Data Aggregation Scheduling Research Articles

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.

Hybrid Delay‐Minimization Scheduling Algorithm of FT and MPTS in WSN Data Aggregation

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay‐minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision‐free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay.

Many-to-Many Data Aggregation Scheduling Based on Multi-Agent Learning for Multi-Channel WSN

Many-to-many data aggregation has become an indispensable technique to realize the simultaneous executions of multiple applications with less data traffic load and less energy consumption in a multi-channel WSN (wireless sensor network). The problem of how to efficiently allocate time slot and channel for each node is one of the most critical problems for many-to-many data aggregation in multi-channel WSNs, and this problem can be solved with the new distributed scheduling method without communication conflict outlined in this paper. The many-to-many data aggregation scheduling process is abstracted as a decentralized partially observable Markov decision model in a multi-agent system. In the case of embedding cooperative multi-agent learning technology, sensor nodes with group observability work in a distributed manner. These nodes cooperated and exploit local feedback information to automatically learn the optimal scheduling strategy, then select the best time slot and channel for wireless communication. Simulation results show that the new scheduling method has advantages in performance when comparing with the existing methods.

Sensory Data Aggregation in Internet of Things: Period-Driven Pipeline Scheduling Approach

The Smart City contexts and the adoption of Industry 4.0 are being upgraded with the latest technologies throughout all systems. New data are continuously collected in huge amounts; therefore, an efficient data aggregation scheduling scheme is highly demanded. This paper addresses the minimum time aggregation scheduling problem in duty-cycled sensor networks. Existing solutions schedule the sensory data through predefined routing structures, which limit the utilization of diverse active time slots of sensors. We propose a period-driven pipeline scheduling approach, namely PDA, that simultaneously grows the aggregation tree and assigns a transmission schedule for each node being added to the tree. Particularly, this process is performed in a top-down manner. In each iteration, corresponding to a time slot, PDA uses a multi-level ranking strategy to schedule several <inline-formula><tex-math notation="LaTeX">$\langle sender, receiver \rangle$</tex-math></inline-formula> pairs, so that in a working period ahead, the possibility to pipeline as many transmissions as possible is high. Intensive simulation results show that the proposed scheme notably works better than the best known recent algorithms by having up to 35 percent shorter aggregation time, as well as having a significantly improved network throughput and time utilization.

Data-Driven Flight Control of Internet-of-Drones for Sensor Data Aggregation Using Multi-Agent Deep Reinforcement Learning

Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight cont rol decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round. LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

Multi-Channel Data Aggregation Scheduling Based on the Chaotic Firework Algorithm for the Battery-Free Wireless Sensor Network

The emergence of battery-free wireless sensor networks (benefiting from the ability to collect energy from the surroundings) has broken through the energy and lifetime limitations of traditional wireless sensor network systems, but also brings challenges to the sharing of network resources. In the multi-channel wireless communication environment, in particular, how to coordinate the communication time and occupied channels of a large number of sensor nodes from the perspective of optimizing the global network has become a research problem that must be solved. To reduce the transmission delay and the usage of wireless channels, a new multi-channel data aggregation scheduling method based on the chaotic firework algorithm is proposed in this paper. With the help of the generation function of feasible solutions, one scheduling set and a firework individual can be rapidly converted to each other. By the operations of firework explosions, the Gaussian mutation, and chaotic exploration, a sub-optimal scheduling set could be found during an acceptable time period. Finally, simulation results show that the new scheduling method has advantages in aggregation delay and occupied channels when compared with existing methods.

An Intelligent delay efficient data aggregation scheduling for distributed sensor networks

Data aggregation is the most prominent task in many wireless sensor network (WSN) applications, that enables all data from the network to be collected at the sink node. The most critical issue in data aggregation is data aggregation scheduling. This problem becomes even cumbersome when considered to obtain minimum latency and collision in the networks where fixed amount of data can be aggregated. Many researches have been conducted to resolve the issue of data delivery with low latency and collision. Though, some methods nearly achieved this objective but at the cost of increased complexity. Also, many works assume the system to be centralized having knowledge of every related device in the network. Practically, WSNs are likely to be distributed where each device’s information is dissociated from the other. In this context, a data aggregation scheduling algorithm is proposed to aggregate the data from a tree-based distributed sensor network. An effective aggregation tree construction based on Dijkstra’s algorithm is adopted which avoids the chance of re-transmission of data. Furthermore, a new collision prevention scheduling (CPS) algorithm is designed which ensures the data aggregation through non-collision schedules using minimum time slots. The proposed method shows 54.74% less aggregation latency compared to its competitor data aggregation scheduling approach. The outcome of extensive simulations confirms the efficiency and good performance of the proposed algorithm over previous works.

