Due to the limited storage capacity of Internet of Things (IoT) devices, it is hard for these devices to store all applications locally to meet their various intelligent needs. To solve this deficiency, based on application modularization technology, we propose a Terminal-Edge-Cloud (TEC) collaborative caching framework, which includes module caching, request information uploading, and module loading. Through this framework, IoT devices can load application modules on demand and run diverse and intelligent applications. To manage the caching process, we introduce a utility model consisting of application module request and load delay, caching deployment cost, and caching management cost, and formulate the collaborative caching problem as a utility maximization one, which is NP-complete. To solve the problem, we decouple the problem and propose an effective two-stage collaborative caching strategy. Simulation results show that the proposed algorithm can provide an effective caching strategy by improving users’ Quality of Experience (QoE) and reducing the total system cost.

Mobile Edge Computing (MEC) is a promising paradigm that deploys cloud-based services at the network edge to process computationally intensive tasks with low service delay. However, optimizing MEC systems remains challenging due to the random task arrival patterns of mobile edge devices and the necessity for dynamic service placement. Consequently, decisions regarding service placement significantly influence task offloading choices, and vice versa. This interdependence has been relatively overlooked, limiting the overall performance of MEC systems. To address this challenge, we formulate the cooperative service placement and task offloading problem as a Partially Observable Markov Decision Process (POMDP). We propose a simple yet efficient approach named HAC-LSTM, which integrates Long Short-Term Memory (LSTM) networks with a hybrid actor-critic (HAC) algorithm. HAC-LSTM employs an LSTM-based state encoder to effectively extract hidden information and manage partial future information of the MEC system. Subsequently, the HAC algorithm leverages hybrid actors—a discrete actor for service placement and a continuous actor for task offloading—to make optimized joint decisions. We evaluate our approach through extensive experiments using real-world mobility datasets and varying key parameters. The results demonstrate that HACLSTM outperforms baseline algorithms by significantly minimizing average delay, thereby enhancing the overall efficiency and responsiveness of MEC systems.

Autonomous vehicles (AVs) are expected to handle complex tasks in dynamic environments. However, the limited onboard computing capacity of AVs and heterogeneous tasks under strict delay constraints bring performance bottlenecks. To efficiently complete the onboard tasks, we formulate a dynamic Vehicle-to-Vehicle Task Offloading Problem (V2TOP) in Vehicular Ad Hoc Network (VANET), aiming to jointly minimize end-to-end task delay and maximize the average speed of connected vehicles subject to safety constraints. Then, we design a Mobility-Aware Distributed Primal-Dual Offloading Algorithm (MDPDOA) to optimize global resource utilization, explicitly modeling both task diversity and heterogeneous computing capabilities of participating vehicles. Furthermore, a dynamic triggering mechanism is introduced to timely re-allocation in response to topology or resource variations, guaranteeing consistent performance in realtime vehicular networks. Simulation results demonstrate that the V2V collaboration policy efficiently utilizes vehicle computing resources and enhances driving safety.

The number of Internet of Things (IoT) devices has increased rapidly in recent years, but lack effective methods to integrate their computational power. In this article, we propose NC-Load, which couples IoT devices into a multiprocessor system, allowing process scheduling across different devices to share their computing power and improve overall throughput. Specifically, NC-Load consists of three key designs, i.e., remote page fault (RPF), lightweight program cropping, and identical memory layout migration, contributing to three merits compared to existing systems: 1) high storage efficiency: the target device launches the program with a locally stored lightweight icon and leverages RPFs to retrieve the required code/data from the source device; 2) on-demand memory loading: only the required memory portions are transmitted when scheduling programs across different devices, which ensures quick recovery of the program; and 3) consistent memory layout: to ensure consistency of addresses after program offloading, the virtual memory area layout of the source device is migrated to the target device. We implement NC-Load on Linux 6.1 and conduct performance evaluation using unmodified programs and the N-Queens cases. The results demonstrate that NC-Load can achieve superior performance in terms of storage efficiency, program performance, memory usage, and throughput.

