Neighboring Sensor Nodes Research Articles

Tessellation-Based Construction of Air Route for Wireless Sensor Networks Employing UAV.

In this paper, we consider a wireless sensor network consisting of an unmanned aerial vehicle (UAV) acting as a sink node and a number of sensor nodes scattered uncertainly on the ground. In the network, the UAV flies to a spatial point called point of interest and hovers to collect environmental data from neighboring sensor nodes. Then, the UAV proceeds to the next point of interest. The UAV must gather data from all the sensor nodes. On the other hand, a shorter round-trip air route of the UAV is more preferred since a battery-operated UAV needs regular recharging. To satisfy the requirement and to adhere to the recommendation as well, especially in the situation where only vague locational information about sensor nodes is available, we propose a scheme that follows three steps. First, it covers the sensor field of the wireless sensor network with three categories of hexagonal tessellations. Secondly, it establishes a point of interest at the centroid of each tile. Thirdly, it constructs an air route of the UAV, which visits every point of interest along a Hamiltonian cycle on the induced graph. Next, we develop a closed-form expression for the exact flight distance attained by the proposed scheme. For comparative evaluation, we discover some optimal schemes that minimize the flight distance by completely inspecting all patterns and corroborating the property of Hamiltonicity. The flight distance along the air route constructed by the proposed scheme is found to be only slightly longer than the flight distance yielded by an optimal scheme. Furthermore, the proposed scheme is proven to be practically valid when a common multicopter is employed as the sink node.

Fuzzy TOPSIS-based Secure Neighbor Discovery Mechanism for Improving Reliable Data Dissemination in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) being an indispensable entity of the Internet of Things (IoT) are found to be more and more widely utilized for the rapid advent of IoT environment. The reliability of data dissemination in the IoT environment completely depends on the secure neighbor discovery mechanism that are utilized for effective and efficient communication among the sensor nodes. Secure neighbor discovery mechanisms that significantly determine trustworthy sensor nodes are essential for maintaining potential connectivity and sustaining reliable data delivery in the energy-constrained self organizing WSN. In this paper, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (TOPSIS)-based Secure Neighbor Discovery Mechanism (FTOPSIS-SNDM) is proposed for estimating the trust of each sensor node in the established routing path for the objective of enhancing reliable data delivery in WSNs. This proposed FTOPSIS-SNDM is proposed as an attempt to integrate the merits of Fuzzy Set Theory (FST) and TOPSIS-based Multi-criteria Decision Making (MCDM) approach, since the discovery of secure neighbors involves the exchange of imprecise data and uncertain behavior of sensor nodes. This secure neighbor is also influenced by the factors of packet forwarding potential, delay, distance from the Base Station (BS) and residual energy, which in turn depends on multiple constraints that could be possibly included into the process of secure neighbor discovery. The simulation investigations of the proposed FTOPSIS-SNDM confirmed its predominance over the benchmarked approaches in terms of throughput, energy consumption, network latency, communication overhead for varying number of genuine and malicious neighboring sensor nodes in network.

Complete outlier detection and classification framework for WSNs based on OPTICS

Wireless Sensor Networks (WSNs) consist of multi-functional sensors with limited resources that collect information in various applications (medical, manufacturing, militarily, etc.). However, data gathered by sensors are susceptible to outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. This paper proposes a centralised outlier detection and classification approach for WSNs, which can distinguish between errors due to a faulty sensor and those due to an event. Also, it considers the spatial–temporal correlation between neighbouring sensor nodes and sensors’ data values. Our approach, titled OPTICS-K, combines the benefits of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm for clustering, the Inter-Cluster Distance (ICD) method and the K-Nearest Neighbours (KNN) algorithm for outlier detection. Furthermore, OPTICS-K uses a new method based on Kriging interpolation to classify the outliers. For evaluation, we conduct a comparison study between OPTICS-K and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the OPTICS-K outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy rate (ACC), F1_score and Area Under the Curve (AUC).

Interleaved Honeypot-Framing Model with Secure MAC Policies for Wireless Sensor Networks

