Sparse Wireless Sensor Networks Research Articles

Excess Emission Sources Identification via Sparse Monitoring Networks

Abnormality detection and identification is of great concern for complex processes monitoring. In chemical industrial parks (CIPs), the primary task is to identify abnormal (excess) sources when unexpected excess emissions occur. However, it is an ill-posed problem to detect, especially to identify them in dense industrial areas where a vast number of emission sources are concentrated in limited space, challenging the relatively sparse wireless sensor networks (WSNs). Meanwhile, barely detecting the existence rather than identifying excess sources can hardly meet the requirements of fine management in CIPs. In this paper, a QR decomposition-based method for excess sources identification (QR-ESI) has been proposed. By introducing equivalent sources (ES) as a substitute for real sources (RS), the semi-independent relation between ES and RS has been developed. By monitoring ES, excess sources can be inferred progressively through the iteration of logical judgments. The performance was evaluated with simulated data and then validated and compared with a state-of-the-art method in a case study in a real-world chemical industrial park.

On transmission range of sensors in sparse wireless sensor networks

One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Game of Sensors: An Energy-Efficient Method to Enhance Network Lifetime in Wireless Sensor Networks Using the Game of Life Cellular Automaton

Wireless sensor networks (WSNs) have increased in popularity since they play a relevant role in many applications, such as environmental monitoring, fire detection, and health care, to mention a few. These applications periodically collect data that is relayed to a single sink employing a many-to-one communication pattern. This pattern requires energy-efficient routing protocols since sensors closer to the sink node deplete their energy faster than those sensors located further away. As a result, several techniques have been proposed to solve this problem. For instance, some pieces of research split the network into concentric coronas to provide more energy resources in areas with heavier traffic. However, these techniques use either a predefined network deployment that is not well-suited for all sensor applications or do not always guarantee homogeneous energy consumption. This paper proposes a simple energy-aware routing method based on the Game of Life cellular automaton, which provides a homogeneous energy depletion while extending the network lifespan by considering factors such as residual energy, number of active neighbors, and a sleep schedule. To this end, a discrete dynamic model that takes into account different behaviors of WSN through a set of rules combined with a variation of the A-star algorithm is proposed. Simulation results show that the proposed model accurately balances the energy consumption rate while expanding the network lifetime compared to most recent research works. Furthermore, the proposed method can be combined with path-planning algorithms to improve energy consumption in sparse WSNs.

Beyond RSS: A PRR and SNR Aided Localization System for Transceiver-free Target in Sparse Wireless Networks

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.

Fast Consensus-Based Time Synchronization Protocol Using Virtual Topology for Wireless Sensor Networks

Slow convergence is a major drawback of average-based consensus time synchronization protocols, particularly in large or sparse wireless sensor networks. The convergence speed can be increased by adding more nodes or increasing the transmission range of nodes, because the network becomes strongly connected. However, these solutions are not always feasible owing to hardware constraints. In this article, a virtual topology-based time synchronization protocol (VTSP) is proposed to address the aforementioned drawback of consensus-based protocols. Notably, VTSP performs the consensus process on a virtual topology that has a stronger algebraic connectivity than a physical one. Therefore, VTSP can significantly accelerate convergence speed without modifying the physical structure of the network. The virtual topology is formed by creating virtual links between each node and its two-hop neighbors. Moreover, two optimization techniques are suggested for reducing data redundancy, which is caused during the creation of the virtual links, in timing messages and for speeding up the convergence by excluding edge nodes from the consensus process. Simulation results demonstrate that VTSP can achieve convergence three times faster than the gradient time synchronization protocol, a well-known consensus-based time synchronization protocol, in various topologies while preserving the same level of accuracy.

Sensing Coverage Algorithm of Sparse Mobile Sensor Node with Trade-Off between Packet Loss Rate and Transmission Delay

To solve the problem of sensing coverage of sparse wireless sensor networks, the movement of sensor nodes is considered and a sensing coverage algorithm of sparse mobile sensor node with trade-off between packet loss rate and transmission delay (SCA_SM) is proposed. Firstly, SCA_SM divides the monitoring area into several grids of same size and establishes a path planning model of multisensor nodes’ movement. Secondly, the social foraging behavior of Escherichia coli in bacterial foraging is used. A fitness function formula of sensor nodes’ moving paths is proposed. The optimal moving paths of all mobile sensor nodes which can cover the entire monitoring area are obtained through the operations of chemotaxis, replication, and migration. The simulation results show that SCA_SM can fully cover the monitoring area and reduce the packet loss rate and data transmission delay in the process of data transmission. Under certain conditions, SCA_SM is better than RAND_D, HILBERT, and TCM.

