The rapid development of IoT technology has promoted the integration and networked fusion of massive heterogeneous sensors. However, traditional Gaussian-Markov frameworks struggle to characterize nonlinear long-term correlated errors within systems and network cooperative effects, limiting inter-node interoperability. To address this, we propose a generalized nonlinear stochastic process (GNSP) theoretical framework, constructing nonlinear operators with long-term memory characteristics, and employing fractional calculus tools to achieve unified analysis of multiple error types within and across nodes. Based on this, we derive distributed error propagation laws on Lie group spaces, establish boundary criteria based on Wasserstein metrics, and improve nonlinear filters to design a dual-layer fusion architecture combining single-node hybrid adaptive filtering and inter-node collaborative optimization. Using vehicle networking GNSS/INS systems as an example, experimental results show that the proposed single-node strategy improves position, velocity, and attitude accuracy by an average of 11.71%, 9.82%, and 12.83% respectively in complex environments such as urban canyons and mountainous forest areas. Meanwhile, through inter-node parameter sharing and cooperative strategies of federated filtering, further V2V collaboration improves performance by 8.5%-12.7%, maintaining average improvements of 16.85%, 18.37%, and 21.14% even under limited communication conditions. This effectively addresses the challenges of distributed sensing errors in IoT environments.

Adaptive antenna arrays have been used to mitigate the interference on global navigation satellite system (GNSS) receivers. The performance of interference mitigation depends on the beamforming algorithms adopted by the antenna array. However, the adaptive beamforming will change the array pattern in realtime, which has the potential to introduce phase center biases into the antenna array. For precise applications, these phase biases must be mitigated or compensated because they will bring errors in code phase and carrier phase measurements. A novel adaptive beamforming algorithm is proposed firstly, then the phase bias induced by the proposed algorithm is estimated, and finally a compensation strategy is addressed. Simulations demonstrate that the proposed beamforming algorithm suppresses effectively the strong interference and improves significantly the capturing performance of GNSS signals. Simultaneously, the bias compensation method avoids the loss of the carrier phase lock and reduces the phase measurement errors for GNSS receivers.

In target tracking WSNs, the tree-based and cluster-based network should be rebuilt when the location of target changes, which involves energy and time overheads. Although the chain structure solves this problem, it involves lots of unnecessary nodes and unacceptable transmitting delay. Therefore, we propose a dynamic chain-based collaboration (DCBC) approach for efficient target tracking and data gathering in this paper. DCBC completes the target tracking task by forming a dynamic tracking chain around the target. With the target moving, the dynamic tracking chain is adjusted by pruning some nodes as long as they are out of the range of the target and adding the nodes which entered the range of the target, so that the chain can adapt to the changing location of the target. The structure of the dynamic tracking chain does not need to be re-established when the target position changes. Furthermore, the sensing data are fused at every step when they are transmitted along the dynamic tracking chain. Using this way, the sensing data can be collected by tracking nodes and fused locally. In addition, we propose a dynamic and energy-efficient sleep scheduling for data transmission in the target tracking chain. The nodes in the tracking chain send their own data in turn. Thus it improves the energy utilization and data transmission efficiency of the network. The experimental results verify that DCBC can reduce and balance the average energy consumption of the network compared with the state-of-the-art approaches, and prolong the lifetime of networks.

One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.

Global navigation satellite system signals are easily distorted by the interferences or disturbances, and global navigation satellite system receivers cannot offer continuous effective navigation results in challenging environments. As a representative regional augmentation technology, pseudolite has the potential to provide accurate positioning service to satisfy specific performance requirements in various applications. In this article, we developed a dynamic localization network based on pseudolite technology for regional augmentation navigation purpose. First, the collaborative positioning algorithm is given, and the architecture of localization system is proposed. Then the error sources of localization system are analyzed for performance evaluation. Finally, the proposed system is verified by experiments conducted in both static and kinenatic scenarios. The experiment results demonstrate that the positioning accuracy of the proposed localization system is nearly 10 m, which is close to the global navigation satellite system single-point positioning accuracy. Therefore, it can be used for emergency dynamic positioning of critical areas under the global navigation satellite system denial environments.