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Deep neural network (DNN)-task enabled mobile edge computing (MEC) is gaining ubiquity due to outstanding performance of artificial intelligence. By virtue of characteristics of DNN, this paper develops a joint design of task partitioning and offloading for a DNN task enabled MEC network that consists of a single server and multiple mobile devices (MDs), where the server and each MD employ the welltrained DNNs for task computation. The main contributions of this paper are as follows: First, we propose a layer-level computation partitioning strategy for DNN to partition each MDs task into the subtasks that are either locally computed at the MD or offloaded to the server. Second, we develop a delay prediction model for DNN to characterize the computation delay of each subtask at the MD and the server. Third, we design a slot model and a dynamic pricing strategy for the server to efficiently schedule the offloaded subtasks. Fourth, we jointly optimize the design of task partitioning and offloading to minimize each MDs cost that includes the computation delay, the energy consumption, and the price paid to the server. In particular, we propose two distributed algorithms based on the aggregative game theory to solve the optimization problem.

The 5G ecosystem is comprised of the cellular 5G System, as well as a managed and orchestrated infrastructure providing virtualized network and service functions. The automotive industry with its stringent requirements for connected vehicles is a promising and yet challenging consumer of such 5G ecosystem. Deployment of service instances at distributed cloud resources of cellular network infrastructure edges enables localized low-latency access to these services from moving vehicles but comes along with challenges, such as the need for fast recon-figuration of the distributed deployment according to mobility pattern and associated service and resource demand. In this paper, we investigate a solution for the collaborative orchestration of services for Connected, Cooperative and Automated Mobility (CCAM) within such 5G ecosystem. A key objective is the service continuity for a highly dynamic automotive scenario, achieved by the associated management and orchestration of these services in distributed edge clouds. The proposed solution leverages a multi-tier orchestration system as well as localized management and protocol operations for collaborative edge resources. By means of both analytical and experimental evaluations, the paper draws conclusions on the gain in accelerating orchestration decisions and enforcements, while balancing associated protocol and computational load over the highly distributed and multi-layered orchestration system.

With the massive deployment of geographically distributed data centers (DCs) and the demand surge for cloud services, their high energy consumption and carbon pollution are increasingly problematic. Therefore, mitigating the harmful effects of the carbon footprint of DCs has become a critical challenge. This paper takes the first step toward analyzing the complementary characteristics of global multi-regional renewable energy sources (RES). There is an opportunity to schedule workloads flexibly and track RES to reduce emissions. Integrating DCs and inter-DC network to form a refined emission model for the trade-off between emission cutting effects from scheduling and carbon costs of workload migration, we propose the spatio-temporal task migration mechanism to pursue low carbon in dual-dimension: This paper shifts intensive workloads to locations sufficient in RES at a coarse scale, and adjust the execution time of the workload in response to real-time RES fluctuations at a fine scale. Thus, the emission overage is shifted and offset by RES in a complementary manner; meanwhile, the acceptance of RES is enhanced. Finally, experiments with real-world data show that our method can optimally coordinate demand with RES and mitigate carbon pollution in geographically distributed DCs, and verify the performance and applicability with various-parameter scenarios.

Network measurement provides operators an efficient tool for many network management tasks such as performance diagnosis, traffic engineering and intrusion prevention. However, with the rapid and continuous growth of traffic speed, it needs more computing and memory resources to monitor traffic in per-flow or per-packet granularity. Sample-based measurement systems (e.g., NetFlow, sFlow) have been developed to perform coarse-grained measurement, but they may miss part of records, especially for mice flows, which are important for some network management tasks (e.g., anomaly detection, performance diagnosis). To address these issues, data streaming algorithms such as hash tables and sketches have been introduced to balance the trade-off among accuracy, speed, and memory usage. In this paper, we present a systematic survey of various data structures, algorithms and systems which have been proposed in recent years to perform fine-grained measurement for high-speed networks. We organize these methods and systems from a software-defined perspective. In particular, we abstract fine-grained network measurement into three-layer architecture. We introduce the responsibility of each layer and categorize existing state-of-the-art works into this architecture. Finally, we conclude the paper and discuss the future directions of fine-grained network measurement.

In Industrial Internet of Things (IIoT), a number of mutually untrusted devices with diverse computing resources and application requirements often need to work together to serve industrial production. When applying permissionless blockchain to process transactions among these devices, we are concerned with two types of fairness: mining fairness (i.e., miners should obtain commensurate rewards according to their respective invested computing power) and transaction-processing fairness (i.e., transaction packing and confirming is in a desired fair order). To address the two types of fairness simultaneously, this paper proposes GT-Chain, in which miners select transactions according to Geometric-distribution and propagate transactions with a Time-to-live limit for achieving transaction-processing fairness as well as balancing the processing efficiency and fairness. Besides, GT-Chain inherits properties of a famous blockchain called FruitChain for achieving mining fairness. We then conduct a probability analysis to quantify the efficiency of transaction processing and define a Euclidean-distance-based fair degree to measure the fairness of transaction processing. Extensive simulations verify that GT-Chain can well achieve our design objectives, and our theoretical model is accurate. This paper is helpful in better designing blockchain protocols for IIoT.

