Wireless Sensor Network Infrastructure Research Articles

Machine Learning Algorithms for Intrusion Detection in WSNs

Machine learning (ML) has emerged as a potent tool for optimizing network performance and extending network lifespans. By automating complex data processing tasks, ML algorithms enable real-time solutions that optimize resource allocation and utilization. ML's ability to process vast, intricate datasets with speed and precision empowers networks to adapt dynamically to evolving demands. [13]. Wireless Sensor Networks (WSNs), composed of interconnected sensor and sink nodes, exemplify the transformative potential of ML. These distributed, decentralized networks inherently possess self-organization and self-healing capabilities. By integrating ML techniques, WSNs can significantly enhance their efficiency, reliability, and scalability, enabling a wide array of applications, from environmental monitoring to military operations. As advancements in electronics and wireless communication continue to drive WSN evolution, the fusion of ML and WSNs promises to unlock new horizons in network intelligence and performance. However, WSNs face several challenges, including resource constraints such as limited memory, processing power, and energy. Additionally, the physical infrastructure of WSNs must be secured to protect sensitive data, especially in privacy-critical applications. To address these challenges, the integration of machine learning offers a promising solution. By leveraging ML techniques, WSNs can adapt to dynamic environments, optimize energy consumption, and detect and mitigate potential security threats. This enables the deployment of WSNs in diverse applications, from environmental monitoring to smart cities, while ensuring data privacy and system security. Keywords: Intrusion Detection System (IDS), Security, Wireless Sensor Network (WSN), Attacks, Reinforcement Learning (RL), Denial-of-Service (DoS), Networks, Machine Learning (ML)

A literature study and performance gaps on Centralized server-based load balancing and routing strategies under cloud-IoT-WSN

ABSTRACT Wireless Sensor Network (WSN) infrastructure has to be balanced for maintaining the Quality of Service (QoS) in the network. However, load balancing technology is complex for meeting the requirements of high flexibility and adaptability due to the conventional WSN architecture. The major complication for developing distributed techniques for these models is load balancing that is the major aim of this study. These distributed methodologies use routing decisions that must be efficient without the need for more information about the system, where one of the efficient techniques is probabilistic routing. This paper plans to conduct a critical literature review on a diverse centralised server for controlling the load balancing in the cloud and routing strategies in WSN as well as linked with an IoT systems. This study reviews a set of contributions regarding load balancing in the cloud and routing strategies in WSN with clear algorithmic analysis. The simulation tools and recorded performance measures are also analysed and sorted out. Finally, the existing research gaps and the future directions are expedited that to be adaptable to attain better protocol performance with a centralised server in balancing node traffic, load balancing in the cloud and improving the throughput of the entire network.

Wireless Sensor Networks for Green Cities: A Comprehensive Review of Environmental Pollution Monitoring

This research paper investigates the design and implementation of a Wireless Sensor Network (WSN)-Based Data Acquisition System tailored for collecting environmental pollution factors with a specific focus on contributing to the realization of a Green City. Leveraging various literature surveys on WSN and its applications with different techniques, a comprehensive analysis of the existing body of knowledge in the field. The paper explores the deployment of WSN technology as a cost-effective and scalable solution for real-time monitoring of pollution-related parameters, including air and water quality, noise levels, and other relevant environmental factors.By synthesizing insights from diverse literature sources, propose an intelligent and adaptable WSN framework capable of capturing, analyzing, and transmitting real-time environmental data. The research highlights the importance of integrating advanced sensor technologies within the WSN infrastructure to ensure a holistic monitoring of pollution factors. This analysis also delves into various data acquisition methodologies and explores the potential for advanced data analytics and visualization techniques to derive meaningful insights

Energy Efficient Cluster based Routing Scheme for WSN based IoT to Extend Network Lifetime

