Search

Abstract Acquiring multi-dimensional optical information, such as intensity, spectrum, polarization, and phase, can significantly enhance the performance of photodetectors. Incorporating these dimensions allows for improved image contrast, enhanced recognition capabilities, reduced interference, and better adaptation to complex environments. However, the challenge lies in obtaining these dimensions on a single photodetector. Here we propose a misaligned unipolar barrier photodetector based on van der Waals heterojunction to address this issue. This structure enables spectral detection by switching between two absorbing layers with different cut-off wavelengths for dual-band detection. For polarization detection, anisotropic semiconductors like black phosphorus and black arsenic phosphorus inherently possess polarization-detection capabilities without additional complex elements. By manipulating the crystal direction of these materials during heterojunction fabrication, the device becomes sensitive to incident light at different polarization angles. This research showcases the potential of the misaligned unipolar barrier photodetector in capturing multi-dimensional optical information, paving the way for next-generation photodetectors.

With the rapid development of mobile networks and widespread use of mobile devices, there is an increasing focus on assigning location-based tasks to mobile users in the context of Mobile Crowd Sensing (MCS). One of the primary challenges in MCS is task assignment, i.e., distributing tasks to suitable users for completion. However, existing work often assumes static offline scenarios where the spatiotemporal information of all users and tasks is pre-determined and known. Neglecting the dynamic spatiotemporal distribution of users and tasks can lead to suboptimal assignment results. In this study, we investigate a novel task assignment problem called Community Task Assignment (CTA). The objective is to enhance the effectiveness and precision of task distribution by considering the sociality of current users and distributing location-based tasks through communities. Initially, we partition users into different communities by abstracting and identifying behavior patterns through the computation of minimum spanning trees, connectivity parameters, and community cohesion. Subsequently, we calculate the match between perception tasks and community behavior pattern features, and task distribution is carried out by the central nodes of the communities based on this match. Experimental validation first confirms the effectiveness of the community partitioning algorithm. Compared to existing algorithms, the proposed method more accurately detects community structures with similar behavioral features in the network. Furthermore, a comparison with existing task assignment algorithms verifies the superiority of the proposed method in terms of average task completion time, task matching rate, and overall utility of task assignments.

Abstract The realization of a controllable transparent conducting system with selective light transparency is crucial for exploring many of the most intriguing effects in top‐illuminated optoelectronic devices. However, the performance is limited by insufficient electrical conductivity, low work function, and vulnerable interface of traditional transparent conducting materials, such as tin‐doped indium oxide. Here, it is reported that two‐dimensional (2D) titanium carbide (Ti3C2Tx) MXene film acts as an efficient transparent conducting electrode for the lead sulfide (PbS) colloidal quantum dots (CQDs) photodiode with controllable near infrared transmittance. The solution‐processed interface engineering of MXene and PbS layers remarkably reduces the interface defects of MXene/PbS CQDs and the carrier concentration in the PbS layer. The stable Ti3C2Tx/PbS CQDs photodiodes give rise to a high specific detectivity of 5.51 × 1012 cm W−1 Hz1/2, a large dynamic response range of 140 dB, and a large bandwidth of 0.76 MHz at 940 nm in the self‐powered state, ranking among the most exceptional in terms of comprehensive performance among reported PbS CQDs photodiodes. In contrast with the traditional photodiode technologies, this efficient and stable approach opens a new horizon to construct widely used infrared photodiodes with CQDs and MXenes.

Abstract 2D materials are considered as potential candidates for the next generation of optoelectronic materials. However, their optical absorption is typically weak due to thickness limitations, greatly restricting the photodetection capabilities of devices. To enhance the photoelectric gain of 2D materials or devices and improve detection sensitivity, various modulation methods such as strain, electric field, and magnetic field are commonly introduced. Among them, surface acoustic wave (SAW) represents a unique and effective modulation approach. In this study, photodetectors are fabricated based on few‐layer MoS2 on a SAW delay line on a LiTaO3 substrate. The interaction between SAW and MoS2 successfully manipulates the optoelectronic performance of the MoS2‐based devices. Under the influence of SAW, the dark current of the devices is significantly reduced by more than two orders of magnitude, while the photocurrent remains almost unchanged, resulting in excellent photoresponse performance. The devices provide a promising pathway for high‐performance optoelectronic applications and reveal a new possibility for acoustic devices in optoelectronics.