An integrated space-terrestrial network based on the ultra-dense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet-of-Thing (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This paper focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep-neural-networks (DNNs), this paper proposes to combine the non-linear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced non-orthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.

Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this paper, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions., This paper appears in IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2020.3039336

Owing to power limitations and hardware constraints of the internet of things (IoT) device, it requires simple, low power, low complex, energy-efficient communication technology. In contrast, LiFi users require high data rates and reliable connectivity. Motivated by the diverse requirements of these heterogeneous users, this paper proposes novel green communication schemes that can be used for the coexistence of LiFi users and light communication (LC) enabled IoT devices under a common LiFi access point. The proposed coexistence schemes utilize the amalgamation of wavelength division multiplexing, OFDMA, Hartley transform based DCO-OFDM (DCO-OFDMH), null DC element, interleaved subcarrier mapping, modified data sequence to achieve concurrent interference-free, low complex and reliable communication. Additionally, as the multiple access (MA) techniques affect the choice of modulation techniques and overall performance in the system. Therefore, this paper includes an analytical delay and throughput framework to corroborate the decision of an appropriate combination of MA and modulation techniques in the coexistence scheme. This paper presents a comprehensive analysis of the proposed coexistence schemes against conventional DCO-OFDMH coexistence schemes based on various performance metrics. The results suggest that the proposed DL and UL coexistence schemes reduce the complexity and increase the energy efficiency at the user’s and device’s terminals.

In this paper, a novel management framework for fog computing with strategic computing speed control at fog nodes (FNs) is studied. In the considered model, mobile users declare requests of offloading resource-hungry computation tasks that are dynamically collected at a dedicated edge server (ES). Upon receiving these requests, the ES can decide to either self-process or delegate some workloads to third-party FNs for maximizing the overall management profit. Unlike the existing work, this paper takes into account strategic behaviors of FNs in computing speed control, i.e., each FN can strategically allocate its computing resource to maximize its utility, which consists of the benefit gained from executing offloaded tasks and the cost incurred by dissatisfied (delayed) service to its own subscribed tasks. To jointly address the long-term system performance and FNs strategic interactions, a scheduling mechanism integrating a noncooperative game and a queueing model is formulated. We then investigate two delegation reward settings, i.e., constant and utility-dependent delegation prices, and propose efficient adaptive algorithms to determine the optimal workload distribution at the ES and the computing speed equilibrium among FNs. Both theoretical analyses and simulations are conducted to evaluate the performance of the proposed solutions and demonstrate their superiorities over counterparts.

The computing power of various Internet of Things (IoT) devices is quite different. To enable IoT devices with lower computing power to perform machine learning, all nodes can only train smaller models, which results in the waste of computing power for high-performance devices. In this paper, a heterogeneous model fusion federated learning mechanism is proposed. Each node trains learning models of different scales according to its own computing capabilities. After receiving the gradient trained by each node, the parameter server (PS) corrects the received gradient with the repeat matrix, and then update the corresponding region of the global model according to the mapping matrix. After all update operations are over, the PS assigns the compressed model to the corresponding node. The paper uses a variety of experimental schemes to evaluate the proposed method, including three datasets, two model structures, and three computational complexity levels. The proposed method have been proved it not only maximizes the use of the unbalanced computing power of edge nodes, but also enables different structural models to compensate for the shortcomings of others, improving the overall performance.

Mobile Crowdsensing (MCS) is a newly-emerged sensing paradigm where a large group of mobile workers collectively sense and share data for real-time services. However, one major problem that hinders the further development of MCS is the potential leakage of workers’ data privacy. In this paper, we integrate Federated Learning (FL) with MCS and introduce a novel sensing system called Federated Mobile Crowdsensing (F-MCS). In F-MCS, the workers can optimize the global model while keeping all the sensitive training data locally, thus ensuring their data privacy. Nevertheless, there are still two major issues in F-MCS. The first issue is that in F-MCS services, the workers are heterogeneous in terms of computational capacities and data resources. Hence, qualified workers should be appropriately selected to improve the efficiency of the training process. The second issue is that F-MCS is a cross-device FL system where the platform will finally get the global model after multiple training rounds. However, most privacy-preserving techniques are designed for cross-silo FL platforms, which cannot be applied to real-world F-MCS scenarios. To tackle the above problems, in this paper, we propose a privacy-preserving scheme for F-MCS, namely FedSky. Mainly, by extending the classic FedAvg algorithm, FedSky selects qualified workers based on constrained group skyline (CG-skyline) and securely aggregates model updates based on the homomorphic encryption technique. Comprehensive security analysis demonstrates the privacy-preservation of FedSky. Extensive experiments are conducted on an image classification task, where the comparison results validate the proposed scheme’s efficiency and effectiveness.

