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The aim of this paper is to discuss the usability of vibrations as energy sources, for the implementation of energy self-sufficient wireless sensing platforms within the Industrial Internet of Things (IIoT) framework. In this context, this paper proposes to equip vibrating assets like machinery with piezoelectric sensors, used to set up energy self-sufficient sensing platforms for hard-to-reach positions. Preliminary measurements as well as extended laboratory tests are proposed to understand the behavior of commercial piezoelectric sensors when employed as energy harvesters. First, a general architecture for a vibration-powered LoRaWAN-based sensor node is proposed. Final tests are then performed to identify an ideal trade-off between sensor sampling rates and energy availability. The target is to ensure continuous operation of the device while guaranteeing a charging trend of the storage component connected to the system. In this context, an Ultra-Low-Power Energy-Harvesting Integrated Circuit plays a crucial role by ensuring the correct regulation of the output with very high efficiency. [ABSTRACT FROM AUTHOR]

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of −24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management. [ABSTRACT FROM AUTHOR]

Converting otherwise wasted kinetic energy present in the environment into usable electrical energy to power wireless sensor nodes, is a green strategy to avoid the use of batteries and wires. Most of the energy harvesters presented in the literature are based on the exploitation of a one-degree-of-freedom arrangement, consisting of a tuned spring-mass system oscillating in the main direction of the exciting vibration source. However, if the direction of excitation changes, the efficiency of the harvester decreases. This paper thus proposes the idea of a curved cantilever beam with a two-degree-of-freedom arrangement, where the two bending natural frequencies of the mechanical resonator are designed to be equal. This is thought to lead to a configuration design that can be used in practical circumstances where excitation varies its direction in the plane. This, in turn, may possibly lead to a more effective energy-harvesting solution to power nodes in a wireless sensor network. [ABSTRACT FROM AUTHOR]

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined. [ABSTRACT FROM AUTHOR]

Multihop transmission is one of the important techniques to overcome the transmission coverage of each node in wireless sensor networks (WSNs). However, multihop transmission has a security issue due to the nature of a wireless medium. Additionally, the eavesdropper also attempts to interrupt the legitimate users' transmission. Thus, in this paper, we study the secrecy performance of a multihop transmission under various eavesdropping attacks for WSNs. To improve the secrecy performance, we propose two node selection schemes in each cluster, namely, minimum node selection (MNS) and optimal node selection (ONS) schemes. To exploit the impact of the network parameters on the secrecy performance, we derive the closed-form expression of the secrecy outage probability (SOP) under different eavesdropping attacks. From the numerical results, the ONS scheme shows the most robust secrecy performance compared with the other schemes. However, the ONS scheme requires a lot of channel information to select the node in each cluster and transmit information. On the other side, the MNS scheme can reduce the amount of channel information compared with the ONS scheme, while the MNS scheme still provides secure transmission. In addition, the impact of the network parameters on the secrecy performance is also insightfully discussed in this paper. Moreover, we evaluate the trade-off of the proposed schemes between secrecy performance and computational complexity. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT) and wireless sensor networks (WSN), the modern world requires advanced solutions for the wireless powering of low-power autonomous devices. The present study addresses the wireless power transfer (WPT) efficiency problem by exploiting a multi-hop concept-based technique to increase the received power at the end sensor node (ESN). The current work adopts efficient multi-hop technology from the communications field to examine its impact on WPT performance. The investigation involves power transfer modeling and experimental measurements in a sub-GHz frequency range, chosen for being capable of providing a greater distance to transmit power. The paper proposes a multi-hop (MH) WPT concept based on signal amplification and demonstrates the fabricated multi-hop node (MHN) prototype. The experimental verification of the MHN is performed in the laboratory environment. The present paper examines two WPT scenarios: line-of-sight (LoS) and non-line-of-sight (NLoS). The turn-on angle of 90 degrees on MHN is used for the NLoS case. The received power and RF-DC converted voltage on the ESN are measured for all investigated scenarios. Moreover, the paper proposes an efficient simulation approach for the performance evaluation of MH WPT technology, providing an opportunity to analyze and optimize wireless sensor nodes' spatial distribution to increase the received power. [ABSTRACT FROM AUTHOR]

