Things (IoT) wireless network consisting of tags equipped with sensor(s). These tags forward data via tag-to-tag communications to a gateway. Our aim is for the gateway to collect the maximum amount of data from tags over a given time frame. To do so, we optimize the time used by tags to sample their environment and data transmission, which involves solving an NP-hard link scheduling problem. We present a mixed integer nonlinear program (MINLP) to determine the transmitting tags in each time slot as well as the sensing duration of tags. We also propose a heuristic, called Max-L, that aims to maximize the number of links in each transmission set in order to reduce the transmission time of samples. Our results show that Max-L collects 85% of the optimal amount of samples.

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 article 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 performance. This is because a small window allows MPC-MILP to execute all tasks sooner as opposed to waiting idly for incorrectly estimated good channel conditions.

Virtualization of devices operating in Internet of Things (IoT) 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 article 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. Furthermore, 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 serve a service request. Furthermore, DGA consumes up to 36.43% less energy than competing methods.

This article 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 the Gaussian mixture model (GMM) and a so-calledbackoffthat 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%.

This article studies the complete targets coverage problem in a novel and practical context: sensor nodes operating in an Internet of Things (IoT) network that have a dual-battery system with nonideal properties. Specifically, sensor nodes have batteries that cannot be charged and discharged simultaneously. Also, sensor nodes must fully discharge/charge a battery before it is charged/discharged again. We outline a mixed-integer linear program (MILP) and use it to optimize the activation schedule of sensor nodes. Its objective is to maximize complete targets coverage lifetime. We also propose a heuristic solution named battery switching aware algorithm (BSAA) to solve large problem instances. Simulation results show the performance of BSAA achieves approximately 98% of the coverage lifetime of MILP. In addition, equipping sensor nodes with a dual battery system prolongs the coverage lifetime by up to 70.2%.

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. Our simulation results show that min-max AoI increases with the number of sources, but reduces with increasing number of PBs. The number of paths available to a source, the number of frames, and solar panel size have limited impact on performance. The min-max AoI of CLR and DPS is$1.60\times $and$1.95\times $higher than that of MILP.

This paper considers the problem of activating links in a rechargeable Wireless Sensor Network (rWSN). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that accounts for memory effects, and (iii) nodes with a dual-battery system. It outlines a greedy algorithm that schedules links according to the earliest time in which a battery at the end nodes of each link can be discharged or is full. Our results show that equipping nodes with a dual-battery system decreases link schedules by up to 35.19% and 15.12% as compared to equipping nodes with a single battery with and without the said battery cycle, respectively. Such a system also respectively reduces the number of charge/discharge cycles by up to 15% and 87.13%. Finally, a longer energy harvesting time increases link schedules linearly, but has no impact on the number of charge/discharge cycles.

This paper considers the problem of upgrading a legacy network into a Software Defined Network (SDN) over multiple stages and maximizing energy saving (ES) in the resulting upgraded network or hybrid SDN. In each stage, an operator needs to select and replace legacy switches with SDN switches and seek to switch off as many cables as possible over each link. This paper addresses the said problem where it considers (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) each non SDN switch must comply with the Open Shortest Path First (OSPF)-Equal Cost Multi-Path (ECMP) protocol. It outlines a Mixed Integer Program (MIP) and a heuristic algorithm called M-GMSU. The results show that (i) MIP and M-GMSU achieve ES of up to 71.93%, (ii) using a larger budget and/or number of stages increases ES, and (iii) the ES of M-GMSU is within 3.55% away from the optimal ES computed by MIP.

This article considers a novel Internet of Things network comprising of sensor devices and power beacons (PBs); both types of nodes are equipped with a cognitive radio (CR). In addition, these sensor devices are powered by radio-frequency signals from PBs. Our aim is to maximize the minimum rate of devices acting as sources. We outline the first mixed integer linear program (MILP) that jointly optimizes the channel assignment of PBs and devices, beamforming vector of PBs, data routing over multiple hops and link activation schedule of devices. We also design a distributed protocol called distributed max–min rate with CR (D-MRCR) for use by devices and PBs. Devices set their operation mode using local information and use a game theory-based approach to iteratively adjust their transmit power. On the other hand, each PB employs a linear program to determine its beamforming vector. Our results show that the max–min rate of D-MRCR is within 51.84% that of MILP.