The Wireless Medium Access Control (WMAC) protocol functions by handling various data frames in order to forward them to neighbor sensor nodes. Under this circumstance, WMAC policies need secure data communication rules and intrusion detection procedures to safeguard the data from attackers. The existing secure Medium Access Control (MAC) policies provide expected and predictable practices against channel attackers. These security policies can be easily breached by any intelligent attacks or malicious actions. The proposed Wireless Interleaved Honeypot-Framing Model (WIHFM) newly implements distributed honeypot-based security mechanisms in each sensor node to act reactively against various attackers. The proposed WIHFM creates an optimal Wireless Sensor Network (WSN) channel model, Wireless Interleaved Honeypot Frames (WIHFs), secure hash-based random frame-interleaving principles, node-centric honeypot engines, and channel-covering techniques. Compared to various existing MAC security policies, the proposed model transforms unpredictable IHFs into legitimate frame sequences against channel attackers. Additionally, introducing WIHFs is a new-fangled approach for distributed WSNs. The successful development of the proposed WIHFM ensures resilient security standards and neighbor-based intrusion alert procedures for protecting MAC frames. Particularly, the proposed wireless honeypot methodology creates a novel idea of using honeypot frame traps against open wireless channel attacks. The development of a novel wireless honeypot traps deals with various challenges such as distributed honeypot management principles (node-centric honeypot, secretly interleaved-framing principles, and interleaving/de-interleaving procedures), dynamic network backbone management principles (On Demand Acyclic Connectivity model), and distributed attack isolation policies. This effort provides an effective wireless attack-trapping solution in dynamic WSNs. The simulation results show the advantage of the proposed WIHFM over the existing techniques such as Secure Zebra MAC (SZ-MAC), Blockchain-Assisted Secure-Routing Mechanism (BASR), and the Trust-Based Node Evaluation (TBNE) procedure. The experimental section confirms the proposed model attains a 10% to 14% superior performance compared to the existing techniques.

An improved approach for energy consumption minimizing in WSN using Harris hawks optimization

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations.

Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

The performance of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) depends on the routing protocol and the deployment technique in modern applications. In a plethora of IoT-WSNs applications, the IoT nodes are essential equipment to prolong the network lifetime with limited resources. Data similarity-based clustering protocols exploit the temporal correlation among the neighbouring sensor nodes through the subset of data. In bendy supervision, IoT-based Software Defined WSNs provide an optimistic resolution by allowing the control logic to be separated from the sensor nodes. The benefit of this SDN-based IoT architecture, allows the unified control of the entire IoT network, making it easier to implement on-demand network management protocols and applications. To this end, in this paper, we design a Multi-hop Similarity-based Clustering framework for IoT-oriented Software-Defined wireless sensor Networks (MSCSDNs). In particular, we construct data-similar application-aware clusters in order to minimise the communication overhead. Also, we adapt inter-cluster and intra-cluster multi-hop communication using adaptive normalised least mean square and merged them with the proposed MSCSDN framework that helps prolong the network lifespan. The proposed framework is compared with the state-of-the-art approaches in terms of network lifespan, stability period, instability period, report delay, report delivery, and cluster leader nodes generations. The MSCSDN achieves optimal data accuracy concerning the collected data.

Collection tree-oriented mesh routing optimization for extending the lifetime of wireless sensor networks

Routing optimization in wireless sensor networks facilitates to reduce the overhead of the maintaining of wireless sensor networks and extend the lifetime of wireless sensor networks. Collection tree-based routing protocol, which does not require route discovery, has been widely used for low overheads of calculation and storage. However, with collection tree-based routing protocol, some nodes easily become the bottleneck points and quickly run out of the energy. To deal with this drawback, this article proposes a collection tree-oriented mesh routing strategy with cooperatively consuming the residual energy among the neighboring sensor nodes. The collection tree-oriented mesh routing is formulated into a linear programming problem with the purpose to maximize the network lifetime. By solving the optimization problem, the optimal mesh routing and data forwarding scheme is derived. Experimental simulations show that the proposed collection tree-oriented mesh routing optimization strategy can extend the network lifetime by more than 20%.

Applying of (SOM, HAC, and RBF) algorithms for data aggregation in wireless sensors networks

Wireless sensor network (WSN) is one of the most promising technologies due to its size, cost-effective nature, and its ability to easily deploy in the target environment, as well as for its entry into many sensitive applications. However, making the most of the potential of this network is very difficult due to many issues, including the data received from the sensor nodes contains a huge amount of data redundant that negatively affects the overall network performance. Recent years have witnessed an increasing interest in data aggregation technology intending to eliminate redundant data from neighboring sensor nodes before transferring to the base station, thus improve performance efficiency and increasing the wireless sensor networks lifespan. This paper focused on applying three intelligent algorithms (SOM, HAC, and RBF) and describing the impact of data aggregation strategy on WSNs through the results obtained. As well as, an accurate description of the literature that applied these algorithms. A Competitive classification accuracy has been achieved when the proposed work is implemented and tested via the intel berkeley research lab dataset.

