Actual Wireless Sensor Network Research Articles

IoT Nodes Authentication and ID Spoofing Detection Based on Joint Use of Physical Layer Security and Machine Learning

The wide variety of services and applications that shall be supported by future wireless systems will lead to a high amount of sensitive data exchanged via radio, thus introducing a significant challenge for security. Moreover, in new networking paradigms, such as the Internet of Things, traditional methods of security may be difficult to implement due to the radical change of requirements and constraints. In such contexts, physical layer security is a promising additional means to realize communication security with low complexity. In particular, this paper focuses on node authentication and spoofing detection in an actual wireless sensor network (WSN), where multiple nodes communicate with a sink node. Nodes are in fixed positions, but the communication channels varies due to the scatterers’ movement. In the proposed security framework, the sink node is able to perform a continuous authentication of nodes during communication based on wireless fingerprinting. In particular, a machine learning approach is used for authorized nodes classification by means of the identification of specific attributes of their wireless channel. Then classification results are compared with the node ID in order to detect if the message has been generated by a node other than its claimed source. Finally, in order to increase the spoofing detection performance in small networks, the use of low-complexity sentinel nodes is proposed here. Results show the good performance of the proposed method that is suitable for actual implementation in a WSN.

An improved coverage gap fixing method for heterogenous wireless sensor network based on Voronoi polygons

The traditional fixing method for coverage gap in wireless sensor network (WSN) mainly targets the homogenous networks, in which all nodes have the same detection radius. By this method, the gaps in the network are covered randomly by mobile nodes, leading to low coverage and resource waste. Considering the different detection radii of actual WSN nodes, this paper explores the gap fixing in heterogenous WSNs, and proposes an improved gap fixing method based on fixing priority. Specifically, the gap fixing points were determined and prioritized with the aid of Voronoi polygons, and then fixed by mobile nodes. After that, the nodes with overlapping sensing ranges were removed to reduce the redundancy. Simulation results show that the improved method can effectively enhance the coverage of the entire network.

On the Realistic Radio and Network Planning of IoT Sensor Networks.

Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.

An Advanced Battery Model for WSN Simulation in Environments With Temperature Variations

Wireless sensor networks (WSNs) can be used to support monitoring activities in a wide range of applications and communication environments. Its usage in extreme conditions, in what concerns pressure, temperature, and humidity, must be carefully assessed before the network deployment. In particular, the temperature variations have a direct impact upon the behavior of WSNs through the batteries of sensor nodes. These electrochemical devices are highly susceptible to temperature variations, which modifies the offered effective charge capacity. In this context, it is difficult to estimate the behavior of batteries over time, impairing the extraction of relevant information for energy-aware approaches. Such information, particularly battery state of charge, voltage, and lifetime, is often used by WSN simulators to predict the communication behavior in different scenarios. Nevertheless, WSN simulators generally use simplistic battery models, causing significant deviations in simulation results when compared with actual WSN deployments. This paper describes the implementation of the Temperature-Dependent Kinetic Battery Model (T-KiBaM) in the Castalia simulator, which enables a considerable improvement of the accuracy of simulations in communication environments with different temperature conditions. An experimental assessment has been performed with temperature variations over time to validate the usage of the T-KiBaM battery model. The experimental results indicate that the T-KiBaM model is quite accurate when estimating battery behavior under both different temperature set points and different temperature variations.

A new hierarchical belief-rule-based method for reliability evaluation of wireless sensor network