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node's power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery's lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems. [ABSTRACT FROM AUTHOR]

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are widely used in industrial applications. However, many of them have limited lifetimes, which has been a considerable constraint on their widespread use. As a typical application of WSNs, distributed measurement of the electric field under high-voltage direct-current (HVDC) transmission lines also suffers from this issue. This paper first introduces the composition of the electric-field measurement system (EFMS) and its working principle. Considering the actual power supply of the system, this paper mainly introduces the composition of the wireless sensor node (WSND) and analyzes the power consumption and potential working state transformation of the WSND, together with a comprehensive study on parameters affecting the power consumption of the wireless communication unit. Moreover, an energy-efficient scheduling approach is proposed after specially designing a working sequence and the study on system parameters. The proposed approach is verified by experiments on not only the experimental line of the national HVDC test base, but also a commercial operation HVDC transmission line with the challenge of long endurance, which is considered in this paper with a new strategy. The results show that the proposed method can greatly extend the lifetime of the WSND. [ABSTRACT FROM AUTHOR]

Current modulation recognition methods in wireless sensor networks rely too much on simulation datasets. Its practical application effect cannot reach the expected results. To address this issue, in this paper we collect a large amount of real-world wireless signal data based on the software radio device USRP 2920. We then propose a real radio frequency (RF) database architecture and preprocessing operators to manage real-world wireless signal data, conduct signal preprocessing, and export the dataset. Based on different feature datasets derived from the RF database, we propose a multidimensional feature hybrid network (MFHN), which is used to identify unknown signals by analyzing different kinds of signal features. Further, we improve MFHN and design a multifeatured joint migration network (MJMN) to identify small-sample targets. The experimental results show that the recognition rates for unknown target signals of the MFHN and MJMN are 82.7% and 93.2%, respectively. The proposed methods improve the recognition performance in the single node of wireless sensor networks in complex electromagnetic environments, which provides reference for subsequent decision fusion. [ABSTRACT FROM AUTHOR]

The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for judging whether an adjacent pair of sensors are consistent in terms of their readings. Inspired by the system-level fault diagnosis, we introduce a set of watchdogs into a WSN as comparators between adjacent pairs of sensors of the WSN, and we propose an algorithm for diagnosing the WSN based on the collection of the consistency outcomes. Simulation results show that the proposed diagnosis scheme achieves a higher probability of correct diagnosis. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC–DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices. [ABSTRACT FROM AUTHOR]

This article proposes a queueing model of the operation of a wireless sensor network node, in which a threshold strategy for starting the node after a period of no transmission is used. In this model, transmission of packets is resumed when the number of packets in the accumulation buffer reaches a predefined level. In the literature, most of the results for models with limited access to the service station are obtained in equilibrium. In this paper, a formula for the Laplace transform of the transient queue-size distribution is obtained and written using the key input parameters of the system. The analytical apparatus uses the concept of the embedded Markov chain, the formula for total probability, renewal theory and some supporting algebraic results. Numerical examples are attached as well. [ABSTRACT FROM AUTHOR]

Graph theory is a useful mathematical structure used to model pairwise relations between sensor nodes in wireless sensor networks. Graph equations are nothing but equations in which the unknown factors are graphs. Many problems and results in graph theory can be formulated in terms of graph equations. In this paper, we solved some graph equations of detour two-distance graphs, detour three-distance graphs, detour antipodal graphs involving with the line graphs. [ABSTRACT FROM AUTHOR]

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme. [ABSTRACT FROM AUTHOR]