Recursive Distributed Filter Design for 2-D Systems Over Sensor Networks: On Component-Based, Node-Wise and Dynamic Event-Triggered Scheme

In this paper, the recursive distributed filtering problem is investigated for a class of discrete shift-varying two-dimensional systems over sensor networks. To alleviate the resource consumption, a new component-based, node-wise yet dynamic event-triggered scheme is proposed to regulate data transmissions among the neighboring sensor nodes over the sensor-to-filter channels. The aim is to devise a distributed filter to ensure the existence of an upper bound on the filtering error variance and subsequently minimize such a bound at each iteration. In virtue of stochastic analysis techniques and mathematical induction principle, a recursive algorithm is developed to calculate the desired upper bound which is then locally minimized by appropriately parameterizing the filter gains. Furthermore, the effect of the event-triggered scheme on the estimation accuracy is rigorously evaluated. The validity of the established filter design strategy is finally demonstrated by a numerical simulation.

Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection.

Wireless sensor networks have found many applications in detecting events such as security threats, natural hazards, or technical malfunctions. An essential requirement for event detection systems is the long lifetime of battery-powered sensor nodes. This paper introduces a new method for prolonging the wireless sensor network’s lifetime by reducing data transmissions between neighboring sensor nodes that cooperate in event detection. The proposed method allows sensor nodes to decide whether they need to exchange sensor readings for correctly detecting events. The sensor node takes into account the detection algorithm and verifies whether its current sensor readings can impact the event detection performed by another node. The data are transmitted only when they are found to be necessary for event detection. The proposed method was implemented in a wireless sensor network to detect the instability of cargo boxes during transportation. Experimental evaluation confirmed that the proposed method significantly extends the network lifetime and ensures the accurate detection of events. It was also shown that the introduced method is more effective in reducing data transmissions than the state-of-the-art event-triggered transmission and dual prediction algorithms.

Sensor network data denoising via recursive graph median filters

In wireless sensor networks (WSN) for environmental monitoring, the sensor nodes typically have limited computational and communication resources. Furthermore, the sensors often operate in a harsh environment, and the measurements can be subjected to significant levels of noise. In this work, with these considerations in mind, we propose an efficient method to denoise the sensor data. Using concepts from graph signal processing, the WSN is first modelled using an extended graph. The time-vertex graph jointly models the correlations between neighbouring sensor nodes and across the time dimension. A recursive graph median filter is developed that can be highly localized, and can be implemented with distributed processing. The filter is applied to the denoising of data that is subjected, simultaneously, to Gaussian noise and impulsive noise. Extensive experimental results, using three real-world measurement datasets, will demonstrate that the recursive filter significantly outperforms the equivalent linear filter and nonrecursive median filter, at high noise levels.

Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) play an essential role in exploring the marine world applications in rivers, ponds, and oceans. In recent years, prominent developments have addressed and resolved the numerous issues of critical underwater applications. Routing is one of the vital problems discussed widely, and various protocols are designed to solve the routing-related issues effectively. In this paper, we propose reinforcement-learning based routing using Steiner-points to minimize energy consumption and enhance the network lifetime of UASNs. Initially, the sensor nodes form a group of three nodes and calculate the Steiner point for each such group. The isolated nodes and the Steiner points in the network are vertices of the Steiner tree, which is the routing path to be constructed dynamically by the sink node agent to travel with the assistance of AUV. The sink node agent in the unknown environment initiates the Steiner tree construction process by visiting either the isolated neighbor node or neighbor sensor node’s Steiner point to collect the data from sensor nodes based on the reward function and the environmental conditions. If agent visits the isolated node, then it collects the data from that node itself. If the agent visits the Steiner point, the agent broadcasts its arrival through the beacon message and intended members of that group communicate to the agent and forwards the sensed data. The agent processes the data to remove the data’s redundancy and selects the next point to be visited and constructs the Steiner tree dynamically. The performance evaluation of the proposed scheme minimized each sensor node’s energy consumption and enhanced the network’s lifetime.

Sensor Localization Using Time of Arrival Measurements in a Multi-Media and Multi-Path Application of In-Situ Wireless Soil Sensing

The problem of localization of nodes of a wireless sensor network placed in different physical media (anchor nodes above ground and sensor nodes underground) is addressed in this article. We use time of arrival of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as the ranging measurement. The localization problem is formulated as a parameter estimation of the joint distribution of the time of arrival values. The probability distribution of the time of arrival of a signal is derived based on rigorous statistical analysis and its parameters are expressed in terms of the location coordinates of the sensor nodes. Maximum likelihood estimates of the nodes’ location coordinates as parameters of the joint distribution of the various time of arrival variables in the network are computed. Sensitivity analysis to study the variation in the estimates with respect to error in measured soil complex permittivity and magnetic permeability is presented to validate the model and methodology.