Indoor Sound Source Localization (ISSL) is under growing focus with the rapid development of smart IOT intelligence. The predominant approaches typically involve constructing large microphone (Mic) array systems or extracting multiple angles of arrival (AOAs). However, the performance of these solutions is often constrained by the physical size of the array. Besides, there has been limited focus on 3D localization with a single small-sized Mic array. In this paper, we propose HearLoc, an ISSL system that can directly locate 3D sources with a ten-$cm$Mic array. We demonstrate that the localization ability and dimensional capability can be significantly enhanced by incorporating the time differences of arrival (TDOAs) between the line-of-sight (LOS) and ECHO signals from nearby reflective surfaces. Our approach involves a localization method that selectively sums the correlation powers at useful TDOAs induced by each location. We also design a data processing pipeline with interpolation, normalization and pruning techniques to improve system accuracy and efficiency. To further enhance scalability, we design an iterative algorithm for the ISSL problem with multiple sources and an array location calibration scheme. Experiments demonstrate that the HearLoc can effectively locate sound sources, exhibiting$2\times$/$3.7\times$improvements in accuracy for 2D and 3D localization, respectively, and a$4\times$increase in efficiency compared to the existing AOA-based ISSL solutions.

Mobile Edge Computing (MEC) plays an increasingly important role in the rapidly increasing mobile applications by providing high-quality computing services. The majority of current research has focused on designing efficient computing task offloading schemes to ensure the effectiveness of the MEC system. However, the configuration and resource management of the MEC system, which are crucial for its scattered feature, have not received due attention. This paper investigates the configuration and computation resource management problem for the MEC system by formulating a profit maximization problem. To address this problem, we first analyze the relationship among mobile users' offloading decisions, the configuration and computation management of the MEC system, and the service quality. Then, we design an optimal configuration and computation management scheme of the MEC system, which can not only maintain the efficiency of computing processes but also make a good trade-off between the profitability and the service quality. In such a way, the total expected profit of the MEC system can be maximized. Numerical evaluations show that the proposed optimal configuration and computation management scheme can efficiently improve the total profit of the MEC system.

As wireless communication and Internet of Things (IoT) technologies advance, edge computing brings computing and storage capabilities closer to users, providing low-latency and high-quality services. However, the limited resources of edge servers and the diverse resource demands of heterogeneous tasks may result in high latency and poor energy efficiency. To address this challenge, we investigate a Docker-based resource management framework for edge servers, involving the allocation of heterogeneous tasks and the configuration of the Docker container clusters. Then, we formulate the problem as one of minimizing energy consumption and task latency, concerning task allocation and server resource management, subject to server resource constraints. To solve this problem, we propose an effective task assignment and resource management strategy, which is developed based on convex optimization theory, aiming to achieve an approximate optimal solution. Simulation results demonstrate that, compared to other algorithms, the proposed algorithm significantly reduces server operating power consumption and task response latency.

As society progresses, liquid identification plays an increasingly important role in human life. But for now, minority of existing liquid identification solutions on the market can meet daily requirements of being ubiquitous, cost-effective and non-intrusive enough. In this work, we propose ASLiquid, the first liquid counterfeit identification system with commercial off-the-shelf earphones. Our core insight is that earphones can effectively induce acoustic resonance in container, and this phenomenon is observed highly associated with the changes in liquid density and solute compositions. Deploying ASLiquid introduces three main challenges: hardware heterogeneity among different earphones, diversity of user operations, and data complexity due to variations in liquid volume and device placement. To address these issues, we first propose to eliminate the existence of hardware noise and frequency response diversity for an earphone-irrelevant solution. Afterwards, we design a user operation adaptation algorithm to extract valuable feature data during each measurement period. To alleviate problems in data complexity, we propose a spectrum projection algorithm that can effectively generate CFR data of unknown liquid volumes and a VAE based anomaly detection model for counterfeit identification. We evaluate our system with six different earphones and under various conditions. Experimental results reveal that ASLiquid can achieve F1 scores of 95%-99.25% for seven frequently occurring liquid counterfeit tasks, even in specialized attacks on liquids with 1% difference in mass fraction and different types of solutions but with the same density.

With the improvement in quality of life, ensuring liquid safety has become increasingly important, leading to a growing focus on liquid identification technologies. While extensive prior work has employed RF signals for this purpose, such methods typically require expensive and bulky signal transmitters or receivers. In this work, we explore a novel modality for liquid identification, i.e. container acoustic resonance. Our key observation is that the acoustic resonance spectrum of each liquid-container system is strongly correlated with the liquid's density and solute composition. We design and implement a simple prototype for liquid identification with low-cost acoustic sensors. The spectrum of channel frequency response for each liquid-container system is extracted as the criterion. The results show that the average accuracy for classifying 10 common liquids is 99.4% with ResNet-5.

