Energy-efficient Data Gathering Scheme Research Articles

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

The sixth-generation (6G) wireless communication networks are anticipated in integrating aerial, terrestrial, and maritime communication into a robust system to accomplish trustworthy, quick, and low latency needs. It enables to achieve maximum throughput and delay for several applications. Besides, the evolution of 6G leads to the design of unmanned aerial vehicles (UAVs) in providing inexpensive and effective solutions in various application areas such as healthcare, environment monitoring, and so on. In the UAV network, effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication. It can be addressed by the use of clustering techniques for UAVs in 6G networks. In this aspect, this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles (MEEDG-CUAV). The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head (CH) to reduce the overall energy utilization. Besides, the quantum chaotic butterfly optimization algorithm (QCBOA) with a fitness function is derived to choose CHs and construct clusters. The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

An energy efficient data gathering scheme for wireless sensor networks using hybrid crow search algorithm

The most important challenge in Wireless Sensor Networks (WSNs) is to investigate mechanisms for energy-efficient data gathering. Within the operating range of Wireless Sensor Network (WSN), the sensor nodes are distributed to send the sensed data to the base station. During the transaction of data, some amount of energy is wasted. So, this work is focused on providing an energy-efficient data gathering scheme by Circular Clustering and Hybrid Crow Search Algorithm for enhancing the lifetime of network. Learning to maximize the lifetime of network and to attain supreme energy efficiency. In the proposed scheme, for robust data gather node selection; initially, the circular cell clusters are accomplished by separating the entire area of the sensor network. Hereafter, to select precise data gathering node in the circular cell cluster region, a multi objective based weighted sum approach is employed for proximity, communication cost, residual energy and coverage. Further, a routing and dynamic mobile sink relocation mechanism are performed to gather data form cluster head using hybrid crow search algorithm (HCSA). Based on the parameters such as total energy consumption, many alive nodes, and network lifetime, the capability of the proposed technique has been investigated. The proposed strategy has better performance analysis when contrasted to the existing techniques.

An energy-efficient data gathering scheme in underwater wireless sensor networks using a mobile sink

Distribution of nodes in different depths leads unbalanced energy consumption in underwater wireless sensor networks. This issue is one of the most important challenges in routing protocols based on depth because nodes with larger depth rely on smaller depth nodes to send their data to the sink through multi-hop communications. This situation applies high load on nodes with small depth as they die faster than other nodes. Besides, low bandwidth and speed of acoustic channels in the underwater environment result in increase of the probability of collision especially when several nodes send their data simultaneously and increase of delay in sending and collecting data. The aim of the proposed protocol which is called energy-efficient data gathering scheme (EEDG) is to relatively fix the aforementioned problems in three steps. Firstly, the balance of energy consumption is achieved by grouping nodes into smaller sets that are managed by temporary forwarder nodes. Forwarder nodes are responsible for gathering data from their subset nodes in one-hop communication in each round. Secondly, collision rates and packet loss are improved using the proposed MAC protocol by which ordinary nodes send their data to their forwarder nodes only at dedicated time slots. Finally, delay in data collection process from the whole network is decreased using the proposed graph structure, that is, forwarder nodes are met by the mobile sink according to the specified degree for them in the graph. Simulation results using NS2 show efficiency of the EEDG in terms of energy consumption, end-to-end delay, and throughput.

Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.

An energy-efficient transmission approach using compressed sensing

In the process of transmission in wireless sensor networks (WSN), a vital problem is that a centre region close to the sink will form in which sensors have to cost vast amount of energy. To communicate in an energy-efficient manner, compressed sensing (CS) has been employed gradually. However, the performance of plain CS is significantly dependant on the specific data gathering strategy in practice. In this paper, we propose an energy-efficient data gathering scheme based on regionalisation CS. Subsequently, advanced methods for practical applications are considered. Experiments reveal that our scheme outperforms distributed CS, the straight forward and the mixed schemes by comparing different parameters of the data package, and the considered methods also guarantee its feasibility.

Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.

BTDGS: Binary-Tree based Data Gathering Scheme with Mobile Sink for Wireless Multimedia Sensor Networks

In wireless multimedia sensor networks (WMSNs), the energy consumption of multimedia data type are much higher than that of traditional wireless sensor networks (WSNs). Heavy multimedia data relaying operation causes not only single sensor node dead early, but also `hot spots' problem. In this paper, a novel energy efficient data gathering scheme with a mobile sink for WMSNs, BTDGS, is proposed. It is based on a virtual binary-tree infrastructure. The mobile sink moves along a predefined circle trajectory, and sensor nodes relay data packages in a greedy manner. The process of BTDGS data gathering includes sink location broadcasting phase, data collection phase, and sink leaving broadcasting phase. The simulation results show that our BTDGS is an energy effective, reliable, timely, and sojourn time adaptive data gathering scheme. It is feasible and suitable for WMSNs.

Min-Distance Hop Count based Multi-Hop Clustering In Non-uniform Wireless Sensor Networks

In wireless sensor networks, an energy efficient data gathering scheme is one of core technologies to process a query. The clusterbased data gathering methods minimize the energy consumption of sensor nodes by maximizing the efficiency of data aggregation. However, since the existing clustering methods consider only uniform network environments, they are not suitable for the real world applications that sensor nodes can be distributed unevenly. To solve such a problem, we propose a balanced multi-hop clustering scheme in non-uniform wireless sensor networks. The proposed scheme constructs a cluster based on the logical distance to the cluster head using a min-distance hop count. To show the superiority of our proposed scheme, we compare it with the existing clustering schemes in sensor networks. Our experimental results show that our proposed scheme prolongs about 48% lifetime over the existing methods on average.

Synchronization-Based Data Gathering Scheme for Sensor Networks

By deploying hundreds or thousands of microsensors and organizing a network of them, one can monitor and obtain information of environments or objects for use by users, applications, or systems. Since sensor nodes are usually powered by batteries, an energy-efficient data gathering scheme is needed to prolong the lifetime of the sensor network. In this paper, we propose a novel scheme for data gathering where sensor information periodically propagates from the edge of a sensor network to a base station as the propagation forms a concentric circle. Since it is unrealistic to assume any type of centralized control in a sensor network whose nodes are deployed in an uncontrolled way, a sensor node independently determines the cycle and the timing at which it emits sensor information in synchrony by observing the radio signals emitted by sensor nodes in its vicinity. For this purpose, we adopt a pulse-coupled oscillator model based on biological mutual synchronization such as that used by flashing fireflies, chirping crickets, and pacemaker cells. We conducted simulation experiments, and verified that our scheme could gather sensor information in a fully-distributed, self-organizing, robust, adaptive, scalable, and energy-efficient manner.