The security of data propagation is a critical challenge for the widespread use of networked control systems (NCSs), while the conventional strategies based on estimation techniques cannot solve this issue well. Furthermore, the contradiction between the security and real-time nature of the control method has not been settled yet. The aim of this paper is to propose a novel real-time control method empowered by blockchain technologies to resolve the security problems of NCSs under potential cyberattack. To facilitate the analysis of blockchain-assisted NCSs, a cyber-authentic-physical system (CAPS) framework is proposed. One of the significant features of CAPS is that the defense of NCSs against attacks is built by introducing a blockchain protocol. Theoretical analysis reveals the relationship among the scale of the peer-to-peer (P2P) network, the security of CAPS, and the latency of NCSs, as well as clarifies the impact of the introduction of blockchain mechanism on the performance of NCSs. With the networked secure control mechanism designed in this paper, the security and stability of NCSs can be achieved simultaneously, and sufficient conditions of which are also given. Finally, simulations and practical experiments are presented to show the efficacy of the suggested secure control strategy.

In this paper, the output regulation problem of fully distributed linear heterogeneous multi-agent systems via event-triggered communication is investigated. A novel information acquisition scheme is proposed, which can effectively save the computing resources of the controller. The time-invariant matrix S is obtained directly through completely distributed communication. Therefore, the computational burden caused by the introduction of matrix observer is reduced. At the same time, to save communication resources, this paper adopts a communication scheme and designs a Zeno-free event-triggered mechanism for each agent. The trigger mechanism in this paper can avoid the continuous monitoring of the system state, and does not need to supplement the self-triggering algorithm. In addition, the control strategy designed in this paper is fully distributed with fixed parameters, and the use of global information can be avoided without using dynamic parameters in the controller. Finally, a numerical example is given to verify the effectiveness of the proposed method.

This paper 1) constructs a cloud intelligence model that specifies the desirable intelligent properties of cloud agents, 2) provides a tutorial on agent-based cloud, fog, and edge resource management, and 3) paves the way for designing intelligent interclouds, fogs, and edges. An intelligent intercloud is a “cloud of clouds” populated by a society of agents that automates intercloud resource management activities. By contributing a tutorial on agent-based cloud resource management techniques, this paper provides researchers with the foundational knowledge for devising intelligent intercloud resource management techniques. By contributing an architectural blueprint, and suggesting and comparing different intelligent resource management techniques, this paper provides an overall design together with pointers to and guidelines for constructing the components of an intelligent intercloud. This paper describes a proof-of-concept prototype of an intelligent intercloud and provides an application example of the prototype. The need of IoT devices for reduced response time triggered technological advancements towards fog and edge computing. By providing a tutorial on agent-based fog and edge resource management techniques, this paper provides researchers with the foundational knowledge for devising intelligent fog and edge agents. Relevant techniques are also suggested for improving and optimizing the performances of future intercloud, fog, and edge agents.

Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been recognized as a promising technique to overcome spectrum scarcity and massive connectivity issues envisioned in next-generation wireless networks. This paper investigates the deployment of an unmanned aerial vehicle (UAV) as a relay that fairly serves many secondary users in a hot-spot region. The UAV deployment algorithm must jointly account for user clustering, channel assignment, and resource allocation sub-problems. We propose a solution methodology that obtains user clustering and channel assignment based on the optimal resource allocations for a given UAV location. This paper is the first to jointly derive closed-form optimal power and time allocations for generic cluster sizes of CCR-NOMA networks. Derivations consider many practical limitations, such as hardware impairments, imperfect channel estimates, and interference temperature constraints. Compared to numerical benchmarks, proposed solutions reach optimal max-min fair data rate by consuming and spending much less transmission power and computational time. The proposed clustering uses the optimal data rates and channel assignment approaches based on a linear bottleneck assignment (LBA) algorithm. Numerical results show that the LBA achieves 100% accuracy in more than five orders of magnitude less time than the optimal integer linear programming benchmark.

UAV swarms have triggered wide concern due to their potential application values in recent years. While there are studies proposed in terms of the architecture design for UAV swarms, two main challenges still exist: (1) Scalability, supporting a large scale of vehicles; (2) Versatility, integrating diversified missions. To this end, a multi-layered and distributed architecture for mission oriented miniature fixed-wing UAV swarms is presented in this paper. The proposed architecture is built on the concept of modularity. It divides the overall system to five layers: low-level control, high-level control, coordination, communication and human interaction layers, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced, but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this paper is the first work which successfully demonstrates formation flight, target recognition and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources including communication resources and computing resources needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this paper studies the multi-user computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multi-user computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this paper proposes a better reply based distributed multi-user computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.

