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With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node’s Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely “the alternating backpack problem”, and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes’ energy requirements and MC’s capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC’s staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

Abstract The secrecy outage performance for a cooperative cognitive radio energy‐harvesting network is analyzed. The cognitive network is composed of an energy‐constrained cognitive source (CS), multiple energy‐constrained cognitive relays (CRs) and a cognitive destination (CD) as well as an eavesdropper (E) coexists with a primary network consisting of a primary transmitter (PT) and a primary receiver (PR). The CS and CRs are equipped with energy harvesters for collecting energy from the radio frequency signal from PT and their transmit powers are limited by the interference threshold at PR. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed‐form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. For the purpose of comparison, the classical round‐robin relay selection (RRRS) is also analyzed in terms of secrecy outage probability. Furthermore, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability.

Aiming at the security problem of edge server, a trust evaluation algorithm was proposed, which integrated server cooperation reputation and device user feedback to improve the security of edge computing context.In the process of interaction, a fusion algorithm based on objective information entropy theory was used to aggregate the cooperation reputation between servers, and a partial homomorphic encryption algorithm was used to prevent the leakage of user data in the process of interaction.After the interaction, the highly trusted device node was selected to calculate the feedback trust, which overcame the malicious feedback of the traditional mechanism.When calculating the global trust, the expected score of the server and the adaptive weight calculation method were considered, which overcame the limitations of the traditional trust scheme.The experimental results show that the proposed trust calculation scheme has low time complexity and spatial complexity, and can effectively resist the attack of malicious nodes.

Abstract In this paper, the secrecy performance in a MIMO cognitive radio (CR) relay network with energy harvesting (EH) and transmit antenna selection/maximal ratio combining (TAS/MRC) is invstigated, where the DF relaying protocol and multiple colluding passive eavesdroppers are considered. To improve the security of wireless transmission, two antenna selection schemes are proposed, namely, the optimal transmit antenna selection (OTAS) scheme and suboptimal transmit antenna selection (STAS) scheme. For the purpose of comparison, the space‐time transmission (STT) scheme is introduced as a baseline. The exact and asymptotic closed‐form secrecy outage probability (SOP) expressions for OTAS, STAS and STT schemes are derived over Rayleigh fading channels. An extension of the TAS framework to an artificial noise (AN) aided MIMO network is further presented and an AN aided transmit antenna selection (AN‐TAS) scheme is proposed, in which the unselected antennas at R are used to emit AN for interfering with Es. Numerical results show that the OTAS and STAS schemes perform better than STT scheme in terms of SOP. Meanwhile, the SOP of AN‐TAS scheme is much smaller than that of OTAS, STAS and STT schemes in the high SNR region, indicating the benefit of applying AN in MIMO network.

Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.

In-air gesture interaction enables a natural communication between a man and a machine with its clear semantics and humane mode of operation. In this paper, we propose a real-time recognition system on multiple gestures in the air. It uses the commodity off-the-shelf (COTS) reader with three antennas to detect the radio frequency (RF) signals of the passive radio frequency identification (RFID) Tags attached to the fingers. The idea derives from the crucial insight that the sequential phase profile of the backscatter RF signals is a reliable and well-regulated indicator insinuating space-time situation of the tagged object, which presents a close interdependency with tag's movements and positions. The KL divergence is utilized to extract the dynamic gesture segment by confirming the endpoints of the data flow. To achieve the template matching and classification, we bring in the dynamic time warping (DTW) and k-nearest neighbors (KNN) for similarity scores calculation and appropriate gesture recognition. The experiment results show that the recognition rates for static and dynamic gestures can reach 85% and 90%, respectively. Moreover, it can maintain satisfying performance under different situations, such as diverse antenna-to-user distances and being hidden from view by nonconducting obstacles.

