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Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption. [ABSTRACT FROM AUTHOR]

Data collection in underwater acoustic sensor networks (UASNs) and the exposure of node location information pose a threat to the security of the entire network. Therefore, the main challenge for underwater acoustic sensor network security is to protect the security and privacy of the node locations. Compared to active attacks, the characteristics of passive attacks are indistinguishable. Therefore, this research focuses on passive attacks in underwater acoustic sensor networks, and an autonomous underwater vehicle (AUV)-aided data-importance-based scheme for protecting location privacy (DIS-PLP) is proposed. The DIS-PLP comprises three main parts. First, the anchor node calculates the importance of the current data based on the historical dataset and decides whether to send data packets to the base station according to the importance result. Subsequently, an AUV privacy violation is defined, and an AUV path planning method is proposed to prevent attackers from tracking the AUV to obtain the location of the source node. Finally, to prevent nodes from waiting too long for AUVs, a secure multi-hop transmission method is proposed that uses fake source nodes to send fake source data packets to hide the real traffic. The simulation results show that the DIS-PLP offers well performance in terms of safety time and delay, which has strong practical significance for Smart Ocean to improve the level of network security. [ABSTRACT FROM AUTHOR]

The diffusion of toxic gas, biochemical material, and radio-active contamination – known as continuous objects – endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm of industrial wireless sensor networks (IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose a motion behavior learning predictive tracking (MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizes Bayesian model averaging (BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) have been widely promoted for developing various categories of marine applications, where the sensor nodes cooperate to complete specific tasks. Given the fact that the sensor nodes are unattended while continuously exposed to harsh environments, an associated trust model plays a significant role in node trustworthiness evaluation and defective node detection, such as the case of adverse attacks on the network. However, the existing trust models only evaluate the communication behavior and the energy of the sensor nodes, ignoring the effects of underwater environmental noise on trust reliability. Further, most trust models are designed with arbitraty weighted trust metrics, causing inevitable evaluation errors. To achieve the accurate calculation of node trust, we propose a new anomaly and attack resilient trust model, based on the isolation forest. We refer to this model as ITrust. The proposed ITrust model consists of two phases: trust metrics specifics and defective node detection. In the first phase, the trust dataset is integrated from four types of trust metrics: communication trust, data trust, energy trust, and environment trust. In the second stage, trust is evaluated with the obtained trust dataset using the isolation forest algorithm. Simulation results demonstrate that the proposed ITrust can detect defective nodes effectively, and achieves higher detection accuracy than that of the existing trust models in a noisy environment. [ABSTRACT FROM AUTHOR]

In recent years, developments in data collection schemes based on multiple autonomous underwater vehicles (AUVs) are facilitating the realization of the so-called underwater acoustic sensor networks (UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose a state prediction-based data collection (SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other’s status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime. [ABSTRACT FROM AUTHOR]

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50 percent. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes ($\approx 9\%$ ≈ 9 % ). [ABSTRACT FROM AUTHOR]

Submarine pipelines are among the most efficient methods of marine oil transportation, and they are widely used in offshore oil exploitation. However, the complex environment of the ocean and malicious attackers may damage the pipeline. They can be efficiently monitored by deploying a multiplicity of sensor nodes, organized in underwater acoustic sensor networks (UASNs). UASNs is located near oil wells, so that UASNs can sense, broadcast, and receive data surveying the status of the submarine infrastructure timely. Inevitably, UASNs may experience security problems, owing to the open nature of the underwater environment. To address these security issues, this paper proposes a new source location privacy protection scheme based on an offshore oil acquisition platform. In the proposed scheme, the nodes are defined as pipeline and environment locations, and are allocated to different clusters. The concept of forming anonymous clusters by exchanging the identities of a source and a cluster head is introduced to determine fake confounding sources. In addition, a data fragmentation method is devised to reduce the energy consumption resulting from fake packet transmission. Particularly, we propose the deployment of disguised AUVs that collect data from fake sources, as to conceal and protect the location of the actual sources. Simulation results show that the safety time of the network is increased by about 6%. Due to the adoption of the data fragmentation method, the energy consumption of nodes is also decreased. [ABSTRACT FROM AUTHOR]

