In recent years, the Internet of Things (IoT) has rapidly advanced, with applications ranging from smart homes to industrial manufacturing, often involving densely deployed nodes such as temperature and humidity sensors. Since these nodes have limited computation and energy, the use of stuffed Wi-Fi management frames for data transmission has emerged as a promising way to avoid the association overhead of the traditional transmission mode. However, this unassociated data transmission mode continues to encounter significant channel contention in dense deployments. To this end, we propose ODGMAC, an on-demand grouping-based MAC solution that dynamically groups transmission-awaiting nodes and allocates time slots on a per-group basis, thereby enabling intra-group contention to improve transmission efficiency and reduce node energy consumption. Firstly, we present a fuzzy control-based algorithm at the access point (AP) to dynamically identify nodes with transmission demands in the current beacon period. On this basis, we then propose a hierarchical group-based time slot allocation methodology. Specifically, the nodes are initially clustered according to their per-packet airtime requirements. Within each cluster, we evenly partition nodes into multiple groups and assign each group to a unique time slot for channel contention, where the optimal slot count is determined by a renewal-theory-based analytical model with a discrete search over candidate counts. Finally, we implement the ODGMAC testbed with one AP and 100 IoT nodes, and conduct real-world experiments in a dense environment. The experimental results show that our solution outperforms existing methods in terms of both data delivery rate and node power consumption. Specifically, under severe channel collision conditions, our solution achieves an average increase of 14.77% in data delivery rate and an average reduction of 8.21% in node power consumption, while maintaining excellent fairness. Moreover, extended simulations show that our solution scales to 1000 nodes and maintains excellent performance under node mobility.

Resource-constrained IoT devices in Industrial Internet environments are highly vulnerable to DDoS attacks due to infrequent security updates and insufficient built-in protection mechanisms. Existing defense solutions primarily rely on external filtering servers or programmable switches, but these approaches fail to simultaneously meet the stringent real-time performance and high accuracy requirements of industrial applications. To address these limitations, we propose a novel cross-plane defense framework that exploits the temporal invariance characteristics of attack traffic patterns. In the data plane, an adaptive variance threshold mechanism immediately mitigates high-volume, low-variance traffic flows, while a bidirectional dual-hash table captures low-collision flow features for efficient export to the control plane. The control plane constructs temporally-enhanced flow sequences that enable deep learning models to perform accurate attack detection, subsequently directing the data plane to block identified malicious sources. We implemented and evaluated a prototype of this framework on a software switch platform using both real-world attack datasets and custom-generated traffic patterns. Experimental results demonstrate that our framework successfully mitigates 86% of attack traffic within milliseconds and achieves complete source blocking within 52 seconds. Compared to baseline methods, our framework can effectively counter both DoS and DDoS attacks without generating false positives on benign traffic.

As a growth engine for smart cities, the emergence of the low-altitude economy presents significant scheduling challenges for UAV fleets that perform cooperative tasks, such as logistics and infrastructure inspection. This paper focuses on scheduling a fleet of cooperative, homogeneous UAVs to perform a series of tasks distributed along a predefined route between docking stations, with the objective of minimizing the total completion time. The complexity stems from temporal dependencies that affect subsequent tasks and from idle-time constraints, where early-arriving UAVs must wait for collaborators. To tackle this, we propose a novel framework based on a geometric visualization of the scheduling problem, introducing the concept of a “skyline” to create a canonical representation of resource availability over time. A provably optimal schedule is found based on this skyline concept, and computational efficiency heuristics that incorporate a lookahead mechanism is proposed for large-scale, practical applications. Comprehensive simulations demonstrate that our skyline-based methods consistently and significantly outperform conventional baseline algorithms in solution quality.

Unmanned Aerial Vehicles (UAVs) have been widely employed to execute tasks in diverse scenarios. However, most existing studies focus on task scheduling using a fixed number of UAVs, which often leads to resource waste or the inability to complete all deadline-driven tasks-a critical issue particularly in practical scenarios such as search and rescue, surveillance and mobile edge computing. Distinct from previous works, we investigate the novel problem of scheduling deadline-driven tasks with a minimum number of UAVs (termed SLIM). This optimization is non-trivial as it requires both minimizing UAV count and ensuring optimal task execution within deadline and energy constraints. To address these challenges, we develop two complementary algorithms: (1) a dynamic programming based algorithm that provides optimal solutions for small-scale scenarios, and (2) an approximation algorithm that ensures theoretical performance guarantees for efficiently handling largescale scenarios. Extensive simulations demonstrate that our approximation algorithm achieves approximately 85% of the optimal dynamic programming solution's performance while reducing the average number of UAVs by 21.8%-39.9% compared to state-of-the-art approaches.