This article studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless-powered Internet of Things (IoT) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a mixed-integer linear program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively-both of which exploit AmBC.

This article considers data collection in a wireless-powered Internet-of-Things (IoT) network. Specifically, it addresses the novel problem of determining the mode of each time slot, where a hybrid access point (HAP) needs to decide whether to charge or collect data from devices. Also, in data time slots, HAP has to decide on a device for data transmission. To this end, we outline an integer linear program (ILP) to determine the mode and the transmitting device over a given planning time horizon. We also propose a rolling horizon (RollH) approach that uses a Gaussian mixture model (GMM) to estimate channel gains. Our results indicate that the amount of data collected by HAP is affected by its charging power, distance between HAP and each device, number of devices, and planning horizon length. The RollH approach allows the HAP to collect 740% more data as compared to competing approaches.

A multi‐cell network that uses orthogonal frequency division multiplexing access (OFDMA) is studied here. Unlike prior works, the proposed network consists of both (i) legacy data users that do not have energy harvesting capability and have a minimum data rate requirement and (ii) radio frequency (RF)‐energy harvesting devices with a minimum energy requirement. It studies sub‐band allocation to users and transmits power allocation at base stations. The authors formulate a mixed‐integer non‐linear program and also present two heuristics to assign sub‐band to base stations. Numerical results show that RF energy harvesting devices will not affect network capacity if legacy data users require a high data rate. In addition, the results obtained from the two proposed heuristics are 95% of the optimal solution.

Future Internet-of-Things (IoT) networks will consist of radio-frequency (RF) energy harvesting devices that are charged by solar-powered power beacons (PBs). To this end, this article aims to maximize the minimum data rate of devices acting as sources operating in a multihop IoT network. The main problem is to decide the amount of energy delivered by solar-powered PBs, routing of data from each source, and link scheduling, which determines the capacity of links. To this end, we make two contributions. First, we present a linear program (LP) to optimize the max-min rate of sources. Our LP considers nonlinear RF conversion at devices, energy storage loss at devices due to the imperfect battery, and time-varying channel quality, which affect the amount of energy harvested by devices. The second contribution is a novel distributed protocol called distributed max-min rate allocation (D-MRA), whereby devices only need local information, such as their battery and data buffer state to make decisions. Our results show that the max-min rate of D-MRA is 58.25% that of LP, which requires global information, in all tested cases.

Backscatter communication can potentially find wide Internet of Things (IoT) applications because of its negligible energy consumption. Traditional backscatter communication relies on either dedicated radio-frequency (RF) sources, such as RF identification readers and power beacons, or ambient RF sources, e.g., TV and cellular signals. In this article, we study a backscatter-assisted wirelessly powered IoT system where devices can backscatter when nearby devices are actively transmitting via RF. Our objective is to determine the transmission schedule for all devices that maximizes the minimum system throughput. We formulate such a scheduling problem as a linear program for both linear and random networks. A key step in the formulation is to identify the groups of devices that can simultaneously backscatter without causing interference. Simulation results show that the max-min system throughput in linear and random networks can be increased by 46 and 180 times, respectively, by using the proposed backscatter-assisted schedule as compared with the traditional time-division multiple access (TDMA).