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network. [ABSTRACT FROM AUTHOR]

Intelligent data collection is a key component in underwater acoustic sensor networks, and it plays an important role in seabed environment monitoring, marine resource detection and marine disaster early warning. Owing to the particularities of the underwater environment, such as reduced infrastructure and noisy communication channels, the data collected by underwater nodes are more efficiently transmitted to a control center on the surface by way of an autonomous underwater vehicle (AUV). However, with the increasing complexity of the underwater tasks, using a single AUV for data collection cannot meet the requirements of low latency and low power consumption. To solve this problem, a multi-AUV collaborative data collection algorithm that reduces the load of data collection task on a single AUV is proposed. The algorithm is divided into two stages: multi-AUV task allocation and Q-learning-based AUV path planning. The data transmission of the clusters is regarded as a set of different tasks, which are assigned to the AUVs for completion. Subsequently, path planning is performed to guide the AUVs, so that the tasks are completed promptly and at a reduced cost. Simulation results show that the proposed algorithm can leverage the energy consumption of a network and extend its lifetime. The performance of the proposed algorithm in energy consumption is increased by about 10%, and the delay of data collection is also significantly reduced. [ABSTRACT FROM AUTHOR]

Topology control has drawn considerable attention in underwater wireless sensor networks. With the complexity of the underwater environment considered, the topological model of a three-dimensional dense network is adopted. The partition unit, cluster, and temporary control node are first defined. A clustering dormancy scheduling algorithm is then proposed. Accordingly, a vulnerability repair algorithm is presented with consideration of the vulnerability of sensor nodes to death due to external factors. The failure node, coverage vulnerability, coverage matrix, key position, and supplementary node are defined in the proposed algorithm. The algorithm also uses the coverage matrix and the vulnerability edge nodes to determine whether the overlay vulnerability requires repair. Experimental results indicate that this algorithm is more effective and efficient compared with similar algorithms. [ABSTRACT FROM AUTHOR]

The Industrial Wireless Rechargeable Sensor Network (IWRSN) is a sensor network used in industrial environments. In order to ensure a certain intensity of industrial monitoring and real-time industrial control, the network is equipped with mobile charger to supplement the energy for sensors according to the charging schedule. Because of the complexity of industrial environment, the monitoring area is firstly divided into grids and established a set of paths that can be driven by mobile chargers. On this basis, a newborn particle swarm optimization (NPSO) charging scheduling algorithm is proposed for the constraint of node working time window. The NPSO algorithm borrows the idea of fireworks algorithm to introduce newborn particles into the population, and improves the convergence speed of the algorithm, then applies it to the charging scheduling process. The NPSO charging algorithm firstly plans the initial scheduling path for the node that needs to be priority charging. The remaining nodes to be charged are then designed to search for the location of the initial path near their position and update the time window of the subsequent charging node. The simulation results show that the proposed newborn particle swarm optimization charging scheduling algorithm has superiority in energy utilization and node mortality compared with the existing charging scheduling algorithm. [ABSTRACT FROM AUTHOR]

At present, Underwater Wireless Sensor Networks (UWSNs) have been widely used in enormous applications, and simultaneously face many security threats. The trust management mechanism plays an important role in protecting network security. Many theories, e.g., subjective logic, Bayesian, cloud model, entropy theory, evidence theory, etc., have been adopted to evaluate the node trust of wireless sensor networks. However, due to the unique characteristics of the underwater dynamic environment, the existing trust mechanisms used in traditional networks (such as P2P networks, Ad-hoc networks, etc.) cannot be directly used in UWSNs. Therefore, this paper proposes a new trust evaluation and update mechanism for underwater wireless sensor networks based on the C4.5 decision tree algorithm (TEUC). In the TEUC, trust evidences are first collected including data-based, link-based and node-based trust evidences. Then, the collected trust evidences are used to train the C4.5 decision tree. In addition, the reward and penalty factors are defined to update trust based on the sliding time window. Finally, simulation results demonstrate that the proposed algorithm outperforms the traditional ones in a dynamic network environment in terms of malicious node detection and energy consumption. [ABSTRACT FROM AUTHOR]

