Abstract
Wireless Sensor Networks (WSNs) consist of large number of constrained wireless sensor nodes for the purpose of data gathering. Due to the limitations of some sensor nodes, especially the limited amount of energy, in-network data processing, such as data fusion, is very important. Meanwhile, data fusion can significantly improve the accuracy of the data gathering. This special issue mainly focuses on the latest research in the area of data fusion technology in WSNs. For this special issue, based on the review results, we have selected twenty papers that address the major issues of data fusion in WSNs and they are summarized as follows.
The paper “Sensor mobility control for multitarget tracking in mobile sensor networks” proposes a novel sensor mobility control framework for the mobile sensor network-based MTT (multitarget tracking). It is formulated as a constrained optimization problem that aims to maximize the overall tracking performance for all targets while conserving network energy and providing tracking coverage guarantee. The optimization problem is relaxed as a convex programming problem for computational tractability and the solution is implemented in a distributed manner. The newly proposed sensor mobility control scheme, implemented on the basis of iterative subgradient search, is shown via computer simulations to have better performance over the static sensor network-based MTT.
The paper “The degree-constrained adaptive algorithm based on the data aggregation tree” proposes a new algorithm called DADAT (degree-based adaptive algorithm for data aggregation tree). The energy consumption and the time of delay are both considered, and a weight model to construct a minimum spanning tree is established. Furthermore, the node degree on the tree is readjusted according to the average degree of the network, and nodes are labeled by red, yellow, and green colors according to its remaining energy; the child nodes of the red nodes are adaptively transferred to their neighbor nodes which are labeled as green. Finally, the authors discuss the weight and the update rounds’ impact on the network lifetime.
The paper “Data fusion algorithm of privacy protection based on QoS and multilayers hierarchically” proposes a data fusion algorithm of privacy protection based on query of server (QoS) and hierarchical multilayers is put forward, which divided the required privacy protection levels according to different safety requirements and set up hierarchical network models. In data fusion process, delay constraint is added to guarantee service quality, and it can reduce the energy consumption. Meanwhile, it can guarantee the accuracy of the data and reduce the probability of the whole network information exposed.
The paper “An efficient secure data aggregation based on homomorphic primitives in wireless sensor networks” proposes a novel secure data aggregation scheme (called SDA-HP) based on homomorphic primitives in WSNs. The proposed scheme adopts a symmetric-key homomorphic encryption to protect data privacy and combines it with homomorphic MAC synchronically to check the aggregation data integrity. It compares the scheme with the previously known methods such as SIES, iPDA, and iCPDA in terms of the data privacy protection efficiency, integrity performance, computation overhead, communication overhead, and data aggregation accuracy.
The paper “Exploiting optimal threshold for decision fusion in wireless sensor networks” proposes a binary decision fusion scheme that reaches a global decision by integrating local decisions made by fusion members. The optimal local thresholds and global threshold are derived by using the min-max criterion based analysis while they are ensuring false alarm rate constraint, without a preestimated target appearance probability. Simulation results show that the scheme can improve the system performance under certain constraints, which can guide the threshold selection for implementing WSN systems in mission-critical applications.
The paper “ESMART: energy-efficient slice-mix-aggregate for wireless sensor network” proposes an energy-efficient secure data aggregation scheme, ESMART, based on the technique of data slicing and mixing. The proposed scheme performs secure data aggregation in a more efficient way while keeping a good performance of privacy preservation. The simulation results show that the security performance of the proposed ESMART scheme is better than that of some existing and widely used schemes.
The paper “An improved optimal linear weighted cooperative spectrum sensing algorithm for cognitive radio sensor networks” proposes an improved optimal linear weighted cooperative spectrum sensing scheme on the assumption that the report channel is not ideal. Through mathematical modeling, the spectrum sensing problem is ultimately converted into a constrained nonconvex optimization problem, and the chaotic harmony search (CHS) algorithm is to be used to find the optimal weighting vector values.