We consider Rechargeable Wireless Sensor Networks (R-WSNs) where nodes harvest energy from both solar and the Radio Frequency (RF) transmissions of their neighbors. Our aim is to maximize the minimum source or sensing rate of nodes. This rate is determined by the available energy at sensor nodes as well as link capacity, which is determined by the set of transmitting nodes. In this paper, we first study and show the benefits of energy sharing. Intuitively, a sensor node should share its energy if doing so increases source rates. We present a novel Linear Program (LP) to determine the routing, link schedule, energy transmission, and reception time that maximize the minimum source rate of a given R-WSN. Our numerical results indicate that, on average, the minimum transmission rate of sensor nodes increased by 16.03 percent when nodes share energy. This motivates the development of a practical protocol called E-RSVP that iteratively increases the time slots of each source node. It also considers using time slots for transmission or reception of energy. Our simulation results show E-RSVP yields minimum source rates that are 14.80 percent higher as compared to the case without energy sharing.

In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a sampling time to collect sensory data, carry out computation, and transmit sensed data to the HAP. A key challenge, however, is random channel gains, which cause sensor nodes to receive varying amounts of Radio Frequency (RF) energy. To this end, we formulate a stochastic program to determine the charging time of the HAP and sampling time of sensor nodes. Our objective is to minimize the expected penalty incurred when sensor nodes experience an energy shortfall. We consider two cases: single and multi time slots. In the former, we determine a suitable HAP charging time and nodes sampling time on a slot-by-slot basis whilst the latter considers the best charging and sampling time for use in the next T slots. We conduct experiments over channel gains drawn from the Gaussian, Rayleigh or Rician distribution. Numerical results confirm our stochastic program can be used to compute good charging and sampling times that incur the minimum penalty over the said distributions.

A link scheduler ensures the transmissions in rechargeable Wireless Sensor Networks (rWSNs) are collision-free. Hence, it plays a critical role in ensuring high network capacity and the energy used for transmission/reception is not wasted due to collisions. This paper proposes a scheduler that generates a Time Division Multiple Access link schedule for use in a rWSN. Different from most prior works, our scheduler considers the time required by each node to harvest sufficient energy to transmit/receive a packet. Further, it utilizes the more efficient Harvest-Use-Store (HUS) model and considers sensor nodes with finite battery capacity. We present a greedy heuristic that activates links according to the earliest time in which their end nodes have sufficient energy to transmit/receive a packet. Our simulation results show that the time to recharge a sensor node significantly increases the link schedule or superframe lengths; i.e., by up to 563.9% as compared to the case where sensor nodes have no energy constraint. Further, in comparison to the Harvest-Store-Use (HSU) model, using HUS can reduce superframe lengths by up to 45.3%. Our experiments also show that increasing battery capacity does not effect the superframe length significantly; i.e., it reduces the length only by up to 2.5%. Finally, our proposed heuristic can generate superframe lengths that are on average 23% longer as compared to the lower bound on the superframe length when nodes have energy constraint.

In a rechargeable wireless sensor network (rWSN), the amount of data forwarded by source nodes to one or more sinks is bounded by the energy harvesting rate of sensor nodes. To improve sensing quality, we consider a novel approach whereby we place a finite number of auxiliary chargers (ACs) with wireless power transfer and energy harvesting ability to boost the energy harvesting rate of some sensor nodes. We formulate a mixed integer linear program (MILP) to determine the subset of nodes that if upgraded will maximize the minimum source rate. We also propose two heuristic algorithms to place ACs in large-scale rWSNs: greedy node deployment (GND), which checks every nonupgraded sensor node and places an AC next to the one yielding the highest increase in max-min rate; and one-unit energy deployment (OUED), which uses a relaxed version of the MILP to first share one unit of energy among sensor nodes. It then upgrades the sensor node with the highest one-unit share. Our results show that the max-min rate obtained by GND and OUED is, respectively, within 99.60% and 97.82% of the max-min rate derived by MILP in small networks with at most 90 nodes. In large networks with 200 nodes, the maximum gap between OUED and GND is only 0.191 kb/s. Lastly, OUED runs at least five times faster than GND.

