Secure Data Fusion in Wireless Multimedia Sensor Networks via Compressed Sensing

The paper proposes a novel secure data fusion strategy based on compressed image sensing and watermarking; namely, the algorithm exploits the sparsity in the image encryption. The approach relies on l1-norm regularization, common in compressive sensing, to enhance the detection of sparsity over wireless multimedia sensor networks. The resulting algorithms endow sensor nodes with learning abilities and allow them to learn the sparse structure from the still image data, and also utilize the watermarking approach to achieve authentication mechanism. We provide the total transmission volume and the energy consumption performance analysis of each node, and summarize the peak signal to noise ratio values of the proposed method. We also show how to adaptively select the sampling parameter. Simulation results illustrate the advantage of the proposed strategy for secure data fusion

Energy Efficient Moving Target Tracking in Wireless Sensor Networks

Moving target tracking in wireless sensor networks is of paramount importance. This paper considers the problem of state estimation for L-sensor linear dynamic systems. Firstly, the paper establishes the fuzzy model for measurement condition estimation. Then, Generalized Kalman Filter design is performed to incorporate the novel neighborhood function and the target motion information, improving with an increasing number of active sensors. The proposed measurement selection approach has some advantages in time cost. As such, if the desired accuracy has been achieved, the parameter initialization for optimization can be readily resolved, which maximizes the expected lifespan while preserving tracking accuracy. Through theoretical justifications and empirical studies, we demonstrate that the proposed scheme achieves substantially superior performances over conventional methods in terms of moving target tracking under the resource-constrained wireless sensor networks

Secure Data Aggregation in Wireless Multimedia Sensor Networks Based on Similarity Matching

Given its importance, the problem of secure data aggregation in wireless multimedia sensor networks has attracted great attention in the literature. Wireless multimedia sensor networks present some challenges that are common to wireless sensor networks, such as the existence of limited resources, like sensors memory, energy consumption, and CPU performance. Many methods have been proposed to attempt to solve the problem. However, the existing data aggregations do not take into account the redundancy of the multimedia data. In order to improve the energy efficiency for multimedia data, we propose a similarity model and power model. The proposal scheme divides multimedia data into multiple different pieces, and transmits the effective pieces to the selected sensor nodes. Through theoretical justifications and empirical studies, we demonstrate that the proposed scheme achieves substantially superior performances over conventional methods in terms of energy efficiency and data transmission under the resource-constrained wireless multimedia sensor networks

Distributed Movement Control for Building a Ring in Mobile Wireless Sensor Networks

Many applications in wireless sensor networks require some sensors to form a ring or multiring-based shape in the target area, such as intrusion detection, border surveillance, routing overlay formation, and network full coverage. In this paper, we study the problem of sensor redistribution to build a ring-based shape for mobile sensor networks. We first give the theoretical analysis on what is optimal sensor movement with the given random deployment. Then, we propose a fully distributed movement control algorithm to achieve ring-based shape for mobile sensor networks. We formally prove that our algorithm can achieve a ring-based distribution within finite time. We also present the procedures of applying our algorithm to form multiring-based distribution. Finally, we present extensive simulations to verify that our approach outperforms other schemes in terms of both the moving distance and convergence time

Towards an Optimal Energy Consumption for Unattended Mobile Sensor Networks through Autonomous Sensor Redeployment

Energy hole is an inherent problem caused by heavier traffic loads of sensor nodes nearer the sink because of more frequent data transmission, which is strongly dependent on the topology induced by the sensor deployment. In this paper, we propose an autonomous sensor redeployment algorithm to balance energy consumption and mitigate energy hole for unattended mobile sensor networks. First, with the target area divided into several equal width coronas, we present a mathematical problem modeling sensor node layout as well as transmission pattern to maximize network coverage and reduce communication cost. And then, by calculating the optimal node density for each corona to avoid energy hole, a fully distributed movement algorithm is proposed, which can achieve an optimal distribution quickly only by pushing or pulling its one-hop neighbors. The simulation results demonstrate that our algorithm achieves a much smaller average moving distance and a much longer network lifetime than existing algorithms and can eliminate the energy hole problem effectively

A Two-Stage Range-Free Localization Method for Wireless Sensor Networks

Range-free localization plays an important role in low-cost and large scale wireless sensor networks. Many existing range-free localization methods encounter high localization error, especially for the network with a coverage hole. One reason for high localization error is unreasonable distance estimation method. Another reason is that unknown nodes use the shortest distance which has large cumulative distance error to estimate their positions. In this paper, a two-stage centralized range-free localization algorithm (TCRL) is proposed. In the first stage, we design a novel rational distance estimation method to alleviate the distance estimation error between neighbor nodes based on the connectivity information and geometric features. In the second stage, a novel neighborhood function is derived from the estimated distances between neighbor nodes. Then a new localization strategy is proposed based on greedy idea. Finally, the proposed algorithm is compared with the same type algorithms in two network scenarios, namely, random deployment and random deployment with a coverage hole. The simulation results show that TCRL achieves more accurate and reliable results than most of existing range-free methods in the two network scenarios

Bicriteria Optimization in Wireless Sensor Networks: Link Scheduling and Energy Consumption

Link scheduling is important for reliable data communication in wireless sensor networks. Previous works mainly focus on how to find the minimum scheduling length but ignore the impact of energy consumption. In this paper, we integrate them together and solve them by multiobjective genetic algorithms. As a contribution, by jointly modeling the route selection and interference-free link scheduling problem, we give a systematical analysis on the relationship between link scheduling and energy consumption. Considering the specific many-to-one communication nature of WSNs, we propose a novel link scheduling scheme based on NSGA-II (Non-dominated Sorting Genetic Algorithm II). Our approach aims to search the optimal routing tree which satisfies the minimum scheduling length and energy consumption for wireless sensor networks. To achieve this goal, the solution representation based on the routing tree, the genetic operations including tree based recombination and mutation, and the fitness evaluation based on heuristic link scheduling algorithm are well designed. Extensive simulations demonstrate that our algorithm can quickly converge to the Pareto optimal solution between the two performance metrics

Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles

Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art