Multi-hop consensus time synchronization algorithm for sparse wireless sensor network: A distributed constraint-based dynamic programming approach

The recent consensus time synchronization algorithms designed for Wireless Sensor Networks (WSNs) are mostly one-hop in nature, i.e., every node communicates with its one-hop neighbors and performs clock parameters averaging to reach to the consensus or synchronized state. As per consensus theory, apart from the averaging scheme employed by the consensus algorithm, another factor that affects the consensus-based algorithms’ performance is the topological connectivity of the networks. In topologies of lower degree of connectivity like sparse network, these one-hop consensus synchronization algorithms have exhibited poor performance in terms of convergence speed and synchronization accuracy. This motivates the design of multi-hop consensus time synchronization algorithm for sparse WSNs. In this paper, we have proposed a multi-hop consensus time synchronization algorithm, multi-hop Selective Average Time Synchronization (SATS), for sparse and multi-hop WSNs. A distributed, constraint-based dynamic programming approach is suggested for multi-hop clock parameters estimation. Simulation results show that the proposed algorithm outperforms some recent, state-of-the-art one-hop consensus time synchronization algorithms within a restricted hop count.

A Mechanism Filling Sensing Holes for Detecting the Boundary of Continuous Objects in Hybrid Sparse Wireless Sensor Networks

Nowadays, the rapidly developed Internet of Things requires the ability handling information efficiently to deal with the intelligent applications. Wireless sensor networks (WSNs), which act as an important interface between physical environment and Internet of Things, have been applied in numerous applications. As a kind of important application of WSNs, the continuous objects boundary detection is popular in industry. However, the long-term maintenance for the traditional WSNs, which are used to monitor the leakage of continuous objects, is expensive. Thus, we use sparse WSNs to address this issue. But, the inaccuracy of the sparse network is a big problem while the information of continuous objects is used to arrange retreat path for people. To access this problem, we propose our mechanism, which used hybrid network to compromise the accuracy and cost of maintenance. The sensing holes will be detected by using Voronoi diagram, before the network starts to work. After the static sensor nodes get the value of the toxic air, the mechanism can calculate the high variation location, which give weights to the sensing holes, in the static sensor networks. Thus, the sensing holes, which selected by both spatial and data variation factors will be list in a target nodes list for the mobile sensor node. Finally, the optimal path considering both distance and priority for the mobile sensor will be plan out. Experimental evaluation shows that there is an optimal amount of the static nodes decided by the sensing radius and the size of area. And it reduces the energy consumption by the static networks.

Lightweight trust model for the detection of concealed malicious nodes in sparse wireless ad hoc networks

A sparse wireless sensor network for forest fire detection is considered. It is assumed that two types of malicious nodes can exist in this network. The malicious behaviors are assumed to be concealed through some statistical behavior. A lightweight centralized trust-based model is proposed to detect malicious or misbehaving nodes. We assume that all nodes contribute to this process through the gathering of statistical data related to communication with their neighbors. These data are periodically sent to a base station, where all trust functions are executed. A simulation model is built to evaluate system performance, and the results show that the proposed model is efficient in detecting all types of considered malicious nodes.

Variable ferry routing algorithm for sparse wireless sensor networks

In this paper, a ferry-based sparse wireless sensor network is considered, in which information is transferred between ferries and sensors. And a triangulation algorithm and triangular mesh colouring strategy for constructing a ferry route are presented. A moving ferry, while moving along the predetermined route, can cover the entire network, and the related theorems are presented. We propose a variable ferry routing scheme (VFR) in order to reduce the energy consumption of ferries. Through theoretical analysis, the relationship is discussed between key parameters of the system, including message generation interval, message delivery probability and the number of ferries. During the routing process, the number of active ferries is adjusted. Compared to other existing routing algorithms, the simulation results of our scheme demonstrate that VFR minimises the message delivery delay and the overhead radio. Moreover, VFR reduces the energy consumption of ferries.