Wireless sensor networks can be used to monitor targets continuously. This assumes sensor nodes have energy neutral operation, whereby the energy consumed to monitor targets is less than their harvested energy. In this paper, we consider a new problem: minimum energy harvesting node placement for energy neutral coverage and connectivity (MEHNP-ENCC). We aim to determine the locations to place the minimal number of nodes used for sensing and relaying such that deployed nodes 1) cover all targets, 2) have a path to the sink, and 3) have energy neutral operation. We first model MEHNP-ENCC as a mixed integer linear program (MILP). After that we propose an MILP-based approach called greedy MILP (GMILP), whereby a greedy heuristic is used to generate a collection of locations. We also propose two heuristics: 1) DirectSearch considers locations that cover one or more lines connecting targets to the sink, whilst 2) GreedySearch also considers locations farther afield from the said lines that have a high recharging rate. Simulation results show that DirectSearch requires 20% more sensor nodes than the optimal solution whilst this value is 10% for GreedySearch and GMILP.

The capacity of Wireless Mesh Networks (WMNs) has significantly increased with the recent addition of multiple transmit (Tx) and receive (Rx) (MTR) capability or smart antennas. This increase however is predicated on an effective link scheduler. The aim of any scheduler is to derive a superframe comprising the smallest number of slots that affords each link one or more transmission opportunities. In particular, the scheduler is required to solve an instance of the NP-complete, MAX-CUT problem, in each time slot. To this end, there are a number of centralized schedulers, but only a handful of distributed schedulers. However, each of these distributed schedulers has its own drawbacks; either they do not guarantee maximal activated links or do not guarantee all links are activated. Henceforth, in this paper, we add to the state-of-the-art by proposing a novel distributed scheduler, called Algo-d, which approximates the MAX-CUT problem in a distributed manner using only local information. In fact, this is the first distributed solution for MAX-CUT problem. Through theoretical analysis and simulation, we show that Algo-d achieves the following performance: 1) Algo-d schedules on average 12% fewer and 46.5% more links in each time slot than two centralized algorithms, and 2) Algo-d schedules 28% more links than ROMA and 270% more links than JazzyMAC; both state-of-the-art distributed schedulers for MTR WMNs.

Network robustness and throughput can be improved by routing each demand d via two disjoint paths (2DP). However, 2DP routing increases energy usage while providing lower link utilization and redundancy. In this paper, we address an NP-complete problem, called 2DP-EAR, that aims to switch off redundant nodes and links while guaranteeing two constraints: traffic demands must be afforded 2DP, and maximum link utilization. We design an efficient heuristic, called 2DP by Nodes First (2DP-NF). We have extensively evaluated the performance of 2DP-NF on both real and/or synthetic topologies and traffic demands. As compared to using Shortest Path routing, on the GÉANT network, 2DP-NF can save around 20% energy by switching off links only with negligible effects on path delays and link utilization, even for MLU below 30%. Furthermore, 2DP-NF can obtain 39.7% power savings by switching off both nodes and links on the GÉANT network.

Green networking techniques aim to shut down the least utilized links and/or routers during off-peaks hours. In this paper, we show that such techniques negatively impact the operation of the Border Gateway Protocol (BGP). We quantify the impacts of two representative green approaches: (i) GAES, a green technique that modifies link weights, and (ii) ESOL, a green technique that does not involve link weights adjustments. Experiments over the Abilene, AT&T, GEANT and SURFnet topologies show that when using GAES, routing changes and the proportion of rerouted traffic, both of which affect BGP, are in the order of 108% and 141% greater than ESOL. Therefore, we propose Hot Potato Low UtiliZation (HotPLUZ), a green approach that takes hot-potato routing into account. HotPLUZ reroutes traffic from lowly utilized links and aggregate said traffic onto highly utilized links, whilst minimizing any changes to the corresponding egress router of a given destination. In addition, HotPLUZ considers link utilization in order to avoid packet loss and high latencies. Our experimental results indicate an overall saving of up to 21% under low network load.