Unmanned aerial vehicles (UAVs) are being widely employed in wireless communication applications, e.g., collecting data from ground nodes (GNs). Minimizing UAV energy in these applications is crucial due to the limited energy supply onboard. Unlike previous studies that assume UAVs fly at a fixed altitude and simplify the energy consumption model of UAVs, we consider the impact of varying UAV altitudes on the ground-to-air communication and utilize a general communication model for GN. Furthermore, we conduct real-world flight tests and introduce a practical speed-related flight energy consumption model of UAVs. This paper focuses on the UAV altitude-speed scheduling and GN transmission switching (UASS-GTS) problem, specifically in scenarios where the UAV flies straight for monitoring applications such as power transmission lines, roads, and water/oil/gas pipes. However, minimizing energy consumption presents challenges due to the tight coupling of altitude scheduling and speed scheduling. To tackle this, first, we develop the looking before crossing algorithm for speed scheduling. We then extend this algorithm by integrating altitude scheduling to propose the Altitude-Speed Scheduling of UAV for Minimizing Energy (ASSUME) algorithm, using a dynamic programming method. The ASSUME algorithm is theoretically proven to be optimal. Additionally, based on ASSUME, we propose an offline-inspired online heuristic algorithm to handle agnostic situations where GN information is not available unless flies close. Simulations indicate that the ASSUME algorithm saves an average of 26.1%–62.7% energy compared to the baseline methods, and the performance gap between the online algorithm and the offline optimal algorithm ASSUME is 22.8%.

Unmanned aerial vehicles (UAVs) play a critical role in disaster response, swiftly gathering information from various points-of-interest (PoIs) across extensive areas. The freshness of this information is measured by the age of information (AoI), representing the time since the latest information acquisition of a specific PoI. However, devising AoI-minimizing routes for UAVs in obstructed post-disaster environments poses unique challenges that have yet to be fully overcome. Obstacles, like post-disaster barriers, can impede direct flight paths between PoIs, and limited battery life requires energy-conscious route planning. Additionally, existing solutions fail to universally minimize varying data freshness requirements. This research addresses the AoI-driven UAV travel problem, seeking to establish periodic routes that optimize AoI metrics while considering energy and general graph constraints. We develop a learning-based algorithm to enhance the current route iteratively, utilizing guidance from a deep reinforcement learning (DRL) agent and executing a series of operations to potentially decrease AoI while adhering to topological and energy constraints. The algorithm is validated on real post-disaster datasets, demonstrating significant improvements in various AoI metrics compared to other learning-based approaches. Furthermore, our algorithm outperforms approximation algorithms and can approach the global optimum when tailored to existing AoI-minimizing problems.

Low-altitude airspace in major cities across the world is increasingly congested with unmanned aerial vehicles (UAVs) and other aircraft. Emerging technologies, innovative business models, and supportive government policies are driving the growth of the low-altitude economy, where UAVs play a crucial role. Given the limited on-board energy of UAVs, this paper investigates the Joint UAV Speed and Altitude Scheduling (JUSAS) problem for data collection from sensors deployed along power transmission lines, bridges, highways, railways, water/gas/oil pipelines, or rivers/coasts. Distinct from existing work, the paper focuses on jointly optimizing UAV speed and altitude scheduling while determining the wireless sensor collection order. It accounts for the altitude-specific sensor transmission range model and the complexities of overlapping range relationships. We first propose theSlowest Segment First(SSF) policy to obtain an optimal UAV speed scheduling for fixed-altitude scenarios. Building upon this, we then reformulate JUSAS as a shortest-path-type problem using our novel flight scheduling graph, solved efficiently through theSSF-based Ant Colony Optimization(SSF-ACO) algorithm. To handle practical scenarios without prior sensor information along the path, we develop SSF-ACO-Online for real-time scheduling. Extensive simulations demonstrate that SSF-ACO significantly outperforms four other algorithms (i.e., SSF-Only, SSF-GA, SSF-PSO, and SSF-SA) in energy efficiency, and reduces 13.11% energy consumption on average. SSF-ACO-Online achieves comparable performance with energy consumption 1.24% higher than offline counterpart in average.