This paper presents a price-searching model in which a source node (Alice) seeks friendly jammers that prevent eavesdroppers (Eves) from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations and are allowed to access Alice's channel if it can transmit sufficient jamming power, which is referred to as collaborative jamming in this paper. For the power used to deliver its own signal, the jammer should pay Alice. The price of the jammers' signal power is set by Alice and provides a tradeoff between the signal and the jamming power. This paper presents, in closed-form, an optimal price that maximizes Alice's benefit and the corresponding optimal power allocation from a jammers' perspective by assuming that the network-wide channel knowledge is shared by Alice and jammers. For a multiple-jammer scenario where Alice hardly has the channel knowledge, this paper provides a distributed and interactive price-searching procedure that geometrically converges to an optimal price and shows that Alice by a greedy selection policy achieves certain diversity gain, which increases log-linearly as the number of (potential) jammers grows. Various numerical examples are presented to illustrate the behavior of the proposed model. [ABSTRACT FROM AUTHOR]

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence, their effects need to be properly assessed before deployment, to obtain a good level of expected performance; and during the operation, in order to avoid dangerous unexpected results. In this paper, we propose a methodology that aims at assessing and improving the dependability level of WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool is applicable to homogeneous WSNs with static routing topologies. It allows the automatic generation of formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime for WSNs where an acceptable percentage of faults can be defined. During the runtime, we can check the behavior of the WSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, as proof-of-concept, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of the WSN at runtime. Although the method scales up to very large WSNs, the applicability of the methodology may be compromised by the state space explosion of the reasoning model, which must be faced by partitioning large topologies into sub-topologies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k-1 nodes while the rest of nodes remain connected, the network is called k-connected. k is one of the most important indicators for WSNs' self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k-connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs. [ABSTRACT FROM AUTHOR]

Technological advances have led to the emergence of wireless sensor nodes in wireless networks. Sensor nodes are usually battery powered and hence have strict energy constraints. As a result, energy conservation is very important in the wireless sensor network protocol design and the limited power resources are the biggest challenge in wireless network channels. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper we present an adaptive packet size strategy for energy efficient wireless sensor networks. The main goal is to reduce power consumption and extend the whole network life. In order to achieve this goal, the paper introduces the concept of a bounded MAB to find the optimal packet size to transfer by formulating different packet sizes for different arms under the channel condition. At the same time, in achieve fast convergence, we consider the bandwidth evaluation according to ACK. The experiment shows that the packet size is adaptive when the channel quality changes and our algorithm can obtain the optimal packet size. We observe that the MAB packet size adaptation scheme achieves the best energy efficiency across the whole simulation duration in comparison with the fixed frame size scheme, the random packet size and the extended Kalman filter (EKF). [ABSTRACT FROM AUTHOR]

In this paper, the optimal working schemes for wireless sensor networks with multiple base stations and wireless energy transfer devices are proposed. The wireless energy transfer devices also work as data gatherers while charging sensor nodes. The wireless sensor network is firstly divided into sub networks according to the concept of Voronoi diagram. Then, the entire energy replenishing procedure is split into the pre-normal and normal energy replenishing stages. With the objective of maximizing the sojourn time ratio of the wireless energy transfer device, a continuous time optimization problem for the normal energy replenishing cycle is formed according to constraints with which sensor nodes and wireless energy transfer devices should comply. Later on, the continuous time optimization problem is reshaped into a discrete multi-phased optimization problem, which yields the identical optimality. After linearizing it, we obtain a linear programming problem that can be solved efficiently. The working strategies of both sensor nodes and wireless energy transfer devices in the pre-normal replenishing stage are also discussed in this paper. The intensive simulations exhibit the dynamic and cyclic working schemes for the entire energy replenishing procedure. Additionally, a way of eliminating "bottleneck" sensor nodes is also developed in this paper. [ABSTRACT FROM AUTHOR]