Building structural health monitoring using dense and sparse topology wireless sensor network

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node\'s size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

Event‐driven probabilistic topology management in sparse wireless sensor network

In a sparse wireless sensor network (WSN), event parameters such as event location and affected area cannot be directly estimated from the sensor observations, as the average distortion of sensor observations, because of the sparsity of network, is greater than in the dense ones. Moreover, observations of all the nodes which detect an event do not have the same significance in the estimation of event parameters. The authors propose a sensor collaboration scheme, named probabilistic event monitoring in sparse network (PEMS), which assists, in a distributed manner, the sensor nodes with significant observations to participate in the event monitoring process. The proposed collaboration scheme, PEMS, is based on the principle of Monte Carlo methods. Further, the event monitoring nodes restructure the network topology with the help of facility location theory for efficient energy management. Extensive simulations show that the event parameters with nominal distortion can be estimated from the observations transmitted by the PEMS-assisted event-monitoring nodes and those nodes can enhance their energy efficiency at the time of event monitoring.

Distributed Byzantine fault detection technique in wireless sensor networks based on hypothesis testing

Distributed fault detection in wireless sensor network is an important problem where every sensor node identifies its own fault status based on the information from its neighboring sensor nodes. This paper presents a novel distributed fault detection algorithm to detect the soft faulty sensor nodes in sparse wireless sensor networks. In the proposed scheme, every sensor node gathers the information only from their neighboring nodes in order to reduce the communication overhead. The Neyman–Pearson testing method is used to predict the fault status of each sensor node and the neighboring sensor nodes. A voting scheme is applied on the fault status information to obtain the final fault status of each sensor node. The generic parameters such as detection accuracy, false alarm rate, time complexity, message complexity, detection latency, network life time and energy consumption are considered to evaluate the performance of proposed scheme analytically as well as through simulation. The result shows that the proposed scheme significantly improves the performance over the existing algorithms.

Autonomous Deployment for Load Balancing <inline-formula> <tex-math notation="TeX">$k$</tex-math></inline-formula>-Surface Coverage in Sensor Networks

Although the problem of $k$ -area coverage has been intensively investigated for dense wireless sensor networks (WSNs), how to arrive at a $k$ -coverage sensor deployment that optimizes certain objectives in relatively sparse WSNs still faces both theoretical and practical difficulties. Moreover, only a handful of centralized algorithms have been proposed to elevate 2-D area coverage to 3-D surface coverage. In this paper, we present a practical algorithm, i.e., the Autonomous dePlOyment for Load baLancing $k$ -surface cOverage (APOLLO), to move sensor nodes toward $k$ -surface coverage, aiming at minimizing the maximum sensing range required by the nodes. APOLLO enables purely autonomous node deployment as it only entails localized computations. We prove the termination of the algorithm and the (local) optimality of the output. We also show that our optimization objective is closely related to other frequently considered objectives for 2-D area coverage. Therefore, our practical algorithm design also contributes to the theoretical understanding of the 2-D $k$ -area coverage problem. Finally, we use extensive simulation results to both confirm our theoretical claims and demonstrate the efficacy of APOLLO.

Information weighted consensus‐based distributed particle filter for large‐scale sparse wireless sensor networks

To the problem of information fusion estimation for large-scale sparse wireless sensor networks (WSNs), a novel algorithm, named the information weighted consensus-based distributed particle filter, is presented. The proposed filter can avoid the divergence of the consensus error introduced by the naive nodes in the large-scale sparse WSNs. This is achieved by embedding an information weighted local particle filter (LPF) and a weighted-average consensus filter as the underlying filter and the top filter, respectively in each sensor node. The information weighted LPF will enable the weighted-average consensus filter to be used in the information space to communicate the information matrix and information state in a distributed fashion. And the weighted-average consensus filter will guarantee that a weighted average consensus for all initial states can be reached with some consensus error, which will not be divergent. Moreover, at the same time, the cross correlation between each pair of networked nodes can be approximately computed, which will further inhibit the divergence among the local estimated states of the filters embedded in each node. Finally, some examples are presented to illustrate the reasonability of the theoretical derivation.

Using Mobile Elements as Dynamic Bridges in Sparse Wireless Sensor Networks

Sparse Wireless Sensor Networks using several mobile nodes and a small number of static sensor nodes have been widely used for many applications, especially for traffic-generated pollution monitoring. This paper proposes a method for data collection and forwarding using Mobile Elements (MEs), which are moving on predefined trajectories in contrast to previous works that use a mixture of MEsand static nodes. In our method, MEscan be used as data collector as well as dynamic bridges for data transfer. We design the trajectories in such a way, that they completely cover the deployed area and data will be gradually forwarded from outermost trajectories to the center whenever a pair of MEs contacts each other on an overlapping road distance of respective trajectories. The method is based on direction-oriented level and weight assignment. We analyze the contact opportunity for data exchange while MEs move. The method has been successfully tested for traffic pollution monitoring in an urban area.