Supply Chain Networks (SCNs) play a vital role in achieving strategic decision-making for production and distribution facilities, aiming to meet market demands and gain competitive advantages. With the application of new-generation information technology in the supply chain, enterprises within SCNs generate a substantial volume of relevant business data. Sharing this data among SCN enterprises can effectively reduce operating costs, optimize business processes, and enhance the overall efficiency of the supply chain. However, effective data sharing among SCN participants faces challenges, such as data leakage, data quality assurance, and fair data value allocation. To address these challenges, this paper proposes a secure cross-domain data sharing model in SCNs (named SCN-CDSM) based on consortium blockchain technology. The model introduces trust, enables cross-domain data exchange, and promotes cooperation among supply chain enterprises. To ensure privacy, group signatures and access control smart contracts are designed, along with an approach to reduce blockchain throughput limitations. Furthermore, a sharing incentive mechanism utilizing the Stackelberg game model based on data value is designed to foster fairness and collaboration. Extensive numerical simulations are conducted to demonstrate the effectiveness of the proposed schemes, achieving both security and efficiency in data sharing within SCNs.

Energy harvesting is a promising technique to address the energy hunger problem for thousands of wireless devices. In Radio Frequency (RF) energy harvesting systems, a wireless device first harvests energy and then transmits data with this energy, hence the ‘harvest-then-transmit’ (HTT) principle is widely adopted. We must carefully design the HTT schedule, i.e., schedule the timing between harvesting and transmission, and decide the data transmission power such that the throughput can be maximized with the limited harvested energy. Distinct from existing work, we assume energy harvested from RF sources is time-varying, which is more practical but more difficult to handle. We first discover a surprising result that the optimal transmission power is independent of the transmission time, but solely depends on the RF harvesting power, for a simple case when the energy harvesting is stable. We then obtain an optimal offline HTT-scheduling for the general case that allows the RF harvesting power to vary with time. To the best of our knowledge, it is the first optimal HTT-scheduling algorithm that achieves maximum data throughput for time-varying RF powered systems. Finally, an efficient online heuristic algorithm is designed based on the offline optimality properties. Simulations show that the proposed online algorithm has superior performance, which achieves more than 90% of the offline maximum throughput in most cases.

Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this article, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.

Summary With their high flexibility and versatility, unmanned aerial vehicles (UAVs) have maneuvered their way into many applications. Thanks to their ability to plan and coordinate, multiple UAVs complete tasks more effectively, which boosts their popularity in battlefield surveys, formation performances, and targeted searches. However, the risk of security threats also rises alongside their popularity. The UAV ad hoc network (UANET) has endeavored to contend with such risks through the optimized link state routing (OLSR) protocol. To test the security and strength of this effort, we present a sky black hole attack (SBHA) algorithm for OLSR, which is undetectable, based on the UANET's multi‐hop routing and the OLSR's known topology. This algorithm obtains the network's maximum profits by approaching and then replacing the calculated topology center and traffic center in UANET. Because of the ever‐changing topology, SBHA aims at UANET's single central node that cannot be detected in advance. This attack is difficult to detect by UANET and therefore difficult to defend. The simulation results show that SBHA can cause greater damage to UANET compared to a traditional black hole attack, and ordinary defense algorithms cannot reduce the negative impact of SBHA on UANET. In addition, SBHA also gains UANET control, and leads to drastic changes in UAVs' movement trajectory, which has more intuitive effects.

