IoT Driven Wireless Sensor Network Routing Optimization using Improved Firefly Algorithm and Block Chain Technology

With rapid development of wireless communication, sensor, micro power system and electronic technology, the research on wireless sensor network has attracted more and more attention. The work proposed routing algorithm in wireless sensor network based on ant colony optimization by analyzing routing protocol and utilizing advanced idea. Ant colony optimization algorithm has advantages in implementing local work, supporting multiple paths and integrating link quality into pheromone formation. In routing selection, the work calculated probability that node is selected as the next hop according to pheromone concentration on the path. With characteristics including self-organization, dynamic and multipath, ant colony optimization algorithm is suitable for routing in wireless sensor network. With low routing cost, good adaptability and multipath, the algorithm balanced energy consumption to prolong network lifetime. In terms of simulation and experiments, ant colony algorithm was proved to be suitable for finding optimal routing in wireless sensor network, thus achieving design goal of routing algorithm.

Wireless sensor networks are generally composed of densely deployed sensor nodes that collaboratively sense, compute and communicate the sensed information. Routing in sensor networks is based on energy optimisation and quality of service. In this paper, a meta-heuristic technique comprising particle swarm optimisation and Tabu search (TS) is used for energy efficient routing in sensor network. The hybrid approach has better network performance compared with the traditional approaches, simple particle swarm algorithm and TS, respectively. Experimental results penlight the efficacy of the proposed approach.

Nowadays, Wireless Sensor Networks (WSNs) are widely used for collecting, communicating, and sharing information in various applications. Due to its limited resources in terms of computation, power, battery lifetime, and memory storage for sensor nodes, it is difficult to add confidentiality and integrity security features. It is worth noting that blockchain (BC) technology is one of the most promising technologies, because it provides security, avoids centralization, and a trusted third party. However, to apply BCs in WSNs is not an easy task because BC is typically resource-hungry for energy, computation, and memory. In this paper, the additional complication of adding BC in WSNs is compensated by an energy minimization strategy, which basically depends on minimizing the processing load of generating the blockchain hash value, and encrypting and compressing the data that travel from the cluster-heads to the base station to reduce the overall traffic, leading to reduced energy per node. A specific (dedicated) circuit is designed to implement the compression technique, generate the blockchain hash values and data encryption. The compression algorithm is based on chaotic theory. A comparison of the power consumed by a WSN using a blockchain implementation with and without the dedicated circuit, illustrates that the hardware design contributes considerably to reduce the consumption of power. When simulating both approaches, the energy consumed when replacing functions by hardware decreases up to 63%.

A wireless sensor network is an enormous assortment of sensor nodes with inadequate power supply and self-conscious computational competence. Due to the circumscribed communication range and high density of sensor nodes, packet dispatching in sensor networks is usually performed through multi-hop data transmission. For that reason, routing in wireless sensor networks has been deliberated as an imperative field of research over the past decade. These days, multipath routing techniques is widely used in wireless sensor networks to improve network performance over and done with efficient utilization of accessible network resources. Consequently, the main intention of this review is to present the conception of the various routing approaches and its fundamental experiments, as well as the basic motivations for exploiting these techniques in wireless sensor networks. Routing procedures for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in inadequate periods. The energy constriction of WSNs make energy saving become the most significant objective of various routing algorithms. In this paper, a review of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goalmouth.

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Energy efficiency is a critical problem for wireless sensor networks. In past decades, network coding has been proved as a promising technology to reduce the number of transmissions, save energy, and improve network throughput, especially suitable for the wireless network that motivates the emergence of network coding aware routings. However, existing network coding aware routings have the failure decoding problem, because of defective network coding conditions. On the other hand, existing network coding aware routings usually consider the ideal traffic scheduling and neglect nodes’ energy and load which are important to wireless sensor networks. Therefore, it is not appropriate to directly apply existing network coding aware routings in wireless sensor networks. Regarding the problems above problems, this paper proposes traffic-shaped network coding aware routing (TSCAR) for wireless sensor networks. In the TSCAR, a universal network coding condition is presented to solve the false decoding problem. And traffic shaping mechanism is proposed to shape the traffic of different flows to create more coding actions when coding opportunities exist. In addition, network calculus is used to analyze the delay of the TSCAR. Besides, a coding, load, and energy aware routing metrics are proposed to evaluate the discovered paths. Extensive simulation results demonstrate that the TSCAR increases the number of coding opportunities and the proportion of coded packets, improves network throughput, and extends the network lifetime of wireless sensor networks.

Routing in sensor networks is very challenging compared to contemporary communication and wireless ad–hoc networks. This is attributed to several characteristics that distinguish them from such networks. Transmitting data from every sensor node within the deployment region might result in significant and unnecessary redundancy in data and incur avoidable energy and traffic expenditure; thus, a routing protocol which is able to select a set of sensor nodes and utilise data aggregation during the relaying of data is of great importance. Furthermore, timely delivery of data is required in many applications of Wireless Sensor Networks (WSNs), such as real–time target tracking in battle environments, emergent event triggering in monitoring applications impose the need of efficient routing protocol. In this paper, we propose a data routing scheme which ensures increased network lifetime by exploiting energy efficiently, supporting reliability, fast delivery of delay–sensitive data and achieving low–cost sensor design. Simulation results demonstrate that, the proposed routing scheme outperforms related existing approaches in terms of all the evaluation metrics such as packet delivery ratio, energy efficiency and timely delivery of data.

