Data Aggregation Time Research Articles

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.

Impacts of Rainfall Data Aggregation Time on Pluvial Flood Hazard in Urban Watersheds

Pluvial floods occur when heavy rainstorms cause the surcharge of the sewer network drainage, representing one of the most impacting natural hazard in urban watersheds. Pluvial flood hazard is usually assessed considering the effect of annual maxima rainfall of short duration, comparable with the typical concentration times of small urban watersheds. However, short duration annual maxima can be affected by an error of underestimation due to the time resolution as well as the aggregation time of the rainfall time series. This study aims at determining the impact of rainfall data aggregation on pluvial flood hazard assessment. Tuscany region (Central Italy) is selected as a case study to perform the assessment of the annual maxima rainfall underestimation error, since the entire region has the same temporal aggregation of rainfall data. Pluvial flood hazard is then evaluated for an urban watershed in the city of Florence (Tuscany) comparing the results obtained using observed (uncorrected) and corrected annual maxima rainfall as meteorological forcing. The results show how the design of rainfall events with a duration of 30 min or shorter is significantly affected by the temporal aggregation, highlighting the importance of correcting annual maxima rainfall for a proper pluvial flood hazard evaluation.

A case study on parallel HDF5 dataset concatenation for high energy physics data analysis

In High Energy Physics (HEP), experimentalists generate large volumes of data that, when analyzed, helps us better understand the fundamental particles and their interactions. This data is often captured in many files of small size, creating a data management challenge for scientists. In order to better facilitate data management, transfer, and analysis on large scale platforms, it is advantageous to aggregate data further into a smaller number of larger files. However, this translation process can consume significant time and resources, and if performed incorrectly the resulting aggregated files can be inefficient for highly parallel access during analysis on large scale platforms. In this paper, we present our case study on parallel I/O strategies and HDF5 features for reducing data aggregation time, making effective use of compression, and ensuring efficient access to the resulting data during analysis at scale. We focus on NOvA detector data in this case study, a large-scale HEP experiment generating many terabytes of data. The lessons learned from our case study inform the handling of similar datasets, thus expanding community knowledge related to this common data management task.

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Wireless sensor network (WSN) comprises the distributed sensors for aggregating and organizing the data. Data aggregation is the major concern in WSN since it relies on several factors, namely energy constraints of sensors, network topology, links conditions and so on. The conventional approach does not perform efficient data aggregation due to their battery power of nodes and degrade the network lifetime. To improve data aggregation and network lifetime, An Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering based Data Aggregation (EEEEFCC-DA) method is designed. Initially, residual energy of each sensor node (SN) is calculated. To determine the number of clusters, the elbow method is used in fuzzy c-means clustering algorithm. Then, Centroids value is calculated for every cluster to group SNs. Bray-Curtis Similarity Index is used to compute the similarity between the SN and Centroids value of cluster. SNs are grouped depends on the similarity value. The process gets iterated until every SNs gets clustered to the suitable clusters. After that, the SN with higher residual energy is selected as cluster head (CH). CH gathers data from each SNs and send to sink node. This, assist to enhance the data gathering accuracy and lessen the energy consumption. Simulation of EEEEFCC-DA method is carried out with various metrics namely energy consumption, network lifetime, data aggregation accuracy (DAA) and data aggregation time with number of SNs and number of data packets (DP). Results show that EEEEFCC-DA method provides better performance in term of DAA , network lifetime , energy consumption and data aggregation time than the conventional methods.

An Energy‐Efficient One‐Shot Scheduling Algorithm for Wireless Sensor Networks

In low‐load wireless sensor networks, the power consumption of the node consists mainly of two parts: data transmission and node state switching. The lower node workload causes low energy consumption on data transmission, and the state switching energy of the node cannot be ignored. This paper proposes a one‐shot time division multiple access (TMDA) scheduling with unlimited channels (SUC) on the assumption that the number of available channels is unlimited. SUC combines the receiver‐based consecutive slot allocation with channel allocation, which minimises the number of node state switching and optimizes energy efficiency. Theoretical analysis demonstrates that the number of channels required by SUC does not exceed , where N indicates the number of nodes. Seeing that the number of available wireless channels is limited in practice, the paper proposes the scheduling with limited channels (SLC) and uses a Lookahead Search mechanism to solve slot conflict. For the scalability of the algorithm, a distributed implementation based on the token change is proposed. The algorithm uses the depth‐first‐search (DFS) to pass the token to all nodes and terminates slot and channel assignment. The simulation results show our algorithm can reduce the energy consumption by minimizing the number of state switching and shorten the data aggregation time by reusing slots among nodes.

