Efficient Data Collection Scheme Research Articles

Efficient Data Collection Scheme for IRS-Based V2I Backscatter Communications

Efficient Data Collection Scheme for IRS-Based V2I Backscatter Communications

Privacy Preserving and Efficient Data Collection Scheme for AMI Networks Using Deep Learning

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks

In this paper, we take an in-depth study on the data collection scheduling issue in underwater acoustic sensor networks (UASNs). To address this issue, we propose the use of software-defined networking (SDN) technique, based on which the paradigm of software-defined underwater acoustic sensor networks (SD-UASNs) is proposed. With SD-UASNs, we propose an efficient data collection scheme on account of node clustering. We take the data scale into account, propose a fast node clustering approach based on an improved Birch algorithm including node pre-clustering and node re-clustering. In particular, for the node pre-clustering procedure, we improve the cluster feature tree of the Birch algorithm and limit the nodes in each cluster to be within an optimal distance. Based on the proposed pre-clustering approach, we propose a greedy data collection scheme that can heuristically seek optimal solutions when the compressed sampling is taken into account. Evaluation results demonstrate that our scheme performs better in network efficiency than some popular schemes.

Efficient Collection of Connected Vehicles Data With Precision Guarantees

Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and control of transportation systems. The outstanding challenge for such analysis is how to continuously collect and process extremely large volumes of data. To address this challenge, efficient collection of data is critical to prevent overburdening the communication systems and overreaching computational and memory capacity. In this work, we propose an efficient data collection scheme that selects and transmits only a small subset of the data to alleviate data transmission burden. As a demonstration, we have used the proposed approach to select the data points to be transmitted from 10,000 connected vehicles trips available in the Safety Pilot Model Deployment dataset. The presented results show that collection ratio can be as small as 0.05 depending on the required precision. Moreover, a simulation study was performed to evaluate the travel time estimation accuracy using the proposed data collection approach. Results show that the proposed data collection approach can significantly improve travel time estimation accuracy.

Energy Efficient Data Collection Algorithm for Mobile Wireless Sensor Network

Wireless sensor network (WSN) with mobile sink serves a lot of industrial and agricultural monitoring applications. The data collection with WSN has been popularly known in many applications such as smart environments, health monitoring, habitat monitoring, surveillance and tracking systems. The mobile sink moves to the corresponding cluster and gathers the data and moves to the next cluster head (CH) for the same. The data loss and waiting time of the data in the CH results from energy consumption and work repetition. This article provides a novel energy efficient data collection scheme. The data collection is done by the sink in a Polling based M/G/1 server model. The cluster member (CM) sends the data in N threshold model to the CH. The CH gathers the data from the CM and reports to sink once it arrives nearby. The energy efficient cluster head selection algorithm is evaluated with the proposed model and the waiting time is analyzed. The data arrival from the CM to the CH is considered as Poisson in nature. The sink mobility with respect to polling system is analyzed. The sink data collection is done through dynamic polling mechanism based on the CH arrival rate. The proposed algorithm outperforms the modified low energy adaptive clustering hierarchy and energy-aware multi-hop routing protocol (M-GEAR) protocols in terms of lifetime, waiting time and throughput. The proposed algorithm provides high resistance to energy hole and HOTSPOT problem.

On the Design of an Energy Efficient Data Collection Scheme for Body Area Nano Networks

With the recent advancements in nanotechnologies, Body Area Nanonetworks (BANNETs) are expected to be a promising solution for many critical biomedical applications. Due to the extremely small size of nanomachines, serious energy limitation becomes a challenging roadblock stunting the development of BANNETs. As an initial step towards this end, this paper focuses on the design of an energy-efficient data collection scheme in BANNETs. First, a sleep/wake-up mechanism is introduced to avoid the unnecessary energy consumption when no external request comes. Then, with a careful consideration of both node available energy and transmission energy consumption, we design a new node selection strategy to further reduce the energy consumption in the data collection process. Finally, we conduct extensive simulations for both the proposed data collection scheme and the benchmark greedy scheme to illustrate the energy efficiency of our scheme as well as to discuss the impacts of network parameters on network performance.

On the Design of an Energy Efficient Data Collection Scheme for Body Area Nano Networks

With the recent advancements in nanotechnologies, Body Area Nanonetworks (BANNETs) are expected to be a promising solution for many critical biomedical applications. Due to the extremely small size of nanomachines, serious energy limitation becomes a challenging roadblock stunting the development of BANNETs. As an initial step towards this end, this paper focuses on the design of an energy-efficient data collection scheme in BANNETs. First, a sleep/wake-up mechanism is introduced to avoid the unnecessary energy consumption when no external request comes. Then, with a careful consideration of both node available energy and transmission energy consumption, we design a new node selection strategy to further reduce the energy consumption in the data collection process. Finally, we conduct extensive simulations for both the proposed data collection scheme and the benchmark greedy scheme to illustrate the energy efficiency of our scheme as well as to discuss the impacts of network parameters on network performance.

