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

Abstract

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%.

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

“…In WSNs, sensor nodes are usually powered by battery, and it would be very challenging or sometimes infeasible to replace or recharge them after being deployed [8,9]. Obviously, it becomes important to reduce energy consumption in WSNs and maximize the network lifetime [10][11][12].…”

“…In addition, how to ensure that data is quickly transmitted to the sink node is also a field that deserves research [12,19]. Since WSNs are often applied to occasions that require rapid response to events, there are certain requirements on the time taken by the sink to receive data from the sink, or delay.…”

“…In order to deal with the loss of the data packet, retransmission mechanism often is used to ensure that the data is collected to meet the reliability requirements of the application. But there are two deficiencies for data retransmission: First, with the packet loss, the retransmission of lost data packets takes more time, which will result in high delay [12]. Second, the retransmission of lost data packets will increase the energy consumption of nodes that will reduce the network lifetime [9].…”

Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks

“…The wireless sensor networks have been developed for a long time [21][22][23][24][25][26]; with the development of wireless portable devices and sensor technology, the application of wireless sensor network in the industrial field becomes the focus of attention [2,4]. Industrial wireless sensor networks (IWSNs) is emerging in this background; it does not require wiring to be deployed at any time and has simple requirements for complex industrial sites; the device is small and easy to deploy and has powerful functions that can be used to detect and monitor a variety of visible and invisible physical phenomena in close range and high precision; and the strong practicality makes it to have broad application prospects in various fields of industrial production [1,2,9,10], especially with the rise of cloud computing [27] and fog computing [28][29][30], to make its development face greater opportunities.…”

A fuzzy-rule-based packet reproduction routing for sensor networks

“…The typical solutions are Automatic Repeat-reQuest (ARQ) protocol and multi-path routing. 8,[12][13][14] In the ARQ, the data transmission reliability is guaranteed by retransmitting the lost data. In the multi-path routing, data packets are transmitted by multiple paths 1 to achieve reliable data transmission.…”