A Secure Data Gathering Scheme Based on Properties of Primes and Compressive Sensing for IoT-Based WSNs

Wireless Sensor Networks (WSNs) have a wide range of applications across multiple platforms within the Internet of Things (IoT), yet face serious challenges like constrained resources, energy, and memory limitations. Current techniques often struggle to efficiently manage energy consumption, resulting in faster battery drain and reduced network lifetimes. Additionally, memory limitations in sensor nodes can affect data storage, further reducing the productivity and scalability of WSNs. To overcome these issues, this research work presents NSPL-HCS (Novel Smoothed Projected Landweber based Hybrid Compressive Sensing), a novel framework that integrates enhanced Particle Swarm Optimization and Grey Wolf Optimization with the compressive sensing technique. NSPL-HCS enhances essential WSN operations, including the creation of cluster, cluster head selection, data compression, data transmission, and data reconstruction. Compared with the available standard and optimized techniques in this context, NSPL-HCS achieves improvements in throughput, residual energy, alive nodes, first and half dead nodes and, largely in network lifetime. Simulation results validated with relevant test functions revealed the potential of NSPL-HCS, showing its competency to improve WSN performance simultaneously retaining reliability and feasibility, as a result, setting the path for wider implementation of WSN in a number of applications of IoT.

In this paper, we propose a greedy localization approach in Wireless Sensors Network (WSN) which has received much attention in the emerging area of Internet of Things (IoT). The proposed method is based on Compressive Sensing (CS) formulation and uses the inherent sparsity of the localization problem. While this is not the first work on applying CS to localize targets, in this paper, we propose a simple and novel formulation and deduce an algorithm to solve this problem, more suitable to the time and complexity constraints of IoT context. Moreover, we propose a Denoising - Greedy Recovery Algorithm (D-GRA) to deal with noise measurement affecting the received signal strength used to the sensor localization. The proposed algorithm has been compared to the classical trilateration method by performing different simulations. The obtained results demonstrate that the proposed algorithms outperform the trilateration method in a noisy environment.

In this paper we propose a greedy probabilistic approach for localization in Wireless Sensors Network (WSN). This topic has received much attention since the WSN are considered as the basis in the emerging area of Internet of Things (IoT). The proposed method aims at increasing the performance of the grid based Compressed Sensing (CS) localization algorithm. This latter is based on the sparse nature of localization problem and select one grid point as user position. The grid point is selected based on correlation property. We propose in this paper to select a grid point based on probabilistic approach where grid point probabilities are calculated from the received signal strength. In a second step we propose to combine the grid positions weighted with their probabilities. The performance of the proposed approaches is evaluated through simulations and compared to CS algorithm results.

This paper considers energy efficient distributed data gathering methods for correlated data field monitoring in multi-hop wireless sensor networks (WSNs) via compressed sensing (CS). This signal compression and acquisition method can be used to exploit the inherent temporal and spatial correlation of the sensor signals. Many existing CS-based data gathering methods exploit correlation structures only in one signal dimension in WSNs. Thus, we propose a distributed method which utilizes the joint correlation pattern of a multi-dimensional WSN signal ensemble via Kronecker sparsity basis. Numerical results how that the proposed method can significantly reduce the data traffic in correlated data gathering multi-hop WSNs.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

The emerging compressed sensing (CS) technique provides lightweight data compression with zero-cost encryption. Therefore, CS enables reduced-complexity designs for sensor nodes and saves transmission power in wireless sensor networks (WSNs). However, CS's linear encoding process makes it vulnerable to several attacks, which may lead to privacy leakage issues. In this article, leveraging the characteristic that CS reconstruction is sensitive to measurement noise, we propose a CS-based watermark cryptosystem for WSNs. In the front-end sensor, a low-dimension watermark is embedded in measurements. In the back-end solver, we present a CS-based watermark decryption/reconstruction engine for the Internet of Things (IoT) gateway. Without synchronization of the key, the proposed cryptosystem can resist ciphertext-only attack and known-plaintext attack effectively. Furthermore, leveraging watermarks as a digital signature, the proposed engine can detect denial of service attack effectively before signal reconstruction. For real-time signal processing, multiple-indices updating algorithm and VLSI architecture are applied to eliminate the throughput degradation from watermark removal. Finally, this CS decoder is fabricated in 40-nm CMOS, and it can support the simultaneous reconstruction of over 10 000 wireless sensors in real time while offering synchronization-free watermark decryption. Therefore, the proposed cryptosystem is suitable for the emerging IoT applications that need encryption strength with limited complexity.

