The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

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While many scheduling algorithms for periodic tasks ignore security requirements posed by sensitive applications and are, consequently, unable to perform properly in embedded systems with security constraints, in this paper, we present an approach to scheduling periodic tasks in embedded systems subject to security and timing constraints. We design a necessary and sufficient feasibility check for a set of periodic tasks with security requirements. With the feasibility test in place, we propose a scheduling algorithm, or SASES (security-aware scheduling for embedded systems), which accounts for both security and timing requirements. SASES judiciously distributes slack times among a variety of security services for a set of periodic tasks, thereby optimizing security for embedded systems without sacrificing schedulability. To demonstrate the effectiveness of SASES, we apply the proposed SASES to real-world embedded systems such as an automated flight control system. We show, through extensive simulations, that SASES is able to maximize security for embedded systems while guaranteeing timeliness. In particular, SASES significantly improves security over three baseline algorithms by up to 107%.

Wireless sensor networks are widely used in smart environments to capture and detect the activities of human beings, and achieving reliable transmission between sensor nodes has become one of the main challenges of practical applications. This paper presents a scheme for path planning that is designed to achieve optimal coverage by using active nodes to periodically fill in the blank areas and to replace the failed nodes. This approach can effectively avoid uneven energy consumption while maintaining complete link states. Meanwhile, the curl field of the nodes is used to model the effects of the residual energy and the distance between nodes, thereby effectively relaxing the requirements on the spatial positions of the nodes. Experiments show that in the case of directional transmission, the proposed method demonstrates better performance than other algorithms in terms of the network lifecycle, coverage, and transmission reliability. This method can effectively address the problem of cross-node failure along the transmission paths in complex and dynamic networks.

The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.