Source location privacy (SLP) protection is an important means of security protection in wireless sensor networks (WSNs). With the development of underwater acoustic sensor networks (UASNs), security and privacy have attracted increasing attention. In this study, we incorporated SLP into UASNs as the basis for a novel stratification-based source location privacy (SSLP) scheme. In the SSLP scheme, SLP is protected through the cooperation of autonomous underwater vehicles (AUVs) in each network layer. Because fake source nodes and fake data streams are commonly used in WSNs, methods that have similar functions in UASNs are required. Hence, we incorporate a fake source node into the underwater cluster structure to add randomness to the underwater network. Furthermore, fake data streams have been included within the AUV data collection and transmission for each cluster. Simulation results confirm that the SSLP scheme offers improved security in comparison with existing underwater data transmission schemes. [ABSTRACT FROM AUTHOR]

In this paper, a high-availability data collection scheme based on multiple autonomous underwater vehicles (AUVs) (HAMA) is proposed to improve the performance of the sensor network and guarantee the high availability of the data collection service. Multi-AUVs move in the network and their trajectory is predefined. The nodes near the trajectory of an AUV directly send their data to the AUV while the others transmit data to nodes that are closer to the trajectory. Malfunction discovery and repair mechanisms are applied to ensure that the network operates appropriately when an AUV fails to communicate with the nodes while collecting data. Compared with existing methods, the proposed HAMA method increases the packet delivery ratio and the network lifetime. [ABSTRACT FROM AUTHOR]

Poisonous pollutants produced in chemical, plastics, or nuclear power industry are easy to leak and result in a large-scale hazardous event region. Recently, industrial wireless sensor networks (IWSNs) are intended to provide situational awareness in industry site and thus hold the promise of profiling the event region. However, low-cost nodes in IWSNs are prone to fail due to prolonged exposure to harsh environment. This article targets the detection of hazardous event region for IWSNs with faulty nodes. A fault-tolerant event region detection algorithm named TPE-FTED is proposed to formulate faulty nodes identification as a trajectory pattern extraction problem. Through online learning of probabilistic model, each node characterizes the distribution of sensing values under different sensing states. A specific set of probabilistic models can be formed as a trajectory which indicates something special happens. Based on the implicit knowledge from generated trajectories, TPE-FTED conducts pattern matching and checks spatiotemporal constraint to identify the declaration of faulty nodes. Simulation results demonstrate that TPE-FTED achieves low false alarm rate as well as high detection accuracy. [ABSTRACT FROM AUTHOR]

Wireless rechargeable sensor networks (WRSN) have attracted considerable attention in recent years due to the constant energy supply for battery-powered sensor nodes. However, current technologies only enable the mobile charger to replenish energy for one single node at a time. This method has poor scalability and is not suitable for large-scale WRSNs. Recently, wireless energy transfer technology based on multi-hop energy transfer has made great progress. It provides fundamental support to alleviate the scalability problem. In this paper, the node energy replenishment problem is formulated into an optimization problem. The optimization objective is to minimize the number of non-functional nodes. We propose the uneven cluster-based mobile charging (UCMC) algorithm for WRSNs. An uneven clustering scheme and a novel charging path planning scheme are incorporated in the UCMC algorithm. The simulation results verify that the proposed algorithm can achieve energy balance, reduce the number of dead nodes, and prolong the network lifetime. [ABSTRACT FROM AUTHOR]

Constant energy supply for sensor nodes is essential for the development of the green Internet of Things (IoT). Recently, WRSNs have been proposed to resolve the energy limitations of nodes, aiming to realize continuous functioning. In this article, a coverage-aware hierarchical charging algorithm in WRSNs is proposed, considering energy consumption and the degree of node coverage. The algorithm first performs network clustering using the K-means algorithm. In addition, nodes are classified into multiple levels in each cluster to calculate respective anchor points based on the energy consumption rate and coverage degree of nodes. Then, the anchor points converge to an optimized anchor point in each cluster. To reduce charging latency, the optimized anchor points form two disjoint internal and external polygons. Next, mobile chargers travel along the internal and external polygons, respectively. Experimental results indicate that the proposed algorithm can improve charging efficiency and reduce charging latency substantially. [ABSTRACT FROM AUTHOR]