Hypergraph-Based Active Minimum Delay Data Aggregation Scheduling in Wireless-Powered IoT

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

3S-hierarchical cluster-based energy-efficient data aggregation protocol in wireless sensor network

Energy utilisation is one of the most common challenges in wireless sensor networks, as frequent communication between the sensor nodes results in a huge energy drain. To overcome this challenge, a 3S-hierarchical cluster-based energy-efficient data aggregation protocol in wireless sensor network is proposed, where 3S represents smart sensing, selective cluster head nomination, and data aggregation schedule which reduce the energy consumption for prolonging network lifetime. The proposed technique has three phases. Smart data sensing which uses an unbounded buffer to store data values and reduces transmissions. Cluster selection where high value nodes are treated as extended nodes to become candidate for cluster head selection. An advance aggregation schedule, which alternatively demands sensor nodes to send data buffer based on node identification value. Simulation analysis and the results proved that the proposed algorithm could effectively enhance the network lifetime and reduce energy consumption while maintaining acceptable packet delivery ratios and delay values.

Energy-Aware Concurrent Data Aggregation Scheduling for Wireless Powered IoT Leveraging Hypergraph Theory

This letter investigates the minimum-delay data aggregation scheduling problem for wireless powered Internet of Things (WPIoT). Unlike traditional Internet of Things (IoT), WPIoT devices should harvest enough energy before data transmission or reception. Hence, many existing solutions for traditional IoT cannot be directly applied to WPIoT. In this letter, we propose a delay-efficient algorithm by fully considering the energy features and cumulative interference effects of WPIoT. This algorithm constructs the data aggregation tree based on the fewest-children-first rule and implements a novel energy-aware concurrent link scheduling method leveraging the hypergraph theory. This novel link scheduling method can satisfy both the residual energy constraint and signal interference constraint. The performance of our algorithm is evaluated by extensive experiments based on a real-world dataset under non-linear energy harvesting models with both perfect and imperfect channel state information. The results confirm the superiority of our algorithm over the baseline algorithms.

Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor Networks

The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Latency-and-Coverage Aware Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

Battery-Free Wireless Sensor Networks (BF-WSNs) have been attracting increasing interests in the recent years. To reduce the latency in BF-WSNs, the Minimum Latency Aggregation Scheduling (MLAS) problem with coverage requirement $q$ is proposed recently, which tries to choose $q$ percent of nodes for communication and aggregation. In the existing method, the authors try to select nodes adaptively according to their energy status and schedule these nodes to achieve the minimum latency. Unfortunately, it cannot guarantee the distribution of the aggregated nodes and may result in these nodes being squeezed in a small area and a poor aggregation quality. Thus, we re-investigate the $q$ -coverage MLAS problem in this article, which can guarantee that the aggregated nodes are distributed evenly. Firstly, the 1-coverage MLAS problem, in which each node can be covered by at least one aggregated node, is studied. To reduce the latency, we intertwine the selection of aggregated nodes and the computation of a collision-free communication schedule simultaneously. Two algorithms are proposed by scheduling the communication tasks in the bottom-up and top-down manner respectively. Secondly, to satisfy the arbitrary coverage requirement $q$ , three algorithms are proposed to guarantee the aggregated nodes are evenly distributed in the network with a low latency. Additionally, the method to extend the proposed algorithms for the BF-WSNs with multiple channels is also studied. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Data Aggregation Scheduling in Battery-Free Wireless Sensor Networks

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named battery-free wireless sensor network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> , instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is a fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

Joint source coding rate allocation and flow scheduling for data aggregation in collaborative sensing networks

In collaborative sensing networks such as WSNs (Wireless Sensor Networks), due to overlapping converge areas among neighbor nodes, they may percept a large number of similar or identical data, which incurs sensing data redundancy and unnecessary energy consumption. Slepian-wolf theorem based source coding is an effective method to reduce redundancy in data aggregation. However, the exponential growth of constraints prevent the method from practical application. Therefore, in this paper, we propose a cross-layer optimization framework to solve data aggregation problem by jointly considering optimal source encoding rate and flow scheduling. By proving the convex of constraints of Spelian-Wolf theorem, we relax original constraints so that the optimal encoding rate scheme can be adopted. The relaxation makes the optimization problem feasible. Furthermore, we employ dual decomposition to separate the original problem into two sub-problems. By solving the two subproblem distributedly, we provide optimal encoding rate allocation for each node and flow scheduling for each link. Simulation results demonstrate that our framework can reduce data redundancy and network traffic significantly compared to the exiting algorithms.