With the development and advancement of wireless sensor networks (WSN), the emergence of the Internet of Things (IoT) has achieved prominence in the modern era. With the increasing number of connected devices, WSN has become a key factor in the communication component of the IoT. In IoT-based WSN infrastructure, devices are equipped with intelligent sensors that sense the environment to collect data, process data, and deliver information to the sink or base station (BS). WSN-assisted IoT has become a key technology for various data-centric applications such as health care, smart cities, and the military. Sensor nodes in IoT devices are equipped with bound and irreplaceable batteries. An increased number of connected devices face serious issues of energy depletion, maintenance, and load balancing, which might result in device failure. Energy efficiency is considered a vital parameter in the design of an IoT based WSN, and this can be accomplished through clustering and multihop routing techniques. In this paper, we propose an energy-aware multihop routing scheme (EAMRS) for hierarchical cluster-based WSN-assisted IoT. EAMRS considers the improved low-energy adaptive clustering algorithm (I-LEACH) to select optimal cluster heads (CH). During data transmission, multihop routing is involved by considering routing metrics such as residual energy, distance to BS and optimal route choice to balance the energy load. However, conventional routing schemes fail to achieve the flexibility and adaptability prerequisites of load balancing mechanisms. EAMRS decreases computation overhead and restricts energy usage, resulting in a prolonged network lifetime.

Development of E-Service Provision System Architecture Based on IoT and WSNs for Monitoring and Management of Freight Intermodal Transportation.

The problems of developing intelligent service provision systems face difficulties in the representation of dynamic aspects of cargo transportation processes and integration of different and heterogeneous ICT components to support the systems' necessary functionality. This research aims to develop the architecture of the e-service provision system that can help in traffic management, coordination of works at trans-shipment terminals, and provide intellectual service support during intermodal transportation cycles. The objectives concern the secure application of the Internet of Things (IoT) technology and wireless sensor networks (WSNs) to monitor transport objects and context data recognition. The means for safety recognition of moving objects by integrating them with the infrastructure of IoT and WSNs are proposed. The architecture of the construction of the e-service provision system is proposed. The algorithms of identification, authentication, and safety connection of moving objects into an IoT platform are developed. The solution of application of blockchain mechanisms for the identification of stages of identification of moving objects is described by analysing ground transport. The methodology combines a multi-layered analysis of intermodal transportation with extensional mechanisms of identification of objects and methods of synchronization of interactions between various components. Adaptable e-service provision system architecture properties are validated during the experiments with NetSIM network modelling laboratory equipment and show their usability.

A survey on network admission control solution in 6LoWPAN using cryptographic mechanism

This paper introduces a robust network admission control (NAC) solution tailored specifically for 6LoWPAN Wireless Sensor Networks (WSN), aiming to enhance the overall security infrastructure. By implementing stringent measures, this solution not only safeguards legitimate nodes but also fortifies the network against potential security threats originating from unauthorized nodes attempting to communicate with both legitimate nodes and the Internet. The solution enhances security through innovative features for administrative nodes, including node presence detection and robust authentication. It incorporates data filtering to swiftly identify and discard unauthorized frames, strengthening network resilience. Established protocols like Neighbor Discovery and DSDV facilitate seamless integration. This strategic utilization minimizes the reliance on additional control messages, thereby optimizing the network's efficiency and resource utilization. The solution utilizes cryptographic methods, particularly the XOR algorithm, to enhance data security in wireless sensor networks. This algorithm ensures authenticity, integrity, and freshness of transmitted data frames, providing robust protection against manipulation and bolstering network security. The proposed network admission control solution addresses security risks in 6LoWPAN WSN and integrates advanced features for a strong and efficient overall security infrastructure in wireless sensor networks.

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard‐to‐reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV‐assisted cluster‐based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV‐WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

Study of Wireless Sensor Networks Topological Protocol and Its Applications

In this paper we studied about the wireless sensor networks and topology protocols and applications of wireless sensor networks (WSN). Many applications of RFID and Wireless Sensors Networks (WSN) in diverse areas including intelligent transport vehicles, defense, environmental monitoring and forecasting such as air pollution monitoring, structural health monitoring, intelligent home, and warehouse management were offered [1-4]. The integration infrastructure of WSN and RFID system with a network was dedicated. Furthermore, the architecture framework of Electronic Production Code (EPC) global sensor network was proposed aiming to establish the infrastructure of global network which is able to aggregate various data [6]. However, RFID and WSN integration was developed by [7]. This research strives to elaborate accurate traceability of different objects by means of RFID which are not conveniently identifiable by applying ordinary sensors. Notwithstanding the fact that the condition of items cannot be monitored by RFID, the environmental condition as well as condition of items can be achieved by sensor nodes. To address this issue, both RFID system and WSN are applied together to overcome some difficulties in industrial environment.