The impact of air quality on health and on life comfort is well established. In many societies, vulnerable elderly and young populations spend most of their time indoors. Therefore, indoor air quality monitoring (IAQM) is of great importance to human health. Engineers and researchers are increasingly focusing their efforts on the design of real-time IAQM systems using wireless sensor networks. This paper presents an end-to-end IAQM system enabling measurement of CO2, CO, SO2, NO2, O3, Cl2, ambient temperature, and relative humidity. In IAQM systems, remote users usually use a local gateway to connect wireless sensor nodes in a given monitoring site to the external world for ubiquitous access of data. In this work, the role of the gateway in processing collected air quality data and its reliable dissemination to end-users through a web-server is emphasized. A mechanism for the backup and the restoration of the collected data in the case of Internet outage is presented. The system is adapted to an open-source Internet-of-Things (IoT) web-server platform, called Emoncms, for live monitoring and long-term storage of the collected IAQM data. A modular IAQM architecture is adopted, which results in a smart scalable system that allows seamless integration of various sensing technologies, wireless sensor networks (WSNs) and smart mobile standards. The paper gives full hardware and software details of the proposed solution. Sample IAQM results collected in various locations are also presented to demonstrate the abilities of the system. [ABSTRACT FROM AUTHOR]

Tracking a mobile target, which aims to timely monitor the invasion of specific target, is one of the most prominent applications in wireless sensor networks (WSNs). Traditional tracking methods in WSNs only based on static sensor nodes (SNs) have several critical problems. For example, to void the loss of mobile target, many SNs must be active to track the target in all possible directions, resulting in excessive energy consumption. Additionally, when entering coverage holes in the monitoring area, the mobile target may be missing and then its state is unknown during this period. To tackle these problems, in this paper, a few mobile sensor nodes (MNs) are introduced to cooperate with SNs to form a hybrid WSN due to their stronger abilities and less constrained energy. Then, we propose a valid target tracking scheme for hybrid WSNs to dynamically schedule the MNs and SNs. Moreover, a novel loss recovery mechanism is proposed to find the lost target and recover the tracking with fewer SNs awakened. Furthermore, to improve the robustness and accuracy of the recovery mechanism, an adaptive unscented Kalman filter (AUKF) algorithm is raised to dynamically adjust the process noise covariance. Simulation results demonstrate that our tracking scheme for maneuvering target in hybrid WSNs can not only track the target effectively even if the target is lost but also maintain an excellent accuracy and robustness with fewer activated nodes. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is emerging as an innovative method for gathering information that will significantly improve the reliability and efficiency of infrastructure systems. Broadcast is a common method to disseminate information in WSNs. A variety of counter-based broadcast schemes have been proposed to mitigate the broadcast-storm problems, using the count threshold value and a random access delay. However, because of the limited propagation of the broadcast-message, there exists a trade-off in a sense that redundant retransmissions of the broadcast-message become low and energy efficiency of a node is enhanced, but reachability become low. Therefore, it is necessary to study an efficient counter-based broadcast scheme that can dynamically adjust the random access delay and count threshold value to ensure high reachability, low redundant of broadcast-messages, and low energy consumption of nodes. Thus, in this paper, we first measure the additional coverage provided by a node that receives the same broadcast-message from two neighbor nodes, in order to achieve high reachability with low redundant retransmissions of broadcast-messages. Second, we propose a new counter-based broadcast scheme considering the size of the additional coverage area, distance between the node and the broadcasting node, remaining battery of the node, and variations of the node density. Finally, we evaluate performance of the proposed scheme compared with the existing counter-based broadcast schemes. Simulation results show that the proposed scheme outperforms the existing schemes in terms of saved rebroadcasts, reachability, and total energy consumption. [ABSTRACT FROM AUTHOR]

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. [ABSTRACT FROM AUTHOR]

Round Robin based Intermittent Periodic Transmit (RR-IPT) has been proposed which achieves highly efficient multi-hop relays in multi-hop wireless backhaul networks (MWBN) where relay nodes are 2-dimensionally deployed. This paper newly investigates multi-channel packet scheduling and forwarding scheme for RR-IPT. Downlink traffic is forwarded by RR-IPT via one of the channels, while uplink traffic and part of downlink are accommodated in the other channel. By comparing IPT and carrier sense multiple access with collision avoidance (CSMA/CA) for uplink/downlink packet forwarding channel, IPT is more effective in reducing packet loss rate whereas CSMA/CA is better in terms of system throughput and packet delay improvement. [ABSTRACT FROM AUTHOR]