In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols. [ABSTRACT FROM AUTHOR]

With the recent developments of wireless sensor networks (WSNs), computing and communication have experienced huge advancement. Meanwhile, security has not received the same attention to go along with such developments. In this paper, we focus on the source location privacy problem in WSNs, a hot research topic in security, and propose a probabilistic source location privacy (PSLP) protection scheme for WSNs. A more powerful adversary, which can use hidden Markov model to estimate the state of the source, is considered in this study. To cope with this type of adversary, phantom nodes and fake sources, which are responsible to mimic the behavior of the source, are utilized to diversify the routing path. Then, the weight of each node is calculated as a criterion to select the next-hop candidate. In addition, two transmission modes are designed to transmit real packets. The simulation results demonstrate that the proposed PSLP scheme improves the safety time without compromising the energy consumption. [ABSTRACT FROM AUTHOR]

In an industrial production process, the leakage of continuous objects poses a serious threat to production safety. In this paper, a diffusion distance-based predictive tracking algorithm is proposed for industrial wireless sensor networks (IWSNs), aiming to timely track the boundary of a continuous object after the occurrence of a leak. Based on the assumption that the motion of the continuous object follows an appropriate diffusion model, sensor nodes are able to capture environmental parameters for establishing the mathematical expression of the model locally. Through building up the relation of diffusion radius with time, each node predicts diffusion scope of the continuous object at different times and makes a judgment about whether it is suitable to be a boundary node. Moreover, to achieve high energy-efficiency, a sleep/wake cycle is introduced to involve a small number of nodes in the process of tracking, while the rest of nodes stay idle until an object approaches. Finally, a cluster-based competitive mechanism is proposed for reporting the location of boundary nodes. Simulation results demonstrated that our proposal is able to track the diffusion of continuous objects with high energy-efficiency. [ABSTRACT FROM AUTHOR]

A space and terrestrial integrated network (STIN) converges satellite communication networks, mobile wireless networking, and the Internet, has greatly extended the scope of cyberspace. The STIN is composed of diverse network elements and supports various access technologies, and the coordinations in the hybrid networks can better support complex and changeable communication tasks. Furthermore, the STIN takes advantage of the merits of low delay and large bandwidth in terrestrial networks, and achieves global coverage without the limitation of geographic conditions, which can support positioning and navigation, emergency relief, space exploration, and so on. However, the research on STIN is still confronted with some fundamental challenges including time-varying network topology, high satellite mobility, large end-to-end delay, scalability, and so on. In this article, we present a composite architecture that integrates space and terrestrial network components for providing anytime anywhere communications by utilizing the software defined networking and mobile edge computing paradigms, which not only facilitates the network management and increases network flexibility, but also provides improved quality of service for global multimedia services. Additionally, we identify a number of challenging issues associated with the proposed STIN architecture, including mobility management, resource management, routing, traffic steering, security, and so on, a [ABSTRACT FROM AUTHOR]

While enjoying the convenience brought by the Internet of Things (IoT), people also encounter many problems with wireless sensor networks (WSNs), the foundation of IoT. Security problems are especially of concern. In this article, we focus on location privacy, which is a major security issue in WSNs, and propose a k-means cluster-based location privacy (KCLP) protection scheme for IoT. To protect the source location, fake source nodes are used to simulate the function of the real sources. Then, to protect the sink location privacy, fake sink nodes and a specific transmission pattern are utilized. In order to improve safety time, a k-means cluster is applied to create clusters and fake packets that must pass through the area. Compared to contrasting algorithms, the KCLP scheme can increase the safety time and reduce delay at minor expense in energy consumption. [ABSTRACT FROM AUTHOR]