Fast Sensory Data Aggregation in IoT Networks: Collision-Resistant Dynamic Approach

Internet-of-Things (IoT) systems and their applications rely on sensory data for operating context-aware functions. To ensure reliability, these data must arrive at the base station in a timely manner. Because IoT sensors are battery powered and expected to last for years, energy consumption has always been a dominant concern. The duty cycling mechanism efficiently extends the lifespan of sensor nodes; however, it increases data aggregation time. This article studies the minimum time aggregation scheduling problem in duty-cycled sensor networks. The existing solutions route the sensory data through a predefined structure, but such structures are constructed without full awareness of collisions in the wireless medium. Therefore, in this study, the collision-resistant dynamic (CORD) scheduling approach has been proposed with an aim to provide fresh data for emerging IoT applications. The proposed approach is applicable to any initial routing structure and it dynamically changes the receiver of a transmission whenever this change can reduce aggregation time. The receiver-changing decision is made on the fly, resisting all collisions between the determined transmissions. We evaluate CORD in various simulation scenarios, and the results demonstrate that the proposed approach yields a notable improvement of up to 62% from the viewpoint of aggregation time while having comparable time complexity compared to state-of-the-art methods.

Energy aware decision stump linear programming boosting node classification based data aggregation in WSN

Data collection is a considerable operation in Wireless sensor network (WSN) to minimize the energy dissipation of sensor nodes and thereby increasing the network lifetime. A lot of research works have been designed for data gathering in WSN. A multi-mobile agent itinerary planning based energy and fault aware data aggregation and zone-based energy-aware data collection routing protocol was introduced to obtain better energy usage and data delivery in WSN. However, data aggregation accuracy was not increased and energy consumption during data aggregation was not reduced. In order to overcome such limitations, an Energy Aware Decision Stump Linear Programming Boosting Node Classification based Data Aggregation (EADSLPBNC-DA) Model is proposed. Initially, residual energy for every sensor node is calculated for performing the node classification. After that, linear programming boosting classification (LPBC) model is applied in EADSLPBNC-DA Model which increased the margin between the training samples (i.e., sensor nodes) of different classes. The LPBC model constructs a strong classifier by combining number of weak decision stump result. Subsequently, strong classifier in EADSLPBNC-DA Model accurately classifies each input sensor node as higher energy or lower energy node which reduced the misclassification error. Then, the lesser energy sensor nodes in WSN transmit the data packets to the neighboring higher energy sensor nodes through calculating the distance by Manhattan distance formula. Finally, the sink node gathers the data packets from the higher energy sensor nodes. As a result, EADSLPBNC-DA Model attains energy efficient data collection in WSN. Experimental evaluation of EADSLPBNC-DA Model is carried out on factors such as energy consumption, delay, data aggregation accuracy, network lifetime and data aggregation time with respect to number of sensor nodes and number of data packets. From the simulation results, proposed EADSLPBNC-DA Model significantly reduces the energy consumption by 22%, delay by 35% and data aggregation time by 28% when compared to the existing techniques. In addition, the data aggregation accuracy and network lifetime gets increased by 13% and 10% respectively compared to state-of-the-art works.