Most Internet of Things (IoT) systems are based on the wireless sensor network (WSN) due to the reduction of the cable layout cost. However, the battery life of nodes is a key issue when the node is powered by a battery. A Low-Power WSN Protocol with ADR and TP Hybrid Control is proposed in this paper to improve battery life significantly. Besides, techniques including the Sub-1GHz star topology network with Time Division Multiple Access (TDMA), adaptive data rate (ADR), and transmission power control (TPC) are also used. The long-term testing results show that the nodes with the proposed algorithm can balance the communication quality and low power consumption simultaneously. The experimental results also show that the power consumption of the node with the algorithm was reduced by 38.46-54.44% compared with the control group. If using AAA battery with 1200 mAh, the node could run approximately 4.2 years with the proposed hybrid control algorithm with an acquisition period of under 5 s. [ABSTRACT FROM AUTHOR]

This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance solutions of a rail trackside typically consist of a large number of integrated sensing systems, which greatly benefit from autonomous source of energy. Although the amount of energy provided by conventional energy harvesting devices is usually only around several milliwatts, it is sufficient as a source of electrical power for low power sensing devices. The main aim of this paper is to design and test a kinetic electromagnetic energy harvesting system that could use energy from a passing train to deliver sufficient electrical power for sensing nodes. Measured mechanical vibrations of regional and express trains were used in laboratory testing of the developed energy harvesting device with an integrated resistive load and wireless transmission system, and based on these tests the proposed technology shows a high potential for railway applications. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management. [ABSTRACT FROM AUTHOR]

Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes. [ABSTRACT FROM AUTHOR]

The problem of finding the number and optimal positions of relay nodes for restoring the network connectivity in partitioned Wireless Sensor Networks (WSNs) is Non-deterministic Polynomial-time hard (NP-hard) and thus heuristic methods are preferred to solve it. This paper proposes a novel polynomial time heuristic algorithm, namely, Relay Placement using Space Network Coding (RPSNC), to solve this problem, where Space Network Coding, also called Space Information Flow (SIF), is a new research paradigm that studies network coding in Euclidean space, in which extra relay nodes can be introduced to reduce the cost of communication. Unlike contemporary schemes that are often based on Minimum Spanning Tree (MST), Euclidean Steiner Minimal Tree (ESMT) or a combination of MST with ESMT, RPSNC is a new min-cost multicast space network coding approach that combines Delaunay triangulation and non-uniform partitioning techniques for generating a number of candidate relay nodes, and then linear programming is applied for choosing the optimal relay nodes and computing their connection links with terminals. Subsequently, an equilibrium method is used to refine the locations of the optimal relay nodes, by moving them to balanced positions. RPSNC can adapt to any density distribution of relay nodes and terminals, as well as any density distribution of terminals. The performance and complexity of RPSNC are analyzed and its performance is validated through simulation experiments. [ABSTRACT FROM AUTHOR]

The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. This paper proposes an algorithm to support node mobility in RPL in an energy-efficient manner and describes its operating principle based on different scenarios. The proposed algorithm supports the mobility of nodes by dynamically adjusting the transmission interval of the messages that request the route based on the speed and direction of the motion of mobile nodes, as well as the costs between neighboring nodes. The performance of the proposed algorithm and previous algorithms for supporting node mobility were examined experimentally. From the experiment, it was observed that the proposed algorithm requires fewer messages per unit time for selecting a new parent node following the movement of a mobile node. Since fewer messages are used to select a parent node, the energy consumption is also less than that of previous algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in a distributed manner. This paper proposed a robust diffusion estimation algorithm based on a minimum error entropy criterion with a self-adjusting step-size, which are referred to as the diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has a fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS algorithm with the diffusion minimum error entropy (DMEE) algorithm, an Improving DMEE-SAS algorithm is proposed for a non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms. [ABSTRACT FROM AUTHOR]