With the wide applications of wireless sensor network (WSN), its reliability evaluation has been attracted more attention. The reliability of a WSN is affected mainly by internal and external factors, which include internal faults and external attacks. In this paper, a reliability evaluation method based on a hierarchical belief rule base (BRB) method is proposed for the reliability evaluation of the WSN. First, the factors affecting the reliability of a WSN are analysed, and the reliability evaluation process that considers the WSN fault evaluation and WSN security evaluation is described. Second, the reliability evaluation model is constructed based on the hierarchical BRB model. The qualitative knowledge is used by the BRB model to build initial belief rules, and the quantitative data are used to optimize the initial parameters of the BRB model, which can utilize various types of uncertainty information effectively. Therefore, the proposed method can be applied to the WSN reliability evaluation, which is a complex and uncertain problem. Finally, a simulation case study and an actual case study of wellhead blowout monitoring are conducted to verify the effectiveness of the proposed method. The reliability results of actual WSN are obtained by the standard testing method, where the loss and accuracy rates of the collected data are treated as the observation factors for obtaining the actual reliability values. The estimated results of hierarchical BRB model are very close to the actual reliability values, which shows that the proposed method can be used for evaluating the reliability of the actual WSN accurately.

Multi‐channel fingerprint localisation algorithm for wireless sensor network in multipath environment

Fingerprint localisation technology based on received signal strength indication (RSSI) is an active area of study in wireless sensor network (WSN) research, mainly because it is cheap and easy to implement. However, due to measurement noise and the multipath effect in RSSI, the accuracy of state-of-the-art fingerprint positioning algorithms is diminished. To solve this problem, the authors propose a multi-channel fingerprint localisation algorithm for WSNs in multipath environments. This algorithm also addresses the issue of existing techniques based on multi-channel signal strength requiring an accurate initial estimate of target–beacon distance or prior knowledge of the target–reference distance. Their proposed algorithm first uses an adaptive Kalman filter to reduce the noise in RSSI measured in different channels, and then calculates the matched fingerprint according to the weight of different channels. Finally, a memetic algorithm is utilised to generate the optimised estimate of fingerprint and location. Extensive experimental results on an actual WSN testbed show that the proposed algorithm improves the accuracy of the existing fingerprint localisation algorithm by at least 50%, regardless of the placement of target node, the number of beacon nodes, and the number of calibration points.

ANCR—An Adaptive Network Coding Routing Scheme for WSNs with Different-Success-Rate Links

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be true in many actual WSNs—the wireless links among nodes are often subject to all kinds of disturbance, obstruction, etc., and may transmit with different success rates. This paper proposes a new routing strategy, named Adaptive Network Coding Routing (ANCR). ANCR firstly establishes a routing path with the traditional network coding routing (NCR), and then applies the neighborhood search algorithm to adaptively determine nodes’ coding opportunities based on the links’ transmission success rates, with the target of reducing the total number of transmission. The simulation results show that, in WSNs with different-success-rate links, ANCR can reduce the network delay by about 50%, and increase the network throughput by about 67%, compared with the traditional NCR.

Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks

Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of its [Formula: see text]-based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. The [Formula: see text]-based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF_CSOR). Furthermore, we study three different greedy algorithms under DAF_CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF_CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF_CSOR exhibits a certain advantage over conventional methods.

Model-Driven-Development-Based Stepwise Software Development Process for Wireless Sensor Networks

To meet future demands for wireless sensor network (WSN) software, both experts and average software developers should be involved in WSN software development. However, WSN software development is difficult for the average software developer because data processing-related design and network-related design are tangled in the software. Here, we propose a software development process for WSN software by stepwise refinement. Our process enables stepwise refinement to separately address data processing-related and network-related concerns, reuse of well-defined designs, and implementations for network-related concerns prepared by the experts, and perform model-driven development to obtain source codes from models by model transformations. Additionally, we used case studies using actual WSN software development and user studies to evaluate how our proposed process can support actual WSN software development.

PROPAGATION ANALYSIS AND DEPLOYMENT OF A WIRELESS SENSOR NETWORK IN A FOREST

A complete study for the deployment of a wireless sensor network in a forest based on ZigBee is presented in this paper. First, due to the lack of propagation models for peer to peer networks in forests, propagation experiments were carried out to determine the propagation model. This model was then used for planning and deploying an actual wireless sensor network. The performance of the network was compared with the expected theoretical behavior to extract some conclusions that are presented in the paper. Finally, some general conclusions, as an estimation of the minimum number of routers necessary to cover a given area, are extracted from the experiments and presented in the paper.