In energy harvesting communication systems, it is possible for a transmitter to schedule the transmission by jointly scaling the rate and turning the transmitter ON/OFF adaptively. Such a joint rate and sleep schedule can greatly increase the throughput achieved by the transmitter with battery constraints. However, most existing works on joint rate and sleep scheduling assume the transition between different states does not have any cost, i.e., energy or time consumption. This is not realistic while the energy and time needed for booting a transmitter, i.e., turning a transmitter from OFF to ON, are not small enough to be ignored in most cases. In this paper, we investigate the joint rate and sleep scheduling on system throughput with more general booting consumption considered in energy harvesting communication systems. We first identify the structural properties of the optimal solution for the model with booting consumption considered. Inspired by these observations, we develop an optimal offline algorithm and an online heuristic algorithm to solve the problem. Experimental results from simulations and real tests show that the proposed algorithms can achieve much higher throughput on average in a realistic energy harvesting communication system, compared to those algorithms that only consider rate scheduling or ignore the booting consumption.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications,e.g., traversing to collect data from ground sensors, patrolling to monitor key facilities, moving to aid mobile edge computing. We summarize these UAV applications and formulate a problem, namely thegeneral waypoint-based PoI-visiting problem. Since energy is critical due to the limited onboard storage capacity, we aim at minimizing flight energy consumption. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which are usually ignored in the literature but play an important role in practical UAV flights according to our real-world measurement experiments. We propose specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem,i.e., general traveling salesman problem, which can be efficiently solved. Theoretical analysis shows that such problem transformation has the graph redefinition approximation ratio upper bound,$max\lbrace \Theta /\delta ,2\rbrace$, where$\Theta$is related to the designed graph parts and$\delta$is a constant. Finally, we evaluate our proposed algorithm by simulations. The results show that it costs less than 107% of the optimal minimum energy consumption for small scale problems and costs only 50% as much energy as a naive algorithm for large scale problems.

Nowadays, not only wearable and portable devices but also aerial and ground robots can be made smaller, lighter, cheaper, and thus as large as hundreds of them may form a swarm to participate in a complicated cooperative application, such as searching, rescuing, mapping, and war-battling. Devices and robots in such a swarm have three important features, namely, large number, high mobility and short distance, hence they form a dynamic and dense wireless network. Successful swarm cooperative applications require low latency communications and real-time localization. This paper proposes to use ultra-wideband (UWB) radio technology to implement both functionalities, because UWB is very time-sensitive that an accurate distance can be calculated using the transmission and reception timestamps of data messages. A UWB swarm ranging protocol is designed to achieve simultaneously wireless data communication and swarm ranging that allows a device/robot to compute the distances to all the peer neighbors at the same time. This protocol is designed for dynamic and dense networks, meanwhile it can also be used in various wireless networks and implemented on various types of devices/robots including low-end ones. In our experiment, this protocol is implemented on Crazyflies, STM32 microcontroller powered micro drones, with onboard UWB wireless transceiver chips DW1000. Extensive real-world experiments are conducted to verify the proposed protocol on various performance aspects, with a total of 9 Crazyflie drones in a compact area. The implemented swarm ranging protocol is open-sourced athttps://github.com/SEU-NetSI/crazyflie-firmware

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying$K$UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the$K$UAVs is connected. We then propose a$\frac {1-1/e}{\lfloor \sqrt {K} \rfloor }$-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is$\frac {1-1/e}{5(\sqrt {K} +1)}$), where$e$is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications, e.g., traverse to collect data from ground sensors, patrol to monitor key facilities, move to aid mobile edge computing. We summarize these UAV applications and formulate an abstract problem, namely the general waypoint-based PoI-visiting problem, aiming at minimizing flight energy consumption, which is critical due to its limited onboard storage capacity. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which is usually ignored in the literature but plays an important role in practical UAV flights. We propose a novel method that uses specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem, i.e., traveling salesman problem, which can be efficiently solved. Finally, we evaluate our proposed algorithm by simulations. The results show it costs less than 107% of the optimal minimum energy consumption for small scale problem and costs only half as much energy as a naive algorithm for large scale problem.

Nowadays, aerial and ground robots, wearable and portable devices are becoming smaller, lighter, cheaper, and thus popular. It is now possible to utilize tens and thousands of them to form a swarm to complete complicated cooperative tasks, such as searching, rescuing, mapping, and battling. A swarm usually contains a large number of robots or devices, which are in short distance to each other and may move dynamically. So this paper studies the dynamic and dense swarms. The ultra-wideband (UWB) technology is proposed to serve as the fundamental technique for both networking and localization, because UWB is so time sensitive that an accurate distance can be calculated using timestamps of the transmit and receive data packets. A UWB swarm ranging protocol is designed in this paper, with key features: simple yet efficient, adaptive and robust, scalable and supportive. This swarm ranging protocol is introduced part by part to uncover its support for each of these features. It is implemented on Crazyflie 2.1 drones, STM32 microcontrollers powered aerial robots, with onboard UWB wireless transceiver chips DW1000. Extensive real world experiments are conducted to verify the proposed protocol with a total of 9 Crazyflie drones in a compact area.

