Data Freshness Research Articles

UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.

Trajectory design and transmission optimization for data freshness in UAVs‐assisted WPCNs

Trajectory design and transmission optimization for data freshness in UAVs‐assisted WPCNs

An energy-aware protocol in wireless sensor networks using the scattered search algorithm and fuzzy logic.

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number of collected data samples to enhance accuracy and robustness in data collection. For this purpose, this research article proposes a clustering-based routing protocol aimed at maximizing the delivery of data samples while minimizing energy consumption and data collection delays. The protocol employs a scattered search algorithm and fuzzy logic to cluster the sensor nodes. By considering the distance to the sink and the remaining energy level of the battery, the network is dynamically divided into clusters using a lightweight clustering approach. To evaluate the effectiveness of the proposed method, simulations were conducted in OPNET using the AFSRP protocol. The results demonstrate superior performance of the proposed method in terms of end-to-end delay by 13.44%, media access delay by 75.2%, throughput rate by 20.55%, energy consumption by 13.52%, signal-to-noise ratio by 43.40% and delivery rate of successfully sending data to the sink is 0.21% higher than the well-known AFSRP method.

Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.

A priority-aware dynamic scheduling algorithm for ensuring data freshness in 5G networks

To ensure the freshness of information in wireless communication systems, a new performance metric named the age of information (AoI) is being adopted in the design of transmission schedulers. However, most AoI schedulers rely on iterative optimization methods, which struggle to adapt to real-time changes, particularly in real-world 5G deployment scenarios, where network conditions are highly dynamic. In addition, they neglect the impact of consecutive AoI deadline violations, which result in prolonged information deficits. To address these limitations, we present a 5G scheduler that can cope with dynamic network conditions, with the aim of minimizing the long-term average AoI under deadline constraints. Specifically, we consider a dense urban massive machine-type communication (mMTC) scenario in which numerous Internet of Things (IoT) devices frequently join or leave the network under time-varying channel conditions. To facilitate real-time adaptation, we develop a per-slot scheduling method that makes locally optimal decisions for each slot without requiring extensive iterations. In addition, we combine the per-slot scheduling method with a priority-rule scheduling algorithm to satisfy the stringent timing requirements of 5G. The simulation results show that the proposed scheduler reduces the average AoI by 10%, deadline violation rate by 40%, and consecutive violation rate by 20% approximately compared with other AoI schedulers.

Learning-Based Sensing and Computing Decision for Data Freshness in Edge Computing-Enabled Networks

Learning-Based Sensing and Computing Decision for Data Freshness in Edge Computing-Enabled Networks

Adaptive Clustering and Scheduling for UAV-Enabled Data Aggregation

Using unmanned aerial vehicles (UAVs) is an effective way to gather data from Internet of Things (IoT) devices. To reduce data gathering time and redundancy, thereby enabling the timely response of state-of-the-art systems, one can partition a network into clusters and perform aggregation within each cluster. Existing works solved the UAV trajectory planning problem, in which the energy consumption and/or flight time of the UAV is the minimization objective. The aggregation scheduling within each cluster was neglected, and they assumed that data must be ready when the UAV arrives at the cluster heads (CHs). This paper addresses the minimum time aggregation scheduling problem in duty-cycled networks with a single UAV. We propose an adaptive clustering method that takes into account the trajectory and speed of the UAV. The transmission schedule of IoT devices and the UAV departure times are jointly computed so that (1) the UAV flies continuously throughout the shortest path among the CHs to minimize the hovering time and energy consumption, and (2) data are aggregated at each CH right before the UAV arrival, to maximize the data freshness. Intensive simulation shows that the proposed scheme reduces up to 35% of the aggregation delay compared to other benchmarking methods.

Simultaneous update of sensing and control data using free-ride codes in vehicular networks: An age and energy perspective

In real-time Internet of Things networks, age of information (AoI) and energy cost (EC) are generally deemed as two significant performance metrics to characterize the information freshness and energy efficiency. This paper aims at updating not just sensing data but sensing data and control data simultaneously in vehicular networks. By employing the promising free-ride coding mechanism, the randomly encoded control data is superposed on the low-density parity-check coded sensing data and then is sent to the receiver, consuming neither additional bandwidth nor transmission power. Considering the time and energy of both status update generation and transmission, the average AoI (AAoI) and average EC (AEC) expressions are derived for preemptive and blocking updating schemes with truncated automatic repeat request protocol. Numerical simulations present the AAoI of both sensing data and control data, as well as the AEC of the transmitter, which demonstrate that free-ride coding mechanism not only can improve the freshness of control data but impose an insignificant influence on the performance of sensing data. Moreover, comparison with the blocking scheme suggests that the preemptive one obtains the smaller AAoI but the larger AEC.

[formula omitted] : An adaptive data aggregation algorithm for IoT infrastructure

In IoT infrastructure, high-frequency sensing and subsequent transmission of sensed data to computational facilities can lead to redundant data storage and processing, consuming significant storage and processing capacity. As a result, the IoT infrastructure needs more data transmission cycles, leading to data redundancy and low network up-time due to the drainage of limited battery capacity. Conversely, if the data is communicated at a lower rate, it may cause absolute data delivery to the processing unit, which is useless. As a result, a well-designed data aggregation algorithm is required. This paper proposes the ADA2−IoT, an Adaptive Data Aggregation Algorithm for IoT Infrastructure tailored to optimize parameters such as low data redundancy, limited data communication cycles, and high IoT infrastructure up times. The proposed algorithm consists of two key components: the Route Data Aggregator (RDA) performs aggregation during data transit towards the Edge node or gateway, and the Node Data Aggregator (NDA) performs data aggregation during capturing or sensing data. The algorithm employs metrics like Age of Information (AoI) and data freshness factor during the node and route data aggregation phase to capture and timely deliver data to the Edge node, where this data is processed for informed decision-making. The proposed algorithm was efficiently tested on a simulation and IoT hardware deployment environment. Both simulation and hardware results demonstrate a substantial improvement in QoS parameters, such as a decrease in data redundancy and packet exchanges, leading to considerable energy savings and prolonging the lifespan of IoT infrastructure.

