Search

Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.

Abstract Remote sensing scene classification aims to automatically assign a specific semantic label to each image. It is challenging to classify remote sensing scene images due to the images' diversity and rich spatial information. Recently, convolutional neural networks have been widely used to overcome these difficulties, such as the famous Visual Geometry Group (VGG) network. However, the VGG network with local receptive fields cannot model the global information of remote sensing images well. It also needs a large number of parameters and floating point operations to achieve satisfactory accuracy. To overcome these challenges, we introduce the self‐attention mechanism to the VGG network. Specifically, we replace the last four convolutional layers in the VGG‐19 network with two cascaded self‐attention blocks, each consisting of two multi‐head self‐attention (MHSA) layers with the residual network structure. The new structure can simultaneously explore the local and global information from remote sensing scenes. Such improvements not only reduce model parameters but also improve the classification performance. The effectiveness of the proposed method is validated through experiments on four public data sets, i.e., NaSC‐TG2, WHU‐RS19, AID and EuroSAT.

Abstract In the medical image analysis domain, medical image segmentation has a significant impact on the quantitative analysis of organ or tissue function, as the first and critical component of diagnosis and treatment pipeline. In this paper, a dense R‐CNN segmentation model based on dual‐attention are proposed for medical images multi‐target instance segmentation. The model combines channel and spatial attention mechanism to extract image features and fuse multi‐scale feature information hierarchically. It combines up‐sampling strategies such as dilated convolution and bilinear interpolation to strengthen the distinguishability between multi‐target instances and pixel‐level features in other regions. The multi‐target detection mechanism of R‐CNN is combined with the multi‐scale feature extraction and fusion ability of dense convolution network. In the encoding stage, the multi‐scale hybrid bottleneck module and deformable convolution are introduced to extract more accurate structural feature information and increase the receptive‐field. In the decoding stage, the bilinear interpolation and the adaptive hierarchical fusion mechanism are used to strengthen the distinguishability between the target region and other regions, and improve the accuracy of instance segmentation. Taking cardiac MRI segmentation as an example, the left and right ventricles, and left ventricular myocardium are selected as segmentation targets. The pixel accuracy is 90.82%, the class pixel accuracy is 87.91%, the mean intersection‐over‐union is 81.52%, the Dice coefficient is 89.82%, and Hausdorff distance is 9.2, which is improved compared with other methods. It verifies the accuracy and applicability of the proposed method for multi‐target instance segmentation of medical images.

Abstract Aiming at the medical images segmentation with low-recognition and high background noise, a deep convolution neural network image segmentation model based on fuzzy attention mechanism is proposed, which is called FA-SegNet. It takes SegNet as the basic framework. In the down-sampling module for image feature extraction, a fuzzy channel-attention module is added to strengthen the discrimination of different target regions. In the up-sampling module for image size restoration and multi-scale feature fusion, a fuzzy spatial-attention module is added to reduce the loss of image details and expand the receptive field. In this paper, fuzzy cognition is introduced into the feature fusion of CNNs. Based on the attention mechanism, fuzzy membership is used to re-calibrate the importance of the pixel value in local regions. It can strengthen the distinguishing ability of image features, and the fusion ability of the contextual information, which improves the segmentation accuracy of the target regions. Taking MRI segmentation as an experimental example, multiple targets such as the left ventricles, right ventricles, and left ventricular myocardium are selected as the segmentation targets. The pixels accuracy is 92.47%, the mean intersection to union is 86.18%, and the Dice coefficient is 92.44%, which are improved compared with other methods. It verifies the accuracy and applicability of the proposed method for the medical images segmentation, especially the targets with low-recognition and serious occlusion.