Efficient sharing of privacy-preserving sensing data on consortium blockchain via group key agreement

Due to the self-organizing and distributed nature of Wireless Sensor Networks (WSN), valuable data in sensor networks faces security and privacy risks. A blockchain-based approach enables secure and convenient sharing of sensor information among different users. Compared to public and private blockchains, consortium blockchain is widely used across different industries and use cases in WSN due to its auditability and high transaction rate. However, sensing data sharing via consortium blockchain raises the privacy issue. Therefore, the data from the sensor node is encrypted by the key (named sensorkey) shared by the sensor node and the sink node and then sent to the blockchain network to not reveal the privacy of the sensing data. Since the infrastructure in large-scale WSN is usually owned and managed by multiple organizations, encrypted sensing data needs to be authorized by these multiple organizations for computation. Organizations requesting privacy-preserving data are referred to as data sharers. Distributing the sensorkey to each data sharer requires separate encryption of the key using the data sharer’s public key. The sink node needs to be online when each data sharer asks for the sensorkey, and one encryption of the sensorkey for each data sharer consumes precious resources. This work proposes GSChain for efficient privacy-preserving sensing data sharing on consortium blockchain. Multiple data sharers resort to asymmetric group key agreement protocol to maintain a shared group encryption key and their respective group decryption keys, enabling efficient sensorkey retrieval from the consortium blockchain. The sensorkey is encrypted only once by the group encryption key and stored on the consortium blockchain along with the privacy-preserving sensing data. Our scheme improves the efficiency of privacy-preserving data sharing among multiple data sharers while reducing the online demand for sink nodes. Although the data-sharing group should remain stable for a long time, we design the group key update scheme. We also discuss how old and new data sharers access different ranges of privacy-preserving sensing data as the data-sharing group changes. We build a complete implementation of GSChain based on the Hyperledger Fabric framework and conduct a comprehensive set of experimental studies. Our experimental results demonstrate that GSChain improves the privacy-preserving sensing data sharing efficiency with tolerable time and storage overhead. In addition, the time overhead caused by the recovery of the GSChain system is tolerable when the membership of the data-sharing group changes.

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues.

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

CDS-MIP: CDS-based Multiple Itineraries Planning for mobile agents in wireless sensor network

using multi agents in the wireless sensor networks (WSNs) for aggregating data has gained significant attention. Planning the optimal itinerary of the mobile agent is an essential step before the process of data gathering. Many approaches have been proposed to solve the problem of planning MAs itineraries, but all of those approaches are assuming that the MAs visit all SNs and large number of intermediate nodes. This assumption imposed a burden; the size of agent increases with the increase in the visited SNs, therefore consume more energy and spend more time in its migration. None of those proposed approaches takes into account the significant role that the connected dominating nodes play as virtual infrastructure in such wireless sensor networks WSNs. This article introduces a novel energy-efficient itinerary planning algorithmic approach based on the minimum connected dominating sets (CDSs) for multi-agents dedicated in data gathering process. In our proposed approach, instead of planning the itineraries over all sensor nodes SNs, we plan the itineraries among subsets of the MCDS in each cluster. Thus, no need to move the agent in all the SNs, and the intermediate nodes (if any) in each itinerary will be few. Simulation results have demonstrated that our approach is more efficient than other approaches in terms of overall energy consumption and task execution time.

