Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our findings reveal that only the principal eigenvalue contributes to enhancing this similarity. Moreover, our theoretical analysis applies universally to both clients and servers. Specifically, employing a specialized eigenvalue selection strategy allows for effective optimization of both local and global fairness. Drawing on these insights, we improve dual-perspective fairness through the lens of spectral graph theory without sacrificing utility. Experimental results on two real-world datasets show the superiority of F3GL over existing baselines.

The uncertainty principle and cross-term can lead to blur, fake signal components and energy oscillation in time-frequency distribution, deteriorate the results of signal tracking, radar/sonar imaging and parameter estimation. Hence in this paper, we propose a time-frequency image enhancement method based on convolutional neural networks for clearer instantaneous frequency curve. The training data are generated by a frequency modulation signal generator, and then an end-to-end training is performed between Wigner-Ville distributions and time-frequency images. Our networks not only extract underlying features of Wigner-Ville distribution, but also understand the semantic of instantaneous frequency curve and use the priori knowledge of the modulation mode. Therefore, it can correctly recognize and eliminate the cross-terms, and transform the Wigner-Ville distribution to an image that can accurate represent the instantaneous frequency curve. The method is tested by three kinds of frequency modulation signals randomly with Gaussian noise. The results show that it can work properly in most cases and has the generalization ability of multi-component signals.

A hybrid active appearance model (AAM) called sequential AAM (SAAM) based on online instance learning is presented. The subspace of the subject-specific AAM component is initially learned with sequential registration results of first frames, and is periodically updated through incremental principal component analysis and online instance fitting process. A drift correction component of the AAM is also updated during tracking by selecting previous ‘good fitting’ frame as a reference image. With the model, facial features can be tracked in a video given theirs locations in the first frame and no other information. Experiments show improved fitting accuracy and computation cost compared with other state-of-the-art AAM.

Recently, with the explosive increase of automobiles in China, people park their cars on streets without following the rules and police has hard time to conduct law enforcement without introducing effective street parking system. To solve the problem, we propose a SPS (street parking system) based on wireless sensor networks. For accurately detecting a parking car, a parking algorithm based on state machine is also proposed. As the vehicle detection in SPS is absolutely critical, we use a Honeywell 3-axis magnetic sensor to detect vehicle. However, the magnetic sensor may be affected by the fluctuation of the outside temperature. To solve the problem, we introduce a moving drift factor. On the parking lot in Shenzhen Institutes of Advanced Technology (SIAT), 62 sensor devices are deployed to evaluate the performance of SPS. By running the system for several months, we observe the vehicle detection accurate rate of the SPS is nearly 99%. The proposed SPS is energy efficient. The end device can run about 7 years with one 2400mAh AA battery.

Large-scale wireless sensor networks have been used for a wide range of applications, one of the most important issues in these applications is how to improve network lifetime of the sensor node powered by battery. In this paper, we propose a novel data collection algorithm using compressive sensing. The network energy consumption is analyzed theoretically. Our approach offers accurate data recovery from a small number compressed data. Its performance is evaluated in Tiny OS. Simulation results show that our approach can effectively prolong the network lifetime for large scale wireless sensor network. Its energy efficiency also outperforms the tree collection protocol stack.

Compared with the conventional one-way counterpart, two-way cooperative communications can significantly improve spectrum efficiency. When no channel state information is available at the receivers, we propose optimal non-coherent detector based on maximum likelihood rule for frequency-shift keying modulated two-way amplify-and-forward (AF) cooperative communications in Rayleigh fading channels. We have also theoretically derived the bit-error-rate performance lower bound of proposed two-way AF cooperative communications.

This work proposes a TDMA jitter minimization algorithm to improve the performance of real-time applications in wireless sensor networks. Our algorithms can significantly reduce jitter in a sensor network using a TDMA-based Medium Access Control protocol. The proposed algorithm basically consists of two parts, the first part is jitter minimization. It minimizes jitter in every node for dynamically arrived message streams. The second part is acceptation ratio optimization. It optimizes slot assignment and leads to less number of stream drops. The performance of the proposed algorithm is evaluated over simulations and analyzed theoretically. The results show that the proposed algorithm has better performance, compared with existing greedy and uniform algorithm, in terms of acceptation ratio and jitter.

Wireless sensor networks (WSNs) consist of a large number of wireless sensor nodes that organize themselves into multi-hop radio networks. With different link quality, different distance to the sink, etc., nodes in a network are not treated equally, especially in the network with high traffic. In this paper, we propose a fairness spatial reuse time division multiple access (TDMA) scheduling algorithm (FSTS). This algorithm, combining several heuristic algorithms, can assign network resources to the nodes to lead to similar end-to-end delivery rate. Meanwhile, considering required data rate, FSTS can achieve the maximum transmission capacity of the network. The simulation results show that the FSTS can significantly reduce the difference in the end-to-end delivery rate.