Secure Energy Efficient Data Collection Scheme Over Wireless Multimedia Sensor Networks

A secure data collection in wireless multimedia sensor networks (WMSNs) has given attention to one of security issues. WMSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. For the energy efficiency, WMSNs adopt mobile sinks to collect data from sensor nodes. Thus, how to gather data securely and efficiently is an important issue WMSNs. In this paper, we propose a secure energy efficient data collection scheme over WMSNs, which are based on Bilinear pairing and symmetric key cryptosystem. First of all, we devise a security model based on a hierarchical key structure for the security mechanisms, authentication, key agreement, confidentiality, and integrity. Based on the model, we propose a secure energy efficient data collection scheme, which could establish secure session in one round. The proposed scheme could efficiently remedy security and efficiency problems in the previous data collection schemes over WMSNs. It has only about 18% of overhead for the security but also has energy efficiency compared with the other related schemes.

Distributed Joint Cluster Formation and Resource Allocation Scheme for Cooperative Data Collection in Virtual MIMO-Based M2M Networks

An efficient data collection scheme plays an important role for the real-time intelligent monitoring in many machine-to-machine (M2M) networks. In this paper, a distributed joint cluster formation and resource allocation scheme for data collection in cluster-based M2M networks is proposed. Specifically, in order to utilize the advantages of cooperation, we first propose a hierarchical transmission model which contains two communication phases. In the first phase, the intracluster information sharing is carried out by all the nodes within the same cluster. Then these nodes transmit the total information to the BS cooperatively with virtual-MIMO (VMIMO) protocol in the second phase. To grasp the properties and advantages of this cooperative transmission strategy, the theoretical analysis results are provided. The key issue in this system is to form the clusters and allocate resources efficiently. Since the optimization problem on this issue is an NP-hard problem, a feasible joint scheme for the cluster formation and resource allocation is proposed in this paper, which is carried out via coalition formation game with a distributed algorithm. This scheme can reduce the complexity while keeping an attractive performance. Simulation results show the properties of the proposed scheme and its advantages when comparing with the noncooperative scheme for the data collection in a practical scenario.

Ferry-based Data Gathering in Wireless Sensor Networks with Path Selection

Depending on the application, mobile ferries can be used for collecting data in a WSN especially those at a large-scale with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry based approach eliminates or minimizes the need for multi-hop forwarding of data, and as a result energy consumption at the nodes will be significantly reduced especially nodes that are near the base station as they are used by other nodes to forward data to the base station. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection scheme using a ferry node is proposed with emphasis of the effect of ferry's path. In this scheme the decision of selecting cluster heads is based on their residual energy and their distance from the ferry path. We simulated the proposed scheme in MATLAB using different scenarios to show their performance in terms of the network lifetime and total energy consumption in the network. We found that the centered and the diagonal fitted paths performed better than the diagonal path in terms of the network lifetime and energy consumed. We also found that increasing the check points increases the lifetime of the network

An efficient data collection scheme through multi-path routing structures in wireless sensor networks

An essential method to overcome the high loss rates of wireless communication in sensor data collection is to use multi-path routing. In this paper, we investigate the construction of multi-path routing structures and develop an enhanced scheme for sensor data collection. In constructing multi-path routing structures, we limit the number of messages that each node transmits and receives in data collection to achieve a required energy efficiency level. Then, we design and analyse an enhanced data collection scheme in which sensor nodes exploit the broadcast nature of wireless transmission to overhear their neighbours. This increases the numbers of data propagation paths from sensor nodes to the base station and improves the robustness of data collection without sacrificing the latency of data collection. Experimental results show that our methods achieve significantly better trade-offs among the robustness, latency and energy efficiency of data collection.

Contour maps: Monitoring and diagnosis in sensor networks

Large-scale sensor networks impose energy and communication constraints, thus it is difficult to collect data from each individual sensor node and process it at the sink. In this paper, we propose an efficient data-collection scheme that can be used for event monitoring or network-wide diagnosis. Our scheme relies on the well-known representation of data—contour maps, which trade off accuracy with the amount of samples. The scheme consists of three novel algorithms to build contour maps: distributed spatial and temporal data suppression, contour reconstruction at the sink via interpolation and smoothing, and an efficient mechanism to convey routing information over multiple hops. By reducing the number of transmissions required to convey relevant information to the sink, the proposed contour mapping scheme saves energy and improves network lifetime. In a sharp contrast to related work in this area, the scheme does not require all nodes to explicitly share information. The contour mapping scheme can be applied for tasks such as: (1) presenting a global picture of the network in both temporal and spatial domains, (2) being used as a diagnosis tool, e.g., to detect faulty sensors and to scan for residual energy, (3) working in concert with in-network aggregation schemes to further reduce the communication overhead of aggregation schemes. The proposed scheme imposes little processing and storage overhead, allowing for the sensor networking paradigm of ‘dumb sensor, smart sink’ which enables economical deployment of large-scale sensor networks. Simulation results show that our scheme is resilient to both high packet loss rate and measurement noise. The design is also energy efficient, resulting in up to an-order-of-magnitude power savings when compared with the base line scheme where every sensor sends its report to the sink.