Recently, researchers have created Wireless Mesh Networks (WMNs) where routers have multiple transmit (Tx) or receive (Rx) capability. A fundamental problem in such WMNs is deriving a transmission schedule that yields minimal end-to-end delays. In this paper, we approach this problem via joint routing and link scheduling. Specifically, we consider two fundamental issues that influence end-to-end delays: superframe length and transmission slot order. We propose two algorithms: JRS-Multi-DEC and JRS-BIP, where the former uses a novel metric to minimize the load of each link whilst the latter uses a binary integer program solver. Both algorithms have the similar aim of minimizing overall delay and to re-order slots such that packets are forwarded quickly along their path. Numerical results show that our algorithms can reduce average delay by approximately 50% as compared to a non joint routing and scheduling algorithm.

Recently, researchers have developed Wireless Mesh Networks (WMNs) where each router is capable of performing multiple transmissions or receptions concurrently; aka Multi Tx-Rx (MTR) WMNs. Consequently, each node is able to transmit (Tx) or receive (Rx) to/from its neighbors simultaneously. A fundamental problem in such WMNs is to derive a transmission schedule with minimal superframe length to maximize network capacity and minimize end-to-end delays. Unfortunately, deriving a minimal superframe length is equivalent to solving the NP-complete, MAX-CUT problem. To this end, there are a number of centralized schedulers, but only but only one distributed scheduler, called JazzyMAC. Henceforth, in this paper, we add to the state-of-the-art by proposing Algo-d, a novel distributed scheduler that solves the MAX-CUT problem using only local information. Experiment results show Algo-d generates superframes that are 37.5% shorter and it activates 264% more links as compared to JazzyMAC. Lastly, as compared to centralized schedulers, Algo-d schedules 50% more links than Algo-1 and at most 7% fewer links than Algo-2.

A fundamental problem in energy harvesting Wireless Sensor Networks (WSNs) is to maximize coverage, whereby the goal is to capture events of interest that occur in one or more target areas. To this end, this paper addresses the problem of maximizing network lifetime whilst ensuring all targets are monitored continuously by at least one sensor node. Specifically, we will address the Distributed Maximum Lifetime Coverage with Energy Harvesting (DMLC-EH) problem. The objective is to determine a distributed algorithm that allows sensor nodes to form a minimal set cover using local information whilst minimizing missed recharging opportunities. We propose an eligibility test that ensures the sensor nodes with higher energy volunteer to monitor targets. After that, we propose a Maximum Energy Protection (MEP) protocol that places an on-duty node with low energy to sleep while maintaining complete targets coverage. Our results show MEP increases network lifetime by 30% and has 10% less redundancy as compared to two similar algorithms developed for finite battery WSNs.

Next generation Wireless Mesh Networks (WMNs) will require a link scheduler that exploits the full advantage of Multi-Transmit-Receive (MTR) communication. To this end, we design a distributed link scheduler called Voting-ALGO that is aware of queue lengths and uses the celebrated max weight policy to achieve 100% throughput.

Current network infrastructures are over-provisioned to increase their resilience against resource failures, e.g., bundled links and nodes, as well as congestion during peak hours. However such strategies waste resources as well as exhibit poor energy efficiency at off-peak periods. To this end, several energy-aware routing algorithms have been proposed to maximally switch off redundant network resource at low traffic load to minimize energy usage. These routing solutions, however, do not consider network reliability as critical back-off links/nodes maybe switched off. Henceforth, we aim to quantify the effects of five recently proposed green routing approaches, namely FGH, GreenTE, MSPF, SSPF, and TLDP, on the following two reliability measures: (i) 2-terminal reliability (ii) path reliability. Experiments using three topologies with real and synthetic traffic demands show that switching off redundant links significantly affects the 2-terminal reliability. Routing traffic through multiple paths has lesser reliability impact while reducing energy, especially when the paths are link disjoint. Interestingly, TDLP and MSPF have better path reliabilities than using shortest path routing.