Traditional workload-based task scheduling has been well studied in edge computing. However, computing tasks with different types may have different sensitivities of processing latency to memory and CPU resources, which makes it challenging to design an efficient task scheduling strategy for edge servers with different configurations to guarantee the quality of service (QoS). To address this challenge, we first conduct the extensive testing on the processing latency of memory and CPU resources in the multi-container environment, and formulate the task scheduling problem as a long-term QoS optimization problem. Secondly, we transform the original problem into a task scheduling sub-problem for each time slot based on Lyapunov optimization theory, and propose an online task scheduling strategy using Genetic Simulated Annealing Algorithm, which can obtain an approximate optimal solution that minimizes makespan while ensuring the queue stability. Thirdly, based on matching theory, we use the backlog tasks information to update the approximate solution and further minimize makespan in real-time. Finally, the simulation results show that the proposed scheme can minimize the makespan comparing to other schemes.

Integrating the advantages of both the edge and the cloud, the edge-cloud computing system emerges to provide high-quality computing services for mobile users. To improve system efficiency, we investigate a hybrid mode of resource collaboration and expansion for the edge-cloud computing system, in which edge servers not only can collaborate with the cloud by purchasing high-priority computation resources temporarily but also can expand their local computation resources permanently. In such a way, the edge server can maximize its long-term profit by making a trade-off between the purchasing cost and the expanding cost. By formulating the resource management problem as a long-term profit maximization one, we first analyze the relationships among the expected minimal purchasing cost, the computation delay, and the available computation resources. Then, we design an efficient resource reserving and expanding scheme to determine the optimal expected amounts of reserving resources and expansion resources. Next, we propose an efficient real-time resource purchasing scheme to obtain the optimal amount of real-time purchasing resources dynamically. Finally, simulation results show that the proposed efficient resource collaboration and expanding scheme can maximize the long-term profit while guaranteeing the computation delay.

As a potential computing solution for fast-growing mobile and IoT applications, edge computing has been developed rapidly. However, due to the relatively limited resources of each edge node, it is difficult for edge nodes to provide quality-guaranteed services to dynamic and massive computation tasks individually. To address this challenge, this paper proposes a two-stage resource expansion and sharing scheme, named SESAME, to enable resource sharing among the edge nodes within/across multiple edge service providers (ESPs), to improve the overall efficiency of the edge computing system. To facilitate the operation and reduce complexity, the resource management scheme has both long-term and short-term decision periods. During the long-term period, an optimal conservative estimation-based resource expansion and pricing strategy has been designed to ensure the system stability and the interests of ESPs. During the short-term period, a resource-sharing strategy considering the internal and external behaviors of ESPs has been proposed to reduce resource-sharing costs while fully utilizing internal resources. In such a way, the resources from different ESPs can collaborate efficiently. Extensive experiments on real datasets show that our algorithm can effectively reduce ESP costs and improve system stability.

Freshness-aware computation offloading has garnered increasing attention recently in the realm of edge computing, driven by the need to promptly obtain up-to-date information and mitigate the transmission of outdated data. However, most of the existing works assume that channels are reliable, neglecting the intrinsic fluctuations and uncertainty in wireless communication. More importantly, offloading tasks typically have diverse freshness requirements. Accommodation of various task priorities in the context of freshness-aware task scheduling and resource allocation remains an open and unresolved problem. To overcome these limitations, we cast the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, prioritized users, and the heterogeneity of edge servers. Building upon the nonlinear fractional programming and the ADMM-Consensus method, we introduce a joint resource allocation and task offloading algorithm to solve the original problem iteratively. In addition, we devise a distributed asynchronous variant for the proposed algorithm to further enhance its communication efficiency. We rigorously analyze the performance and convergence of our approaches and conduct extensive simulations to corroborate their efficacy and superiority over the existing baselines.