Unmanned aerial vehicles (UAVs) are being widely used in wireless communication, e.g., collecting data from ground nodes (GNs), where energy is critical. Existing works combine speed scheduling, i.e., the controlling of speed, with trajectory design for UAVs, making it complicated to solve while loses focus on the fundamental nature of speed scheduling. We focus on speed scheduling by considering straight line flights, with applications in monitoring power transmission lines, roads, water/oil/gas pipes and rivers/coasts. By real-world flight tests, we disclose a speed-related flight energy consumption model, distinct from typical distance-related or duration-related models. Based on such a practical energy model, we develop the looking before crossing (virtual rooms) algorithm, where virtual rooms on the time-distance diagram represent the spatio-temporal constraint of GNs in wireless transmission. This algorithm is proved to be optimal in solving the offline problem, where all information is known before scheduling. For the online problem, i.e., GN information is not unavailable unless flies close, we propose an offline-inspired online heuristic. Simulation shows its performance is near the offline optimal. Our study on the practical flight energy model and speed scheduling sheds light on a new research direction on UAV-aided wireless communication.

Unmanned aerial vehicle (UAV) assisted data collection is a promising technology, where a base station (BS) is mounted on a UAV to collect data from ground sensors (GSs). However, it is very challenging to save the energy of UAV while completing the tasks of data collection. In this work, a novel energy consumption model of UAV is adopted, where the UAV flies at a proper speed is the most energy efficient, i.e., the UAV will cost more energy when it flies faster or slower. According to this model, we investigate the Energy-efficient Trajectory Planning and Speed Scheduling (ETPSS) problem, aiming at minimizing the total energy consumption of UAV by determining flight trajectory and speed of UAV while completing the task of data collection for each GS. To solve this problem, we decompose it into two sub-problems, i.e., trajectory design and speed scheduling, and propose a three-step scheme named Energy-efficient Trajectory and Speed optimization (ETSO). Moreover, the second step of ETSO optimally solves the speed scheduling sub-problem. Finally, we conduct simulation experiments, and the results demonstrate that the ETSO performs well on energy efficiency.

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS , which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.

In cloud computing, it is crucial to maintain service continuity, while power outage is one of the most common and serious threats. To improve the resilience of cloud against power outage, a service provider usually deploys emergency energy supply (e.g., UPSs and generators) in a data center. When a power outage at a data center happens, the cloud service provider needs to make the operation decision on which subset of VMs to keep running and which servers to host such VMs to minimize its loss (or maximize its profit) using the emergency energy supply while the selected VMs are running in the affected data center until they are finished, migrated to other data centers, or normal power supply of the affected data center has been restored. No prior research has theoretically studied such a cloud service continuity problem under power outage. In this paper, we tackle this challenge and investigate the cloud service continuity problem. Specifically, we consider that a profit is associated with maintaining the continuity of a service, denoted as service continuity profit. Based on that we first formulate an optimization problem that aims to maximize the total profit subject to energy constrains. After showing the hardness of the problem, we focus on the design of approximation algorithms for solving the problem, where we consider two practical cases. In the first one with sufficient number of servers for re-provisioning, we develop a constant approximation algorithm of which the worst-case performance approaches the optimal solution within a constant factor (≈4.5-6.4). In the second one, we consider the general case with limited number of servers, and we develop an approximation algorithm with an approximation ratio of around 5.7-8. By combining these two algorithms together, we can achieve both good worst-case performance and average performance. Simulation results demonstrate the efficiency in terms of maximizing the service continuity profit of the proposed algorithms.

Billions of lightweight Internet of Things (IoT) devices have been deployed for various applications nowadays. Most of them first collect interested data and then process them in some degree according to application requirements. Transparent computing (TC) is a promising technique that makes such lightweight devices suitable to process even large-size applications. The advantage of TC is to separate code storage from its execution, allowing IoT devices to load code blocks from nearby TC storage server on demand. Distinct from existing work, this paper allows the TC IoT devices to offload some tasks to servers, since wireless IoT devices are usually powered by batteries, having limited energy resources. If a task is offloaded, a challenging problem is that its input data collected by the IoT device must be transferred as well, which incurs additional transmission time and energy. This paper proposes a two-step approach aiming at minimizing the energy consumption of the IoT device while satisfies the delay constraint. This approach first studies the offloading decision problem that determines for each task whether to offload task data or load task code blocks, while loading code indicates code receiving and executing energy cost. Second, the transmission power scheduling problem is investigated to further reduce offloading energy for a given delay constrained offloading task set. Heuristic decision making algorithms and optimal power scheduling algorithm are proposed, respectively. Such two-step approach is shown by extensive simulation to be near optimal for the original problem thanks to the optimal design of the power scheduling algorithm.