Abstract Power analysis attacks pose a serious threat to the security of many trusted systems. The principle of power analysis attacks is based on the assumption that the power consumption of an electronic device is proportional to the Hamming weight (HW) of the data being processed. However, this power model is defective as it is deviated from the CMOS circuit power consumption theory where power consumption is largely dependent on the switching ability, i.e., the Hamming distance (HD) of the data being processed. This paper presents an HD power analysis model which emulates Hamming distance based on probability distribution of HW, thus conjointly achieving better feasibility and accuracy. The experiment of CPA analysis on smart card chips running DES (Data Encryption Standard) and AES (Advanced Encryption Standard) encryption demonstrates that the proposed model can achieve 10% ∼ 18% better results compared to the existing HW model, which suggests higher success rate of discerning the secret key from the trusted systems. Copyright © 2010 John Wiley & Sons, Ltd.

Two-way cooperative communications are considered to improve the throughput of conventional one-way cooperative communications. For fast Rayleigh fading channels, we propose optimal and suboptimal non-coherent detectors for on-off keying (OOK) and frequency-shift keying (FSK) modulated two-way amplify-and-forward (AF) cooperative communications in this paper. In the proposed system, the relaying node combines the received signals from two nodes, amplifies them and retransmits the conjugate of the combined and amplified signals to the above-mentioned nodes. At the receivers of above-mentioned nodes, the optimal non-coherent detection is employed which is based on maximum likelihood rule. Since it involves integration operation, the optimal detector is simplified to a suboptimal detector which omits the real component of the relayed signal over the frequency which includes interference. The simulation results have shown that compared with the optimal detector, the proposed suboptimal detector reduces the receiver complexity at the expense of acceptable performance degradation. Furthermore, we have analytically studied the bit-error-rate performance upper and lower bounds of proposed non-coherent FSK modulated two-way AF cooperative communications.

Recent years, directional antenna has been extensively used in designing protocols for wireless sensor networks. It provides many advantages over classical omni-directional antenna, such as increased spatial reuse ratio and reduced energy consumption. In wireless sensor networks, location estimation is necessary for sensor nodes to provide meaningful information about their surrounding environment. Numerous localization schemes using anchor nodes equipped with low gain omni-directional antenna have been proposed. Since the omni-directional antenna radiate energy in all directions, wanted signal is easily interfered with wide range of environment noise, leading to big localization error. In contrast, a directional antenna concentrates its energy in a particular direction. The reduction in interference of surrounding sensor nodes increases localization accuracy. In this paper, we tackle the problems in estimating location of randomly deployed sensor nodes with a low gain omni-directional antenna. We propose a novel localization scheme using a mobile anchor node equipped with a directional antenna. Our localization scheme is verified by OPNET simulation software. The result shows that the proposed localization algorithm has higher accuracy and energy efficiency than other localization schemes.

The sensor node localization is a key technology to make application feasible and meaningful. This paper presents and analyzes a proposed localization algorithm by employing a mobile anchor node, called mobile maximum rectangle localization scheme. In the proposed scheme, a mobile anchor node moves around in sensor area and broadcasts beacon message. The sensor node uses the geometry technique to find the maximum rectangle which encloses itself and then obtain its location. Our approach is different from previous schemes in that it works without measuring any angle, distance, and received signal strength at radio transceiver. An irregular radio range is assumed in our discussion. High localization accuracy can be achieved without using expensive hardware on the sensor nodes, as required by other localization schemes. The proposed localization scheme is verified by OPNET simulation software, which shows our method has higher accuracy than Ssu's localization scheme.

The accuracy in location estimation of ad hoc deployed sensor nodes for a wireless sensor network in the presence of noisy environment and obstacle is discussed. Considering estimation accuracy, energy efficiency and range independency, the authors propose a localisation scheme using a mobile anchor node equipped with a directional antenna. Without increasing hardware and computational complexity, sensor nodes estimate their positions using the minimum number of beacon points. Each sensor node estimates its location based on the intersection point of the greatest gain direction of directional antenna. The proposed localisation scheme is verified using OPNET simulation software. The simulation results show that the proposed scheme has higher accuracy and energy efficiency than other localisation schemes.

The performance of data query processing in wireless sensor networks is greatly affected by routing protocol. Currently available data query systems usually use one routing protocol to deal with all kinds of queries. This work demonstrates that properly selecting routing protocol according to different kinds of queries can improve the performance of query processing. We propose a dynamic routing layer which includes two routing protocols: HEED and MintRoute. It adopts a centralized control mechanism to make protocol selection based on query semantic and network density. The routing protocol information is attached on each query and is flooded to the whole network. When sensor node receives the query and the dynamic routing layer can automatically switch to a protocol according to routing protocol information carried by the query. Through simulation, we find that dynamic routing layer is more energy efficient than HEED and MintRoute.