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

Many individuals or businesses outsource their data to remote cloud.Cloud storage provides users the advantages of economic convenience,but data owners no longer physically control over the stored data,which introduces new security challenges,such as no security guarantees of integrity and privacy.The security of two identity-based cloud data integrity verification schemes by Zhang et al and Xu et al respectively are analysed.It shows that Zhang et al.’s scheme is subjected to secret key recovery attack for the cloud servers can recover user’s private key only utilizing stored data.And Xu et al.’s scheme cannot satisfy security requirements of soundness.Based on Xu et al.'s scheme,a modified identity-based cloud data integrity verification scheme is proposed.A comprehensive analysis shows the new scheme can provide the security requirements of soundness and privacy,and has the same communication overhead and computational cost as Xu et al.’s scheme.

Falling is one of the major health threats to the independent living of elders, and falling has received much attention in academia and industry over the past two decades. Previous fall detection methods either require specialized hardware or invade people's daily lives. These limitations make it difficult to widely deploy fall detection systems in houses. On the other hand, a long static case is also one of the health threats to the elderly; these cases indicate that something is abnormal. In this paper, we present the design and implementation of a motion detection system based on passive radio frequency identification tags. The key finding is that static, regular action, and sudden falls make an impact on the RSS and Doppler frequency values differently. Such features help us to detect the status of the elderly. A wavelet transform is used to pre-process the signal data, and a support vector machine is adopted to improve the accuracy of the fall detection. We implemented a prototype monitoring system called TagCare and conducted extensive experiments to demonstrate the accuracy and efficiency of TagCare in movement detection and fall behavior identification for the elderly.

The Internet of Things (IoT) has been integrated into legacy healthcare systems for the purpose of improving healthcare processes. As one of the key technologies of IoT, radio frequency identification (RFID) technology has been applied to offer services like patient monitoring, drug administration, and medical asset tracking. However, people have concerns about the security and privacy of RFID-based healthcare systems, which require a proper solution. To solve the problem, recently in 2019, Fan et al. proposed a lightweight RFID authentication scheme in the IEEE Network. They claimed that their scheme can resist various attacks in RFID systems with low implementation cost, and thus is suitable for RFID-based healthcare systems. In this article, our contributions mainly consist of two parts. First, we analyze the security of Fan et al.’s scheme and find out its security vulnerabilities. Second, we propose a novel lightweight authentication scheme to overcome these security weaknesses. The security analysis shows that our scheme can satisfy the necessary security requirements. Besides, the performance evaluation demonstrates that our scheme is of low cost. Thus, our scheme is well-suited for practical RFID-based healthcare systems.

With the advent of pile-less shared bicycles, the techniques initially used for public bicycle dispatching were unable to fulfill the routine dispatch tasks, resulting in constant bicycle crowding. In this paper, to alleviate the mess of shared bicycles, we propose a hybrid dispatching algorithm based on bicycle demand data. We take the bicycle stations’ imbalance as an optimization index and use greedy ideas to ensure that after each dispatch all stations get the smallest imbalance. In addition, it is suggested that two assessment metrics evaluate the efficiency of the dispatching technique from the users and operators’ perspectives. It is shown that the proposed dispatching algorithm performs better in terms of user satisfaction and operator revenue, and is less affected by bicycle distribution compared with the traditional manual scheduling algorithm.

With the rapid development of the Internet of Things and the popularization of 5G communication technology, the security of resource-constrained IoT devices such as Radio Frequency Identification (RFID)-based applications have received extensive attention. In traditional RFID systems, the communication channel between the tag and the reader is vulnerable to various threats, including denial of service, spoofing, and desynchronization. Thus, the confidentiality and integrity of the transmitted data cannot be guaranteed. In order to solve these security problems, in this paper, we propose a new RFID authentication protocol based on a lightweight block cipher algorithm, SKINNY, (short for LRSAS). Security analysis shows that the LRSAS protocol guarantees mutual authentication and is resistant to various attacks, such as desynchronization attacks, replay attacks, and tracing attacks. Performance evaluations show that the proposed solution is suitable for low-cost tags while meeting security requirements. This protocol reaches a balance between security requirements and costs.