Battery-Free Wireless Sensor Network (BF-WSN) is a newly proposed network architecture to address the limitation of traditional Wireless Sensor Networks (WSNs). The special features of BF-WSNs make the coverage problem quite different and even more challenging from and than that in traditional WSNs. This paper defines a new coverage problem in BF-WSNs which aims at maximizing coverage quality rather than prolonging network lifetime. The newly defined coverage problem is proved to be at least NP-Hard. Two sufficient conditions, under which the optimal solution of the problem can be derived in polynomial time, are given in this paper. Furthermore, three approximate algorithms are proposed to derive nearly optimal coverage when the sufficient conditions are unsatisfied. The time complexity and approximate ratio of the three algorithms are analyzed. Extensive simulations are carried out to examine the performance of the proposed algorithms. The simulation results show that these algorithms are efficient and effective.

Promoting the efficiency of task forwarding is a key issue in search and rescue operations, aerospace applications and so on. In this paper, we introduce a reputation mechanism existing in an evolutionary game into the task forwarding process of a multiagent system to promote the cooperation rate between agents. Combining the reputation mechanism with strategy updating of a multiagent system, a model based on evolutionary game theory is built and tested, and key factors such as the reputation threshold and the percentage of agents who initially select the forwarding strategy are analyzed in this paper. A numerical simulation shows that compared with previous studies, the model in this paper can make the proportion of agents choosing the forwarding strategy in a multiagent system reach a steady state faster. Through an analysis of various parameters, we also make this new model applicable to more realistic situations, which provides a more appropriate method for task forwarding in multiagent systems.

Indoor optical wireless communication (IOWC) is a key technology complementing radio communication in 6G. Typically, the downlink of IOWC is implemented using visible light and uplink is implemented using infrared. Though the downlink of IOWC has been studied well, the uplink of multiple-access IOWC requires further research. In this paper, power allocation strategies in the uplink of IOWC with multiple non-cooperative users is studied. Optimizing power allocation in IOWC that maximize sum-rate with power constraints becomes a non-convex problem. First, this paper proposes an alternating maximization algorithm to compute near optimal power allocation policies for small number of users. Next, a phenomenon of invariance is observed when the number of users in the system is large. Under invariance, the optimal power allocation policy of a user becomes independent of other users, thereby facilitating fast and decentralized computation of power allocation strategies. From simulations, it was observed that the invariance can occur even with five to ten users. For the first time, a rigorous analysis of the invariance phenomenon is provided. An algorithm to compute the optimal power allocation at invariance is proposed; this algorithm is proven to achieve the global maxima in sum-rate without the channel knowledge of other users.

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning.

Node-link visualizations are a familiar and powerful tool for displaying the relationships in a network. The readability of these visualizations highly depends on the spatial layout used for the nodes. In this paper, we focus on computing layered layouts, in which nodes are aligned on a set of parallel axes to better expose hierarchical or sequential relationships. Heuristic-based layouts are widely used as they scale well to larger networks and usually create readable, albeit sub-optimal, visualizations. We instead use a layout optimization model that prioritizes optimality— as compared to scalability— because an optimal solution not only represents the best attainable result, but can also serve as a baseline to evaluate the effectiveness of layout heuristics. We take an important step towards powerful and flexible network visualization by proposing STRATISFIMAL LAYOUT, a modular integer-linear-programming formulation that can consider several important readability criteria simultaneously— crossing reduction, edge bendiness, and nested and multi-layer groups. The layout can be adapted to diverse use cases through its modularity. Individual features can be enabled and customized depending on the application. We provide open-source and documented implementations of the layout, both for web-based and desktop visualizations. As a proof-of-concept, we apply it to the problem of visualizing complicated SQL queries, which have features that we believe cannot be addressed by existing layout optimization models. We also include a benchmark network generator and the results of an empirical evaluation to assess the performance trade-offs of our design choices. A full version of this paper with all appendices, data, and source code is available at osf.io/3vqms with live examples at https://visdunneright.github.io/stratisfimal/.