Radiative wireless power transfer (WPT) is a promising technique to power wireless devices' transmission. In a resource-limited device, receiving energy and transmitting data cannot operate at the same time because they share the same spectrum or hardware. This paper studies the problem for a wireless device to decide when to harvest energy, when to deliver data, and what transmission rate to use. Distinct from the most existing works, we focus on delay minimization in transmitting a sequence of data packets over a point-to-point channel, which is critical for time-sensitive applications. Since the battery is capacitated, the device must repeatedly switch between harvesting energy and transmitting data. For the offline case where packet information is known before scheduling, a surprising result is discovered that for all (energy receiving and data transmitting) cycles, except the last one, the optimal transmission rate should be a constant which is called the wOPT rate. Based on this discovery, the offline delay minimization problem is optimally solved. For the online case where packets arrive dynamically without prior information, we propose a simple online algorithm: using the wOPT rate to transmit whenever both energy and data are ready. It is proved to be 1.16-competitive if the battery is initially empty, namely, its delay is less than 1.16 times the offline optimal delay for any given packet set. When the battery is with arbitrary initial energy, simulation results show that the performance is near optimal. The discovery of the wOPT rate reveals an essential property of WPT and is expected to be significant in solving other related problems.

Wireless power transfer (WPT) is a recently developed technique to perfectly address the energy problem for smart city sensors that do not have a readily wired power supply. In a radio-frequency-powered wireless communication system, sensors first harvest energy via the radio-frequency WPT, and then, transmit sensed data to the receiver. The “harvest-then-transmit” protocol is used to coordinate the two operations, in which we wish to optimally decide when to harvest energy, when to transmit data, and what transmission rate should be used such that the data are maximally transmitted. Unlike existing works, we assumed that the wireless transferred power is dynamically changing, instead of being constant, which is more realistic in green smart city applications, e.g., Industry 4.0 workshop, smart transportation, and smart buildings, where environments are continuously changing, so the wireless transferred power is affected dynamically. In this paper, we present an optimal scheduling algorithm for the offline case where the varying WPT is known in advance. Based on the optimal principles learned from the offline case, we have designed an efficient online algorithm. Finally, we report our simulation results that demonstrate that our online scheduling algorithm can adaptively and efficiently achieve high data throughput.

Energy harvesting communication system enables energy to be dynamically harvested from natural resources and stored in capacitated batteries to be used for future data transmission. In such a system, the amount of future energy to harvest is uncertain and the battery capacity is limited. As a consequence, battery overflow and energy dropping may happen, causing energy underutilization. To maximize the data throughput by using the energy efficiently, a rate-adaptive transmission schedule must address the trade-off between a high-rate transmission which avoids energy overflow and a low-rate transmission which avoids energy shortage. In this paper, we study an online throughput maximization problem without knowing future information. To the best of our knowledge, this is the first work studying the fully-online transmission rate scheduling problem for battery-capacitated energy harvesting communication systems. We consider the problem under two models of the communication channel, a static channel model that assumes the channel status is stable, and a fading channel model that assumes the channel status varies. For the former, we develop an online algorithm that approximates the offline optimal solution within a constant factor for all possible inputs. For the latter, that the channel gains vary in range [hmin; hmax], we propose an online algorithm with a proven ⊖(log(hmax/ hmin))-competitive ratio. Our simulation results further validate the efficiency of the proposed online algorithms.

Wireless power transfer (WPT) technique enables wireless charging/recharging, thus is a promising way to power wireless devices' transmissions. Because current WPT technique requires a wireless device to stop transmitting data when receiving power, and also because the received power in this way is limited, careful scheduling is needed to decide when the device should receive power and when it should transmit such that data can be efficiently transmitted. This paper assumes the most fundamental point-to-point White Gaussian Noise channel is used for data transmission and attempts to obtain an optimal scheduling such that a sequence of data packets can be transmitted with the minimum delay. It is discovered that, for all (energy receiving, data transmitting) cycles, except the last one, the optimal transmission rate should be a constant which is called the wOPT rate. Based on this discovery, this paper optimally solves the offline delay minimization problem. Then, an online heuristic scheduling algorithm is proposed, which either receives energy or transmits at the wOPT rate. Simulations have demonstrated its efficiency. The discovery of the wOPT rate reveals an essential property of WPT, thus is expected to make significant impact in the field of WPT.