With the development of radio frequency identification technology, radio frequency identification system has been deployed in different applications in a large scale. It needs a densely deployed reader environment to cover the work area, especially in radio frequency identification logistics and warehousing applications. If the distribution area and the number of readers are not optimized, and some of them will be redundant, which will reduce the efficiency of the whole radio frequency identification system. Researchers have proposed many algorithms and optimization techniques to solve the problem of redundant readers. In this paper, the authors propose a novel redundant reader elimination algorithm, namely, L-NCD, in P2P-RFID reader network, combined with layered eliminate optimization and neighboring coverage density. The simulation results demonstrate that the proposed algorithm eliminates more redundant readers than the other methods such as redundant-reader elimination under the same conditions.

Recent advances in agriculture-oriented sensors and wireless communication technologies offer great opportunities for the development and application of precision agriculture. This article describes our experience during the deployment of an experimental in-field sensor network that provided real-time monitoring in a 10-year-old date palm orchard. Taking into account the spatial variability of multiple growth-determining soil variables, the sub-regions of the sensor placement were determined by delineating the orchard into several monitoring zones. Then, the specific number of the required sensors was calculated based on the requirement of data accuracy. The sensors were distributed into the target orchard by combining the tree number with the coefficient variability of growth-determining soil variables in the monitoring zones. Finally, the networking was realized by means of synthesizing the received wireless signal strength distribution, the layout of monitoring zones, and the number of the required sensors. Some data from the deployed wireless sensor network are presented and discussed.

An energy consumption optimization algorithm based on ant colony algorithm is proposed for wireless sensor network. The proposed algorithm allows each node in wireless sensor network to save the distance and residual energy of neighbor nodes. Furthermore, in terms of probability selection of the nodes and the pheromone update, this algorithm focuses on the next hop node through the comparison of distance between the nodes and the residual energy, which ensures less possibility of nodes with low energy selected as the next hop. Therefore, the proposed algorithm improves energy load balancing, stability of wireless sensor network and, eventually, extends the life span of the wireless sensor network. The simulation results show that the improved ant colony algorithm avoids too much energy consumption of a certain local node resulting in more uniform energy consumption for each node.

Radio frequency identification is one of the key techniques for Internet of Things, which has been widely adopted in many applications for identification. However, there exist various security and privacy issues in radio frequency identification (RFID) systems. Particularly, one of the most serious threats is to clone tags for the goal of counterfeiting goods, which causes great loss and danger to customers. To solve these issues, lots of authentication protocols are proposed based on physical unclonable functions that can ensure an anti-counterfeiting feature. However, most of the existing schemes require secret parameters to be stored in tags, which are vulnerable to physical attacks that can further lead to the breach of forward secrecy. Furthermore, as far as we know, none of the existing schemes are able to solve the security and privacy problems with good scalability. Since many existing schemes rely on exhaustive searches of the backend server to validate a tag and they are not scalable for applications with a large scale database. Hence, in this paper, we propose a lightweight RFID mutual authentication protocol with physically unclonable functions (PUFs). The performance analysis shows that our proposed scheme can ensure security and privacy efficiently in a scalable way.

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

GPS have been able to offer a commercially available outdoor positioning services; however, since the quality of satellite signals declines after entering into the indoor environment, the GPS system cannot provide indoor positioning services. With the development of cloud computing and the Internet of Things technology, indoor positioning has become a hot research hotspot. Radio Frequency Identification is a wireless communication technology, and the Radio Frequency Identification reader can communicate wirelessly through electromagnetic waves with the tag. Radio Frequency Identification technology has the following advantages: no contact, recognition from the long distance, long life and fast moving objects identification, etc. Radio Frequency Identification is the key component of Internet of Things, and many fusion application systems combining Radio Frequency Identification and cloud computing are emerging. This article designs and implements the Radio Frequency Identification indoor positioning system based on Received Signal Strength Indicator, which obtains three-dimensional coordinates orientation through multiple trilateral positioning algorithm on location tag. C# and Python are used to complete positioning system, where C# dominates user interface and calculates three-dimensional coordinates. Python is responsible for three-dimensional display. The two programs communicate through a socket.