Low-Latency Data Aggregation Scheduling for Cognitive Radio Networks With Non-Predetermined Structure

Data aggregation is a fundamental yet popular operation in wireless networks where the sink needs to obtain the combined information of the whole network. However, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in cognitive radio networks. Few studies have addressed this issue and most previous aggregation methods all assume that a fixed-structure based aggregation tree is constructed in advance, which may result in the selection of a node with limited spectrum opportunities as the parent by many nodes and by extension results in a large latency. Thus, the MLAS problem in cognitive radio networks (MLAS-CR) without the above limitation is investigated in this paper. First, the MLAS-CR problem with primary social behaviors where the activity of primary users can be predicted is studied. To make full use of the limited spectrum opportunities, we integrate the construction of the aggregation tree, and the computation of a conflict-free schedule simultaneously, without any predetermined structures. Second, the MLAS-CR problem without the above assumption is also investigated. To reduce the latency, a two-way aggregation scheduling method is proposed to adaptively choose the parent with only current channel information. To further reduce the latency, we also introduce a new data aggregation mode for CRN, i.e., Data Aggregation Scheduling in The Dark, to utilize the spectrum opportunities of scheduled nodes. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Long-Lifetime and Low Latency Data Aggregation Scheduling for Wireless Sensor Network

Abstract Wireless Sensor Networks contain an incredible number of hubs with limited registering, detecting, and wireless communication capacities. These systems have been utilized as a part of a wide zone of utilizations, such as human services, contamination checking, and target-following frameworks. The dynamic clustering of sensors into bunches is a prevalent procedure to expand the system lifetime and increment adaptability. To accomplish this, in this article the sensor hubs are adjusted to ensure a long lifetime and the activities are arranged into rounds that use fixed time intervals. In the first phase, a clustering topology is found, and a group head is picked in view of the outstanding energy level. Then the cluster head screens the network energy threshold value to identify the energy drain rate of all its cluster members. In the second stage, the Long-Lifetime and Low-Latency Data Aggregation Scheduling method is used. This scheduling method assigns schedule openings to group part information parcels. Here, congestion occurrence is completely kept away from the scenario. In the third stage, an Energy-Efficient Distributed Schedule–based convention is used to keep up greatest residual energy level over the network. The experimental outcome shows the steps proposed in this article ensure an increase in the network lifetime and decrease in the energy utilization.

Delay-Aware Reverse Approach for Data Aggregation Scheduling in Wireless Sensor Networks.

Many time-sensitive applications require data to be aggregated from wireless sensor networks with minimum latency. However, the minimum latency aggregation scheduling problem has not been optimally solved due to its NP-hardness. Most existing ideas rely on local information (e.g., node degree, number of children) to organize the schedule order, hence results in solutions that might be far from optimal. In this work, we propose RADAS: a delay-aware Reverse Approach for Data Aggregation Scheduling that determines the transmissions sequence of sensors in a reverse order. Specifically, RADAS iteratively finds the transmissions starting from the last time slot, in which the last sender delivers data to the sink, down to the first time slot, when the data aggregation begins. In each time slot, RADAS intends to maximize the number of concurrent transmissions, while giving higher priority to the sender with potentially higher aggregation delay. Scheduling such high-priority sender first would benefit the maximum selections in subsequent time slots and eventually shorten the schedule length. Simulation results show that our proposed algorithm dominates the existing state-of-the-art schemes, especially in large and dense networks, and offers up to 30% delay reduction.

Distributed and Efficient Minimum-Latency Data Aggregation Scheduling for Multichannel Wireless Sensor Networks

Data aggregation is a critical operation in wireless sensor networks (WSNs). Many applications have strict requirements for the latency of data aggregation. This paper focuses on the latency problem of data aggregation. Two factors determine the latency of data aggregation. First, because of the existence of interference, efficient collision-free scheduling is crucial for reducing data aggregation latency. Second, the tree structure has an important impact on data aggregation latency. In this paper, we propose a novel approach called distributed and efficient data aggregation scheduling over multichannel links (DEDAS-MC). DEDAS-MC minimizes the latency in routing the aggregated data to the sink over multichannel links. In DEDAS-MC, we first present a scheduling algorithm to schedule sensors to avoid interference and minimize the latency of data aggregation on a given tree. Then, a distributed algorithm for constructing minimum-latency data aggregation trees is proposed by employing the Markov approximation method. In DEDAS-MC, the value of $\beta $ is adaptive. The Markov approximation method-based adaptive- $\beta $ is more flexible and efficient than the single $\beta $ approximation. The experiments show that DEDAS-MC outperforms the existing competing schemes.

Delay Efficient Scheduling Algorithms for Data Aggregation in Multi-Channel Asynchronous Duty-Cycled WSNs

Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.