To accommodate the growing demand for memory capacity in a cost-effective way, multiple types of memory are incorporated in a single system. In such tiered memory systems consisting of small fast and large slow memory components, accurately identifying the performance importance of pages is critical to properly migrate hot pages to fast memory. Meanwhile, growing address translation cost due to the increasing memory footprints, helped adopting huge pages in common systems. Although such page hotness identification problems have existed for a long time, this paper revisits the problem in the new context of tiered memory systems and huge pages. This paper first investigates the memory locality behaviors of applications with three potential migration polices, least-recently-used (LRU), least-frequentlyused (LFU), and random with huge pages. The evaluation shows that none of the three migration policies excel the others, as the effectiveness of each policy depends on application behaviors. In addition, the results show huge pages can be effective even with page migration, if a proper migration policy is used. Based on the observation, this paper proposes a novel dynamic policy selection mechanism, which identifies the best migration policy for a given workload. It allows multiple concurrently running workloads to adopt different policies. To find the optimal one for each workload, this study first identifies key features that must be inferred from limited approximate memory access information collected using accessed bits in page tables. In addition, it proposes a parallel emulation of alternative policies to assess the benefit of possible alternatives. The proposed dynamic policy selection can achieve 23.8% performance improvement compared to a prior approximate mechanism based on LRU lists in Linux systems.

Offloading computation based on mobile edge computing paradigms can augment the computational capabilities of resource-scarce mobile devices. However, the capacity limitations of edge servers constrain the performance improvement achieved through computation offloading. In this paper, we consider a three-tier computation offloading schema with multiple users, edge servers and cloud servers. Computation can be offloaded from mobile devices to edge servers or can be further offloaded to remote cloud servers if necessary. Since a number of mobile devices connected to edge servers will share a common wireless communication network, which may contain both uplink and downlink channels, the assignment of bandwidth assignment the channels also constrains the performance improvement of computation offloading. In this paper, the problems of how to determine the offloading strategy and of how to assign the bandwidth are jointly studied and formulated as a programming problem to minimize the average application response time. We analyze the joint problem and further transform it into a piecewise convex programming problem. We propose an efficient algorithm that can find the optimal solution. Extensive experiments demonstrate that our algorithm significantly outperforms previous algorithms. The experimental results also show that the performance of our algorithm is highly robust.

The cybersecurity of the distributed AC optimal power flow (ACOPF) against false data injection attacks (FDIA) is investigated in this paper. A collaborative distributed ACOPF solver, rooted in the dual decomposition concept, is first formulated, based on which a theoretical framework is then developed to model the distributed ACOPF and its cybersecurity in the presence of FDIA. Under this proposed cybersecurity framework, a reputation-based peer-to-peer trust management system (TMS) is proposed to secure the resilience of the system against FDIA. Finally, the proposed TMS is validated on the IEEE 69-bus benchmark system. The primary contribution of this paper is the proposed holistic resilience framework, in which the FDIA is analytically assessed and effectively defended. In the analytical results, we have established two key propositions: the FDIA generally has one degree of freedom; and the FDIA can be effectively detected and mitigated using the proposed TMS with one-hop redundancy.

A decade back, emergency voice communication was the only target to support the patient in an ambulance. It is now evolved from emergency voice communication to vital signal monitoring and operating the machines from the remote place. This evolution requires support from technology to meet the high data rates along with reliability for the specified applications. The millimeter-wave (mmWave) communication support high data rate requirements of vehicular communication. However, in the case of mmWave, the radio signals vary fast. It poses the implementation challenge to the mmWave system in this scenario. The other implementations challenges of mmWave are high path loss, severe blockage and frequent beam updates which inhibit seamless connectivity (reliability) to vehicular nodes. However, the reliability is always a prime concern for any vehicular communication system. This paper addresses these challenges by implementing a novel energy-efficient strategy based on RSUs deployment and radio access technology (RAT). The strategy is to deploy RSUs on either side of the road and use an optimal combination of mmWave and microwave RAT. The essential analysis of such a hybrid system involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the expression for seamless coverage and connectivity. The analysis is also extended to rate and energy efficiency calculations. The analysis is supported by probabilistic models-based simulations that agree closely with computation results. The results claim that the proposed model leads to improved performance in terms of coverage and rate while maintaining the cost and energy efficiency within the limits.

This paper focuses on the global chance-constrained optimal power flow problem of a multi-regional interconnected grid. In this global problem, however, multiple regional independent system operators (ISOs) participate in the decision-making process, raising the need for distributed but coordinated approaches. Most notably, due to the regulation and security concerns, regional ISOs may refuse to share confidential information with others, including generation cost, load data, system topologies, and line parameters. Accordingly, this paper proposes a distributed CC-OPF method with strict confidentiality preservation, which theoretically enables regional ISOs to determine the exact global optimal dispatchable generations within their regions without disclosing confidential data. The proposed method neither requires parameter tunings nor has convergence issues. Results from a number of test cases show that the proposed method has considerably high accuracy, regardless of the system size, load level, and amount of regions, thereby outperforming the state-of-the-art obfuscation approaches., IEEE Transactions on Power Systems, 38 (4), ISSN:0885-8950, ISSN:1558-0679