Ensuring Energy Efficiency When Dynamically Assigning Tasks in Virtualized Wireless Sensor Networks

Traditional non-virtualized Wireless Sensor Networks (WSNs) suffer from high deployment and maintenance costs, mainly because their applications are embedded in sensor nodes. Virtualization technologies address these challenges by allowing multiple sensing tasks to run over the same deployed WSN infrastructure. However, virtualization comes at an energy-delay cost, making it both essential and challenging to allocate physical and/or virtual resources efficiently to applications with different sensing tasks, especially for delay-sensitive applications. Our goal is to address the challenge of task assignment in virtualized WSNs while minimizing the overall energy consumption and meeting the given deadlines. After formulating the problem as an Integer Linear Programming (ILP), we propose a scalable heuristic. We evaluate the performance of our proposed heuristic in different scenarios and compare it with the optimal solution as well as a recent work from literature. The results indicate that our proposed heuristic leads close-to-optimal solutions with good performance in terms of execution time. It shows that the proposed DTA solution can not only achieve up to a 97% reduction of the execution time for small-scale scenarios, as compared to the optimal solution, but it also outperforms the existing benchmarks in terms of successful task execution rate by 100%.

A Review on Wireless Sensor Networks and its applications

Around the globe, wireless sensor nodes are utilized in a variety of real-time applications. Significant research in the field of wireless sensor networks (WSNs) has been done in the last decade due to an increase in demand in various applications all over the world. WSNs are a new field of research that is being investigated to provide solutions to existing issues such as energy consumption, sensor location, routing algorithms, robustness, efficiency, and many more. Various scholars have grappled with a variety of network design and computational complexity issues during the last decade. Because of its uses in a number of areas, including as healthcare, espionage, environmental monitoring, industrial monitoring, forest fire detection, and natural disaster prevention, WSNs are gaining popularity throughout the world. This article discusses the use of WSN infrastructure and its applicability in many sectors to solve current problems.

An anonymous two-factor authentication protocol for IoT-based applications

Internet of things (IoT) allows people to establish a real-time connection with physical objects. To fulfill this lifelong ambition, the infrastructure of Wireless Sensor Networks (WSNs) plays a pivotal role. However, the public channel that is used by the sensors and users can imperil the security of their connection. Accordingly, many authentication protocol have been proposed to preserve the integrity and confidentiality of the transmitted messages. In this paper, we examine Wu et al.’s protocol as one of the most state-of-the-art protocols and prove that it is susceptible to sensor capture attack, user trace attack, and DoS attack, also it cannot provide forward secrecy. To mitigate these weaknesses, we propose our protocol and analyze that with both formal and informal methods to show that it is secure against various known attacks such as sensor and user trace, sensor capture, offline password guessing, and replay attacks. Finally, we evaluate our protocol in terms of security features and communication and computation costs. The results show that our protocol not only is more secure than other existing protocols but also reduces 62.1% of the computation cost and 30.76% of the communication cost in comparison with the costs of Wu et al.’s protocol.

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

Energy-efficient event pattern recognition in wireless sensor networks using multilayer spiking neural networks

Motivated by the energy-efficient computation of the brain, an energy-efficient wireless sensor network infrastructure is built for solving the pattern recognition task. In this work, spatiotemporal patterns originate from the wireless sensor nodes that use pulse-based networking schemes like pulse position-coded packet data unit and spiked time-division multiple access. Pulse-based networking schemes use spatiotemporal patterns to encode information rather than using traditional energy-expensive packets. These spatiotemporal patterns are learned through biologically plausible learning rule of Tempotron which is a neuronal binary classifier that uses a gradient-descent supervised learning rule to adjust the weights of synaptic inputs. A single Tempotron-trained spiking neuron is shown to read out information encoded in the spike timings of synaptic inputs. However, due to the simplicity of the single Tempotron classifier, its performance is inherently limited. In this work, we devise the multilayer spiking neuron training rules for event pattern classification in distributed wireless sensor networks. We also show that the proposed architecture improves classification accuracy by a considerable amount as compared to a single Tempotron model’s performance.

IoT Platform for Energy Sustainability in University Campuses.

University campuses are normally constituted of large buildings responsible for high energy demand, and are also important as demonstration sites for new technologies and systems. This paper presents the results of achieving energy sustainability in a testbed composed of a set of four buildings that constitute the Telecommunications Engineering School of the Universidad Politécnica de Madrid. In the paper, after characterizing the consumption of university buildings for a complete year, different options to achieve more sustainable use of energy are presented, considering the integration of renewable generation sources, namely photovoltaic generation, and monitoring and controlling electricity demand. To ensure the implementation of the desired monitoring and control, an internet of things (IoT) platform based on wireless sensor network (WSN) infrastructure was designed and installed. Such a platform supports a smart system to control the heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings. Furthermore, the paper presents the developed IoT-based platform, as well as the implemented services. As a result, the paper illustrates how providing old existing buildings with the appropriate technology can contribute to the objective of transforming such buildings into nearly zero-energy buildings (nZEB) at a low cost.