With the boost of IoT, WSNs have been a well-liked know-how. However, due to WSNs' open and unmonitored nature, security vulnerabilities pose a significant problem. In this article, aiming at SLP, a major security problem, we propose the CASLP protection scheme in WSNs for IoT. To increase the scope of the transmitting path, the source starts the random walk in a given direction. Then, to maintain CASLP convergence, nodes only select their next relay nodes in a specific range. Last, to further improve SLP, we concatenate arcs from several rings around the sink to form a new closed loop. Compared to a ringbased routing scheme, CASLP can reduce delays and energy consumption at the minor expense of safety time in WSNs. [ABSTRACT FROM AUTHOR]

Continuous objects monitoring and tracking, which aims to detect the invasion of unauthorized materials of interest, is one of the most prominent applications in wireless sensor networks. In this paper, we propose a novel boundary recognition and tracking algorithm for continuous objects (BRTCO) to ensure the efficiency of objects contour extraction. On precondition of assuring the tracking accuracy, a collaborative filtering scheme is proposed to minimize the number of boundary nodes. Also, in the phase of data transmission, we take the advantage of clustering to ensure energy efficiency. A report node selection mechanism is designed based on the competition of cluster heads. Simulation results demonstrate that BRTCO can significantly reduce the total energy consumption, the number of boundary nodes, and the number of report nodes. [ABSTRACT FROM AUTHOR]

Industrial wireless sensor networks (IWSNs) are an expansion of the Internet of Things paradigm that fully exploits the collective intelligence of smart nodes in industrial processes. The data acquisition of IWSNs in harsh industrial environments is susceptible to transmission failures and time delay while being strict in the performance of reliability and timeliness. This article proposes an SNR-assured anti-jamming clustering routing (SA-AJCR) protocol to address the problem of data transmission in an environment of high ambient noise and complex electromagnetic interference. To ease the influence of the industrial field on wireless communication, the irregular radio range of each node is modeled through field testing, and then its communication range is artificially constrained to ensure that each signal is received under a satisfying SNR. Through mapping out an approximation of jammed areas at the cluster level, SA-AJCR enables IWSNs to be tolerant of node or link failure caused by jamming. Routing paths can be adjusted to greedily bypass the mapped jammed regions in the network without interrupting the data transmission or overriding the CSMA limitation. The simulation results demonstrate that the proposed protocol is effectively able to provide high packet delivery ratio in hostile electromagnetic environments with the existence of jammers. [ABSTRACT FROM AUTHOR]

Smart communities are an emerging communication paradigm that mobilizes physical devices operating in different wireless networks to deliver ubiquitous services. Since wireless communication is vulnerable to physical or link layer jamming, it is essential to map the locations and shapes of jammed regions for adjusting routing policy. However, existing mapping strategies require jammed devices to override the carrier sense multiple access limitation for the declaration of jamming. Besides, the utilization of flat network architectures in existing mapping strategies incurs large communication overhead. In this article, a three-layer hierarchical network structure and a matching top-down mapping mechanism are proposed. Through organizing smart nodes into onehop clusters, the jamming phenomenon can be discovered purely from cluster heads' data reports collected in the sink layer. Each one-hop cluster is also used as a basic mapping unit to identify and measure jammed regions. From cluster-level mapping to sensor-level mapping, jammed areas can be mapped from coarse- to fine-grained. Simulation results demonstrate that the mapping traffic involved in the proposed jammed-area mapping strategy can be reduced substantially. [ABSTRACT FROM PUBLISHER]

Localization is one of the fundamental issues in Wireless Sensor Networks (WSNs). Recently, various localization methods have been proposed. However, using single anchor node introduces quite long localization delay. Therefore, the path planning problem of a Group of Mobile Anchor Nodes (GMAN) is studied in this paper. First, two path planning schemes named GSCAN and GTURN are proposed. Both GSCAN and GTURN use three mobile anchor nodes which traverse the entire network following the designed trajectory to help nearby unknown nodes with localization. Furthermore, in order to improve localization ratio of unknown nodes, two boundary strategies are proposed to guarantee that the movement trajectories of anchor nodes can cover the whole WSN. Simulation results show that GSCAN and GTURN can achieve better localization performance than LMAT, RPGM and Ou’s in terms of higher localization ratio and lower energy consumption. [ABSTRACT FROM AUTHOR]