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

Data integration from heterogeneous data sources in low-voltage (LV) power distribution grids will be valuable to distribution system operators (DSOs). The power measurements from customer premises need to be processed with other data such as grid topology, line parameters etc., to deploy smart grid applications (SGA) such as real-time grid monitoring and voltage regulation. The most challenging task for DSOs is to collect and integrate data from several sources as several entities are involved in the data management and access to databases are restricted. This paper presents an op E n common information ${M}$ odel (CIM) BA sed sma ${R}$ t grid application framewor ${K}$ ( EMBARK ) to address the above-mentioned challenge. EMBARK is developed to be an efficient, modular and scalable architecture for extracting relevant grid related information from various asset management databases. A novel data management functionality is a part of EMBARK that enables data-driven update of settings and parameters of the algorithms behind smart grid applications. The proposed approach is demonstrated and numerically validated using grid data from a medium-sized distribution grid operator in Denmark. The architecture developed and presented in this paper can support all the phases from planning to the actual smart grid operation, i.e., automatically building the models to perform load flows, grid impact studies, planning, asset management etc.

Inter-satellite link (ISL) scheduling is required by the BeiDou Navigation Satellite System (BDS) to guarantee the system ranging and communication performance. In the BDS, a great number of ISL scheduling instances must be addressed every day, which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications. To address the dual requirements of normal and quick-response ISL schedulings, a data-driven heuristic assisted memetic algorithm (DHMA) is proposed in this paper, which includes a high-performance memetic algorithm (MA) and a data-driven heuristic. In normal situations, the high-performance MA that hybridizes parallelism, competition, and evolution strategies is performed for high-quality ISL scheduling solutions over time. When in quick-response situations, the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model, which is trained from the high-quality MA solutions. The main idea of the DHMA is to address normal and quick-response schedulings separately, while high-quality normal scheduling data are trained for quick-response use. In addition, this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness. A seven-day experimental study with 10 080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal (in 84 hours) and quick-response (in 0.62 hour) situations, which can well meet the dual scheduling requirements in real-world BDS applications.

In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms are needed for DDs to select channels. To this end, multiarmed bandit (MAB) algorithms can be used. However, in general, it is difficult to analyze the performance of MAB algorithms in the setting due to the interaction of DDs. Thus, in this paper, instead of analyzing MAB algorithms, we formulate an evolutionary game of DDs in NOMA-based IoT networks and analyze the evolutionary game. It is shown that the game has a unique evolutionary stable state (ESS) in terms of the channel selection probability. The performance according to the ESS can be regarded as a baseline performance, because it is the best performance achieved by the DDs competing for multiple channel resources. Thus, the resulting baseline performance can be used to see if a learning algorithm including MAB can achieve a better performance by avoiding competition between DDs.

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced.

Driven by the increasing demand of real-time mobile application processing, Multi-access Edge Computing (MEC) has been envisioned as a promising paradigm for pushing computational resources to network edges. In this paper, we investigate an MEC network enabled by Unmanned Aerial Vehicles (UAV), and consider both the multi-user computation offloading and edge server deployment to minimize the system-wide computation cost under dynamic environment, where users generate tasks according to time-varying probabilities. We decompose the minimization problem by formulating two stochastic games for multi-user computation offloading and edge server deployment respectively, and prove that each formulated stochastic game has at least one Nash Equilibrium (NE). Two learning algorithms are proposed to reach the NEs with polynomial-time computational complexities. We further incorporate these two algorithms into a chess-like asynchronous updating algorithm to solve the system-wide computation cost minimization problem. Finally, performance evaluations based on real-world data are conducted and analyzed, corroborating that the proposed algorithms can achieve efficient computation offloading coupled with proper server deployment under dynamic environment for multiple users and MEC servers.

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes, connected through fronthaul (FH) links. To reduce the FH deployment costs and the required FH capacity, operators may install a single FH link shared among multiple cells and exploit key enabling techniques, such as modulation compression, to reduce FH data. In shared FH capacity scenarios, it is essential to provide efficient methods to control and optimize the FH resources' utilization with limited impact on the air interface performance. In this paper, a multi-cell multi-user scenario with a shared FH link across multiple cells is considered. We focus on optimizing the resource allocation and modulation compression of each user, in a centralized and dynamic manner, aiming to maximize the air interface performance subject to a shared FH capacity constraint. The problem is formulated as a convex optimization problem, which allows deriving the optimal resource allocation and modulation compression per user. Then, we evaluate the proposed FH-aware scheduling methods against baseline holistic strategies over an end-to-end dynamic 5G NR system-level simulator based on ns-3. Under a tight available FH capacity, results show gains that vary from 16% to 567% in different percentile statistics of the user-perceived throughput.