Directional antenna offers various benefits for wireless sensor networks, such as increased spatial reuse ratio and reduced energy consumption. Numerous localization schemes using anchor nodes equipped with omni-directional antenna to transmit beacon information have been proposed. However, the radiated RF signal from an omni-directional antenna is easier to be interfered by wide range of environment noise, which leads to greater localization error. In this paper, we tackle the problems in estimating location of randomly deployed sensor nodes with low gain omni-directional antennas. Since directional antenna concentrates energy on a particularly direction with a high gain and narrow covering area, we propose a high accuracy, energy efficient, and low cost localization scheme using a mobile anchor node equipped with a directional antenna. After investigating the effect of directional antenna on wireless sensor networks, we show how the location estimation problem can be solved using geometric characteristics on a 2D plane with directional antenna. Our localization scheme is verified by OPNET simulation software. The result shows that the proposed localization algorithm has higher accuracy and energy efficiency than other localization schemes.

The scalar multiplication is the dominant operation in Elliptic Curve Cryptosystems (ECC). It consists of a series of point additions and point doublings. A number of algorithms have been proposed to accelerate the scalar multiplication. Most of the algorithms demand high complexity which makes scalar multiplication hard to implement. In this paper, we propose an efficient algorithm for computing scalar multiplication based on partitioning scalar and propositional logic theory to address the trade-offs between speed and complexity. Our algorithm remains low complexity compared to existing accelerated scalar multiplication algorithms, whilst it is suitable for parallel processing systems.

Wireless sensor networks with many inexpensive sensor nodes allow users to accurately monitor a remote environment. Usually one or several mobile nodes are used to collect and combine sensor data from each individual stationary node. These networks require robust wireless communication protocols that are energy efficient and provide low latency. Motivated by these applications, we develop a novel medium access control (MAC) protocol for this kind of applications. The sensor nodes are expected to remain inactive for most of time, but become active when a mobile node is nearby. A few techniques for energy saving are developed in our protocol, e.g., true-sleep and pseudo-sleep modes are introduced to reduce energy consumption. They are implemented by radio-triggered circuit which wakes up the stationary nodes as a mobile node moves into the stationary nodespsila reception range. The proposed protocol is simulated in OPNET Modeler 10.5. The simulation results show that the proposed protocol has better performance than S-MAC and EAR (Eavesdrop-And-Register) in terms of latency, energy efficiency, and throughput.

Wireless sensor networks (WSNs) are vulnerable to various attacks since they are distributed in unattended environments and have limited energy, storage and computation abilities. Preventive approaches can be applied to protect WSNs from some kinds of attacks. However, preventive methods are not efficient on specific attacks. So it is necessary to develop some mechanisms for intrusion detection. Intrusion detection system (IDS) not only prevents adversaries from attacking the network, but also provides attackspsila features for improving the preventive algorithms. The traditional intrusion detection algorithms canpsilat be applied directly to WSNs due to their constraints of resources. According to the problems in the current intrusion detection systems, based on immunology, we propose a novel IDS which is distributed, robust, and adaptive. The simulation results indicate that the proposed IDS has high accuracy in attack detections.

Localization is one of the crucial issues in wireless sensor networks. In range-based mechanisms, the nodes obtain pairwise distances or angles with extra hardware for high localization accuracy. On the other hand, the range-free schemes obtain lower localization accuracy at low hardware cost. To improve location accuracy, we present a three dimensional range-free localization scheme by using a mobile anchor node equipped with a GPS. The mobile anchor node carried in an aero plane flies over the sensor node area and broadcasts its current position periodically. A sensor node in the area computes its own location using the position of the mobile anchor node where the maximum RSSI is received by the sensor node. In our scheme, neither extra hardware on each sensor node nor communications between sensor nodes is needed. Our proposed scheme is simulated in Opnet and simulation results show that our scheme performs better than other range-free localization algorithms using mobile beacon nodes.

This paper discusses the design of a fast algorithm for motion estimation with emphasis on hardware cost considerations, real-time application, network adaptation, and flexibility. To achieve the best trade-off among hardware cost, computational complexity, and distortion performance, we propose a multi-stage pixel-subsampling motion estimation algorithm. The algorithm has a lower hardware cost than Liu's subsampling algorithm and the three-step hierarchical search algorithm (3SHS) in terms of data flow control, I/O bandwidth, and regularity. Its computational complexity is close to that of 3SHS and its distortion performance, which is better than that of Liu's algorithm and 3SHS, is close to that of full search.