As a high-quality computing service provider for mobile users, the task offloading scheme for existing mobile edge computing (MEC) has been widely studied in recent years. However, how to establish an efficient MEC system for Edge Service Providers (ESPs), especially building on the existing Base Stations (BSs) of Mobile Network Operators (MNOs), has not been well studied. In this paper, we formulate the BS rental problem for MNOs and the edge server deployment problem for ESPs as an auction-based profit optimization problem. Firstly, we model the competition for BSs among the ESPs as a sequential first-price auction and the edge server deployment problem as a profit maximization problem for each ESP. Then, we prove that both of these problems are convex and propose an efficient auction-based edge sever deployment scheme for ESPs. Finally, extensive simulations are conducted to show the effectiveness of the proposed edge server deployment scheme for ESPs.

Federated learning (FL) is an emerging distributed machine learning paradigm, which has shown great potential in collaborative learning with privacy preservation. However, FL clients usually have disparate system resource capabilities (e.g., data, computation, and communication) for model training and aggregation, which can cause a series of system heterogeneity issues with performance degradation. To this end, we propose FedPKD, a Prototype-based Knowledge Distillation framework for FL. FedPKD integrates knowledge distillation and prototype learning with FL, which enables heterogeneous clients and the server to learn collaboratively, with different model architectures and resource capability adaptations. Specifically, FedPKD proposes to transfer dual knowledge of clients including the model output logits and prototypes to the server, and a prototype-based ensemble distillation mechanism is proposed to aggregate the logits and prototypes from clients, which can be used to train the server model with an unlabeled public dataset. The server model knowledge is then transferred back to clients to improve the performance of client models. Moreover, to improve learning performance and reduce communication overhead, we propose a prototype-based data filter mechanism to filter out the samples with low-quality knowledge. Extensive experiments under various settings demonstrate the superiority of FedPKD in learning performance and communication efficiency when compared to state-of-the-art benchmarks.

The rapid proliferation of latency-sensitive Internet of Things (IoT) applications boosts the frequency of offloading compute-intensive tasks from IoT users to mobile edge computing (MEC) due to the limitation resources of IoT devices. It is inevitably for IoT users to compete for the computing resources of the MEC, especially when the computation tasks are dependent and have hard deadline constraints. However, most existing dependent task offloading schemes may not well consider the resource competition issues among IoT users, and possibly lead to limited system performance in multiuser scenario. To address this issue, we intend to design an auction-based dependent task-offloading mechanism to improve the efficiency of task offloading for multiple IoT users. First, we formulate the dependent task offloading as a valuation maximization problem in the trade of computing resources satisfying users’ latency requirements, which has been proved to be NP-hard. Then, by jointly considering the task graph structure and the current status of the MEC, we propose a truthful auction mechanism, named greedy winner selection strategy, in which a heuristic dependent task assignment for winners is designed to improve the efficiency of the task offloading. By conducting extensive simulations, we validate that the performance of the proposed dependent task offloading strategy is superior to existing competition algorithms, in terms of total valuations, average makespans, and success rates.

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

With the rapid growth of global mobile data traffic, Wi-Fi plays an increasingly important role in expanding network capacity. To overcome the geographical coverage limit of Wi-Fi APs, especially for mobile users, the crowdsourced wireless community network has emerged as a cost-effcient way for providing Internet access services. For instance, it is plausible to share their private residential Wi-Fi APs with each other by designing some tailored incentive/pricing mechanisms. Thus motivated, we propose a distributed pricing and bandwidth allocation scheme to maximize the profit of Wi-Fi providers and provide better Internet services to mobile users. Firstly, we study the stationary networks with incomplete information of users and propose distributed pricing and bandwidth allocation algorithms for single-AP regions and AP group regions, respectively. Then, we generalize the study to dynamic networks and explore distributed pricing based on the statistics of users mobility. Further, we design an online bandwidth allocation algorithm according to the real-time user information. Simulation results demonstrate that the proposed distributed pricing and bandwidth allocation scheme, comparing with the operators pricing scheme, has a better performance on both Wi-Fi APs profit and user experience.

With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.

With the explosive growth of mobile and Internet of Things (IoT) applications, increasing Mobile Edge Computing (MEC) systems have been developed by diverse Edge Service Providers (ESPs), opening a new computing market with stiff competition. However, considering the spatiotemporally varying features of computation tasks, taking over all the received tasks alone may greatly degrade the service performance of the MEC system and lead to poor economical benefit. To this end, this paper proposes a two-layer collaboration model for ESPs. Each ESP can balance the computation workload among the internal edge nodes from the ESP and offload part of computation tasks to the ESP external edge servers from other ESPs. For internal load balancing, we propose a task balancing scheme based on the Alternating Direction Method of Multipliers (ADMM) to manage the computation tasks within the edge nodes of the ESP, such that the computation delay can be minimized. For external task offloading, we formulate a game-based pricing and task allocation scheme to derive the best game strategy, aiming at maximizing the total revenue of each ESP. Extensive simulation results demonstrate that the proposed schemes can achieve improved performance in terms of system revenue and stability, as well as computation delay.