This study aims to overcome the disadvantages of the original RPL (IPv6 Routing Protocol for Low power and Lossy networks) routing protocol (RPL including problems with energy consumption and energy load balance). We developed a relatively balanced RPL – the improved protocol (IRPL). This protocol is based on an efficient clustering algorithm and an effective topology control model of the loop domain communication route. The clustering algorithm can be used to calculate the optimal number of cluster heads by assumption of the network model. Combined with the clustering probability model and the node competition mechanism, the cluster head node in the wireless sensor network was used to complete the clustering process. In the topology control model, the wireless sensor network was divided into concentric rings with equal areas. Nodes determined the best network route, depending on different levels of ring domain and the optimal forwarding communication area defined in this study. Simulation results indicate that the IRPL routing protocol can reduce overall network energy consumption, balance network energy consumption, and prolong network lifetime. [ABSTRACT FROM AUTHOR]

In order to improve the frequency spectrum availability and evade insecurity frequency range, the cognitive radio is introduced in wireless sensor network (WSN), which constructs the cognitive wireless network (CWN). The dynamic spectrum access (DSA) is used in CWN as the spectrum access scheme. In this paper, sensor nodes of mobile wireless sensor network (MWSN) are deployed based on the prior information of the deployment environment. The idea of CWN is introduced in MWSN. A distributed direction-of-arrival (DOA) estimation algorithm is proposed. The clustering of nodes constructs a sub-NWSN which acts as the sensor array used for DOA estimation. The Fourier domain (FD) root multiple signal classification (root-MUSIC) algorithm is applied for DOA estimation of sub-MWSN with arbitrary topology structure. The weight values of sub-MWSNs can be formulated as a function of the number of nodes, snapshot number and battery capacity of nodes. The total cost spectrum function is achieved finally. The improved performance of distributed FD root-MUSIC algorithm is verified by comparing with the manifold separation technique. [ABSTRACT FROM AUTHOR]

Nowadays, the study of underwater WSNs (UWSNs) has become a hot topic. However, UWSNs have not been fully utilized in the complex underwater environment, since there are some difficulties in controlling mobile sensor nodes and underwater environment conditions. In addition, how to ensure the security of UWSNs and the safety of underwater mobile sensor nodes has not been solved well. In this article, we study the problem of trust establishment between nodes in UWSNs. We first give a detailed overview of existing trust management mechanisms. Since UWSNs possess specific characteristics, it is noted that those existing mechanisms are not applicable for UWSNs. We then introduce a trust cloud model that is suitable for trust management in UWSNs. [ABSTRACT FROM PUBLISHER]

Recent breakthroughs in wireless technologies have greatly spurred the emergence of industrial wireless sensor networks (IWSNs). To facilitate the adaptation of IWSNs to industrial applications, concerns about networks’ full coverage and connectivity must be addressed to fulfill reliability and real-time requirements. Although connected target coverage (CTC) algorithms in general sensor networks have been extensively studied, little attention has been paid to reveal both the applicability and limitations of different coverage strategies from an industrial viewpoint. In this paper, we analyze characteristics of four recent energy-efficient coverage strategies by carefully choosing four representative connected coverage algorithms: 1) communication weighted greedy cover; 2) optimized connected coverage heuristic; 3) overlapped target and connected coverage; and 4) adjustable range set covers. Through a detailed comparison in terms of network lifetime, coverage time, average energy consumption, ratio of dead nodes, etc., characteristics of basic design ideas used to optimize coverage and network connectivity of IWSNs are embodied. Various network parameters are simulated in a noisy environment to obtain the optimal network coverage. The most appropriate industrial field for each algorithm is also described based on coverage properties. Our study aims to provide IWSNs designers with useful insights to choose an appropriate coverage strategy and achieve expected performance indicators in different industrial applications. [ABSTRACT FROM PUBLISHER]