This paper considers the problem of deriving a link schedule for Time Division Multiple Access (TDMA)-based concurrent transmit/receive Wireless Mesh Networks (WMNs) that results in low end-to-end delays as well as high network capacity. We first propose a MAX-CUT heuristic approach, called Algo-2, that maximizes link activations in each slot of a super-frame. Algo-2 is shown to produce better network capacity as compared to existing heuristic approaches and significantly improves the super-frame length of an existing MAX-CUT approach that enforces 2-phase transmit-receive restriction - a node that transmits (receives) in slot i ≥ 1 is to become a receiver (transmitter) in slot i + 1. Then, we propose a heuristic solution, called BDA, as a complement to existing schedulers to reduce transmission delays. Since BDA only reorders slots in the superframe, it maintains each original schedule's super-frame length, and hence capacity, while reducing delays by up to 70% in 6-node random topology networks.

Network robustness and throughput can be improved by routing each source-to-terminal (s, t) demand via two link-disjoint paths (TLDP). However, the use of TLDP incurs higher energy cost. Henceforth, we address the problem of minimizing the energy usage of networks that use TLDP. Specifically, our problem is to maximally switch off redundant network links while maintaining at least 0≤T≤100% of (s, t) TLDP in the network, for a given T, and limiting the maximum link utilization (MLU) to no greater than a configured threshold. To address this problem, we present a fast heuristic, called TLDP by Shortest Path First (TLDP-SPF), and extensively evaluate its performance on both real and/or synthetic topologies and traffic demands. Our simulation results show that TLDP-SPF can reduce network energy usage, on average, by more than 20%, even for MLU below 50%. As compared to using Shortest Path routing, while reducing energy by about 20%, TLDP-SPF does not significantly affect (s, t) path length, even for MLU<50%.

Broadcast is a fundamental operation in wireless networks and is well supported by the wireless channel. However, the interference resulting from a node's transmission pose a key challenge to the design of any broadcast algorithms/protocols. In particular, it is well known that a node's interference range is much larger than its transmission range and thus limits the number of transmitting and receiving nodes, which inevitably prolong broadcast. To this end, a number of past studies have designed broadcast algorithms that account for this interference range with the goal of deriving a broadcast schedule that minimizes latency. However, these works have only taken into account interference that occurs within the transmission range of a sender. Therefore, the resulting latency is non-optimal given that collision occurs at the receiver. In this paper, we address the Interference-Aware Broadcast Scheduling (IABS) problem, which aims to find a schedule with minimum broadcast latency subject to the constraint that a receiver is not within the interference range of any senders. We study the IABS problem under the protocol interference model, and present a constant approximation algorithm, called IABBS, and its enhanced version, IAEBS, that produces a maximum latency of at most 2⌊π/√(3)(α+1)2+ (π/2+1)(α+1)+1⌋ R, where α is the ratio between the interference range and the transmission range, i.e., α ≥ 1, and R is the radius of the network with respect to the source node of the broadcast. We have evaluated our algorithms under different network configurations and confirmed that the latencies achieved by our algorithms are much lower than existing schemes. In particular, compared to CABS, the best constant approximation broadcast algorithm to date, the broadcast latency achieved by IAEBS is 5 over 8 that of CABS.

This paper addresses the NP-hard problem of switching off bundled links whilst retaining the QoS provided to existing applications. We propose a fast heuristic, called Multiple Paths by Shortest Path First (MSPF), and evaluated its performance against two state-of-the-art techniques: GreenTE, and FGH. MSPF improves the energy saving on average by 5% as compared to GreenTE with only 1% CPU time. While yielding equivalent energy savings, MSPF requires only 0.35% of the running time of FGH. Finally, for Maximum Link Utilization (MLU) below 50% and delay no longer than the network diameter, MSPF reduces the power usage of the GÉANT topology by up to 91%.

The success of wireless mesh networks hinges on their ability to support bandwidth intensive, multi-media applications. A key approach to increasing network capacity is to equip wireless routers with smart antennas. These routers, therefore, are capable of focusing their transmission on specific neighbors whilst causing little interference to other nodes. This, however, assumes there is a link scheduling algorithm that activates links in a way that maximizes network capacity. To this end, we propose a novel link activation algorithm that maximally creates a bipartite graph, which is then used to derive the link activation schedule of each router. We have verified the proposed algorithm on various topologies with increasing node degrees as well as node numbers. From extensive simulation studies, we find that our algorithm outperforms existing algorithms in terms of the number of links activated per slot, superframe length, computation time, route length and end-to-end delay.

