Latency Data Aggregation Research Articles

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.

Utilizing Multiple-Access Communication to Realize FNN Computation in Wireless Networks

Driven by the visions of the Internet of Things, more and more devices will join the network and generate large amounts of data. To analyze these data and provide useful information for intelligent applications, artificial neural networks are expected to be widely configured in data centers. However, it incurs excessive latency for the centers to aggregate data from massive distributed devices. To tackle this challenge, we propose wireless-network-based feedforward neural network (WN-FNN), a novel design that utilizes multiple-access communication to perform FNN computation in wireless networks. Based on a technology known as computation over multiple-access channel, the proposed design integrates communication and computation, which dramatically reduces the data aggregation latency. Furthermore, we analyze the impacts of the input noise and received noise on WN-FNN. A regularization strategy is proposed to improve the denoising ability of WN-FNN. An indoor localization application is simulated to verify the performances of WN-FNN and the regularization strategy.

Distributed Data Aggregation Scheduling in Multi-Channel and Multi-Power Wireless Sensor Networks

Large amounts of sensor data are frequently generated and streamed from sensors deployed on various buildings, in forests or in other application areas. In many of these areas, one difficulty is managing the velocity and volume of the big sensor data while still providing low time latency support for data analysis. Data aggregation can reduce the volume of big sensor data. However, data aggregation is a fundamental yet time-consuming operation in wireless sensor networks (WSNs), particularly in high-density WSNs. Therefore, researchers have started focusing on minimizing the latency of data aggregation, which has been proven to be an NP-hard problem. This paper proposes a cluster-based distributed data aggregation scheduling algorithm, distributed multi-power and multi-channel (DMPMC), that can minimize the data aggregation latency in multi-channel and multi-power WSNs. To save energy, low transmission power is used for packet transmissions inside a cluster, and high power is used for packet transmissions among clusters. Simulations are conducted to compare DMPMC with the best centralized algorithm in a single channel, named E-PAS, the best distributed algorithm in a single channel, named CLU-DDAS, and the best algorithm in multi-channels, named multi-channel. The results show that the DMPMC algorithm proposed in this paper achieves the lowest average latency.

On Latency-Efficient Transmission Scheduling for In-Network Data Aggregation in Duty-Cycled Wireless Sensor Networks

In-network data aggregation is a fundamental traffic pattern in many applications of wireless sensor networks (WSNs). Data aggregation scheduling aims to find a collisionfree transmission schedule scheme for data aggregation while minimizing the total network latency. This paper focuses on the data aggregation scheduling problem in dutycycled WSNs (dc-WSNs), in which low-duty-cycle techniques are employed for energyconsuming operations. Based on greedy strategy, we propose two latency-efficient data aggregation scheduling algorithms, namely GAS-PAS and GAS-SAS for dc-WSNs. We theoretically derive the latency upper bounds of the proposed algorithms, and the results demonstrate that both GAS-PAS and GAS-SAS achieve constant approximation to the optimal latency. We also conduct extensive simulations to show that the proposed scheduling algorithms can improve data aggregation latency in dc-WSNs under various network settings, comparing with state-of-the-art algorithms in the literature.

Near optimal scheduling of data aggregation in wireless sensor networks

Due to the large-scale ad hoc deployments and wireless interference, data aggregation is a fundamental but time consuming task in wireless sensor networks. This paper focuses on the latency of data aggregation. Previously, it has been proved that the problem of minimizing the latency of data aggregation is NP-hard [1]. Many approximate algorithms have been proposed to address this issue. Using maximum independent set and first-fit algorithms, in this study we design a scheduling algorithm, Peony-tree-based Data Aggregation (PDA), which has a latency bound of 15R+Δ−15, where R is the network radius (measured in hops) and Δ is the maximum node degree. We theoretically analyze the performance of PDA based on different network models, and further evaluate it through extensive simulations. Both the analytical and simulation results demonstrate the advantages of PDA over the state-of-art algorithm in [2], which has a latency bound of 23R+Δ−18.

A New Scheduling Algorithm for Reducing Data Aggregation Latency in Wireless Sensor Networks

Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggre- gation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works.