The exploration of the minimal exposure path has significance in guiding the movement of unauthorized objects through monitored areas. In wireless sensor networks, the minimal exposure path can be further used to evaluate the worst-case coverage. This paper analyzes the minimal exposure path in an area that contains heterogeneous sensor nodes and various obstacles. Two exposure-based coverage models are first established to describe the detection patterns of both omnidirectional and directional sensing units. Based on the models, the exposure value of an arbitrary path can be mathematically derived. Next, the obstacle-avoidance minimal exposure path searching (OMEPS) algorithm is proposed to find the minimal exposure path in an environment that includes obstacles. In addition to searching for areas with a poor degree of coverage, OMEPS guides mobile objects through coverage holes caused by obstacles without fear of exposure. Simulation results demonstrate that OMEPS can enable a heterogeneous wireless sensor network to efficiently determine its minimal exposure path. Additional sensors can then be deployed along the minimal exposure path to improve the quality of coverage. [ABSTRACT FROM AUTHOR]

Wireless multimedia sensor networking (WMSN) is an efficient technology and has been widely used in multimedia data transport and applications. The key to the success of multimedia data transport is the transmission quality. Real-time critical multimedia requires efficient routing for data delivery with guaranteed QoS. However, satisfying the stringent QoS requirements of multimedia transmission usually translates to high energy consumption. How to efficiently minimize the energy consumption while ensuring QoS requirements is an acute problem. Therefore, in this article, we call routing protocols that can balance out the trade-off between network lifetime and QoS requirements ?green routing protocols.? First, a detailed survey of existing routing protocols for WMSNs is presented, which are classified into two categories based on network structures. Moreover, the performance of existing routing protocols is compared in detail. Finally, future research issues of green routing protocols in WMSNs are presented and carefully discussed. [ABSTRACT FROM PUBLISHER]

Underwater acoustic sensor networks (UASNs) have been widely used in many applications where a variable number of sensor nodes collaborate with each other to perform monitoring tasks. A trust model plays an important role in realizing collaborations of sensor nodes. Although many trust models have been proposed for terrestrial wireless sensor networks (TWSNs) in recent years, it is not feasible to directly use these trust models in UASNs due to unreliable underwater communication channel and mobile network environment. To achieve accurate and energy efficient trust evaluation in UASNs, an attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper. The ARTMM mainly consists of three types of trust metrics, which are link trust, data trust, and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. Simulation results demonstrate that the proposed trust model is quite suitable for mobile underwater environment. In addition, the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption. [ABSTRACT FROM PUBLISHER]

Recently, underwater wireless sensor networks (UWSNs) have emerged as a promising networking technique for various underwater applications. An energy efficient routing protocol plays a vital role in data transmission and practical applications. However, due to the specific characteristics of UWSNs, such as dynamic structure, narrow bandwidth, rapid energy consumption, and high latency, it is difficult to build routing protocols for UWSNs. In this article we focus on surveying existing routing protocols in UWSNs. First, we classify existing routing protocols into two categories based on a route decision maker. Then the performance of existing routing protocols is compared in detail. Furthermore, future research issues of routing protocols in UWSNs are carefully analyzed. [ABSTRACT FROM PUBLISHER]