In a participatory sensing system, data sensed from smartphone users are shared with the general public who requests data through submitting tasks. When multiple tasks request the data from a mobile user, the mobile user can make a transmission schedule to achieve the balance between the amount of data transmitted and energy consumption. Intuitively, reducing the amount of data transmitted by making use of data sharing between the tasks can save the energy consumption. However, due to the convexity of rate-power function for rate-adaptive transmitting devices, a schedule purely minimizing the amount of data transmitted may not always be the optimal one minimizing the energy consumption. Thus, there exists a tradeoff between the amount of data transmitted and energy consumption. This paper formulates the problem as a bi-objective optimization problem to simultaneously minimize the amount of data transmitted and the energy consumption. Two task models are studied, first-in-first-out (FIFO) task model and arbitrary deadline (AD) task model, respectively. We first provide optimal algorithms for the off-line case. We then study the online case where requests arrive dynamically without prior information. For FIFO tasks, we develop an online algorithm that is O(ln L)-competitive with respect to both the amount of data transmitted and energy consumption, where L is the longest length of the time duration of the tasks. For AD tasks, we devise an online algorithm that is O(ln2L)-competitive with respect to both the amount of data transmitted and energy consumption. Our simulation results validate the efficiency of our online algorithms.

This paper presents an optimal rate scheduling algorithm called Truncation for an energy-harvesting enabled wireless transmitter to transmit a set of dynamically arrived packets with minimum transmission energy. Distinct from existing works, we allow packets to have individual delay constraints, which is the most general model ever assumed but is very much desired to guarantee per-application quality-of-service (QoS). Moreover, we restrict the allowable rates to a set of discrete values, which is more practical and required in many real applications. As the first achievement, we obtain an optimal offline algorithm, which assumes the rate is continuously adjustable. Then, we propose a general framework that transforms any algorithm using the continuous-rate model into an algorithm using only discrete-rates, while preserving the optimality as long as the optimality holds for convex rate-power functions. It is possible that the harvested energy is insufficient to guarantee all packets to meet their deadlines. Should this occur, maximizing throughput with the limited available energy becomes the goal to achieve. Our Truncation algorithm is able to identify this case and produces a schedule that guarantees maximum throughput, if packets share a common deadline. Furthermore, based on the optimal offline algorithms, an efficient online algorithm is designed which has been shown by simulations to produce near optimal results.

Packet scheduling in switches plays an important role in providing guaranteed service to meet Quality-of-Service (QoS) requirements. In this paper, we revisit a historical Maximum Traffic Time-slot Scheduling (MTTS) problem for both circuit- and packet-switched traffic in SS/TDMA satellite systems proposed two decades ago by Bonuccelli et al.. They developed a two-step approach to solve the MTTS problem, which scheduled the circuit-switched traffic first into TDMA frames and then inserted packets from packet-switched traffic into empty slots in these scheduled TDMA frames as many as possible. This problem was referred as Incremental Time-slot Assignment (ITSA) Problem in their paper, and was proved to be NP-hard. Therefore, MTTS problem seems to be NP-hard due the the NP-hardness of ITSA problem induced by their two-step approach. However, in this paper, we show that MTTS problem is NOT NP-hard, since there exists an optimal algorithm for this problem. By aggregating the traffic in scheduled TDMA frames, an optimal algorithm based on network flow method is developed for MTTS problem, which can solve the MTTS problem in polynomial time. Theoretic proof for the optimality of our algorithm is given and simulations results further validate the correctness of our algorithm.

With the tremendous amount of information that is becoming available on the Web, the ability to develop information agents quickly has become a crucial problem. In this paper, a programmable agent is presented for Internet application to retrieve and extract information from the Web with user's guidance. The agent consists of a retrieval script to identify Web sources, an extraction script based on the document object model for the extraction process and a data translator to export the extracted information into knowledge‐based frame structures. A GUI tool called Script Writer supports the generation of extraction script visually.