Internet card (IC) as a new business model emerges, which penetrates rapidly and holds the potential to foster a great business market. However, the understanding of IC user portraits is insufficient, which is the building block to boost the IC business. In this paper, we take the lead to bridge the gap by studying one large-scale dataset collected from a provincial network operator of China, which contains about 4 million IC users and 22 million traditional card (TC) users. Particularly, we first conduct a systematical analysis on usage data by investigating the difference of two types of users, examining the impact of user properties, and characterizing the spatio-temporal networking patterns. After that, we shed light on one specific business case of churn prediction by devising an IC user Churn Prediction model, named ICCP, which consists of a feature extraction component and a learning architecture design. In ICCP, both the static portrait features and temporal sequential features are extracted, and one principal component analysis block and the embedding/transformer layers are devised to learn the respective information of two types of features, which are collectively fed into the classification multilayer perceptron layer for prediction. Extensive experiments corroborate the efficacy of ICCP.

Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including 1) the allocation of dynamic communication and computational resources, 2) delay constraints of heterogeneous tasks, and 3) requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is formulated as a delay-constrained long-term stochastic optimization problem under unknown prior statistical knowledge. To solve this problem, we first provide a technical path to transform and decompose it into several slot-level sub-problems. Then, we devise a distributed online algorithm, namely TODG, to efficiently allocate resources and schedule offloading tasks. Further, we present a comprehensive analysis for TODG in terms of the optimality gap, the worst-case delay, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.

Federated learning (FL) can enable distributed model training over mobile nodes without sharing privacy-sensitive raw data. However, to achieve efficient FL, one significant challenge is the prohibitive communication overhead to commit model updates since frequent cloud model aggregations are usually required to reach a target accuracy, especially when the data distributions at mobile nodes are imbalanced. With pilot experiments, it is verified that frequent cloud model aggregations can be avoided without performance degradation if model aggregations can be conducted at edge. To this end, we shed light on the hierarchical federated learning (HFL) framework, where a subset of distributed nodes are selected as edge aggregators to conduct edge aggregations. Particularly, under the HFL framework, we formulate a communication cost minimization (CCM) problem to minimize the communication cost raised by edge/cloud aggregations with making decisions on edge aggregator selection and distributed node association. Inspired by the insight that the potential of HFL lies in the data distribution at edge aggregators, we propose SHARE, i.e., SHaping dAta distRibution at Edge, to transform and solve the CCM problem. In SHARE, we divide the original problem into two sub-problems to minimize the per-round communication cost and mean Kullback-Leibler divergence of edge aggregator data, and devise two light-weight algorithms to solve them, respectively. Extensive experiments under various settings are carried out to corroborate the efficacy of SHARE.

Owing to the ability to provide better services for latency-intensive tasks than the cloud paradigm, Mobile Edge Computing (MEC) has attracted increasing attention recently. However, due to limited resources, MEC cannot handle large amount of computation tasks as the cloud paradigm. Most of the existing works design offload strategies for MEC by sharing the responsibility of total computation tasks with the cloud to provide more services, but neglecting the fact that the profit can be shared when sharing responsibility, which decreases the profit. To address this issue, we propose a trade collaboration framework for the MEC and the cloud paradigm, where the MEC can purchase resources from the cloud paradigm to process computation tasks under latency constraints. Without accurate information about required resources, this paper has designed an efficient resource trade scheme for the MEC to achieve their optimal purchased resources, such that the expected profit of the MEC can be maximized. Simulation results show that the proposed scheme can maximize the profit of the MEC and guarantee latency requirements.

In this article, we aim at maximizing the data gathering performance of the rechargeable wireless sensor network, where a mobile sink moves along the predefined path to charge sensor nodes through a wireless energy transfer technique and gather data from them. First, we show how to transform the original time-average optimization problem into a queue stability one by using the Lyapunov optimization framework, then we show how to decompose it into multiple subproblems by using the optimization decomposition. A distributed speed control and routing algorithm was proposed to reduce the computing load of the mobile sink and to obtain the near-optimal solution for data collection. Our analysis shows that there is an inherent tradeoff between the network utility and the average data queuing size, and the proposed adaptive transmission scheme can achieve the near-optimal network utility when a certain queueing delay can be tolerated.