Traditional Industrial Wireless Sensor Networks (IWSNs) are constrained by limited battery energy. Recent breakthroughs in wireless power transfer have inspired the emergence of Industrial Wireless Rechargeable Sensor Networks (IWRSNs). IWRSNs usually contain one or more mobile chargers which can traverse the network to replenish energy supply for sensor nodes. The essential problem in mobile energy provisioning is to find the optimum path along which the mobile chargers travel to improve charging performance, prolong the battery lifespan of nodes and reduce the charging latency as much as possible. In this paper, we introduce and analyze the impacts of four traveling paths, namely, SCAN, HILBERT, S-CURVES(ad) and Z-curve on energy provisioning for IWRSNs. This evaluation aims to embody effective and essential properties that a superior traveling path should possess. Our simulations show that S-CURVES(ad) outperforms the other traveling paths in the lifetime of nodes and traveling efficiency. And at the same time, it has relatively small charging latency. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cross-layer optimized geographic node-disjoint multipath routing algorithm, that is, two-phase geographic greedy forwarding plus. To optimize the system as a whole, our algorithm is designed on the basis of multiple layers' interactions, taking into account the following. First is the physical layer, where sensor nodes are developed to scavenge the energy from environment, that is, node rechargeable operation (a kind of idle charging process to nodes). Each node can adjust its transmission power depending on its current energy level (the main object for nodes with energy harvesting is to avoid the routing hole when implementing the routing algorithm). Second is the sleep scheduling layer, where an energy-balanced sleep scheduling scheme, that is, duty cycle (a kind of node sleep schedule that aims at putting the idle listening nodes in the network into sleep state such that the nodes will be awake only when they are needed), and energy-consumption-based connected k-neighborhood is applied to allow sensor nodes to have enough time to recharge energy, which takes nodes' current energy level as the parameter to dynamically schedule nodes to be active or asleep. Third is the routing layer, in which a forwarding node chooses the next-hop node based on 2-hop neighbor information rather than 1-hop. Performance of two-phase geographic greedy forwarding plus algorithm is evaluated under three different forwarding policies, to meet different application requirements. Our extensive simulations show that by cross-layer optimization, more shorter paths are found, resulting in shorter average path length, yet without causing much energy consumption. On top of these, a considerable increase of the network sleep rate is achieved. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In wireless multimedia sensor networks (WMSNs), the energy consumption of multimedia data type are much higher than that of traditional wireless sensor networks (WSNs). Heavy multimedia data relaying operation causes not only single sensor node dead early, but also 'hot spots' problem. In this paper, a novel energy efficient data gathering scheme with a mobile sink for WMSNs, BTDGS, is proposed. It is based on a virtual binary-tree infrastructure. The mobile sink moves along a predefined circle trajectory, and sensor nodes relay data packages in a greedy manner. The process of BTDGS data gathering includes sink location broadcasting phase, data collection phase, and sink leaving broadcasting phase. The simulation results show that our BTDGS is an energy effective, reliable, timely, and sojourn time adaptive data gathering scheme. It is feasible and suitable for WMSNs. [ABSTRACT FROM AUTHOR]

Fault detection is a method of crucial importance to guarantee Quality of Service (QoS) in industrial wireless sensor network (IWSN). Timely and accurate detection for fault nodes can increase the robustness of IWSN. Therefore, in order to reduce the network detection load and improve the system scalability, a cluster-based fault detection (CFD) algorithm is proposed. In this algorithm, the IWSN is clustered based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol at the beginning, and then the reliability of all cluster head nodes is verified. In each cluster, the cluster head is responsible for fault detection in it. In addition, the introduction of node credibility mechanism enables CFD algorithm to tolerate transient faults. Simulation results demonstrate that CFD algorithm has better performances in terms of network energy consumption and detection accuracy. [ABSTRACT FROM AUTHOR]

Trust models have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs). Considerable research has been done on modeling trust. However, most current research work only takes communication behavior into account to calculate sensor nodes’ trust value, which is not enough for trust evaluation due to the widespread malicious attacks. In this paper, we propose an Efficient Distributed Trust Model (EDTM) for WSNs. First, according to the number of packets received by sensor nodes, direct trust and recommendation trust are selectively calculated. Then, communication trust, energy trust and data trust are considered during the calculation of direct trust. Furthermore, trust reliability and familiarity are defined to improve the accuracy of recommendation trust. The proposed EDTM can evaluate trustworthiness of sensor nodes more precisely and prevent the security breaches more effectively. Simulation results show that EDTM outperforms other similar models, e.g., NBBTE trust model. [ABSTRACT FROM AUTHOR]

When setting up an underwater acoustic sensor network (UASN), node deployment is the first and foremost task, upon which many fundamental network services, such as network topology control, routing, and boundary detection, will be built. While node deployment in 2-D terrestrial wireless sensor networks has been extensively studied, little attention has been received by their 3-D counterparts. This paper aims at analyzing the impacts of node deployment strategies on localization performances in a 3-D environment. More specifically, the simulations conducted in this paper reveal that the regular tetrahedron deployment scheme outperforms the random deployment scheme and the cube deployment scheme in terms of reducing localization error and increasing localization ratio while maintaining the average number of neighboring anchor nodes and network connectivity. Given the fact that random deployment is the primary choice for most of practical applications to date, our results are expected to shed some light on the design of UASNs in the near future. [ABSTRACT FROM PUBLISHER]