A wireless sensor is a small, low-cost, low-powered device with sensing, wireless communication, and computation capabilities. While the tiny size and low cost of sensors make them lucrative for use in various applications and services, the constraints associated with those also curb exploitation of computation intensive tasks. This in fact comes as the major concern while thinking about using public key cryptography (PKC) in wireless sensor network (WSN). Within the last few years, we have seen a steep growth of research works addressing various aspects of security in wireless sensor networks. While most of the works suggest using symmetric key cryptography, a good number of works also present their success stories of using PKC in WSN. In this chapter, we present a survey on these noteworthy implementations done so far in this area. Alongside presenting the survey of the significant implementations, we also give an overview of PKC, discuss why and how it is needed for WSN, and the major hurdles to its implementation in WSN.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

The Internet of Things (IoT) with its pervasive connectivity imposes substantial challenges regarding privacy, security, and trust. Traditional security measures, such as cryptography and access control, often prove inadequate in the dynamic and resource-constrained IoT environment. The decentralized nature of IoT networks, coupled with the heterogeneity of devices, demands innovative security solutions. Trust is essential in human interaction and plays a crucial role in reducing challenges and security risks within IoT networks. This article focuses on trust-based methods for securing IoT systems, particularly within the context of Wireless Sensor Networks (WSNs). Trust-based approaches offer advantages such as lower computational complexity, leading to improved power management compared to traditional authentication and encryption methods. This article examines the latest trust models, as well as machine learning and deep learning algorithms specifically designed and implemented to enhance the security and reliability of trust-based WSN-assisted IoT applications.

This chapter presents a combined approach to visualize the IoT and cyber-physical system in any wireless communication networks. The brief overview of IoT and its applications in wireless sensor network (WSN) are elaborated in current era. In today’s present situation and the challenges faced during COVID-19 pandemic how the things can be monitored through IoT is being focused in this chapter. Also, the threats challenges and monitoring of cyber-physical system for security is presented in analysis form. WSN provides a bridge between the physical and virtual world and finds use in numerous applications like environmental monitoring, civil structural health monitoring and industrial process monitoring. Lately, it has found much use in the latest technology called Internet of Things (IoT). It seems that IoT has a great potential in shaping our future where everything will be connected together all the time. The major challenges faced by WSN include energy-efficiency, heterogeneity, and systematic design. WSN comprise of autonomous sensors which are capable of wirelessly transmitting their sensed parameter-values (also called packets) to remotely 188located sink node. The sink node is a WSN node which has the capability to assemble the data from all the nodes and forward it to farther distances to the remote base station through wired connections. The data transmission from source node to sink node is done by taking multi-hops which means that data is not sent directly to the destination node but through jumping from one intermediate node to another until the sink is reached. The sensor nodes also act as wireless routers which transmit data to other sensor-routers. So, sensor devices are very useful in deployment, collection of data, act as a IoT sensor devices as an application in technology. IoT is a device which can be used as application while a cyber-physical system is a collection of various mechanical components which are controlled and managed by different devices and algorithms. An IFM (Information Flow Monitor) is an application to backtrack queries and find the culprit in an error that has occurred in the cyber system. However, while backtracking and flow monitoring is not always guaranteed in terms of secure and efficient transaction. In some cases, the sender may deny the authority of the data so the information flow monitors may fail to provide a trustful and authentic source of data. Thus, the chapter focuses on both the issues of IoT integration with cyber-physical security (CPS) system. The key challenges and some solutions are proposed for the researchers for future work.

Internet of Things (IOT) is experiencing a super-fast growing phase. Wireless Sensor Networks (WSN), as the “five sense organs” and teleneuron of IOT, is developing fast along. Owing to the application-oriented characteristics of WSN, WSN applications may differ enormously in implementation methods. The WSN developers may have to rewrite the code for each and every application. Along with the increasing demand to WSN applications, WSN developers may have heavier tasks of programming for these applications. Thanks to TinyOS and NesC, an operating system and programming language designed especially for WSN, raises the WSN application's programming to a higher level. Large number of supported sensor hardware plus system provided and user-de-fined libraries eases WSN application programmers' jobs. How-ever, the massive amount of nodes' communication and networking, and security protection slows down the development pace. A unified and empirical WSN application implementation platform is one of the available solutions to the problem. This paper introduces an empirical WSN application implementation platform, which avails the WSN developers build an application with unified code template and network packet generation and processing strategies.

