Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary... Show More

Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary tasks for many MSN users. For example, drivers are not required to report traffic condition although their vehicles with advanced sensors have easy access to critical information. Therefore, incentive mechanisms are needed to recruit sensing users (SUs). Incentive mechanisms proposed for traditional MSNs cannot be applied directly due to limited information of SU used for recruitment. In this article, the authors propose a novel cloud-based MSN model that consists of three parties, including data request party, cloud-based platform and SUs. To better utilize information of SUs, a data quality model is proposed to measure the credit level of SUs. The proposed SU recruitment strategy takes into consideration social connections of users. According to the strategy, SUs are divided into two separate levels. Moreover, the authors propose an incentive mechanism using a Stackelberg game theoretical approach to achieve the maximum utility of each recruited SU. The simulation results demonstrate that the proposed incentive mechanism can recruit SUs more efficiently while providing data quality guarantee.

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale... Show More

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

Wireless Sensor Networks (WSNs) are widely used in numerous critical applications, and require the network to have a prolonged lifetime and high tolerance to failures. However, the battery-operated... Show More

Wireless Sensor Networks (WSNs) are widely used in numerous critical applications, and require the network to have a prolonged lifetime and high tolerance to failures. However, the battery-operated sensor nodes used in WSNs cause the network to be resource-constrained. On the one hand, there is a continuous urge to efficiently exploit the WSN energy, and hence, prolong the network lifetime. On the other hand, WSN node failures are not only attributed to battery drain. Node failures can be caused by hardware or software malfunctioning. In this article, the authors assess the impact of the death criterion on the network lifetime and reliability. It is related how the data from the different sensors are aggregated to the death criterion. Additionally, the impact of the number of sensing cycles per network master on the network lifetime and energy efficiency for the different considered death criteria. The effect of the network master selection process on the energy efficiency is also examined. Finally, the impact of the death criterion on the reliability of the WSN is evaluated.

This article presents results of a comparative study of recursive techniques for robust identification of time-varying electrical equivalent circuit models of li-ion batteries. Two such methods are... Show More

This article presents results of a comparative study of recursive techniques for robust identification of time-varying electrical equivalent circuit models of li-ion batteries. Two such methods are studied, a direct continuous time system identification method and an indirect discrete time technique. The results of this study indicate that although both methods work equally well for identification of time-invariant circuit models from clean voltage-current data, the direct continuous time method outperforms indirect discrete time technique for identification of time-varying circuit models. Similar conclusions can also be drawn for identification of equivalent circuit models in the presence of noise and/or unmodeled dynamics.