Abstract Many shortest link scheduling algorithms adopt non-fading SINR interference model, which assumes that the received signal power will always remain determinate as long as the transmission power of the corresponding sender is fixed. In fact, because environment always influences the propagation of radio signals, the received signal power is by no means a certain value. Rayleigh fading is a statistical model for radio signals propagation. It assumes that the strength of a signal on a receiver is a random variable, varying with the Rayleigh distribution. This paper proposes a shortest link scheduling algorithm under the Rayleigh fading model (SLSRF). The SLSRF partitions the wireless network area into hexagons and colors the hexagons with three different colors such that two neighboring hexagons have different colors. The senders of the links scheduled simultaneously are arranged in hexagons with the same color. The correctness of the SLSRF is proved through theoretical analysis, and the efficiency is illustrated by elaborate simulations. Our simulation results demonstrate that the schedule delay of SLSRF is less than that of some results under the non-fading SINR interference model. Furthermore, we extend the SLSRF to a distributed version, which is suitable for large wireless networks.

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d (d⩾1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.

The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user’s privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k -medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k -medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k -medoids clustering, multiple partition differential privacy k -medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user’s privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.

Abstract Wireless sensor networks (WSNs) have become one of the most vigorous techniques in the network domain. However, the sensor nodes of WSNs tend to become the target of attackers due to the broadcast communication mode and the unattended deployment nature. Although it can prevent the sensitive data from being compromised, Slice-Mix-AggRegaTe (SMART) needs to exchange messages frequently in a network, which put tremendous overhead on the sensor nodes with limited resources. Faced with these issues, this paper proposes an energy-efficient privacy-preserving data aggregation protocol based on slicing (EPPA) where a novel slicing mode is adopted to reduce the numbers of slices, which can significantly prevent the data from being compromised and decrease the communication overhead. Meanwhile, an enhanced scheme based on EPPA, called multi-function privacy-preserving data aggregation protocol (MPPA), is presented and it supports multiple functions in the process of data aggregation, such as max/min, count, and mean. The theoretical analysis and the simulation evaluation show that the proposed aggregation protocols demonstrate a better performance in the privacy preserving and the communication efficiency.

For the shortest link scheduling (SLS), i.e., scheduling a given set of links with minimum time slots, we consider the distributed algorithm design by using the locality of the protocol model with high fidelity under the Rayleigh fading. Different from most previous works, focusing on distributed algorithm design under the deterministic SINR model, which ignores the fading effects in signal propagation, we first show that a successful link of protocol model is also feasible under the deterministic SINR model, then it can be scheduled successfully with high probability under the Rayleigh fading, by upper-bounding interference outside interference range of protocol model. Then on the basis of this key conclusion, we design LLS-SLS algorithm to solve SLS within (2eΔmaxT + 1)δ log2 ΔmaxT time slots for a constant δ. Specifically, ΔmaxT is the number of a sender's neighbors within some certain range, and can be upper-bounded. Next, based on the concept of random contention, we design CLLS algorithm to schedule all links after costing 4(δ + 1)ΔmaxT ln (ΔmaxT + 1) time slots. In addition, extensive simulations evaluate the performance of two proposed algorithms.

A body area network (BAN) consists of wireless sensors and actuators deployed on a patient's body for real-time health monitoring and personalized medical care. It is essential and challenging to secure wireless communications in a BAN to protect the patient's privacy while also allowing the authorized healthcare practitioners (e.g., emergency room doctors and nurses) to easily communicate with and configure the BAN devices transparent to the patient or even when the patient loses consciousness. With the existing schemes, the devices are based on a pre-installed secret password or a physiological signal feature to authenticate each other and to agree upon a cryptographic key for secure communications. The former requires a patient's input to access and configure the BAN, and the latter is not sufficiently reliable or secure due to signal dynamics. This motivates us to design a new key agreement scheme in this paper, called multi-biometric and physiological signal-based key agreement (MBPSKA), to achieve more secure and reliable authentication and communication session establishment between the BAN devices while providing flexibility to authorized personnel to access, control, and adjust the BAN without patient involvement. The proposed scheme exploits both the reliable biometric traits and the time-variant physiological signal features of a patient along with the efficient fuzzy crypto-algorithms and key distribution protocols. The devices use multiple biometric and physiological features for mutual authentication and cryptographic key protection. We analyze the security characteristics of MBPSKA, including its capabilities against various attacks. Our evaluation results using the real-world datasets demonstrate that MBPSKA outperforms the existing physiological signal-based key agreement schemes in terms of security, authentication reliability, and accuracy.