Freshness-in-air: An AoI-inspired UAV-assisted wireless sensor networks

The Age-of-Information (AoI) metric has emerged as a performance measurement metric for evaluating time-sensitive wireless communications systems. Maintaining the freshness and reliability of data is critical in time-critical wireless networks, where outdated information can have significant consequences. Moreover, short packet transmissions are used in wireless sensor networks (WSNs) to maintain energy efficiency and low latency. This paper proposes a theoretical model that utilizes the AoI metric and finite block length information theory to estimate information freshness in an unmanned aerial vehicle (UAV)-assisted WSN. This network includes multiple sensing nodes and relies on short-packet communication for transmission. In this paper, closed-form expressions for average AoI (AAoI) and the block error rate are derived. Furthermore, the optimal altitude and block length that ensures the freshness of received information at the destination is determined. The results of the analysis provide valuable insights into the performance characteristics of UAV-assisted WSNs and have important implications for the design and optimization of such systems.

AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

Advancing Edge Computing with Federated Deep Learning: Strategies and Challenges

Abstract: Edge computation (EC) represents a transformative architecture in which cloud computing services are decentralized to the locations where data originates. This shift has been facilitated by the integration of deep learning (DL) technologies, notably in eliminating latency issues commonly referred to as the "echo effect" across various platforms. In typical EC-enabled DL frameworks where data producers are directly involved, it is often necessary to share data with third parties or edge/cloud servers to facilitate model training. This process, however, raises significant concerns regarding synchronization with high data rates, seamless migration, and security, consequently exposing the system to privacy vulnerabilities. These challenges can be addressed through the adoption of Federated Learning (FL), which provides a robust mechanism to mitigate risks associated with data loss, ensure data freshness, and enhance privacy.

Cost Minimization of Digital Twin Placements in Mobile Edge Computing

In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behavior simulations and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this paper, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

Tradeoff Between Age of Information and Operation Time for UAV Sensing Over Multi-Cell Cellular Networks

Unmanned aerial vehicles (UAVs) have a significant potential for sensing applications in further cellular networks due to their extensive coverage and flexible deployment. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV, which senses data with onboard sensors and uploads sensory data to the ground base stations (BSs). To evaluate the freshness of sensory data, we employ the concept of age of information (AoI), which is defined as the time elapsed since the latest successful transmission of sensory data. A lower AoI implies fresher sensory data, which may lead to the increase of UAV operation time. To balance such tradeoff, we aim to minimize the weighted sum of operation time and total AoI for the UAV by jointly optimizing transmission scheduling, BS association, as well as UAV trajectory. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve due to the time-varying propagation channels. To this end, we first characterize the average communication performance with statistic channel information, and then develop a search algorithm to obtain the optimal solution via employing the optimal structure as well as convex optimization techniques, while a low-complexity Double Graph based Algorithm (DGA) is developed to obtain a suboptimal solution. Then, by taking into account the site-specific performance and making fast decisions online, we propose a Deep reinforcement Learning Algorithm (DLA). Compared to DGA, DLA can adapt to the specific local environment and obtain a solution more rapidly once the training process is completed. Simulation results show that the proposed algorithms outperform the benchmarks about 30%, and achieve flexible tradeoff between operation time and AoI of UAV sensing, which is not available by considering just one objective.

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks.

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

AoI-Aware Resource Scheduling for Industrial IoT with Deep Reinforcement Learning

Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.

Precoding Design for Ensuring Data Freshness in Multi-User MISO Networks

Precoding Design for Ensuring Data Freshness in Multi-User MISO Networks

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.

Optimize the Age of Useful Information in Edge-assisted Energy-harvesting Sensor Networks

The energy-harvesting sensor network is a new network architecture to further prolong the lifetime of sensor networks and enhance the quality of IoT services. Due to the inherent problems of energy-harvesting sensor networks, it is really hard to collect fresh and useful sensory data. To solve the above problems, we investigate the data collection scheme in edge-assisted energy-harvesting sensor networks and try to collect fresh and useful sensory data from such networks. Enlightened by the concept of the age of information, we define a new metric, the age of useful information (AoUI), to measure the usefulness and freshness of the sensory data. Furthermore, we define the Minimizing the Maximum Age of Useful Information problem (Min-AoUI) to construct a sensory data collection method to minimize the AoUI of the sensory data. We prove that the Min-AoUI problem is NP-Hard, and approximation algorithms are proposed to solve this problem. The time complexity and the approximation ratio of this algorithm are analyzed. The performance of the algorithm is also verified by extensive experimental results.

Transient Data Caching Based on Maximum Entropy Actor–Critic in Internet-of-Things Networks

AbstractWith the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by industrial devices. Due to their inefficiency, traditional caching strategies in edge networks are inadequate for handling transient data. Thus, to improve the efficiency of transient data caching, we construct a freshness model of transient data and propose a maximum entropy Actor–Critic-based caching strategy, TD-MEAC-which can improve the freshness of cached data and reduce the long-term caching cost. Simulation results show that the proposed TD-MEAC achieves a higher cache hit rate and maintains a higher average freshness of cached transient data compared with the existing DRL and baseline caching strategies.