Energy-constrained ferry route design for sparse wireless sensor networks

In recent years, using message ferries as mechanical carriers of data has been shown to be an effective way to collect information in sparse wireless sensor networks. As the sensors are far away from each other in such highly partitioned scenario, a message ferry needs to travel a long route to access all the sensors and carry the data collected from the sensors to the sink. Typically, practical constraints (e.g., the energy) preclude a ferry from visiting all sensors in a single tour. In such case, the ferry can only access part of the sensors in each tour and move back to the sink to get the energy refilled. So, the energy-constrained ferry route design (ECFRD) problem is discussed, which leads to the optimization problem of minimizing the total route length of the ferry, while keeping the route length of each tour below a given constraint. The ECFRD problem is proved to be NP-hard problem, and the integer linear programming (ILP) formulation is given. After that, efficient heuristic algorithms are proposed to solve this problem. The experimental results show that the performances of the proposed algorithms are effective in practice compared to the optimal solution.

Joint k -coverage and data gathering in sparsely deployed sensor networks -- Impact of purposeful mobility and heterogeneity

Coverage is one of the fundamental concepts in the design of wireless sensor networks (WSNs) in the sense that the monitoring quality of a phenomenon depends on the quality of service provided by the sensors in terms of how well a field of interest is covered. It enables the sensors to detect any event that may occur in the field, thus, meeting the application-specific requirements. Several applications require k - coverage , where each point in the field is covered by at least k sensors, which helps increase data availability to ensure better data reliability. Achieving k -coverage of a field of interest becomes a more challenging issue in sparsely deployed WSNs. Though the problem of coverage in WSNs has been well studied in the literature, only little research efforts have been devoted to the case of sparsely deployed WSNs. Thus, in this article, we investigate the problem of k -coverage in sparse WSNs using static and mobile sensors, which do not necessarily have the same communication range, sensing range, and energy supply. Precisely, we propose an optimized, generalized framework for k -coverage in sparsely deployed WSNs, called k -SCHEMES, which exploits sensor heterogeneity and mobility. First, we characterize k -coverage using heterogeneous sensors based on Helly 's Theorem. Second, we introduce our energy-efficient four-tier architecture to achieve mobile k -coverage of a region of interest in a field. Third, on top of this architecture, we suggest two data-gathering protocols, called direct data-gathering and forwarding chain-based data-gathering, using the concept of mobile proxy sink. We found that the second data-gathering protocol outperforms the first one. For energy-efficient forwarding, we compute the minimum transmission distance between any pair of consecutive mobile proxy sinks forming the forwarding chain as well as the corresponding optimum number of mobile proxy sinks in this chain. We corroborate our analysis with several simulation results.

An Angle Partitioning Based Algorithm for the ECFRD Problem in Wireless Sensor Network

Energy-Constrained Ferry Route Design (ECFRD) Problem is an NP-hard problem to minimize the total route length of a message ferry to access all the sensor nodes in a sparse wireless sensor network, while the route length of a tour under a given value due to the energy constraint. In this paper, we propose an angle partitioning based algorithm (APBA) to solve the ECFRD problem. In APBA, the nodes are partitioned into groups according to the tangent angles of their coordinates, and the route length of each group will not exceed the energy constraint. The experimental results show that APBA can greatly reduce the total route length of the ferry. In the best case, 35% of the total route length can be saved, comparing previous nearest neighbor based split and route algorithms.

An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks

In sparse wireless sensor networks, a mobile robot is usually exploited to collect the sensing data. Each sensor has a limited transmission range and the mobile robot must get into the coverage of each sensor node to obtain the sensing data. To minimize the energy consumption on the traveling of the mobile robot, it is significant to plan a data collection path with the minimum length to complete the data collection task. In this paper, we observe that this problem can be formulated as traveling salesman problem with neighborhoods, which is known to be NP-hard. To address this problem, we apply the concept of artificial bee colony (ABC) and design an ABC-based path planning algorithm. Simulation results validate the correctness and high efficiency of our proposal.