Wastewater management is a mechanism that is used to extract and refine pollutants from wastewater or drainage that can be recycled to the water supply with minimal environmental effects. New methods and techniques are required to ensure safe and smart wastewater management systems in smart cities because of the present deteriorating environmental state. Wireless sensor networks and the Internet of Things (IoT) represent promising wastewater treatment technologies. The elaborated literature survey formulates a conceptual framework with an Internet of Things (IoT)-based wastewater management system in smart cities (IoT-WMS) using blockchain technology. Blockchain technology is now being used to store information to develop an incentive model for encouraging the reuse of wastewater. Concerning the quality and quantity of recycled wastewater, tokens are issued to households/industries in smart cities. Nevertheless, this often encourages tampering with the information from which these tokens are awarded to include certain rewards. Anomaly detector algorithms are used to identify the possible IoT sensor data which has been tampered with by intruders. The model employs IoT sensors together with quality metrics to measure the amount of wastewater produced and reused. The simulation analysis shows that the proposed method achieves a high wastewater recycling rate of 96.3%, an efficiency ratio of 88.7%, a low moisture content ratio of 32.4%, an increased wastewater reuse of 90.8%, and a prediction ratio of 92.5%.

Precision agriculture is a trending technology that helps in improving the agricultural productivity. This technique combines various technologies like Internet of Things (IoT), remote sensing, information technology and wireless sensor networks (WSN). In this research, we propose a new technique for adaptive water scheduling in precision agriculture based on wireless sensors like moisture and temperature sensors. In the proposed framework, the secure data transfer in the cloud is achieved using blockchain technology. Further, energy efficient data transfer is attained by means of Improved LEACH algorithm. The data from the sensors are acquired using PIC micro controller module. The acquired data are then transmitted to the cloud using Raspberry Pi module. This data is then secured and verified using block chain-based IoT technique. In this way, the blockchain technology plays a pivotal role in sharing the agricultural data in the cloud in a reliable and transparent manner. Further, blockchain technology also provides data security in a decentralized manner. The main drawback in wireless sensor-based precision agricultural system is the limitation of battery power. To overcome this drawback, we have implemented Improved LEACH protocol for achieving energy efficiency. In this protocol, a new threshold limit is introduced to increase the level of energy efficiency. The usage of blockchain and ILEACH protocol helps in achieving an intelligent farming system. The proposed technique helped in achieving an increased throughput of 63%. Further, the data from other sensors like pH sensor and humidity sensor were transmitted to the cloud in real-time. The entire sensor data were available to the farmers using IoT-based smart phone module to enable remote monitoring.

A non-coherent detection scheme for the IEEE 802.15.4 offset-quadrature phase shift keying (O-QPSK) modulation receivers is proposed to achieve perfect communication, which is an important assumption in blockchain technology. More specifically, the residual carrier frequency offset (CFO) in the chip sample is first estimated and compensated, and then the conventional non-coherent detection method is directly configured for data demodulation. Simulation results show that, compared with the conventional scheme, this augmented detector can achieve rather encouraging reduction in packet-error rate (PER) as well as implementation complexity. Then, a more reasonable balance between receiver complexity and reliability is provided, which is clearly desirable for easy application of blockchain technology in wireless sensor networks (WSNs) .

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.

International audience

Use of blockchain in health sensor networks to secure information integrity and accountability

Today, blockchain technology is no longer confined to the financial sector for which it was originally designed, but has been integrated across diverse fields, achieving noteworthy advancements. Researchers have endeavored to demonstrate the feasibility and potential of blockchain applications beyond finance. This paper introduces the characteristics and workflow of blockchain technology. It provides an overview of the current state of blockchain applications in healthcare, logistics management, and transportation, analyzing several specific examples and presenting their corresponding simplified frameworks. These frameworks are conductive to traditional sectors by offering additional functionalities. In healthcare, blockchain is utilized to enhance the security and integrity of the sensitive health data records, as well as to facilitate remote patient monitoring. In logistics management, blockchain is employed to effectively enhance data security and ensure cold chain traceability, thereby improving the transparency and reliability of supply chain operations. In transportation, blockchain supports the development of intelligent transportation systems. Due to the inherent limitations of blockchain technology, such as issues with scalability, and the specific deficiencies of certain proposed frameworks, including the inaccuracies of Body Area Sensor Networks (BSN) and the drawbacks associated with Rivest-Shamir-Adleman (RSA) encryption, these integration efforts continue to face challenges. Various approaches, such as sharding, have been proposed to address these drawbacks and improve the efficacy of blockchain implementations in these domains. Despite these challenges, the ongoing research and development efforts indicate a promising future for blockchain technology in a wide array of applications.

Landmark-based routing (LR) provides a promising approach for scalable point-to-point routing in wireless sensor networks (WSNs). Though various approaches have been proposed for landmark-based routing, they either introduce significant computational complexity or are inefficient in realistic, dynamic environments. In this paper, we identify three design principles that could form the basis of efficiency: algorithmic simplicity, update efficiency, and application awareness. Motivated by these principles, we present XLR, a new, flexible and comprehensive framework that tackles the inefficiency of landmark-based routing. XLR consists of four components: Relay Selection (RS), Parametric P-Norm distance function (PPN), Efficient Update with Coordinate Difference (EUCD) and General Forwarding (GF). The key advantage of XLR is that any subset of XLR's components can be independently incorporated into most landmark-based routing protocols.We perform extensive simulations to demonstrate that: (i) RS, a simple method, yields good performance comparable with previous methods, (ii) PPN increases LR performance considerably, (iii) EUCD reduces coordinate update overhead by up to 39%, and (iv) our GF outperforms previous approaches that consider factors such as link quality, delay and power consumption independently.