There are a lot of information and network security threats along with the rapid development of Internet of things (IoT). This paper according to the environmental features of the IOT node limited hardware resource and its requirements, and takes aim at the privacy information security issues for the IoT, an encryption method of lightweight data encryption is designed based on data encryption standard (DES). In order to reduce the complexity of the algorithm but ensuring the security, this algorithm simplified the data packet and the iterative rounds; at the same time added the random selection of S boxes, and improved sub key generation scheme to achieve some degree of different keys in each encryption. Experiments show that, the algorithm reduces the computational complexity and resource cost, improve operation efficiency. It is a suitable method of privacy data protection in the IoT.

A Wireless Sensor Network (WSN) is a collection of sensor nodes, spatially deployed in an ad hoc fashion, which performs distributed sensing tasks in a collaborative manner, without relying on any underlying infrastructure support. WSNs have usually been treated as passive networks, where sensors measure some physical phenomena periodically, or detect some events, and then send the data thus generated to the sink. Another kind of application is emerging, both in the context of a stand-alone WSN and of distributed WSNs, for the Internet of Things (IoT) where, instead of serving only as passive, information gathering mechanisms, sensors can actively process and respond to user queries to report the sensed data on demand. Irrespective of the query types, query processing tasks in WSNs pose several technical challenges, such as (i) selecting the appropriate sensors, within a stand- alone WSN or the appropriate WSNs among the distributed WSNs in the IoT, which satisfy the query specifications; (ii) providing the query response within the user-specified delay bound; (iii) ensuring higher query response accuracy; and (iv) determining the query execution plan dynamically to satisfy the QoS requirements on either delay or accuracy, or a combined specification of both delay and accuracy. This thesis aims to address these issues by developing a hierarchical data model for representing the sensor data of a stand-alone WSN at sensor, fusion node and sink levels. In the case of an aggregate query, the model can be used to select the appropriate sensors which satisfy the query specifications. In the case of an approximate query, the model can provide the approximate query response directly from the model. In both instances, the data model enables retrieving the query response with higher accuracy and less data transmission. To execute a query in delay sensitive applications, a delay aware spanning tree is designed, which provides a routing path for query dissemination to the target sensors as well as collecting a response from them. The tree is constructed based on the query load of the sensors so that the sensors with a higher query load can be reached quickly thus causing less delay in retrieving the query response. Moreover, to support the query execution according to the QoS requirements on delay and accuracy, the hierarchical data model is applied on the delay aware spanning tree to generate a QoS aware sensor model. The model organises sensor data hierarchically, with different levels of accuracy, on different hop distances of the tree. It creates the provision to collect the query response from any hop distance of the tree to satisfy the query-specified delay and accuracy bound. Then, the formulations are extended for the distributed WSNs in the context of the IoT. In doing so, a distributed WSNs information model is maintained in the web server which gathers the data models and the spatial properties of the WSNs from their associated QoS aware sensor models. The information model exploits the overlapping information among the participating WSNs and facilitates selecting the appropriate WSNs it is necessary to visit according to the query specifications. Moreover, based on the QoS requirements of the query, the information model determines the cost effective query execution plan, with the help of the QoS aware sensor models of the respective WSNs. Extensive analytical and simulation analyses confirm the efficacy of the proposed new strategies compared with notable work in this area.

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Wireless Sensor Network (WSN) technology has developed substantially over the past decade or so and now numerous solutions exist across a diverse range of innovative applications. The expanding Internet of Things (IoT) sector is becoming an ever more important aspect of modern technology and a key motivator for improving security and privacy in WSNs. Typically, WSN protocols form an integral part of the overall IoT infrastructure by enabling the sensor to access point communication links. These wireless links inherently encompass security challenges, frequently due to external interference and intrusions. As IoT applications incorporate WSNs in their architecture, the incentive to attack and compromise these WSNs escalates. Often, commercial off the shelf devices and standardized open-access protocols combine to achieve specific WSN deployments. Numerous WSN vulnerabilities exist, whilst attack approaches are abundant and change frequently. Thus, to ensure acceptable performance, safety and privacy in many IoT applications, the adopted WSN must be secure. This paper discusses IoT security and privacy, by evaluating a machine learning approach for interference detection focused entirely on analyzing received In-phase (I) and Quadrature-phase (Q) samples. Significantly, once an intrusion is detected, mitigation strategies can be implemented, thus emphasizing the requirement for interference detection. Random Forest is chosen as the machine learning classifier as it consists of a large number of individual decision trees operating as an ensemble. An intrusion detection system (IDS) is developed based on Matlab simulated ZigBee data as an initial insight into whether a real wireless data approach may be viable.