Fog/Edge computing is a novel computing paradigm supporting resource-constrained Internet of Things (IoT) devices by the placement of their tasks on the edge and/or cloud servers. Recently, several Deep Reinforcement Learning (DRL)-based placement techniques have been proposed in fog/edge computing environments, which are only suitable for centralized setups. The training of well-performed DRL agents requires manifold training data while obtaining training data is costly. Hence, these centralized DRL-based techniques lack generalizability and quick adaptability, thus failing to efficiently tackle application placement problems. Moreover, many IoT applications are modeled as Directed Acyclic Graphs (DAGs) with diverse topologies. Satisfying dependencies of DAG-based IoT applications incur additional constraints and increase the complexity of placement problems. To overcome these challenges, we propose an actor-critic-based distributed application placement technique, working based on the IMPortance weighted Actor-Learner Architectures (IMPALA). IMPALA is known for efficient distributed experience trajectory generation that significantly reduces the exploration costs of agents. Besides, it uses an adaptive off-policy correction method for faster convergence to optimal solutions. Our technique uses recurrent layers to capture temporal behaviors of input data and a replay buffer to improve the sample efficiency. The performance results, obtained from simulation and testbed experiments, demonstrate that our technique significantly improves the execution cost of IoT applications up to 30\% compared to its counterparts., Comment: This Paper is accepted in IEEE Transactions on Mobile Computing (TMC), on 23 October 2021

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.

The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness.

This paper investigates a multi-user wireless powered fog computing (FC) network, where energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP) and then, compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. An optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, an alternating direction method of multipliers (ADMM)-based algorithm is designed. With our presented ADMM-based algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI). For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also designed. Simulation results show that the proposed distributed algorithm achieves the comparable performance with the centralized algorithm or by using the exhaustive search one. Moreover, to minimize the transmit power, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional design.

Multi-access edge computing (MEC) has been recently considered in challenging environments lacking available terrestrial infrastructures by extending the computing resources to the air for further enhancing the computation capability of the new aerial user equipment (AUE). Additionally, wireless power transfer (WPT) is a promising solution to prolong the battery lifetime of energy-constrained wireless devices like AUEs. In this paper, we investigate the integration of laser-beamed WPT in the high-altitude platform (HAP) aided MEC systems for the HAP-connected AUEs. By discretizing the three-dimensional coverage space of the HAP, we present a multi-tier tile grid-based spatial structure to provide aerial locations for laser charging. With this setup, we identify a new privacy vulnerability caused by the openness during the air-to-air transmission of WPT signaling messages in the presence of a terrestrial adversary. A privacy-aware laser-powered aerial MEC framework is developed that addresses this vulnerability and enhances the location privacy of AUEs for laser WPT. Specifically, the interaction between the HAP as a defender and the adversary in their tile grid allocation as charging locations to AUEs is formulated as a Colonel Blotto game, which models the competition of the players for limited resources over multiple battlefields for a finite time horizon. Moreover, we derive the mixed-strategy Nash equilibria of the tile grid allocation game for both symmetric and asymmetric tile grids between the defender and the adversary. Simulations results show that the proposed framework significantly outperforms the design baselines with a given privacy protection level in terms of system-wide expected total utilities.

This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. We aim to jointly maximize the number of served IMDs, the user scheduling, the number of UAVs, and their $3$ D placement. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. Due to its non-convexity, we break the problem into two subproblems. We propose a machine-learning-based algorithm for the first subproblem, i.e., optimizing the UAVs' number and $3$ D placements, and the user association. Unlike existing literature, our algorithm achieves low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the $\ell_p$ -norm concept and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until convergence, and a near-optimal solution is achieved. Moreover, the computational complexity of the proposed scheme is analyzed. Regarding fast convergence and low computational complexity of the proposed algorithm, we confirm its superior performance through simulation results.

Machine Learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this paper, we present a privacy-preserving machine learning (ML) framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using Differential Privacy (DP) and Radial Basis Function Network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model’s performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.

Internet of Things (IoT) has brought changes in many fields by joining physical space with the cyber space. The IoT devices are becoming increasingly complex. With the rapid deployment of cameras, tasks in IoT like visual information are more important, but IoT devices have limited computing resources, including power, computing ability, storage, etc. Some tasks that might be perfectly normal to perform on a computer would be rather challenging on an IoT device. Therefore, how to maintain acceptable performance while minimizing resources is becoming a more consequential part in IoT. In the paper, we aim to solve the problem of object detection and tracking in IoT while minimizing resources. The traditional algorithms need to use convolutional neural network (CNN) to identify different objects in each frame, and then determine the tracking target from identified objects, which typically requires a lot of computing resources. By incorporating spatial attention, and multi-domain network, we proposed a novel algorithm named as Spatial Attention Powered Multi-Domain Network (SA-MDNet). By adding spatial attention mechanism to the original MDNet model, and using multi class cross entropy loss, we are able to distinguish the background and the target in different video sequences effectively and efficiently. This novel algorithm achieves similar performance on the OTB 50/100/2013 datasets compared to several state-of-art models, while uses only a fraction of the memory compared to MDNet.