The advancement of the Internet of Things has enabled numerous applications ranging from smart wearable to connected vehicles. However, the limited energy and memory resources of low-end IoT devices significantly impede their further flourish. In this article, we consider a system that consists of an IoT device and an edge server. The edge server stores code blocks for the IoT device and loads required blocks to the IoT device for execution thereby alleviates the latter from the limited memory resource. Furthermore, the IoT device can harvest energy from the ambient energy sources to achieve a sustainable operation. To deal with the dynamic energy harvesting process and block request process, we propose a stochastic block prefetching framework (BPF) to optimize the user experienced delay. The BPF assists the IoT device to intelligently prefetch blocks from the edge server according to the historical user behaviors. The BPF consists of three modules, i.e., estimation module, prefetching module, and dual learning module. The estimation module measures the probability of block being requested in the future. The prefetching module requests blocks from the edge server according to the available energy and memory. The dual learning module helps to accelerate the convergence of the framework. The numerous simulation results are provided to verify the effectiveness of the proposed framework.

In this paper, we consider a wireless powered cooperative non-orthogonal multiple access (NOMA) relay network, in which one source is supposed to send independent messages to two users with the assistance of one energy-constrained relay that harvests energy from the source. Firstly, we study the minimum power consumption at the source node to fulfill the least required transmission rates by two users in both time switching relaying (TSR) strategy and power splitting relaying (PSR) one. Secondly, when the relay is provisioned with data buffer and energy storage, the long-term average power consumption minimization problem is formulated to take into account of the data and energy queue causality, peak transmit power constraint, and transmission mode selection. By using Lyapunov optimization framework, a novel buffer-aided transmission scheme (BATS) is proposed to asymptotically approach the optimal solution. Our analysis shows that, the PSR outperforms the TSR in terms of the realized energy efficiency, and BATS can be utilized to further improve the energy efficiency. It is disclosed that, there is an inherent trade-off between the long-term power consumption and the average queuing delay. In addition, larger user rates or less power consumption can be realized if a larger delay can be tolerated.

Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

We study the computation resource management problem in mobile edge-cloud computing networks. Mobile edge servers shall first satisfy the computation requirements of mobile users and Internet of Things (IoT) devices, and then wholesale redundant computation resources to the cloud networks to maximize their profit. Due to the coarse time granularity of wholesales, computation resource buyback may happen occasionally to deal with traffic bursts. Thus, the mobile edge servers need to make a tradeoff between the wholesale profit and the buyback cost. In this paper, the computation resource management problem is modeled as profit maximization. To solve this problem, we first analyze the relationship among the reserved computation resources, the computation tasks of mobile users and IoT devices, and the buyback cost. Then, we design an efficient wholesale scheme to determine the amount of the wholesaled computation resources, by which the total expected profit of the mobile edge server can be maximized. Given the reserved computation resources, we also propose a fast-convergent realtime buyback scheme for mobile edge servers to minimize the buyback cost. Finally, the simulation results show that our proposed efficient wholesale and buyback scheme can increase the total profit while guaranteeing the computation delay of all the computation tasks, especially when the computation workloads are time-varying.

With the increasing penetration of electric vehicles (EVs) and various user preferences, charging stations often provide several different charging modes to satisfy the various requirements of EVs. How to effectively utilize the charging capacity to minimize the service dropping rate is a pressing and open issue for charging stations. Given that EV owners are price-sensitive to the charging modes, we intend to design an optimal pricing scheme to minimize the service dropping rate of the charging station. First, we formulate the operation of a dual-mode charging station as a queuing network with multiple servers and heterogeneous service rates, and analyze the relationship between the service dropping rate of the charging station and the selections of EVs. Then, we formulate a customer attrition minimization problem to minimize the number of EVs that leave the charging station without being charged and propose an optimal pricing approach to guide and coordinate the charging processes of EVs in the charging station. The simulation has been conducted to evaluate the performance of the proposed charging scheduling scheme and show the efficiency of the proposed pricing scheme.

