Greedy probabilistic approach for localization in IoT context

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.

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.

Bitcoin and Ethereum's low transaction per second (TPS) rate has prohibited the adoption of Blockchain in areas such as Internet of Things (IoT) and Internet of Medical Things (IoMT). Despite these constraints, there is considerable research in this area, outlining the benefits of employing Blockchain in IoT applications. Such benefits relate to ensuring that the applications and their data are secure, and integrity is maintained. However, the relatively low TPS rate is still an impediment for the widespread adoption of Blockchain in IoT. This research seeks to explore whether Solana, a new Blockchain technology designed to have a high TPS rate, can overcome this challenge. Can the Solana Blockchain achieve a TPS rate higher than that achieved in other works in the literature? Can a sufficient rate be achieved that could help Blockchain technologies become an enabler in the IoT industry? These are the questions that this research aims to answer. Our experiments conducted on an application coded in Rust on Solana Blockchain demonstrated that it can successfully achieve a 663 TPS rate. Consequently, we conclude that Solana, by overcoming the aforementioned TPS rate issue, could advance the further adoption of Blockchain in IoT context.

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

TDL: Two-dimensional localization for mobile targets using compressive sensing in wireless sensor networks

Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing and transmitting data. However, existing protocols may suffer from one or more security flaws. Recently, Butt et al. proposed an energy-efficient three-factor authentication protocol for wireless sensor networks. However, their protocol is vulnerable to several attacks, and lacks certain security properties. In this paper, the causes of these design flaws are analyzed. Furthermore, we propose a novel three-factor authentication protocol (password, smart card, and biometric information) for wireless sensor networks in Internet of Things contexts. A dynamic anonymous strategy is designed to prevent privacy disclosure and to resist sensor node capture attacks, tracking attacks, and desynchronization attacks. The Find–Guess model and random oracle model are combined to prove the security of the proposed protocol. A comparative analysis with related schemes shows that the proposed protocol has higher security and is able to maintain a low computational overhead.

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.

Today’s networked society is populated by complex business ecosystems that have moved away from the centralised firm-centric structure to a more distributed, crowd-driven ecosystem structure that adopts an open participatory value creation model. Such crowd-driven ecosystems take advantage of technological advancements and underlying network effects in order to harness the collective power and intelligence of the crowd. This ability to co-create value by actively collaborating with the crowd has been fueled by the Internet of Things (IoT) introducing new user-centric paradigms, such as mobile crowd sensing (MCS), which leverage the power and the wisdom of the crowd to observe, measure, and make sense of particular phenomena by exploiting user-owned mobile and wearable devices. Crowd-driven IoT ecosystems have emerged taking advantage of technological advancements and underlying network effects in order to harness the collective power and intelligence of the crowd. However, although research in the context of business ecosystems and business models has gained substantial importance in recent years, there is still a limited understanding of the business dynamics of these emerging, technologically advanced, crowddriven ecosystems. The vast majority of existingresearch efforts place emphasis on business models in the IoT context and only a few studies consider business models for IoT ecosystems. However, one can identify a research gap in relation to business models for crowd-driven ecosystems in the IoT context. This study aims to contribute to the emerging crowd-driven IoT ecosystems literature by examining business models in such environments. For this reason both an exploratory and explanatory research approach is employed. This study provides an exploratory review analysis that synthesizes current scientific knowledge and an explanatory framework that facilitates the analysis of business model components and frameworks that correspond to the needs of firm-centric and ecosystem-centric IoT environments. The study concludes by providing suggestions for the business model development for technologically advanced, crowd-driven ecosystems as in the area of IoT based on its findings, establishing this way a solid foundation for future research in the area of crowd-driven IoT ecosystems.

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.

Most of the current RSS (Received Signal Strength)-based localization algorithms in Wireless Sensor Networks (WSNs) rely on isotropic radio propagation model to infer the distance between a pair of nodes from received signal strength, which however has been proved to be quite unreliable in recent research work. Performance analysis is important to evaluate the applicability of a localization algorithm, however little work has been done on evaluating the existing localization algorithms in simulated realistic settings. This paper firstly presents the motivation and detailed implementation of a proposed Link-State Based Annulus (LSBA) localization algorithm, and then gives a panorama of performance comparison among LSBA and other four localization algorithms in terms of estimation error, convergence speed, computational complexity and communication cost in the simulated realistic environment. Simulation results show that LSBA achieves the best tradeoff among all the four metrics in WSNs with moderate number of anchors, and has good adaptability to irregular node deployment as well.

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.

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.

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 emergence of the internet of things (IoT) is changing the behavior of people, companies, and their businesses. The IoT can enhance e-commerce by reducing the gaps between the virtual and physical worlds. Although the integration of the IoT into e-commerce has several positive effects, security concerns are greater in the IoT context than in traditional e-commerce settings. The proposed research model is based on the unified theory of acceptance and use of technology 2 (UTAUT2) along with trust, privacy, and confidential data concerns to explain the intention to use the IoT in e-commerce. The authors developed a questionnaire and received a total of 328 responses. The results show that among the IoT security factors, trust and privacy are significant determinants of the intention to use the IoT in e-commerce.