Wireless rechargeable sensor networks (WRSNs) have emerged as a promising solution to overcome the energy bottleneck in traditional battery-powered sensor networks. However, the uncertain energy demands and dense deployment of sensor nodes pose significant challenges to efficient charging scheduling in WRSNs. To address these challenges, this article proposes a novel multi-AAV assisted on-demand partial charging scheduling (MOPCS) algorithm. MOPCS integrates the advantages of one-to-many charging, partial charging, and dynamic multi-AAV coordination to maximize the network lifetime and energy utilization. The key contributions of this work include a real-time adaptive charging scheduling trigger mechanism, an energy-efficient charging cluster division method, a spatiotemporally balanced task allocation among multiple autonomous aerial vehicles (AAVs), and a hybrid priority-based charging path planning algorithm. Extensive simulations demonstrate that MOPCS significantly outperforms state-of-the-art algorithms in terms of charging request response timeliness, node survival rate, and AAV energy efficiency, especially in dense network deployments. This work provides valuable insights and practical solutions for the design and optimization of AAV-assisted charging scheduling in WRSNs, paving the way for more sustainable and scalable wireless sensor networks in various application scenarios.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is significant packet loss in LoRa transmission due to link performance degradation. Existing studies take little account of end-devices' movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance theLink Performance forMobile LoRa networks in general scenarios for both single-gateway and multi-gateway applications. The key observation is that, due to LoRa's unique feature, repeating the original packet content enables the use of smaller, more energy-saving transmission parameters, which not only enhances link performance but also reduces energy consumption. Technically, we propose a link performance estimation model based on packet content repetition for both single-gateway and multi-gateway mobile networks. Then, we propose the corresponding channel frequency selection model to avoid transmission collisions. Finally, we design low-overhead communication mechanisms to operate the system. To evaluate the performance of LMLoRa in various scenarios, we design and implement real-world testbeds and a simulation platform for both single-gateway and multi-gateway scenarios. Extensive results show that LMLoRa improves packet delivery ratio by an average of 33.4% to 69.2% compared with the state-of-the-art.

LoRa, as a Low-Power Wide Area Networks (LP-WAN) technology, is extensively employed for connecting Internet of Things (IoT) applications. LoRa time-slotted networks have gained popularity due to their high channel utilization and robust anti-interference capability. However, the queueing latency of end-devices (EDs) in these networks is often overlooked in the time-slot-scheduled LoRa network, leading to data obsolescence and insufficient notification time. Existing research mainly focuses on reducing transmission delay and avoiding collisions in LoRa networks, while neglecting the importance of ensuring low queueing latency for EDs. In this paper, we propose a semidefinite relaxation (SDR)-based channel scheduler called Q-MAC to achieve low queueing latency in time-slotted LoRa networks. The core idea is to allocate time slots and channels effectively for packets while avoiding collisions. To accomplish this, we formulate an optimization model to minimize latency and packet collisions. This model is a multivariable-coupled non-convex integer problem, we transform the model into a Quadratically Constrained Quadratic Programming (QCQP) problem. Subsequently, we employ the SDR and heuristic algorithms to obtain feasible solutions. Simulation results demonstrate that Q-MAC can significantly reduce queueing latency, achieving an average improvement of 8.57 × compared to existing approaches.

LoRa, as a typical representative of Low Power Wide Area Networks (LPWAN), has been widely used to connect massive IoT devices. However, in mobile applications, there is massive packet loss in LoRa transmission due to link performance degradation, especially when LoRa end-devices move far from the gateway or into obstructed areas. Existing studies take little account of end-device movement, particularly when the movement pattern is unknown. We propose LMLoRa to enhance the Link Performance for Mobile LoRa networks in general scenarios. The key observation is that repeating the original packet content enhances link performance and allows smaller and more energy-efficient transmission parameter selections. Technically, LMLoRa proposes a link performance estimation model for mobile LoRa networks based on packet content repetition. Second, we exploit key hardware features of LoRa to obtain much continuous RSSI information for link quality prediction. Additionally, LMLoRa develops a channel frequency allocation policy to mitigate transmission collisions. Finally, LMLoRa designs a communication mechanism to assist the estimation model and work the whole system with low communication overhead. We design and implement LMLoRa in complex realworld environments, results show that LMLoRa enhances packet reception rate by 33.4% and energy efficiency by 14.4% on average compared with the state-of-the-art.

LoRa Localization, a fundamental service in LoRa networks, has garnered significant attention due to its long-range capabilities and low power consumption. However, existing approaches for LoRa localization are either incompatible with commercial devices or highly susceptible to environmental factors. To tackle this challenge, we propose SyncLoc, a TDoA-based LoRa localization framework that integrates a dedicated node for multi-dimensional time-drift correction. Our proposal is built on two key observations: firstly, the nanosecond-level measurement of time differences between gateways, and secondly, the substantial impact of SNR on gateway time drift. To accomplish our objective, we present three progressively enhanced versions of SyncLoc, each intended to comprehensively analyze the factors influencing LoRa time synchronization accuracy across different deployment scenarios involving nodes, carrier frequencies, and spreading factors. In addition to improving accuracy, we identify inefficiencies in LoRa’s multi-node concurrent localization, and introduce SyncLoc-4, a multi-node localization scheduling mechanism that optimizes efficiency with a 2-approximation ratio. Extensive experiments utilizing commercial LoRa devices in real-world demonstrates a 2.44× improvement in accuracy. Furthermore, simulations of large-scale networks exhibit a 2.47× boost in localization scalability (i.e., the number of concurrently located nodes) when employing SyncLoc instead of LoRaWAN.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under thesamespreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences underdifferentSF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of > 95%.