Abstract The regulation of neural stem cell (NSC) proliferation and differentiation during brain development is a precisely controlled process, with the production of different neuronal subtypes governed by strict timelines. Glutamate is predominantly used as a neurotransmitter by the subtypes of neurons in the various layers of the cerebral cortex. The expression pattern of BCAT1, a gene involved in glutamate metabolism, in the different layers of neurons has yet to be fully understood. Using single-cell data, we have identified seven different states of NSCs and found that state 4 is closely associated with the development of projection neurons. By inferring the developmental trajectory of different neuronal subtypes from NSC subsets of this state, we discovered that BCAT1 is involved in the regulation of NSC proliferation and differentiation and is specifically highly expressed in layer II/III and IV neurons. Suppression of BCAT1 through shRNA resulted in a reduction in NSC proliferation and an abnormal development of layer II/III and IV neurons. These findings provide new insights into the role of BCAT1 in the regulation of NSC behavior and neuronal development.

Due to the widespread use of mobile intelligent terminal devices, Mobile Crowd Sensing (MCS) applications have gained significant research attention. However, ensuring users privacy remains a critical challenge, as it can hinder users’ willingness to participate actively in tasks. To address the limitations of existing differential privacy protection methods, this paper proposes a novel privacy protection approach based on Artificial Immune Computing (AICppm). Specifically, private information is concealed within a masking carrier, and data scrambling is avoided. The proposed method involves two main steps: first, a carrier preprocessing approach based on a high-pass filter bank is designed to identify candidate positions for perturbation. Then, a carrier steganography algorithm based on multi-objective optimization is used, transforming the perturbation position into an antibody using the artificial immune algorithm. By iteratively searching for antibodies with higher fitness, the optimal perturbation of the offspring population is identified using the improved Strength Pareto Evolution Algorithm (SPEA2). The experimental results demonstrate that the proposed algorithm can withstand the attacks of malicious steganalysis tools, preserving the integrity of the sensing data and enabling real-time processing of private information.

Hypertensive cerebral hemorrhage, the most common prevalent of spontaneous cerebral hemorrhage, poses a significant threat to patient mortality and morbidity, while therapeutic options remain limited, making the disease a burden not only for patients’ families but also a major challenge for national healthcare systems. The elevation of intracranial pressure subsequent to hypertensive cerebral hemorrhage is a critical contributor to mortality. However, it often manifests before the onset of clinical symptoms, which are typically atypical, leading to delayed treatment and irreversible consequences for the patient. Hence, early detection of intracranial pressure variations can aid in timely, efficient, and precise treatment, reducing patient mortality. Invasive intracranial pressure monitoring enables real-time, accurate monitoring of intracranial pressure changes, providing clinicians with therapeutic guidance and overcoming the limitations of empirical treatment. This article aims to review the use of invasive intracranial pressure monitoring in postoperative hypertensive cerebral hemorrhage and hopes to contribute to clinical and scientific research.

Imaging of multiples, as a supplement to imaging of primaries, can provide a wider range of subsurface illumination. Therefore, it can provide more detailed information on subsurface structures. However, imaging of multiples suffers from crosstalk issues generated by unrelated events. Many strategies have been proposed to attenuate crosstalk, among which the angle domain Radon crosstalk attenuation algorithm achieves good application effect. In the angle domain, the true imaging is flat, while the crosstalk events have moveouts. Therefore, it is convenient to identify the crosstalk in angle gathers using the Radon transform. However, the conventional Radon transform lacks a quantitative description for crosstalk in angle gathers, which would affect the accuracy of crosstalk attenuation. In this paper, residual moveout kernels are derived with a Radon transform to attenuate crosstalk in angle gathers for imaging of multiples. First, two types of residual moveout (RMO) equations are derived based on the causality of crosstalk. A three-layer model is used to verify the correctness of the analytical solutions. Then, based on the derived equations, the two types of crosstalk can be attenuated respectively in the Radon domain. Synthetic experiments demonstrate that the derived RMO equations can effectively attenuate the crosstalk events in imaging of multiples.

The scalar images (PP and PS) can be effectively obtained in vector-based elastic reverse time migration by applying dot product–based scalar imaging conditions to the separated vector wavefields. However, the PP image suffers from polarity reversal issues when opening angles are greater than 90∘ and backscattering artifacts when opening angles are close to 180∘. To address these issues, we propose the pseudo-Laplace filter for the dot product–based scalar imaging condition. Based on the analysis of the Laplace filter in the scalar image of vector-based wavefields, the second-order parallel-oriented partial derivatives of Cartesian components cross-correlation results are selected to construct the pseudo-Laplace filter. In contrast, second-order normal-oriented partial derivatives of the Cartesian component’s cross-correlation results are omitted. The theoretical analysis with the plane wave assumption shows that the proposed pseudo-Laplace filter can solve the problems of backscattering artifacts and polarity reversal in PP images by the scalar imaging condition. Due to additional polarity correction and backscattering attenuation, numerical examples show excellent performance in PP images with a pseudo-Laplace filter. Furthermore, the application of the pseudo-Laplace filter requires trivial additional computation or storage.