Connected electric vehicles (EVs) are a key component of future intelligent and green transportation systems, and the penetration of EVs depends on convenient and cost-effective charging services. In addition to being charged at home or on parking lots, a charging network is needed for EVs right off the road. This paper first focuses on the optimal charging network design for charging service providers, considering the time-varying and location-dependent demands from vehicles and constraints of power grids. To optimize the charging station locations and the number of chargers in each station, we first model the coverage area of each possible location to estimate the dynamic charging requirements of EVs. Then, we formulate the problem as profit maximization, which is a mixed-integer program. To make the problem tractable, we investigate the features of the problem and obtain a necessary condition to deploy a charging station and derive the upper and lower bounds of the number of chargers in each station. Given the analysis, we take two steps to transform and relax the problem to convex optimization. A fast-converging search algorithm is further proposed based on the profit of each possible location. Using real vehicle traces, simulation results show that the proposed algorithm can maximize the total profit when fewer charging stations and chargers are initially needed, which is more attractive for charging service providers.

This paper addresses the optimal charging scheduling problem for Electric Vehicles (EVs) in an intelligent workplace parking lot powered by both the Photovoltaic Power (PV) System and the Power Grid. Due to the uncertain charging requirements of different EVs and time-varying available renewable energy, the charging load from the parking lot may bring a new challenge to the Power Grid. By minimizing total cost of the parking lot, we design a dynamic charging scheduling scheme to manage the charging processes of EVs based on the real- time information of EVs and renewable energy from the PV system. Numerical simulations are carried out to demonstrate the efficiency of the designed charging scheduling scheme.

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated.

In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

Data gathering in wireless sensor networks typically involves two steps: data sensing and data transmission, which dominate the energy consumption of each sensor. In Rechargeable Sensor Networks (RSNs), in order to optimize data gathering, energy should be carefully allocated to data sensing and data transmission due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the optimal data transmission. In this paper, we strive to optimize data gathering by jointly considering data sensing and transmission. To this end, we first design a Balanced Energy Allocation Scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then we propose a Distributed Sensing Rate and Routing Control (DS2RC) algorithm to jointly optimize data sensing and transmission, while guaranteeing network fairness. In DS2RC, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. We theoretically prove the optimality and the convergence of the proposed algorithms. Extensive simulations are performed to demonstrate the efficiency of BEAS and DS2RC by comparing with existing algorithms.

Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms.

Energy harvesting is a promising technology for extending the lifetime of sensor networks with the restrictions of limited battery energy. One of the main challenging issues is to maximize the sampling rates of all sensor nodes. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize overall network utility. To solve the problem, we firstly propose an adaptive energy allocation scheme for each node to manage its energy use in an efficient way. Then we develop a distributed sampling rate control (DSRC) algorithm to obtain the optimal sampling rate. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are performed to demonstrate the efficiency of our algorithm.

Traditional measurement-while drilling (MWD) logging tools use magnetic dipoles (coils) as transmitting and receiving antennas and operate at a fixed frequency. In this paper, a new MWD tool using electrical dipoles and using pulses as transmitting signals is investigated and its performance is compared to the coil-type MWD tool in the same formation using a numerical simulation technique. The performances of these two different tools are compared both in the time domain and in the frequency domain. A time-domain transmission-line-matrix (TLM) method is used to perform the analysis. It is shown that a pulsed electric-dipole type MWD tool is superior to a coil-type MWD tool in detection of formation boundaries in all the tested cases. It is further suggested that an MWD tool using electric dipole antennas may be more sensitive in practical applications.

An iterative algorithm is developed to reconstruct the image of formation conductivity surrounding a borehole using time-domain data. The forward modeling employed in the algorithm is derived from the transmission line matrix (TLM) method, which is used to simulate electromagnetic waves propagating in formations with two-dimensional variations in cylindrical coordinates. A new structure of a transmission line mode is used to simulate a coil-type transmitter antenna in a borehole. Since the inversion algorithm proceeds iteratively and the part of the formation involved in the inversion marches in space step by step, no optimization is necessary, and problems caused by optimization procedure such as inverting large-scale matrix and computation of Jacobian matrix numerically, are avoided. This method is especially useful in cases where the analytic gradient is not available. The inversion algorithm is tested in formations having both one- and two-dimensional conductivity variations with coil-type transmitters. Investigation depth and resolution for noise-free cases are also discussed.>