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

Deployment quality and cost are two conflicting aspects in wireless sensor networks. Random deployment, where the monitored field is covered by randomly and uniformly deployed sensor nodes, is an appropriate approach for large-scale network applications. However, their successful applications depend considerably on the deployment quality that uses the minimum number of sensors to achieve a desired coverage. Currently, the number of sensors required to meet the desired coverage is based on asymptotic analysis, which cannot meet deployment quality due to coverage overestimation in real applications. In this paper, we first investigate the coverage overestimation and address the challenge of designing coverage-guaranteed deployment strategies. To overcome this problem, we propose two deployment strategies, namely, the Expected-area Coverage Deployment (ECD) and BOundary Assistant Deployment (BOAD). The deployment quality of the two strategies is analyzed mathematically. Under the analysis, a lower bound on the number of deployed sensor nodes is given to satisfy the desired deployment quality. We justify the correctness of our analysis through rigorous proof, and validate the effectiveness of the two strategies through extensive simulation experiments. The simulation results show that both strategies alleviate the coverage overestimation significantly. In addition, we also evaluate two proposed strategies in the context of target detection application. The comparison results demonstrate that if the target appears at the boundary of monitored region in a given random deployment, the average intrusion distance of BOAD is considerably shorter than that of ECD with the same desired deployment quality. In contrast, ECD has better performance in terms of the average intrusion distance when the invasion of intruder is from the inside of monitored region.

Evaluation of security risks in radio frequency identification (RFID) systems is a challenging problem in Internet of Things (IoT). This paper proposes an extended attack tree (EAT) model to identify RFID system's flaws and vulnerabilities. A corresponding formal description of the model is described which adds a probability SAND node together with the probability attribute of the node attack. In addition, we model the process of an RFID data privacy attack based on EAT, taking a sensitive information theft attack on an RFID tag as an example. To resolve the problem of assessing the risk probability of each node in EAT, we present the information feature entropy evaluation method for RFID privacy assessment. Finally, an evaluation is carried out to calculate the RFID privacy attack tree sequences and information feature entropy of the atomic node. Analysis shows that our scheme can calculate the overall risk evaluation result value for RFID privacy and comprehensively determine the risk of the weakest atomic node in RFID system.

Scenarios in which two nodes who distrust each other in wireless sensor networks (WSNs) would like to know the distance between them are considered. The scenario is designed to protect the private information of WSNs, in this case each node's location, from the other nodes and from a passive attacker. The goal of the present work is to provide two novel and secure two-party distance computation protocols based on a semihonest model, the first with aid of a third party and the second based on randomization technique. Both of these protocols can extend the calculated value into a real number field. The output of the distance computation and the intermediate values in the proposed protocols are also private and not accessible to a third party or any other attackers. When executing these two protocols, security is guaranteed, and the performances of communication and computation of them are found to be satisfactory when compared to those of other similar protocols.

The RPL (Routing Protocol for LLN) protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource, so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfigurations.

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.

With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past few years. In order to construct an autonomous and robust biometric security system, this paper explores the application of face recognition technique in wireless sensor networks. Given the limited technological resources of sensor nodes, new challenges remain to be met. In this work, a facial component-based recognition mechanism is firstly applied to ensure the recognition accuracy. Secondly, in order to address the problem of resource constraints, a distributed scheme based on K-d trees is deployed for both the face image transmission and retrieval. According to the simulation results, the proposed method is capable of achieving considerable energy efficiency, while assuring the recognition accuracy.