With the popularity of the smart phones embedded in a large number of sensors, mobile crowd sensing (MCS) applications have developed rapidly. These applications often require the participants to collect the location-related data. However, this process faces the risk of the privacy leakage. The existing anonymous and cloaking methods can still analyze the user's identity and sensing locations by obtaining enough data with spatiotemporal correlation. In addition, the existing encryption methods not only take a lot of computation cost but also need to assume a full trusted sensing platform. In order to preserve the users' privacy, we need to separate the user's true identity and the sensing location. We propose a privacy-preserving method based on server-aided (or cloud-assisted) reverse oblivious transfer (ROT) protocol containing a cloud server which can compute the results of the encrypted sensing data to avoid the full trust in the sensing platform and enhance the computing efficiency in MCS. It overcomes the derivation of the malicious users in the standard malicious model through constructing the coefficients of the polynomial. Finally, the analysis demonstrates that the proposed method possesses the most efficient computing time, communication overhead and storage overhead comparing with other methods and can achieve the quality-privacy tradeoff optimization.

Sensing task allocation is one of the most challenging issues in the field of mobile sensor networks (MSNs). Many existing studies have focused on various aspects of sensing task allocation, such as the relationship between sensing task allocation and the number of participants, the sensing task completion time, and the reward required to complete the task. However, few studies have focused on the relationship between sensing tasks and agent attributes. To address this issue, we first analyze the relationship between the structural characteristics of the mobile perceptron network and the agents. Further, we model the relationship between agents using multi-attribute fusion. Then, according to this model and its preference for sensing tasks, we proposed the sensing task allocation algorithm based on Dempster-Shafer (D-S) theory and multi-attribute fusion(STADMF). Finally, STADMF is compared with three algorithms on large-scale real data sets and a synthetic mobile crowdsensing (MCS) trace. The results show that the proposed algorithm achieves good performance in terms of the similarity and accuracy of task sensing.

Ionization scattering of electron beams with residual gas molecules causes ion trapping in electron rings, both in a collider and electron cooling system. These trapped ions may cause emittance growth, tune shift, halo formation, and coherent coupled bunch instabilities. In order to clear the ions and prevent them from accumulating turn after turn, the gaps in a temporal structure of the beam are typically used. Typically, the gap in the bunch train has a length of a few percent of the ring circumference. In those regions, the extraction electrodes with high pulsed voltages are introduced. In this paper, we present the design consideration and initial test results of the high-voltage pulsed kicker hardware that includes vacuum device and pulsed voltage driver, capable of achieving over 3 kV of deflecting voltage amplitude, rise and fall times of less than 10 ns, 100 ns flat-top duration at 1.4 MHz repetition rate.

GaAs-class strained superlattice (SSL) photocathodes can provide electron beams with electron spin polarization (ESP) exceeding the theoretical maximum 50% of bulk GaAs. In this paper, we describe the evaluation of a SSL structure composed of GaAsSb/AlGaAs and grown on a GaAs substrate. Theoretical analysis and numerical calculations show GaAsSb/AlGaAs SSL structures have the largest heavy-hole and light-hole energy splitting of all existing GaAs-class SSL structures, which should lead to the highest initial ESP. Five GaAsSb/AlGaAs SSL photocathode samples with different constituent species concentrations, number of layer pairs, and layer thicknesses were fabricated and evaluated. The highest ESP was ∼77% obtained from a photocathode based on the GaAsSb0.15/Al0.38GaAs (1.55/4.1 nm ×15 layer pairs) SSL structure.

With the development of mobile smartphones equipped with a large number of sensor devices, mobile crowd computing has become the hotspot of current research. In the application, the publishers use the application platform to release the task and then select the appropriate users to participate in the task by bidding and collect their data, in which the users’ identity, location, and other private information face the risk of disclosure. To solve the problem of privacy disclosure in mobile crowd computing, we propose a privacy preserving method based on Voronoi Polygon (PP - Voronoi). The users construct the Voronoi diagram to form a cloaked region hiding users identity and then calculate the anchor point of the cloaked region, by which the users communicate with the task publisher to achieve privacy protection of the cloaked region and K anonymity. The experimental results show that compared with other privacy preserving methods, PP-Voronoi method has better effectiveness of privacy protection and lower network traffic.

Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users’ search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach.

Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances’ uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user’s spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

The event detection is one of the fundamental researches in wireless sensor networks (WSNs). Due to the consideration of various properties that reflect events status, the Composite event is more consistent with the objective world. Thus, the research of the Composite event becomes more realistic. In this paper, we analyze the characteristics of the Composite event; then we propose a criterion to determine the area of the Composite event and put forward a dominating set based network topology construction algorithm under random deployment. For the unreliability of partial data in detection process and fuzziness of the event definitions in nature, we propose a cluster-based two-dimensional τ-GAS algorithm and fuzzy-decision based composite event decision mechanism. In the case that the sensory data of most nodes are normal, the two-dimensional τ-GAS algorithm can filter the fault node data effectively and reduce the influence of erroneous data on the event determination. The Composite event judgment mechanism which is based on fuzzy-decision holds the superiority of the fuzzy-logic based algorithm; moreover, it does not need the support of a huge rule base and its computational complexity is small. Compared to CollECT algorithm and CDS algorithm, this algorithm improves the detection accuracy and reduces the traffic.

Bulk GaAs samples with different surface cleave planes were implanted with 100 and 10 000 V hydrogen ions inside an ultrahigh vacuum test apparatus to simulate ion back-bombardment of the photocathode inside a DC high voltage photogun. The photocathode yield, or quantum efficiency, could easily be recovered following implantation with 100 V hydrogen ions but not for 10 000 V ions. Moreover, the implantation damage with 10 000 V hydrogen ions was more pronounced for GaAs photocathode samples with (100) and (111A) cleave planes, compared to the photocathode with (110) cleave plane. This result is consistent with enhanced ion channeling for the (110) cleave plane compared to the other cleave planes, with ions penetrating deeper into the photocathode material beyond the absorption depth of the laser light and beyond the region of the photocathode where the photoemitted electrons originate.

It is of great significance to conduct researches on the message diffusion process for the node coverage problem, which can be generally abstracted as a random sampling model in the cooperative communication systems including the ad hoc and unstructured P2P networks. However, the message diffusion in the ad hoc network is not a completely independent random process. When forwarding the messages, the nodes will be influenced by such factors as degrees, visit times, and network connectivity. But the random sampling model does not take these factors into consideration, resulting in the overestimated node coverage degree. Discussing the message diffusion process of the cooperative communication systems like ad hoc network, this paper analyzes the causes of the inaccuracy problems of random sampling model and solves the problems by specially introducing the factors such as node degree and visit times. As for the E-R random network topology, it validates the effectiveness of the model proposed herein in contrast with the simulation experiment results. Compared with the random sampling model, the model proposed herein coincides better with the simulation results of the ad hoc network message diffusion process in the condition of network connectivity and its accuracy can meet the requirements for 3 and 5 visits.

Wireless sensor networks (WSNs) integrate sensor technology, communication technology and information processing technology; it is a synthetic discipline. WSNs have been applied into almost all walks of life. However, in many special fields, network security is a basic and important factor which we must concern for. This paper introduces the key management scheme after analyzing the security threats of LEACH protocol. At the same time, we adopt several related mechanisms to protect the communication among the sensor nodes and the confidentiality of the data transmission. Also, we use the mixed multipath mechanism among the clusters to improve the security of the network and strengthen the intrusion tolerance.

Deployment is a fundamental issue in wireless sensor networks (WSNs), which affects the performance and lifetime of the networks. Usually the sensor locations are precomputed based on “perfect” sensor detection model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is very important to take into account this uncertainty in the deployment process. In this paper, we address the problem of sensor deployment in a mixed sensor network where the mobile and static nodes work collaboratively to perform deployment optimization task. We consider the Gaussian white noise in the environment and present a centralized algorithm (FABGM for short) which discoveres vacancies by using detection model based on false alarm and moves the mobile nodes according to the method based on bipartite graph matching in this study. In this algorithm, the management node of the WSNs collects the geographical information of all of the static and mobile sensors. Then, the management node executes the algorithm to get the best matches between mobile sensors and coverage holes. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

With GPS devices spreading, more mobile nodes are guided by the navigation systems to select better paths. The performance of mobile networks can be improved with the navigation information in nodes. In this paper, we propose a trajectory-based network coding (TBNC) method to disseminate data in mobile wireless sensor network (MWSN). It is designed according to the characteristics of MWSN and is appropriate for the mobile nodes that share anonymously its GPS pretrajectory for the higher bandwidth. Data are disseminated in the topology that is predicted on the basis of the trajectory information. Network coding is used to adapt for the dynamics velocity of mobile nodes. Simulation results show that TBNC is able to cut down 1/2 overhead messages of PRoPHET when mobile nodes share GPS trajectory. It improves the reliability and scalability of MWSN.