In order to ensure the reliability and credibility of the data in wireless sensor networks (WSNs), this paper proposes a trust evaluation model and data fusion mechanism based on trust. First of all, it gives the model structure. Then, the calculation rules of trust are given. In the trust evaluation model, comprehensive trust consists of three parts: behavior trust, data trust, and historical trust. Data trust can be calculated by processing the sensor data. Based on the behavior of nodes in sensing and forwarding, the behavior trust is obtained. The initial value of historical trust is set to the maximum and updated with comprehensive trust. Comprehensive trust can be obtained by weighted calculation, and then the model is used to construct the trust list and guide the process of data fusion. Using the trust model, simulation results indicate that energy consumption can be reduced by an average of 15%. The detection rate of abnormal nodes is at least 10% higher than that of the lightweight and dependable trust system (LDTS) model. Therefore, this model has good performance in ensuring the reliability and credibility of the data. Moreover, the energy consumption of transmitting was greatly reduced. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more low-power sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting. [ABSTRACT FROM AUTHOR]

A wireless sensor network that consists of nodes with a sound sensor can be used to obtain context awareness in home environments. However, the limited processing power of wireless nodes offers a challenge when extracting features from the signal, and subsequently, classifying the source. Although multiple papers can be found on different methods of sound classification, none of these are aimed at limited hardware or take the efficiency of the algorithms into account. In this paper, we compare and evaluate several classification methods on a real sensor platform using different feature types and classifiers, in order to find an approach that results in a good classifier that can run on limited hardware. To be as realistic as possible, we trained our classifiers using sound waves from many different sources. We conclude that despite the fact that the classifiers are often of low quality due to the highly restricted hardware resources, sufficient performance can be achieved when (1) the window length for our classifiers is increased, and (2) if we apply a two-step approach that uses a refined classification after a global classification has been performed. [ABSTRACT FROM AUTHOR]

Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm. [ABSTRACT FROM AUTHOR]

Current generation wireless sensor routing algorithms and protocols have been designed based on a myopic routing approach, where the motes are assumed to have the same sensing and communication capabilities. Myopic routing is not a natural fit for the IoT, as it may lead to energy imbalance and subsequent short-lived sensor networks, routing the sensor readings over the most service-intensive sensor nodes, while leaving the least active nodes idle. This paper revisits the issue of energy efficiency in sensor networks to propose a clustering model where sensor devices' service delivery is mapped into an energy awareness model, used to design a clustering algorithm that finds service-aware clustering (SAC) configurations in IoT settings. The performance evaluation reveals the relative energy efficiency of the proposed SAC algorithm compared to related routing algorithms in terms of energy consumption, the sensor nodes' life span and its traffic engineering efficiency in terms of throughput and delay. These include the well-known low energy adaptive clustering hierarchy (LEACH) and LEACH-centralized (LEACH-C) algorithms, as well as the most recent algorithms, such as DECSA and MOCRN. [ABSTRACT FROM AUTHOR]

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b. [ABSTRACT FROM AUTHOR]

Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios. [ABSTRACT FROM AUTHOR]

By 2030, it is expected that a trillion things will be connected. In such a scenario, the power required for the trillion nodes would necessitate using trillions of batteries, resulting in maintenance challenges and significant management costs. The objective of this research is to contribute to sustainable wireless sensor nodes through the introduction of an energy-autonomous wireless sensor node (EAWSN) designed to be an energy-autonomous, self-sufficient, and maintenance-free device, to be suitable for long-term mass-scale internet of things (IoT) applications in remote and inaccessible environments. The EAWSN utilizes Low-Power Wide Area Networks (LPWANs) via LoRaWAN connectivity, and it is powered by a commercial photovoltaic cell, which can also harvest ambient light in an indoor environment. Storage components include a capacitor of 2 mF, which allows EAWSN to successfully transmit 30-byte data packets up to 560 m, thanks to opportunistic LoRaWAN data rate selection that enables a significant trade-off between energy consumption and network coverage. The reliability of the designed platform is demonstrated through validation in an urban environment, showing exceptional performance over remarkable distances. [ABSTRACT FROM AUTHOR]