Abstract Two-way communication is required to support control functions like packet acknowledgement and channel feedback. Most previous works on the transmission capacity of wireless ad hoc networks, however, focused on one-way communication; reverse communication from the destination to the source was ignored. In this paper, we first establish mathematical expression for two-way transmission capacity under the fixing transmission distance (i.e., the distance between the source and the destination is a constant), by introducing the concept of two-way outage and setting different rate requirements in both directions. Next, based on the concept of guard zone and cooperative communication, methods of increasing two-way transmission capacity are proposed. Simulation results show that the proposed methods can improve two-way transmission capacity significantly.

Abstract Background The purpose of this study is to discuss whether genetic variants (rs2230199, rs1047286, rs2230205, and rs2250656) in the C3 gene account for a significant risk of advanced AMD. Methods We performed a meta-analysis using electronic databases to search relevant articles. A total of 40 case-control studies from 38 available articles (20,673 cases and 20,025 controls) were included in our study. Results In our meta-analysis, the pooled results showed that the carriage of G allele for rs2230199 and the T allele for rs1047286 had a tendency to the risk of advanced AMD (OR = 1.49, 95% CI = 1.39–1.59, P

Abstract Finding the minimum connected dominating set (MCDS) is a key problem in wireless sensor networks, which is crucial for efficient routing and broadcasting. However, the MCDS problem is NP-hard. In this paper, a new approximation algorithm with approximation ratio H(Δ)+3 in time O(n 2) is proposed to approach the MCDS problem. The key idea is to divide the sensors in CDS into core sensors and supporting sensors. The core sensors dominate the supporting sensors in CDS, while the supporting sensors dominate other sensors that are not in CDS. To minimize the number of both the cores and the supporters, a three-phased algorithm is proposed. (1) Finding the base-core sensors by constructing independent set (denoted as S 1), in which the sensors who have the largest |N2(v)||N(v)| $\frac {|N^{2}(v)|}{|N(v)|}$ (number of two-hop neighbors over the number of one-hop neighbors) will be selected greedily into S 1; (2) Connecting all base-core sensors in S 1 to form a connected subgraph, the sensors in the subgraph are called cores; (3) Adding the one-hop neighbors of the core sensors to the supporter set S 2. This guarantees a small number of sensors can be added into CDS, which is a novel scheme for MCDS construction. Extensive simulation results are shown to validate the performance of our algorithm.

The proliferation of ubiquitous Internet and mobile devices has brought about the exponential growth of individual data in big data era. The network user data has been confronted with serious privacy concerns for extracting valuable information during the process of data mining. Differential privacy preservation is a new paradigm independent of the adversaries' prior knowledge, which protects sensitive data while maintaining certain statistical properties by adding random noise. In this paper, we put forward a differential privacy preservation multiple cores DBSCAN clustering schema based on the powerful differential privacy and DBSCAN algorithm for network user data to effectively leverage the privacy leakage issue in the process of data mining, enhancing data clustering efficaciously by adding Laplace noise. We perform extensive theoretical analysis and simulations to evaluate our schema and the results show better efficiency, accuracy, and privacy preservation effect than previous schemas.

Clustering is a fundamental and effective technique for utilizing sensor nodes’ energy and extending the network lifetime for wireless sensor networks. In this paper, we propose a novel clustering protocol, LECP-CP (local energy consumption prediction-based clustering protocol), the core of which includes a novel cluster head election algorithm and an inter-cluster communication routing tree construction algorithm, both based on the predicted local energy consumption ratio of nodes. We also provide a more accurate and realistic cluster radius to minimize the energy consumption of the entire network. The global energy consumption can be optimized by the optimization of the local energy consumption, and the energy consumption among nodes can be balanced well. Simulation results validate our theoretical analysis and show that LECP-CP has high efficiency of energy utilization, good scalability and significant improvement in the network lifetime.

The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach “Time–Location–Frequency”–aware Service Selection Approach is put forward in this article. In Time–Location–Frequency–aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM , we validate the feasibility of our proposal.

Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, L2,1-norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes.

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L2,1-norm (NMF-L2,1). While L2,1-norm minimization is applied to both the error function and the regularization term, our method is robust to outliers and noise in the data and generates sparse results. The application of our method to plant and tumor gene expression data demonstrates that NMF-L2,1 can extract more characteristic genes than other existing state-of-the-art methods.

Due to the imbalanced energy consumption among nodes in wireless sensor networks, some nodes die prematurely, which decreases the network lifetime. To solve this problem, existing clustering protocols usually construct unequal clusters by exploiting uneven competition radius. Taking their imperfection on designing the uneven competition radius and intercluster communication into consideration, this paper proposes an improved distributed unequal clustering protocol (IDUC) for wireless sensor networks, where nodes are energy heterogeneous and scattered unevenly. The cores of IDUC are the formation of unequal cluster topology and the construction of intercluster communication routing tree. Compared with previous protocols, IDUC is suitable for various network scenarios, and it can balance the energy consumption more efficiently and extend the lifetime of networks significantly.

Periodical broadcasts of individual vehicle's safety information such as velocity and acceleration are critical in vehicular ad hoc networks (VANETs). However, current contention-based media access control (MAC) protocols cannot guarantee high performance in high density VANETs. In this paper, we propose a MAC protocol based on s -disjunct code to achieve reliable and real-time broadcasts via assigning a channel to each vehicle as the vehicle enters the network. Our protocol is adaptive as vehicles are required to adjust their communication ranges according to the network density in order to avert the interference among them. We conduct an extensive simulation study, and our results indicate that the proposed scheme can successfully achieve the requirements of the dedicated short range communication (DSRC) standard, that is, to broadcast safety information 10 times per second. Even at a high vehicle density, our scheme still guarantees that the success rates of both transmission and reception are greater than 95%, and the collision rates of access collision and merging collision are lower than 10 collisions/period.

One-slot link scheduling is important for enhancing the throughput capacity of wireless sensor networks. It includes two aspects: maximum links scheduling (MLS) and maximum weighted links scheduling (MWLS). In this paper we propose two heuristic algorithms for the two NP-hard problems with obvious power assignments under the SINR (signal-to-interference-plus-noise-ratio) model. For MLS, we propose an algorithm MTMA (maximum tolerance and minimum affectance ), which improves the currently best approximation algorithm by 28%–62% on average. For MWLS, we give an effective heuristic algorithm MWMA (maximum weighted and minimum affectance ), which performs better on improving the throughput and reducing the running time. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations.

To investigate the bilateral symmetry of the global corneal topography in normal corneas with a wide range of curvature, astigmatism and thickness values.Cross-Sectional Study.Topography images were recorded for the anterior and posterior surfaces of 342 participants using a Pentacam. Elevation data were fitted to a general quadratic model that considered both translational and rotational displacements. Comparisons between fellow corneas of estimates of corneal shape parameters (elevation, radius in two main directions, Rx and Ry, and corresponding shape factors, Qx and Qy) and corneal position parameters (translational displacements: x0, y0 and z0, and rotational displacements: α, β and γ) were statistically analyzed.The general quadratic model provided average RMS of fit errors with the topography data of 1.7±0.6 µm and 5.7±1.3 µm in anterior and posterior corneal surfaces. The comparisons showed highly significant bilateral correlations with the differences between fellow corneas in Rx, Ry, Qx and Qy of anterior and posterior surfaces remaining insignificantly different from zero. Bilateral differences in elevation measurements at randomly-selected points in both corneal central and peripheral areas indicated strong mirror symmetry between fellow corneas. The mean geometric center (x0, y0, z0) of both right and left corneas was located on the temporal side and inferior-temporal side of the apex in anterior and posterior topography map, respectively. Rotational displacement angle α along X axis had similar distributions in bilateral corneas, while rotation angle β along Y axis showed both eyes tilting towards the nasal side. Further, rotation angle γ along Z axis, which is related to corneal astigmatism, showed clear mirror symmetry.Analysis of corneal topography demonstrated strong and statistically-significant mirror symmetry between bilateral corneas. This characteristic could help in detection of pathological abnormalities, disease diagnosis, measurement validation and surgery planning.