Nowadays, more and more APs are deployed for the wireless access. The roadside APs are able to improve the bandwidth of VANETs. In this paper, we design a Multicast Network Coding (MCNC) method to aggregate and disseminate data in VANETs. It is appropriate for the areas where APs have been deployed but cannot cover the all range of vehicles movement. Data transmission is divided into 3 stages in MCNC. Network coding, multicast aggregating, and decoding are implemented in the 3 stages, respectively. Simulation results show that MCNC is able to cut down 2/3 overhead messages of Epidemic when the cover area of the APs is only 1/5. Its loss rate and delay are lower than the other methods. We also discuss its benefit for the deployment of VANETs.

The congestion control algorithm based on the weighted directed graph is designed for the network congestion over the wireless sensor network. The congestion problem is modeled as a distributed dynamic system with time-varying delay, and it can be proven that the sent rate for all nodes converges to the available bandwidth of the sink by the proposed congestion control algorithm. Via Lyapunov function, the validity of the proposed algorithm is shown under the varying network topologies. Ns simulation results indicate that the proposed algorithm restrains the congestion over the wireless sensor network, maintains a high throughput and a low delay time, and also improves the quality of service for the whole network.

The intelligent vehicle is a complex system equipped with advanced technologies such as the artificial intelligence, automatic control, and computer, communication. It is a combination of multiple academic subjects and the latest technologies representing the developing tendency of future automobile technology and attracts more and more attention. In this paper, we made some useful explorations in the fields of intelligent vehicle control technology and obstacle avoidance, and a deep research is carried out about the distance of vehicle, vehicle tracking, vehicle lane changing and intersections obstacle avoidance and communication protocols, and some innovative ideas are proposed during the research. By using VANET, some predictable status of tracing and tracking of intelligent vehicle technology was researched, and the communication protocols between two vehicles, the safe spacing algorithm, the method of computing the actual distance at corners and straights, and the trajectory of lane change were designed. In addition, a series of vehicle tracing and tracking technologies, such as without knowing the road conditions, homeostatic mechanism, keeping a safe spacing to the target vehicle, and adjusting its own speed to track the target vehicle smoothly to the destination by comparing the actual distance and safe spacing between vehicles, were discussed here.

Considering city road environment as the background, by researching GPSR greedy algorithm and the movement characteristics of vehicle nodes in VANET, this paper proposes the concept of circle changing trends angle in vehicle speed fluctuation curve and the movement domain and designs an SWF routing algorithm based on the vehicle speed point forecasted and the changing trends time computation. Simulation experiments are carried out through using a combination of NS-2 and VanetMobiSim software. Compared with the performance of the SWF-GPSR protocol with general GPSR, 2-hop C-GEDIR, and the GRA and AODV protocols, we find that the SWF algorithm has a certain degree of improvement in routing hops, the packet delivery ratio, delay performance, and link stability.

Navigation with wireless sensor networks (WSNs) is the key to provide an effective path for the mobile node. Without any location information, the path planning algorithm generates a big challenge. Many algorithms provided efficient paths based on tracking sensor nodes which forms a competitive method. However, most previous works have overlooked the distance cost of the path. In this paper, the problem is how to obtain a path with minimum distance cost and effectively organize the network to ensure the availability of this path. We first present a distributed algorithm to construct a path planning infrastructure by uniting the neighbors’ information of each sensor node into an improved connected dominating set. Then, a path planning algorithm is proposed which could produce a path with its length at most c times the shortest Euclidean length from initial position to destination. We prove that the distributed algorithm has low time and message complexity and c is no more than a constant. Under different deployed environments, extensive simulations evaluate the effectiveness of our work. The results show that factor c is within the upper bound proved in this paper and our distributed algorithm achieves a smaller infrastructure size.