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, Mobile Edge Computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this paper, we propose an autonomous partial offloading system for delay sensitive computation tasks in multi-user IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of Reinforcement Learning (RL), we propose two RL based offloading strategies to automatically optimize the delay performance. Specifically, we first implement Q-learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on Deep Deterministic Policy Gradient (DDPG). The simulation results show that, the Q-learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

With the evolutionary development of the latency-sensitive Industrial Internet of Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this paper, we introduce a new Deep Reinforcement Learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Further, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. Experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

Edge computing (EC) offers cloud-like services at the edge of mobile networks to satisfy the delay-sensitive and rapid computation applications in meeting the demands of rapidly increasing mobile devices and other IoTs. EC is known to be constrained with limited resources that its efficacy greatly depends on an effective and efficient resource allocation to provide optimal resources utilization. Focusing on the fact, this paper presents an adaptive resource allocation mechanism, abbreviated as A-PBRA, for effective resources utilization in the EC paradigm. To realize optimal utilization, the available resources are allocated dynamically (adaptability) by considering the nature of the incoming requests. The proposed scheme shall adapt to the resource demands and priorities of the incoming requests. After identifying the received request which can be either priority-based or normal request, each of them is processed with three possibilities. The available resources are thus allocated as per the priorities of the incoming requests to satisfy the constraints accordingly. The proposed mechanism is adaptable to a maximum number of incoming requests along with optimizing the utilization of limited resources at the edge node. Extensive simulations were performed through ifogsim to evaluate the performance of the proposed method. Critical comparisons were made against closely related algorithms and techniques i.e., the NBIHA and the CORA-GT. The simulation results from the proposed scheme optimistically showing that it performed better in terms of resources utilization, average response time, task execution time, and energy consumption.

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for Financial Technology (FinTech). The emergence of the Next-Generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyse the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this paper, we develop a ranked-based service deployment strategy and an Artificial Intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using Hall’s theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVM) algorithm is used at edge networks for analysing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles~(EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7\% and 18\% energy savings compared to a baseline that executes workload locally and an average of 13\% energy savings compared to a baseline that offloads vehicles workloads to RSUs.

The huge advancement in the field of communication has pushed the innovation pace towards a new concept in the context of Internet of Things (IoT) named IoT for Financial Technology applications (IoT-FinTech). The main intention is to leverage the businesses’ income and reducing cost by facilitating the benefits enabled by IoT-FinTech technology. To do so, some of the challenging problems that mainly related to routing protocols in such highly dynamic, unreliable (due to mobility) and widely distributed network need to be carefully addressed. This paper therefore focuses on developing a new trustworthy and efficient routing mechanism to be used in routing data traffic over IoT-FinTech mobile networks. A new Non-linear Levy Brownian Generalized Normal Distribution Optimization (NLBGNDO) algorithm is proposed to solve the problem of finding an optimal path from source to destination sensor nodes to be used in forwarding FinTech’s related data. We also propose an objective function to be used in maintaining trustworthiness of the selected relay-node candidates by introducing a trust-based friendship mechanism to be measured and applied during each selection process. The formulated model also considering node’s residual energy, experienced response time, and inter-node distance (to figure out density/sparsity ratio of sensor nodes). Results demonstrate that our proposed mechanism could maintain very wise and efficient decisions over the selection period in comparison with other methods.

The future communication technologies like 6G are capable to provide higher mobility and better quality of service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next generation networks. In this paper, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost, load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next generation large scale IoT networks.

Federated learning (FL) has been widely adopted to train machine learning models over massive data in edge computing. However, machine learning faces critical challenges, \eg, data imbalance, edge dynamics, and resource constraints, in edge computing.The existing FL solutions cannot well cope with data imbalance or edge dynamics, and may cause high resource cost. In this paper, we propose an adaptive asynchronous federated learning (AAFL) mechanism. To deal with edge dynamics, the parameter server will aggregate local updated models only from a certain fraction $\alpha$ of all edge nodes in each epoch. Moreover, the system can intelligently vary the number of local updated models for global model aggregation in different epochs with network situations. We then propose experience-driven algorithms based on deep reinforcement learning (DRL) to adaptively determine the optimal value of $\alpha$ in each epoch for two cases of AAFL, single learning task and multiple learning tasks, so as to achieve less completion time of training under resource constraints. Extensive experiments on the classical models and datasets show high effectiveness of the proposed algorithms. Specifically, AAFL can reduce the completion time by about 55\% and improve the learning accuracy by 18\% under resource constraints, compared with the state-of-the-art solutions.