The use of mobile nodes to collect data in a Wireless Sensor Network (WSN) has gained special attention over the last years. Some researchers explore the use of Unmanned Aerial Vehicles (UAVs) as mobile node for such data-collection purposes. Analyzing these works, it is apparent that mobile nodes used in such scenarios are typically equipped with at least two different radio interfaces. The present work presents a Dual-Stack Single-Radio Communication Architecture (DSSRCA), which allows a UAV to communicate in a bidirectional manner with a WSN and a Sink node. The proposed architecture was specifically designed to support different network QoS requirements, such as best-effort and more reliable communications, attending both UAV-to-WSN and UAV-to-Sink communications needs. DSSRCA was implemented and tested on a real UAV, as detailed in this paper. This paper also includes a simulation analysis that addresses bandwidth consumption in an environmental monitoring application scenario. It includes an analysis of the data gathering rate that can be achieved considering different UAV flight speeds. Obtained results show the viability of using a single radio transmitter for collecting data from the WSN and forwarding such data to the Sink node. [ABSTRACT FROM AUTHOR]

A number of research works has studied packet scheduling policies in energy scavenging wireless sensor networks, based on the predicted amount of harvested energy. Most of them aim to achieve energy neutrality, which means that an embedded system can operate perpetually while meeting application requirements. Unlike other renewable energy sources, solar energy has the feature of distinct periodicity in the amount of harvested energy over a day. Using this feature, this paper proposes a packet transmission control policy that can enhance the network performance while keeping sensor nodes alive. Furthermore, this paper suggests a novel solar energy prediction method that exploits the relation between cloudiness and solar radiation. The experimental results and analyses show that the proposed packet transmission policy outperforms others in terms of the deadline miss rate and data throughput. Furthermore, the proposed solar energy prediction method can predict more accurately than others by 6.92%. [ABSTRACT FROM AUTHOR]

A novel network paradigm of mobile edge computing, namely TMWSNs (two-tiered mobile wireless sensor networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, only a few works have considered the security of TMWSNs. In fact, the storage nodes, which are located at the upper layer of TMWSNs, are prone to being attacked by the adversaries because they play a key role in bridging both the sensor nodes and the sink, which may lead to the disclosure of all data stored on them as well as some other potentially devastating results. In this paper, we make a comparative study on two typical schemes, EVTopk and VTMSN, which have been proposed recently for securing Top-k queries in TMWSNs, through both theoretical analysis and extensive simulations, aiming at finding out their disadvantages and advancements. We find that both schemes unsatisfactorily raise communication costs. Specifically, the extra communication cost brought about by transmitting the proof information uses up more than 40% of the total communication cost between the sensor nodes and the storage nodes, and 80% of that between the storage nodes and the sink. We discuss the corresponding reasons and present our suggestions, hoping that it will inspire the researchers researching this subject. [ABSTRACT FROM AUTHOR]

Barrier coverage has been widely used to detect intrusions in wireless sensor networks (WSNs). It can fulfill the monitoring task while extending the lifetime of the network. Though barrier coverage in WSNs has been intensively studied in recent years, previous research failed to consider the problem of intrusion in transversal directions. If an intruder knows the deployment configuration of sensor nodes, then there is a high probability that it may traverse the whole target region from particular directions, without being detected. In this paper, we introduce the concept of crossed barrier coverage that can overcome this defect. We prove that the problem of finding the maximum number of crossed barriers is NP-hard and integer linear programming (ILP) is used to formulate the optimization problem. The branch-and-bound algorithm is adopted to determine the maximum number of crossed barriers. In addition, we also propose a multi-round shortest path algorithm (MSPA) to solve the optimization problem, which works heuristically to guarantee efficiency while maintaining near-optimal solutions. Several conventional algorithms for finding the maximum number of disjoint strong barriers are also modified to solve the crossed barrier problem and for the purpose of comparison. Extensive simulation studies demonstrate the effectiveness of MSPA. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting. [ABSTRACT FROM AUTHOR]

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths. [ABSTRACT FROM AUTHOR]