We study multicasting over wireless lossy links. Instead of downloading all the data from the source node, we allow the destination nodes themselves to locally exchange the packets, as local communication within a cluster achieves higher packet reception probability with less transmission cost. However, when shall we stop the transmission from the source node? If the source stops too early, the destination nodes locally cannot reconstruct all the original packets, while if the source stops too late, the benefit of cooperative data exchange cannot be fully exploited. In this paper, we propose a network coded hybrid source and cooperative exchange scheme to determine when to stop the source sending and start the exchange process, so as to minimize the total transmission cost. For the case when the clusters are predefined, we derive the expected total transmission cost with our hybrid scheme. Our theoretical results show that under a special condition, the source node should keep sending the packets until all the destinations get the complete information. For the case when the clusters are not predefined, we propose a cluster division algorithm such that the destination nodes within each cluster can conduct data exchange locally with energy efficiency. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

We address the modeling and design of linear network coding (LNC) for reliable communication against multiple failures in wireless sensor networks (WSNs). To fulfill the objective, we design a deterministic LNC scheme RDLC based on the average number of path failures simultaneously happening in the network other than the maximum number of path failures. The scheme can significantly improve the network throughput comparing with the traditional approaches. In our study, we also investigate the potential of random linear code RRLC for providing reliable communication in WSNs and prove the low bound of the probability that the RRLC can provide the reliable communication. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed LNC schemes.

In wireless sensor networks, it is of great importance for fault diagnosis to ensure the gathering information accuracy and reduce energy additionally consumed by faulty nodes, for the deployment of a large number of sensor nodes in hostile environment. In this paper, we propose an energy-efficient data collection protocol which consists of clustering and multipath routing. Clustering based on fault diagnosis eliminates the possibility of cluster heads (CHs) acting by faulty nodes which reduce energy consumption and fault information transmission. Multipath routing provided by directed acyclic graph (DAG) increases system fault tolerance. Furthermore, clustering and multihop routing consider residual energy and routing cost, respectively; thus balanced energy consumption is achieved. Performance analysis shows that the message complexity disseminated in clustering and fault diagnosis is acceptable. Simulations demonstrate that the protocol has better energy efficiency compared with other related protocols.

Processing the gathered information efficiently is a key functionality for wireless sensor networks. In generally, the sensor networks often use in-network data aggregation and clustering to optimize network communication. The set of aggregating nodes forms a dominating set of the network graph. Finding the weakly connected dominating set (WCDS) is a promising approach for clustering the WSN. However, finding a minimum WCDS is NP-hard problem for most graphs, and a host of approximation algorithm has been proposed. The aim of the paper is to construct a minimum WCDS as a clustering scheme for WSN. Our clustering schemes construction algorithm includes two phases. First of all, we construct a maximal data aggregation tree (DAT) of the network. The second phase of the algorithm is to choose the nodes (called connectors) to make the WCDS connected. The correctness and performance of our algorithms are confirmed through theoretical analysis and comprehensive simulations.

Mobile agent (MA) systems provide new capabilities for energy-efficient data processing by flexibly planning its itinerary for facilitating agent-based data collection and aggregation. In this paper, we present a cooperative data processing algorithm based on mobile agent (MA-CDP), and considers MA in multihop environments and can autonomously clone and migrate themselves in response to environmental changes. MA accounts for performing data processing and making data aggregation decisions at nodes rather than bringing data back to a central processor, and redundant sensory data will be eliminated. The results of our simulation show that MA-based cooperative data processing provides better performance than directed diffusion in terms of end-to-end delivery latency, packet delivery ratio, and energy consumption.

Cognitive radio technology is the key to realize dynamic spectrum access system and promote the spectrum utilization through exploiting the spectrum holes left by primary users. However, the spatial heterogeneity of spectrum availability imposes special challenges for efficient utilization of the spectrum resources for cognitive radio ad hoc networks (CRAHNs). The cross-layer cooperative transmission scheme is a promising approach to improve the efficiency of spectrum utilization and improve the performance of cognitive radio networks. Such an approach leverages relay-assisted discontiguous OFDM (DOFDM) for data transmission at physical and MAC layers in a basic three-node configuration. With this scheme, a relay node will be selected that can bridge the source and the destination using its common channels between those two nodes. In this paper, we investigate the application of such a cooperative transmission scheme to address the spectrum heterogeneity issue in CRAHNs. In particular, we describe several types of cooperative transmission and formulate a new resource allocation problem with joint relay selection and channel allocation. We propose a heuristic algorithm to solve the resource allocation problem, which is based on the metric of utility-spectrum ratio of transmission groups. Simulations demonstrate the performance improvement of the cooperative transmission over the direct transmission.