QoS-aware query processing for wireless sensor networks

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.

Timeliness in query response is the major quality metric for query processing in the real-time applications of Wireless Sensor Networks (WSNs). The structure of the query routing tree directly affects the whole query processing delay as it provides the path to forward a query to the relevant nodes and return the response to the sink. In the current literature, query routing structure is designed irrespective of the variation in query loads among the sensors. As a consequence, current schemes do not guarantee for the routing tree to provide a faster path to the sensors with higher query load. This motivates the current work to consider query load in constructing and self-reconfiguring the routing tree. In this paper, we present a query load-based spanning tree construction method that reduces the query response delay as well as energy consumption in query execution and provides query response with the best possible accuracy. Simulation results illustrate the efficacy of the proposed framework.

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.

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.

The appearance of wireless sensor networks has attracted worldwide attention. Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs) consist of wirelessly interconnected sensor nodes organised arbitrarily or deterministically in an environmental area which can gather, distribute and route information in different application areas. Power consumption in these networks is the foremost problem. WSNs are a recent generation of sensor networks, have an ample range of applications, their expansion and application will convey about extensive impact in human life and construction of all areas. Some of the applications include disaster management, landslide detection, glacial monitoring, wildlife tracking, healthcare, military applications, environmental monitoring, security surveillance, industrial process control and a large number of applications to robotics and Internet of Things (IOT) projects. This paper will demonstrate the elementary description of WSN followed by different energy-efficient power consumption protocols. Here we have performed the comparative performance analysis of different energy-efficient protocols. In our work we have compared MODLEACH with TSEP followed by TSEP with RELSEP.

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.

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.

The Internet of Things (IoT) is envisioned as a paradigm shift, with a plethora of applications, on the premise of well-established enabling technologies; prominently Wireless Sensor Networks (WSNs) and RFIDs. The former has evolved to improve energy efficiency and resilient operation, yet true scalability has only been recently probed and quite sparsely advanced. Moreover, the traditional approach, whereby most WSN platforms are tailored for a single-application, imposes significant rigidity in re-utilizing platforms for new applications, and limitations on re-using previously deployed ones. In remedy, we present a novel paradigm in WSNs to efficiently utilize network resources, and extend it to a platform for multiple applications to cross-utilize resources over multiple WSNs. We present the approach in three phases; the first calibers resources in the network and their usability. Then applications are represented as finite sets of functional requirements. Finally, we present an optimization approach to find an optimal mapping between applications and resources. This paradigm presents a leap in scalability, not only in a WSN but across multiple ones, dynamically accommodating varying resources being introduced and removed; in addition to utilizing transient resources in their vicinity. To this end, we present an architecture to efficiently adopt WSNs in IoT with changing demands and scale. Our approach is further explained and demonstrated via a detailed use case depicting the premise of IoT applications.

Wireless sensor networks (WSNs) have gained much attention in today’s research domain for supporting a wide variety of applications including the multimedia applications. Multimedia applications that are regarded as the quality-of-service (QoS)-aware, delay sensitive, and bandwidth hungry applications require enough energy and communication resources. WSNs being the energy-scarce network have now been designed in such a way that they can support these delay-sensitive and time-critical applications. In this paper, we propose an energy-efficient routing protocol for heterogeneous WSNs to support the delay sensitive, bandwidth hungry, time-critical, and QoS-aware applications. The proposed QoS-aware and heterogeneously clustered routing (QHCR) protocol not only conserves the energy in the network, but also provides the dedicated paths for the real-time and delay sensitive applications. The inclusion of different energy-levels for the heterogeneous WSNs also provides the stability in the networks while minimizing the delay for the delay-sensitive applications. Extensive simulations have been performed to validate the effectiveness of our proposed scheme. Our proposed routing scheme outperforms other state-of-the-art schemes in terms of the delay performances.

In precision agriculture (PA), the internet of things (IoT) and wireless sensor networks (WSN) can be utilised to more effectively monitor crop fields and make quick choices. The sensors can be installed in crop fields to gather pertinent data, but doing so uses up some of their limited energy. The use of IoT and WSN for smart precision agriculture necessitates energy-efficient operations, location-aware sensors, and secure localization techniques. In this chapter, agricultural problems are identified using IoT and WSN technologies to rectify them. Pests, a lack of water supply, and leaf diseases can be identified for best solutions through pest identification and classification, soil and water conservation, and leaf issues. The integration of Arduino and various sensors is used in the IoT and WSN to solve the issues automatically. Securing energy conservation can be achieved through IoT and sensor systems using efficient programmes.

Wireless Sensor Networks (WSNs) have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things (IoT) and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

Wireless sensor networks (WSNs) are an essential technology for the Internet of Things (IoT) and Machine-to-Machine (M2M), and node locations on them are indispensable for IoT and M2M applications. Therefore, we proposed a Cloud-based Self-Organizing localization (Cloud-SOL), which is composed of a WSN and a cloud computing environment with which the WSN connects. The Cloud-SOL aggregates the WSN's neighbor node list to the cloud computing environment and can estimate precise node locations using the WSN's aggregated topology information. However, it requires multi-hop topology with a high hop count for the WSNs. In this paper, we propose a virtual network topology for Cloud-SOL and show its implementation. The virtual network topology dynamically and ideally configures the WNS topology in cloud computing environment to the Clod-SOL without dependence on actual WSN topology.

Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less error-prone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches