Leveraging Crowdsourcing for Efficient Malicious Users Detection in Large-Scale Social Networks

Wireless Communications and Mobile Computing

et al. 2018

In wireless sensor networks (WSNs), many applications require a high reliability for the sensing data forwarding to sink. Due to the lossy nature of wireless channels, achieving reliable communication through multihop forwarding can be very challenging. Broadcast technology is an effective way to improve the communication reliability so that the data can be received by multiple receiver nodes. As long as the data of any one of the receiver nodes is transmitted to the sink, the data can be transmitted successfully. In this paper, a cross-layer optimization protocol named Adaptive transmission Power control based Reliable data Forwarding (APRF) scheme by using broadcast technology is proposed to improve the reliability of network and reduce communication delay. The main contributions of this paper are as follows: (1) for general data aggregation sensor networks, through the theoretical analysis, the energy consumption characteristics of the network are obtained. (2) According to the case that the energy consumption of nearsink area is high and that in far-sink area is low, a cross-layer optimization method is adopted, which can effectively improve the data communication by increasing the transmission power of the remaining energy nodes. (3) Since the reliability of communication is improved by increasing the transmission power of the node, the number of retransmissions of the data packet is reduced, so that the delay of the packet reaching the sink node is reduced. The theoretical and experimental results show that, applying APRF scheme under initial transmission power of 0 dBm, although the lifetime dropped by 13.77%, delay could be reduced by 40.37%, network reliability could be reduced by 10.08%, and volume of data arriving at sink increased by 10.08% compared with retransmission-only mechanism.

Section: Related Workmentioning

confidence: 99%

Adaptive Transmission Power Control for Reliable Data Forwarding in Sensor Based Networks

Teng

Wireless Communications and Mobile Computing

et al. 2018

“…This strategy has an advantage of wide applicability in effectively overcoming similar attack behavior that impedes the transmission of data to the sink [21]. However, the strategy cannot detect the existence of an attack or identify malicious nodes [15].…”

Section: Related Workmentioning

confidence: 99%

“…where , is the correlation coefficient between nodes and [4,15]. A larger , indicates a higher correlation between the data of nodes and a smaller length of data packet formed after the data aggregation.…”

Section: Systemmentioning

confidence: 99%

“…As one of the most important parts for IoT, the wireless sensor networks (WSNs) are increasingly applied in various aspects of industry [3][4][5][6], environment monitoring [7], life [8,9], and medical health monitoring [10]. With the emergence of edge computing [11], the network architecture is also rapidly developing in computing mode from the cloud computing [11,12] of network center to edge computing [13], where the sensor network plays an important role [14][15][16]. However, preservation of information and sensitive data is one of the biggest challenges in sensor networks [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks

Tang

Security and Communication Networks

Zhao

et al. 2018

An Aggregate Signature based Trust Routing (ASTR) scheme is proposed to guarantee safe data collection in WSNs. In ASTR scheme, firstly, the aggregate signature approach is used to aggregate data and keep data integrity. What is more important, a light aggregate signature based detour routing scheme is proposed to send abstract information which includes the data sending time and ID of data, nodes' ID to the sink over different paths which can verify whether the data reaches the sink safely. In addition, the trust of a path is evaluated according to the success rate of the path. The trust of paths susceptible to frequent attack will be lowered and the path with high trust will be selected for data routing to avoid data gathering through low trust path and thereby increase the success rate of data gathering. Our comprehensive performance analysis has shown that, the ASTR scheme is able to effectively ensure an increase in success rate of data transmission by 23.23%, reduce the data amount loaded by the node by 53.59%, reduce the redundant data by 41.70%.

“…Crowd sensing (CS) refers to sensing a wide range of human activities and their surrounding environment by kinds of sensing devices such as smartphones, iPad, or sensor nodes [32,[39][40][41][42]. Because the sensing devices is so vast, it is given a new name crowdsourcing networks (CN) [43].…”

Section: Related Workmentioning

confidence: 99%

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Wireless Communications and Mobile Computing

et al. 2018

To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named "Quality of Information Satisfaction Degree" (QoISD) is to quantify how much collected sensing data can satisfy the application's QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.