In the study of three-dimensional underwater sensor networks, the nodes would produce changes in perception range under the influence of environmental factors and their own hardware. Requesting all nodes completely isomorphic is unrealistic. Ignoring boundary effects usually causes the coverage effect of the actual deployment of networks to not reach the anticipated result. This paper firstly presents an underwater sensing model with normal distributed node sensing radius. Secondly, it gives the relationship between expected deployment quality and the number of nodes in the premise of considering boundary effects. Then, it deduces nodes’ redundancy formula based on the sensing model with normal distributed node sensing radius, making node could determine whether itself is a redundant node only based on the number of its neighbour nodes. Furthermore, this paper proposes a redundancy model and boundary effects based coverage-enhancing algorithm for three-dimensional underwater sensor networks (RBCT). Simulation results show that RBCT, compared to similar algorithms, has certain advantages in saving energy and enhancing coverage rate.

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor’s current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm’s performance and the effect of number of targets on the resulting subset.

Coverage pattern and deployment strategy are directly related to the optimum allocation of limited resources for wireless sensor networks, such as energy of nodes, communication bandwidth, and computing power, and quality improvement is largely determined by these for wireless sensor networks. A three-dimensional coverage pattern and deployment scheme are proposed in this paper. Firstly, by analyzing the regular polyhedron models in three-dimensional scene, a coverage pattern based on cuboids is proposed, and then relationship between coverage and sensor nodes’ radius is deduced; also the minimum number of sensor nodes to maintain network area’s full coverage is calculated. At last, sensor nodes are deployed according to the coverage pattern after the monitor area is subdivided into finite 3D grid. Experimental results show that, compared with traditional random method, sensor nodes number is reduced effectively while coverage rate of monitor area is ensured using our coverage pattern and deterministic deployment scheme.

The aim of this study is to improve the quality of service (QoS) for peer-to-peer (P2P) streaming media system with the proposed peer selection strategy. P2P technology is promising for large scale streaming media files distribution over the Internet. As peers contribute their resources over multipeer transmission paths, it is particularly challenging to achieve consistently high streaming QoS such as low start-up delay, low jitter, low packet loss ratio, and high bandwidth assurance. Recently, there are a lot of researchers studying peer selection strategy in peer-to-peer streaming system. In this paper, a novel P2P streaming media peer selection strategy based on mobile agent and trust (MATPS) is proposed. The proposed approach is compared with bandwidth first peer selection strategy (BFPSS), GridMedia's peer selection (GridPS), trust-based peer selection strategy (TrBPS), and SmartPeerCast peer selection strategy (SPCPS), and the simulation results show that the proposed strategy improves the streaming QoS.

In opportunistic network, data transmission relies mainly on node mobility. However, the moving patterns of nodes in diverse application scenarios are different. Thus, nodes moving patterns should be considered when designing data transmission strategy. Based on the analysis of node moving characteristics in opportunistic social networks, two parameters, called social rang and social frequency, are introduced and sociability-based Spray and Forward (SSF) scheme is proposed by applying them to the data dissemination and forwarding phase, respectively. Simulation results show that SSF can effectively improve data delivery ratio and reduce latency under the given model.

With its widespread application prospects, opportunistic social network attracts more and more attention. Efficient data transmission strategy is one of the most important issues to ensure its applications. As is well known, most of nodes in opportunistic social network are human-carried devices, so encounters between nodes are predictable when considering the law of human activities. To the best of our knowledge, existing data transmission solutions are less accurate in the prediction of node encounters due to their lack of consideration of the dynamism of users' behavior. To address this problem, a novel data transmission solution, based on time-evolving meeting probability for opportunistic social network, called TEMP is introduced, and corresponding copy management strategy is given to reduce the message redundancy. Simulation results based on real human traces show that TEMP achieves a good compromise in terms of delivery probability and overhead ratio.

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods.

Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings.