This paper studies the energy efficiency of twelve Pure and Slotted Aloha tag reading protocol variants via simulation. We compare their energy consumption in three collision resolution phases: (1) success, (2) collision, and (3) idle. Our extensive simulation results show that Pure Aloha with fast mode and muting has the lowest energy consumption, and hence is most suited for deployment in energy-constrained environments.

Wireless routers equipped with smart antennas are capable of forming beams to neighboring devices to transmit/receive multiple packets simultaneously, hence achieving high network capacity. This however is dependent on the link scheduling algorithm employed by these routers. To this end, we describe a simple link activation algorithm that tradeoffs path length to increase network capacity. We show via analysis and simulation that the proposed algorithm improves network capacity and lowers end-to-end delay despite a slight increase in path length.

This paper presents a dynamic framed slotted Aloha (DFSA) protocol that is energy efficient, and more importantly, is the first protocol capable of monitoring tags. Our protocol uses three separate frames: 1) reservation, 2) body, and 3) monitor. The reservation and body frame are used to identify tags, whereas the monitor frame is used to keep track of identified tags. We have performed extensive simulation studies on all three frames, and compared our protocol with existing framed Aloha protocols. From our results, we confirm that our protocol is suitable for use in RFID-enhanced wireless sensor networks (WSNs).

Deafness is a key problem. It erodes the performance gains provided by directional antennas, and introduces a new hidden terminal problem. To address deafness, and hence the hidden terminal problem, we propose SpotMAC. By exploiting narrow or pencil beams, SpotMAC achieves high spatial reuse, throughput and fairness. In addition, pencil beams simplify the collision avoidance process and constrain the hidden terminal problem to a linear topology which can be solved using an inverse RTS/CTS exchange. From extensive simulation studies, we confirm nodes using SpotMAC have several orders of magnitude higher throughput than those using the IEEE 802.11 MAC with omni-directional antenna.

This paper studies the energy consumption of frame slotted Aloha (FSA) based anti-collision protocols. Specifically, we investigate twelve FSA variants using a detailed qualitative and quantitative methodology to evaluate their energy efficiency with varying tag population. Our results show that the variant that adjusts its frame size in accordance with tag population and incorporates the muting and early-end feature has the lowest energy consumption, hence most suited for RFID-enhanced WSNs.

This paper investigates the energy efficiency of RFID anti-collision protocols and their suitability for use in RFID-enhanced wireless sensor networks (WSNs). We present a detailed analytical methodology and an in-depth qualitative and quantitative energy consumption analysis of Pure and Slotted Aloha anti-collision protocols and their variants. We find that Slotted Aloha variants that employ muting with early-end are the most energy efficient, but are computationally expensive. Overall, for all Aloha variants we investigated, if the offered load is very high, tag responses cause a bottleneck at the reader. Thereby, resulting in no tags being identified and incur significant identification delays - thus severely impacting a sensor node's battery life.

Low throughput due to unfairness is a key problem in multi-rate wireless local area networks. To promote fairness and hence throughput, T2-Fair groups flows according to their average data rate, provides each group fair time allocations and ensures throughput fairness for flows in each group. Since each group is allocated transmission times fairly, T2-Fair isolates high and low rate groups and prevents system capacity from being degraded by low rate flows. We have derived T2-Fair's performance bounds analytically and investigated its performance using the ns-2 simulator in various scenarios with a mix of high and low rate flows. Our results show that T2-Fair is effective in isolating and providing proportional throughput fairness to these flows.