Miejska sieć węzłów pomiarowych jakości powietrza

Smart city is a city that increases the interactivity of its components and put emphasis on their functionality. Internet of Things technology (IoT) is an innovative solution in environmental protection. Usually, information on air quality is very scattered. This paper describes the test stages of pre-implementation works, focusing on the presentation of the technical design of the measurement nodes and the assumptions of the IT project. The goal of the project Intelligent Wireless Sensor Network Infrastructure (IIBSC) is, among others, to create a dense network of air quality measurement nodes at city, district or even street level. The concept is based on Internet of Things (IoT) technology using a matrix construction tool connected to multiple identical measurement nodes located in the test area. The project developed a hardware platform supporting sensors and resistant to external factors, and an ISIMPIO information platform based on edge processing technology for processing data from air quality sensors. Due to the use of Internet of Things (IoT) technology, an edge server using edge processing was designed. Edge server provides a complete ecosystem for building edge applications that are fully optimized for seamless field work. In addition, it allows the implementation of integrated Python software, the MQ Telemetry Transport support protocol (MQTT), time-series database, firmware update over a wireless network, and built-in security system. Measuring the concentration of particulate matter and other substances in the air will be useful for specialists assessing their dynamics. The technology and test installation selected corresponds to the leading solutions in this field in Europe and, in the future, should also be extended to less urbanised areas.

Mid Square Hash Bezier Secured Routing for IoT Enabled Wireless Sensor Networks

The swift uptake of sensors and the increasing demand of Wireless Sensor Network (WSN) applications and services create unmatched appeals for routing data packets on WSN infrastructure. In the era of Internet of Things (IoT), an immense number of sensing devices collect numerous sensor data over time for an extensive span of fields. For IoT-enabled devices routing through WSN, sensor nodes transmit confidential data to the gateway nodes via public channels. In such an environment, secured routing remains a serious issue that has to be addressed. To address this issue, in this work, a robust method using Mid Square Hash-based Bezier Authentication (MSH-BA) is designed to secure the routing for IoT-enabled WSNs. First, a Mid Square Hashing Data Collection algorithm is proposed to minimize the energy consumption through unique key generation. To minimize the running time of IoT sensor network, discrete registration is performed between user and gateway nodes. Finally, the proof of correctness of mutual authentication is performed using the Bezier Authentication method via progressive distance, therefore ensuring packet delivery ratio. The robust authentication method is analyzed comprehensively and compared against the similar authentication methods and the results showed that it is efficient and robust than earlier methods.

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures.

Monitoring contemporary water distribution networks (WDN) relies increasingly on smart metering technologies and wireless sensor network infrastructures. Smart meters and sensor nodes are deployed to capture and transfer information from the WDN to a control center for further analysis. Due to difficulties in accessing the water assets, many water utility companies employ battery-powered nodes, which restricts the use of high sampling rates, thus limiting the knowledge we can extract from the recorder data. To mitigate this issue, compressive sensing (CS) has been introduced as a powerful framework for reducing dramatically the required bandwidth and storage resources, without diminishing the meaningful information content. Despite its well-established and mathematically rigorous foundations, most of the focus is given on the algorithmic perspective, while the real benefits of CS in practical scenarios are still underexplored. To address this problem, this work investigates the advantages of a CS-based implementation on real sensing devices utilized in smart water networks, in terms of execution speedup and reduced ener experimental evaluation revealed that a CS-based scheme can reduce compression execution times around , while achieving significant energy savings compared to lossless compression, by selecting a high compression ratio, without compromising reconstruction fidelity. Most importantly, the above significant savings are achieved by simultaneously enabling a weak encryption of the recorded data without the need for additional encryption hardware or software components.