Coverage is one of the key issues to achieve energy efficiency of a wireless sensor network. Sensor scheduling is one of the most important methods to solve coverage problems. It can ensure the coverage degree of a region and prolong the network lifetime. In this paper, we focus on the k -coverage scheduling problem to guarantee k -coverage sensing and network connectivity. We consider both deterministic and stochastic sensing models of the sensors and adapt the results of deterministic sensing model to solve the sensor scheduling problem under the stochastic sensing model. We use regular pentagons to divide the sensing disks to solve the k -coverage problem. Each sensor node runs a stochastic k -coverage-preserving scheduling algorithm to determine its work modes, and redundant nodes can enter into sleep mode, while active nodes ensure the coverage of the network. Theoretical analysis and simulation results show that our algorithm can reduce the number of active nodes and extend the network lifetime significantly while maintaining a given coverage degree.

Area coverage is one of the key issues for wireless sensor networks. It aims at selecting a minimum number of sensor nodes to cover the whole sensing region and maximizing the lifetime of the network. In this paper, we discuss the energy-efficient area coverage problem considering boundary effects in a new perspective, that is, transforming the area coverage problem to the target coverage problem and then achieving full area coverage by covering all the targets in the converted target coverage problem. Thus, the coverage of every point in the sensing region is transformed to the coverage of a fraction of targets. Two schemes for the converted target coverage are proposed, which can generate cover sets covering all the targets. The network constructed by sensor nodes in the cover set is proved to be connected. Compared with the previous algorithms, simulation results show that the proposed algorithm can prolong the lifetime of the network.

It is well established that unilateral exercise can produce contralateral effects. However, it is unclear whether unilateral exercise that leads to muscle injury and inflammation also affects the homologous contralateral muscles. To test the hypothesis that unilateral muscle injury causes contralateral muscle changes, an experimental rabbit model with unilateral muscle overuse caused by a combination of electrical muscle stimulation and exercise (EMS/E) was used. The soleus and gastrocnemius muscles of both exercised and non-exercised legs were analyzed with enzyme- and immunohistochemical methods after 1, 3 and 6 weeks of repeated EMS/E. After 1 w of unilateral EMS/E there were structural muscle changes such as increased variability in fiber size, fiber splitting, internal myonuclei, necrotic fibers, expression of developmental MyHCs, fibrosis and inflammation in the exercised soleus muscle. Only limited changes were found in the exercised gastrocnemius muscle and in both non-exercised contralateral muscles. After 3 w of EMS/E, muscle fiber changes, presence of developmental MyHCs, inflammation, fibrosis and affections of nerve axons and AChE production were observed bilaterally in both the soleus and gastrocnemius muscles. At 6 w of EMS/E, the severity of these changes significantly increased in the soleus muscles and infiltration of fat was observed bilaterally in both the soleus and the gastrocnemius muscles. The affections of the muscles were in all three experimental groups restricted to focal regions of the muscle samples. We conclude that repetitive unilateral muscle overuse caused by EMS/E overtime leads to both degenerative and regenerative tissue changes and myositis not only in the exercised muscles, but also in the homologous non-exercised muscles of the contralateral leg. Although the mechanism behind the contralateral changes is unclear, we suggest that the nervous system is involved in the cross-transfer effects.

Due to the imbalance of energy consumption of nodes in wireless sensor networks (WSNs), some local nodes die prematurely, which causes the network partitions and then shortens the lifetime of the network. The phenomenon is called “hot spot” or “energy hole” problem. For this problem, an energy-aware distributed unequal clustering protocol (EADUC) in multihop heterogeneous WSNs is proposed. Compared with the previous protocols, the cluster heads obtained by EADUC can achieve balanced energy, good distribution, and seamless coverage for all the nodes. Moreover, the complexity of time and control message is low. Simulation experiments show that EADUC can prolong the lifetime of the network significantly.