The IEEE 802.15.3 MAC enables high-rate communications between devices in a wireless personal area network and has good support for applications requiring quality of service (QoS). To meet applications' QoS requirements, such as delay and jitter, the channel time allocation (CTA) scheduler plays an important role in sizing and positioning CTAs within each super-frame. In this paper, we first present a novel CTA sharing protocol, called VBR-MCTA that enables the sharing of CTAs belonging to streams with the same group identity. This allows our protocol to exploit the statistical characteristics of variable bit rate (VBR) streams by giving unused time units to a How that requires peak rate allocation. We then present two optimizations to VBR-MCTA, namely VBR-Blind and VBR-TokenBus. The former, by giving ownership of a CTA in a round-robin manner without consideration to traffic profiles, does not consume any valuable "air-time" with signaling overheads. The latter allows a CTA to be shared by multiple devices that take turns owning unused "air-time" from CTAs. We have simulated VBR-MCTA and its optimizations in the ns-2 simulator over an implementation of the IEEE 802.15.3 MAC. Our results show that VBR-TokenBus has the best delay and jitter as it provides a one to six milliseconds reduction in both compared to standard CTA methods. VSR-Blind, although having performing poorer than MCTA-Token or VBR-MCTA, is still significantly better than traditional CTA methods at a reduced overhead.

We present a rendezvous based medium access control (MAC), culled ArDeZ, for use in sensor networks. ArDeZ is a TDMA based medium access scheme that does not rely on strict time slot positioning and assignments, making ArDeZ easily deployable in large sensor networks without having to adhere to strict time slot boundaries. ArDeZ establishes two independent peer-to-peer time channels between nodes, and these channels do not necessarily have the same duty cycle. Each channel has a set of rendezvous periods associated with it that are generated from a seed exchanged during the setup process. Further, the duration and frequency of each channel's rendezvous periods are easily changed by an application for each link on a given path, thereby allowing the application to balance traffic and energy requirements. We have implemented ArDeZ in the ns-2 simulator, and our results show that ArDeZ is capable of very low power consumption.

This paper presents the impact of IEEE 802.15.3 MAC's channel time allocation methods on a TCP flow's performance. We show the importance of having super rate and appropriately sized channel time allocations (CTAs)

Mobile ad-hoc networks (MANETs) are dynamic and multi-hop in nature. As nodes continually join and leave the MANET, managing the problem of address conflicts is particularly challenging. In the past, researchers have gone to great lengths to ensure that nodes are assigned unique addresses and various protocols and policies have been designed to resolve address conflicts. In this paper, we argue that current solutions, originally designed for static wired networks, put unnecessary stress on the dynamic operation of a MANET. To solve, this problem, we present a MANET that can continue to operate even when there are conflicting addresses. Unlike previous solutions, our technique does not break applications by requiring nodes to renumber. Further, the overheads introduced by traditional address allocation and maintenance protocols are removed. All these improvements are effected by introducing of a new routing sub-layer that enables a reactive routing protocol to route packets through a MANET that is experiencing address conflicts. This routing sub-layer provides features such as conflict avoidance forwarding, conflict notification, and enhanced address resolution.

In mobile environments, efficient binding update delivery results in fast adaptation to the effects of migration. The scheme discussed performs the update using IP multicast. The proposed active delivery scheme/sup +/ (ADS/sup +/) is an extension of the ADS earlier proposed by the authors. Unlike ADS, ADS/sup +/ does not assume the use of additional network hardware and yet provides a quick, deployable, bandwidth conserving solution to the delivery of binding updates.

In this paper we propose AMTRee, an active network (AN) based multicast tree that is bidirectional, optimizable on demand and adaptive to source migration. We show how AN can be leveraged to enable a multicast tree to be modified and optimized efficiently after handoff. By filtering unnecessary signaling messages, maintaining minimal storage at routers and incorporating features of shared-tree methods we are able to achieve a scalable solution. Furthermore we introduce an AN-based optimization algorithm that is executed on demand by receivers. The performance of AMTree is compared to that of the bidirectional home agent (HA) method and the remote subscription method. We found that compared to the bidirectional HA method AMTree has a much lower handoff and end-to-end latency. The AMTree approach does not require a new multicast tree to be built after each handoff and yet the end-to-end latency is comparable to that of the remote subscription method.