Experimental Data Anomaly Detection at Edge Sensor Nodes Using Physics Laws

Reported measurements by sensor nodes in wireless sensor networks (WSNs) are subject to various anomaly due to sensor/node failures or some events in the environment. Resource scarcity in sensor nodes makes the implementation of sophisticated data anomaly detection techniques challenging in WSNs. We present a multivariate physics-based data anomaly detection technique in WSNs, and we implement the technique to measure the accuracy and efficiency of the proposed technique in detail. The proposed technique examines the natural relationship between various types of sensor measurements to determine the existence of any data anomaly in the node. Furthermore, the technique enables the distinction of data errors and events where a data anomaly is flagged. The technique does not require any exchange of information between neighboring sensor nodes or a central node for data anomaly detection. In a case study, we have applied the proposed technique on temperature and humidity sensor readings and have implemented using TI CC3200 Development Kit, TI BOOSTXL-SENSORS, and TI IMETER-BOOST. To evaluate the accuracy and efficiency of the technique, we have manufactured various data errors and events, measured required memory footage, and metered the amount to current consumption by a sensor node. The detailed analyses have confirmed the high accuracy and efficiency in detecting and separating data errors and environmental events.

Assessing the usefulness of dense sensor network for PM2.5 monitoring on an academic campus area

Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM2.5 monitoring on a campus area of Wrocław University of Science and Technology (Wrocław, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM2.5 concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM2.5 concentrations. Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM2.5 concentrations were even 2–4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM2.5 levels was observed under conditions of temperature inversion.

Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks

Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.

Cognitive Radio Based System for Best Effort Communications in Sound-based Covert Channel for IoT Environments

Nowadays, the use of nodes with different sensing capabilities is pervasive. This leads to many environments capable of monitoring human activity for security, comfort, and connectivity applications among others. However, this also leads to many opportunities for security breaches in personal mobile devices when they are in such environments. In this work, we focus on a covert channel communication system based on sound waves with cognitive radio capabilities. In such system, the mobile nodes are infected with malware that allows the attacker to obtain information from the devices, sending data using a covert channel (hidden communications) to neighbor sensor nodes used for IoT applications through sound waves imperceptible to human ears. A cognitive radio design is based on allowing such hidden communications to occur when the primary channel is unused (i.e., when the user is not using or handling its device). As such, the primary network is considered to be any activity of the mobile (communication, application or service) and the secondary network is the covert channel communication system. Such a system is studied and mathematically analyzed using Continuous Time Markov Chains in order to obtain the throughput in the covert channel for different system parameters. A main result from this work is that, a careful selection of the activation parameter can lead to a more efficient covert data transfer system, irrespective of the rest of the system parameters.

Communication Constrained Distributed Spatial Field estimation Using Mobile Sensor Networks

Abstract In this paper we address the problem of distributed estimation of spatial fields using mobile sensor networks with communication constraints. These constraints consist of a maximum communication bandwidth which limits the amount of data that can be exchanged between any two nodes of the network at each time instant. An algorithm to select the most significant data to be transferred between neighboring sensor nodes is developed starting from derived analytical error bounds. Moreover, the motion of the network nodes is controlled using a coverage control algorithm with the objective of minimizing the estimation uncertainty of each of the nodes. The resulting communication constrained distributed estimation algorithm is deployed on a team of ground mobile robots in the Robotarium, and its performance is evaluated both in terms of estimation accuracy of a simulated spatial field, and of the amount of data transferred.

Weighted-Directed-Hypergraph-Based Spectrum Access for Energy Harvesting Cognitive Radio Sensor Network

In this paper, we investigate the spectrum access for energy harvesting cognitive radio sensor network (EH-CRSN), where sensor nodes (SNs) connected to different sinks share several channels. To maximize the sum of system energy efficiency (EE) and spectrum efficiency (SE), different from traditional works modeling the interference relationship by binary graph and unweighted hypergraph, we apply a novel weighted directed hypergraph (WDH) to accurately characterize the degree of interference among neighbor SNs and formulate the channel access problem in multi-sink network as a WDH game. We analyze the existence of Nash equilibrium (NE) and design a spectrum access algorithm to achieve the optimal solution which is one of NE. Moreover, simulation results are presented to verify the effectiveness of the proposed algorithm.

Design of a Secure Communication Scheme using Channel Shifting in Wireless Sensor Networks

One of the primary challenges to wireless sensor network is to satisfy the security needs of application since it is not practical to recover quickly once the network has been compromised. In order to improve the resilience of the network against wormhole attacks, a security scheme for wireless sensor network (WSN) has been proposed. The proposed scheme hybrids polynomial pool based key pre-distribution scheme and the availability of multiple channels on every sensor node. It allows the sensor nodes not only to establish a secure link with any other sensor node using polynomial pool-based key pre-distribution scheme but also to avoid wormhole attack by shifting from the common channel to any randomly selected channel from its available channels, and hence drifting from the frequency at which adversary is assumed to be listening to launch a wormhole attack. Results show that when almost fifty percent of neighbouring sensor nodes is malicious, the probability of wormhole attack is almost zero.