Aimed at the defects of current coverage-enhancing algorithms, firstly the probability model is introduced into the coverage-enhancing algorithm of the directional sensor network based on the virtual potential field, and the probability model would have an impact on the location of the centroid. Secondly, to the boundary coverage, through the simulation of typical algorithms which consider the boundary repulsion, it finds that the coverage rates of the corners are much lower than the overall coverage. To add a vertex force on the corner is essential and to some extent would reduce the gap between them. Finally, based on the random deployment of sensor nodes, a novel algorithm is proposed which determines whether the sensor node rotates or moves along fixed direction according to the coverage effect, named as PRMCA (probability model based rotate or move along fixed direction coverage-enhancing algorithm). A set of simulation experiments verify the performance of the proposed algorithm.

In wireless multimedia sensor networks, multiple camera sensor nodes generally are used for gaining enhanced observations of a certain area of interest. This brings on the visual information retrieved from adjacent camera nodes usually exhibits high levels of correlation. In this paper, first, based on the analysis of 3D directional sensing model of camera sensor nodes, a correlation model is proposed by measuring the intersection area of multiple camera nodes’ field of views. In this model, there is a asymmetrical relationship of the correlation between two camera nodes. Then, to farthest eliminate the data redundancy and use the node collaboration characteristic of wireless (multimedia) sensor networks, two kinds of cluster structure, camera sensor nodes cluster, and common sensor nodes cluster are established to cooperate on image processing and transmission tasks. A set of experiments are performed to investigate the proposed correlation coefficient. Further simulations based on a sample of monitoring a crossing by three correlative camera nodes show that the proposed network topology and image fusion and transmission scheme released the pressure of camera node greatly and reduce the network energy consumption of communication of the whole network efficiently.

Positioning of the location information of wireless sensor network nodes is one of the key issues in wireless sensor network applications. The traditional node positioning method based on least-square algorithm heavily depends on the ranging accuracy, and therefore cannot guarantee high precision. This paper presents a new method for wireless sensor network node positioning based on nonlinear weighting least-square algorithm. Regarding ranging equation error-weighted sum as a whole, this method starts with the initial iteration point of stepwise refinement to explore the optimal solution and further reduces the positioning computational complexity by the simplification of the Taylor equation. Experimental results demonstrate that promising results have been achieved by using this method.

The needs of diverse environmental information introduce the multimedia data into wireless sensor networks. The characteristics of most multimedia information, such as great amounts of data and high-quality requirement for network service, positively affect traditional wireless sensor networks, which also derive various new research areas. This paper focuses on the multimedia image sensor networks and proposes FVPTR (fuzzy image recognition and virtual-potential-field-based paired tangent point repulsion) method to enhance the perspective coverage of network. This approach utilizes fuzzy image recognition method to process the boundary nodes. Aimed at nonboundary nodes, based on potential field theory, it adopts paired tangent point repulsion mechanism, which attempts to obtain the optimal network sensing coverage through the multiple paired achievements between one current node and several target nodes. Combined with FVPTR, some algorithms such as LRBA, MBAA, and mixed superposition algorithm are put forward to single or multiple-time adjustment by the rotation of the direction angle. The results of simulation and all kinds of comparisons show that three-times pairing method enhances the coverage of networks well.

In conventional directional sensor networks, coverage control for each sensor is based on a 2D directional sensing model. However, 2D directional sensing model failed to accurately characterize the actual application scene of image/video sensor networks. To remedy this deficiency, we propose a 3D directional sensor coverage-control model with tunable orientations. Besides, a novel criterion for judgment is proposed in view of the irrationality that traditional virtual potential field algorithms brought about on the criterion for the generation of virtual force. Furthermore, cross-set test is used to determine whether the sensory region has any overlap and coverage impact factor is introduced to reduce profitless rotation from coverage optimization, thereby the energy cost of nodes was restrained and the performance of the algorithm was improved. The extensive simulations results demonstrate the effectiveness of our proposed 3D sensing model and IPA3D (improved virtual potential field based algorithm in three-dimensional directional sensor networks).