Design Research on Algorithm of Processing Time, Data Aggregation Time, Packet Delivery Ratio and Energy Consumption

This paper presents the preliminary information of research that deals with communication protocol for Wireless Sensor Network (WSN) with retention of energy efficiency. The motive of this research is to accomplish a common goal of energy efficiency, using different forms of methodologies. Hence, the adoption of different methodologies and a common goal of energy efficiency in WSN are achieved. This paper explains the outcome accomplishment the proposed ENLPL Algorithm, Globular topology, load balancing technique of Processing Time, Data Aggregation Time, Packet Delivery Ratio and Energy Consumption pertaining to optimizing energy using probabilistic technique, and Dynamic Reconfiguration. . The work also explains about the comparative analysis among the models to showcase the best scenario of usage in sensorbased applications

The complexity of data aggregation in static and dynamic wireless sensor networks

The contribution of this paper is threefold. First, we give tight bounds for the complexity of the problem of data aggregation in static networks. In more details, we show that the problem remains NP-complete when the graph is of degree at most three. Second, we investigate the complexity of the same problem in a dynamic network, that is, a network whose topology can evolve through time. In the case of dynamic networks, we show that the problem is NP-complete even in the case where the graph is of degree at most two. Third, we give the first lower and upper bounds for the minimum data aggregation time in a dynamic graph. We also observe that even in a well-connected evolving graphs, the optimal solution cannot be found by a distributed algorithm or by a centralized algorithm that does not know the future.

Bitmap-Wise Wireless M-Bus Coordination for Sustainable Real Time Energy Management

Even though WM-Bus is being considered to be the most promising network protocol for smart metering, it is not suitable for a sustainable real-time home energy management system (HEMS), which requires higher reliability and longer lifetime despite real time bi-directional communications. Therefore, in this paper we propose a Bitmap-wise WM-Bus (BWM-Bus), coping well with sustainable real-time HEMS. In particular, the proposed scheme addresses the several problems in WM-Bus for HEMS by introducing novel functions: asynchronous meter trigger, adaptive slot scheduling, and bitmap-wise retransmission request. Through experiments, we demonstrate that BWM-Bus guarantees higher data success ratio with lower data aggregation time, as well as longer lifetime than WM-Bus standard.

On centralized and distributed algorithms for minimizing data aggregation time in duty-cycled wireless sensor networks

We study the problem of minimizing data aggregation time in wireless sensor networks (WSNs) under the practical duty-cycle scenario where nodes switch between active states and dormant states periodically for energy efficiency. Under the protocol interference model, we show that the problem is NP-hard and present a lower bound of delay for any data aggregation scheme. To solve the problem efficiently, we then construct a routing tree based on connected dominator set and propose two aggregation scheduling algorithms, which are the centralized Greedy Aggregation Scheduling (GAS) and the distributed Partitioned Aggregation Scheduling (PAS), so as to generate collision-free transmission schedules for data aggregation in duty-cycled WSNs. To minimize the total delay, GAS tries to achieve maximal concurrent transmissions in each time-slot during each frame by using global information, while PAS leverages a network partition based strategy and local information to ensure the largest degree of channel reuse across space and time domains. Theoretical analysis indicates that each algorithm consumes at most $$O(R+\varDelta)$$ O ( R + Δ ) frames and achieves nearly constant factor approximation on the optimal delay. Here R and $$\varDelta$$ Δ are the network radius and the maximum node degree, respectively. We also evaluate the practicability of our algorithms by extensive simulations under various network conditions and the results corroborate our theoretical analysis.

Improved Algorithm for Minimum Data Aggregation Time Problem in Wireless Sensor Networks

Wireless sensor networks promise a new paradigm for gathering data via collaboration among sensors spreading over a large geometrical region. Many applications impose delay requirements for data gathering and ask for time-efficient schedules for aggregating sensed data and sending to the data sink. In this paper, the authors study the minimum data aggregation time problem under collision-free transmission model. In each time round, data sent by a sensor reaches all sensors within its transmission range, but a sensor can receive data only when it is the only data that reaches the sensor. The goal is to find the method that schedules data transmission and aggregation at sensors so that the time for all requested data to be sent to the data sink is minimal. The authors propose a new approximation algorithm for this NP-hard problem with guaranteed performance ratio \(\frac{{7\Delta }}{{\log _{2} {\left| {\text{S}} \right|}}} + c\), which significantly reduces the current best ratio of Δ 1, where S is the set of sensors containing source data, Δ is the maximal number of sensors within the transmission range of any sensor, and c is a constant. The authors also conduct extensive simulation, the obtained results justify the improvement of proposed algorithm over the existing one.