Mobility Support for Next-Generation Wireless Sensor Networks

Mobility support technologies are much more versatile than traditional technologies as they can be applied to any scenario and combined with other algorithms, modeling, and technologies. Commonly, mobility related elements in wireless sensor networks (WSNs) are mobile sensor nodes, mobile anchor nodes, mobile data collectors, mobile sinks, and so forth. This special issue contains thirteen papers, of which four are related to the location algorithms based on the WSNs, five are related to the data share, data collection, and exchange, and three cover the routing of WSNs. There are single papers focusing on mobile PowerPoint pages, connectivity restoration, and mode decomposition algorithm for Internet of Things (IoT) application, respectively. The special issue opens with four papers related to the location algorithms based on the WSNs. H. Kabir et al. in their paper entitled “A Mathematical Algorithm of Locomotive Source Localization Based on Hyperbolic Technique” propose a noniterative and simpler method to locate the instantaneous position of the moving source in linear sensor array network (LSAN) by estimating the position and velocity based on TDOA and FDOA which does not have the convergence problem. The proposed method achieved the theoretical lower bound for near to far field with same and different velocity and different baseline of sensors in low to moderate noise.

R. Yu et al. in their paper entitled “A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems” propose a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). According to the historical information of mobile nodes trajectories, the state transition matrix is constructed by the location as the transition state and hidden Markov model is used to predict the mobile node location with the certain duration. R. Yu et al. in their paper entitled “LAPM: The Location Aware Prediction Model in Human Sensing Systems” develop a model that collects samples considering the relevancy from the perspective of location from different mobile social networks and estimating the occurrence likelihood of the perceived event with collected samples. C. So-In and W. Katekaew in their paper entitled “Hybrid Fuzzy Centroid with MDV-Hop BAT Localization Algorithms in Wireless Sensor Networks” propose a novel hybrid localization algorithm utilizing key characteristics of Centroid and DV-Hop. This hybrid scheme employs an extra weight derived from a fuzzy logic function to Centroid.

The next five papers focus on data share, data collection, and exchange. J. Li et al. in their paper entitled “Reputation-Based Incentives for Data Dissemination in Mobile Participatory Sensing Networks” propose reputation-based incentive schemes to motivate participants to disseminate reliable data in participatory sensing system, named RIDD, while minimizing incentive cost for maintaining sufficient number of reliable participants. Y. Jo in the paper entitled “Communication Availability-Based Scheduling for Fair Data Collection with Path-Constrained Mobile Sink in Wireless Sensor Networks” proposes the Communication Availability-Based Scheduling for Fair Data Collection (CASF) policy to support fair data collection in wireless sensor networks with a mobile sink. This concept is based on the communication error rates of sensor nodes and the accumulated amounts of data received from each sensor node. T. Ren et al. in their paper entitled “Data Uploading and Exchange Algorithm for Mobile Sensor Networks in City Traffic Environment” propose data uploading and exchange algorithm (DUEA); three-tier network architecture composed of vehicle nodes, relay nodes, and sink nodes is considered. X. Xu et al. in their paper entitled “A Survey on Energy Harvesting and Integrated Data Sharing in Wireless Body Area Networks” make a relatively comprehensive introduction from energy harvesting principle, especially for practical application in wireless body area networks (WBANs). Y. Zhang et al. in their paper entitled “LKM: A LDA-Based K-Means Clustering Algorithm for Data Analysis of Intrusion Detection in Mobile Sensor Networks” propose an improved K-means clustering algorithm based on linear discriminant analysis (LDA), called LKM algorithm, which proves that LKM algorithm enhances the performance of high-dimension data analysis and the abnormal detection rate of intrusion detection system (IDS) in mobile sensor networks (MSNs).

The next three papers are focused on routing of WSNs. B. Diao et al. in their paper entitled “Improving Both Energy and Time Efficiency of Depth-Based Routing for Underwater Sensor Networks” propose two novel protocols, energy-efficient depth-based routing protocol (EE-DBR) and directional depth-based routing protocol (D-DBR). C. Xu et al. in their paper entitled “A Systematic Design Method for Large-Scale Wireless Ad Hoc Network Protocol Based on Optimization Decomposition Theory” propose a systematic design method based on optimization decomposition, which includes primal decomposition method and dual decomposition method. Z. Wu et al. in their paper entitled “Backup Routing Algorithm Based on Delay Constraint in Cognitive Radio Sensor Networks” propose an efficient backup routing algorithm for reliable routing based on the uncorrelated degree and delay constraint in CRSNs.

The last three papers focus on mobile PowerPoint pages, connectivity restoration, and mode decomposition algorithm for Internet of Things (IoT) application, respectively. J. Xiang et al. in their paper entitled “ ${\mathrm{M}\mathrm{R}}^{\ast }$ -Tree: Novel Indexing and Retrieving Mechanism for Spatial Objects in Mobile PowerPoint Pages” propose a spatial objects indexing mechanism, denoted as ${\mathrm{M}\mathrm{R}}^{\ast }$ -tree, leveraging the traditional ${\mathrm{R}}^{\ast }$ -tree method, which can index spatial objects in PowerPoint pages efficiently and effectively. Z. Mi et al. in their paper entitled “Graph-Theoretic Based Connectivity Restoration Algorithms for Mobile Sensor Networks” propose a critical sensor determination and substitution (CSDS) strategy to address the important problem of network partitions in MSNs. X. Yang et al. in their paper entitled “Improved Empirical Mode Decomposition Algorithm of Processing Complex Signal for IoT Application” develop a technique to obtain the extreme points of observation interval boundary by introducing the linear extrapolation into empirical mode decomposition (EMD). A novel envelope fitting method for processing complex signal is proposed, which employs a technique of nonuniform rational B-splines curve.

We are very hopeful that these papers will prove to be useful source of reference for both the researchers and the practitioners.