Wireless sensing has emerged as a powerful environmental sensing technology that is vulnerable to the impact of all kinds of ambient noises. LoRa is a novel interference-resilient technology of low-power wide-area networks (LPWAN), which has attracted wide attention from scientific and industrial communities. However, LoRa transmission suffers from serious latency in those complex wireless sensing environments requiring transmission reliability. In this article, we present CH-MAC, the first MAC-layer protocol based on the local corruption nature of packets and the time-varying nature of channels to reduce end-to-end transmission latency in LPWAN with reliable communication requirements. Specifically, CH-MAC employs Luby Transform code to divide and encode the payload into several blocks such that the receiver can retain part of the coded information in the corrupted packets. In addition, CH-MAC utilizes hopping to transmit different blocks of a packet with various channels to avoid sudden noise collision. Moreover, CH-MAC adopts a dynamic packet length adjustment mechanism to mitigate network congestion. Extensive evaluations on a real-world hardware testbed and a simulation platform show that CH-MAC can reduce end-to-end transmission latency by 2.63× with a communication success rate requirement of >95% compared with state-of-the-art methods.

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability issues in solving a large-scale RDMA resource scheduling problem. In this paper, we present how to optimally share the RDMA resources in large-scale data center networks with a distributed manner. First, we propose Distributed RDMA NUM (DRUM) to model the RDMA resource scheduling problem as a new variation of the NUM problem. Second, we present distributed algorithms to efficiently solve the large-scale, interdependent RDMA resource sharing problem for different RDMA use cases. Through theoretical analysis, the convergence and parallelism of proposed algorithms are guaranteed. Finally, we implement the algorithms as a kernel-level indirection module in the real-world RDMA environment, so as to provide end-to-end resource sharing and performance guarantee. Through extensive evaluations by large-scale simulations and testbed experiments, we show that our method significantly improves applications’ performance under resource contention, achieving$1.7-3.1\times $higher throughput, and in a dynamic context, the largest performance improvement reaches 98.1% and 64.1% in terms of latency and throughput, respectively.

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum, transmissions from multiple LoRa end-devices inevitably collide into each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the same spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under different SF settings, which are resulted by the imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks, and significantly limit the LoRa reliability. In this paper, we present X-MAC, the first scheduler that is aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and further performs dynamic channel scheduling to avoid collisions caused by interferences both under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 2.41× with packet reception rate (PRR) requirement of > 95%.

Low-Power Wide Area Networks (LPWANs) are widely used to connect large-scale Internet of Things (IoT) applications. Long Range (LoRa) is a promising LPWAN technology sensitive to energy consumption, since LoRa nodes are generally battery-powered, and the battery life will influence the lifetime of the LoRa network. In practice, the battery life of LoRa nodes is short in many scenarios, due to the long transmission distance form the gateway leading to high energy consumption. Existing techniques for energy-efficient data transmission mainly focus on static gateways, and will consume huge energy of remote nodes. In this paper, we propose to integrate LoRa with mobility to minimize the energy consumption of nodes by effectively shortening the transmission distances, and design the first mobile LoRa data transmission system called LoRaDrone by leveraging the unmanned aerial vehicle (UAV) gateway flying close to nodes. Specifically, we present a low-power communication mechanism and a dynamic channel allocation policy to minimize the energy consumed in sensing and communicating with the UAV gateway, while considering the distinctive LoRa parallel reception and complex transmission collisions. Then, an optimal speed scheduling strategy is designed to ensure the reliability of data transmission, and minimize the energy consumption of the UAV. Evaluations on various scales verify the effectiveness of LoRaDrone under different nodes' distributions and UAV paths. Compared with the baselines, the energy consumption of nodes using LoRaDrone is at most reduced by$\mathbf{70.37}\times$at 5000 nodes.

With the increment of the scale of users and networks, network virtualization technology has been widely used by service providers in cloud networks to address elastic network demands. As a fundamental network virtualization component realizing cross-tenant traffic routing, the placement of virtual routers has become a considerable factor influencing the network performance. And through in-depth experiments, we also found that CPU load augments may well incur a significant degradation in network throughput performance, revealing the problems of the placement of virtual routers in existing cloud network modes: 1) Ignore the bandwidth loss caused by the CPU load variation. 2) Lack of theoretical support for the optimal scheme. Based on these above, we propose a CPU load-aware virtual router placement strategy, which balances the computing load situation of each virtual router, and adopts branch and bound algorithm and convex optimization to achieve the approximate optimal placement within 0.1% error. We have evaluated our strategy in our cloud testbed, and find a 20% improvement in terms of cross-tenant throughput compared with the worst case of existing strategies,