IoT-based Wireless Sensor Networks Research Articles

RETRACTED ARTICLE: OTP-ER: an ordered transmission paradigm for effective routing in IoT based wireless sensor networks

RETRACTED ARTICLE: OTP-ER: an ordered transmission paradigm for effective routing in IoT based wireless sensor networks

Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security.

Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25–30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25–30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL.

Detecting Sinkhole Attacks in IoT-Based Wireless Sensor Networks Using Distance From Base Station

Wireless sensor networks (WSNs) are infrastructure-less in nature, which contains numbers of autonomous sensor nodes. These sensor nodes are dedicated to monitoring the physical conditions of the environment and are organizing the collected data at a central location. Application area of WSN, like - heath care, military surveillance, are sensitive with respective to information sensed, that’s why security of WSN needs to be very effective. Providing security to WSN plays a major role as it consists of limited resources. The security system should lie within the boundary of the resource potential as well as should be competent enough to handle attacks. Intrusion detection system (IDS) is one such type of defense system, which can fulfill the measure of limitation of resources. In this paper, a detection technique is proposed against sinkhole attack using the Euclidean distance of each node from base station. The main advantage of the proposed technique is that it doesn't require any extra hardware setup as well as doesn't require extra communication cost.

Modern Energy Optimization Approach for Efficient Data Communication in IoT-Based Wireless Sensor Networks

Many researchers are drawn to mobile wireless sensor networks (WSN) and the Internet of Things (IoT) because of the significant challenges of power consumption and network connectivity. A technique that takes into consideration the characteristics such as network probability, the identified region of individual nodes, and the radius of the whole identified region is presented in this article. Free-space propagation is carried out in the region of interest. This approach assures network connection, long-term communication sustainability, and maximum energy efficiency. It was discovered that a mathematical network model can be built using the probability theory. It has been possible to examine and evaluate the changes in sensor nodes as a function of distance from the detection region using this approach. As a result, a correlation has been established between a network’s communication radius and the identified region. Additionally, a novel method has been developed to reduce energy consumption and sustain connectivity through boosting the connectivity feature. Notably, IoT-based WSN architectures require more energy optimization than any other network, because they have resource-limited nodes. Also, a simulation plot of the proposed approach’s mathematical network scheme is shown to show if it works. The proposed method consumes much less energy averagely 40% compared to the existing methods, which are LEACH, ZTR, and DSR when the radius is 100.

Cloud-based virtualization environment for IoT-based WSN: solutions, approaches and challenges.

Internet of Things (IoT) is an ever-growing technology that enables advanced communication among millions of various devices to provide ubiquitous services without human intervention. The potential growth of electronic devices in sensing systems has led to the realization of IoT paradigm where applications depend on sensors to interact with the environment and collect data in a real-time scenario. Nowadays, smart applications require fast data acquisition, parallel processing, and dynamic resource sharing. Unfortunately, these requirements can not be supported efficiently with traditional Wireless Sensor Networks (WSN) due to the deficiency of computing resources and the lack of resource-sharing. Therefore, it is not recommended to develop innovative applications based on these constrained devices without further enhancement and improvement. Hence, this article explores a coeffective solution based on Cloud Computing and Virtualization Techniques to address these challenges. Cloud computing provides efficient computing resources and huge storage space, while the virtualization technique allows resources to be virtualized and shared between various applications. Integrating IoT-WSN with the Cloud-based Virtualization Environment will eliminate the drawbacks and limitations of conventional networks and facilitate the development of novel applications in a more flexible way. Furthermore, this article reviews the recent trends in IoT-WSN, virtualization techniques, and cloud computing. Also, we present the integration process of sensor networks with Cloud-based Virtualization and propose a new general architecture view for the Sensor-Cloud paradigm, and discuss its key elements, basic principles, lifecycle operation, and outline its advantages and disadvantages. Finally, we review the state-of-the-art, present the major challenges, and suggest future work directions.

A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network

A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network

A survey of Sybil attack countermeasures in IoT-based wireless sensor networks.

Wireless sensor networks (WSN) have been among the most prevalent wireless innovations over the years exciting new Internet of Things (IoT) applications. IoT based WSN integrated with Internet Protocol IP allows any physical objects with sensors to be connected ubiquitously and send real-time data to the server connected to the Internet gate. Security in WSN remains an ongoing research trend that falls under the IoT paradigm. A WSN node deployed in a hostile environment is likely to open security attacks such as Sybil attack due to its distributed architecture and network contention implemented in the routing protocol. In a Sybil attack, an adversary illegally advertises several false identities or a single identity that may occur at several locations called Sybil nodes. Therefore, in this paper, we give a survey of the most up-to-date assured methods to defend from the Sybil attack. The Sybil attack countermeasures includes encryption, trust, received signal indicator (RSSI), encryption and artificial intelligence. Specifically, we survey different methods, along with their advantages and disadvantages, to mitigate the Sybil attack. We discussed the lesson learned and the future avenues of study and open issues in WSN security analysis.

An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings

Smart buildings generally adopt a centralized optimal control for heating, ventilation and air-conditioning (HVAC) systems to improve the building system performance. As a crucial technology adopted in smart buildings, Internet of Things (IoT) based wireless sensor networks (WSNs) are promising platforms for implementing novel distributed optimal control to achieve distributed intelligence effectively. Such a future mist computing paradigm can be hindered by the limited energy resource of WSNs. The event-driven optimization method activates the optimization only when events occur. It can save the energy resource of WSNs, and thus pave the way for implementing the distributed optimal control architecture in IoT-based WSNs. This study therefore proposes an event-driven multi-agent based distributed optimal control strategy for HVAC systems for implementation in IoT-based battery-powered WSNs. The strategy consists of two novel schemes. First, an event determination scheme determines the event threshold by comprehensively considering the system performance and the total energy consumption of individual sensors implementing the distributed optimal control architecture. Second, an event-driven distributed optimization scheme solves the optimization problems with distributed optimization algorithms in IoT sensors of limited data processing capacity when an event occurs. Comparison and case studies are conducted to compare different strategies and validate the proposed strategy. Results show that different strategies require very different sensor energy consumption. The proposed strategy can provide satisfactory system performance while reducing the energy consumption of IoT sensors.

An Efficient Routing Approach to Maximize the Lifetime of IoT-Based Wireless Sensor Networks in 5G and Beyond

The Internet of Things grew rapidly, and many services, applications, sensor-embedded electronic devices, and related protocols were created and are still being developed. The Internet of Things (IoT) allows physically existing things to see, hear, think, and perform a significant task by allowing them to interact with one another and exchange valuable knowledge when making decisions and caring out their vital tasks. The fifth-generation (5G) communications require that the Internet of Things (IoT) is aided greatly by wireless sensor networks, which serve as a permanent layer for it. A wireless sensor network comprises a collection of sensor nodes to monitor and transmit data to the destination known as the sink. The sink (or base station) is the endpoint of data transmission in every round. The major concerns of IoT-based WSNs are improving the network lifetime and energy efficiency. In the proposed system, Optimal Cluster-Based Routing (Optimal-CBR), the energy efficiency, and network lifetime are improved using a hierarchical routing approach for applications on the IoT in the 5G environment and beyond. The Optimal-CBR protocol uses the k-means algorithm for clustering the nodes and the multihop approach for chain routing. The clustering phase is invoked until two-thirds of the nodes are dead and then the chaining phase is invoked for the rest of the data transmission. The nodes are clustered using the basic k-means algorithm during the cluster phase and the highest energy of the node nearest to the centroid is selected as the cluster head (CH). The CH collects the packets from its members and forwards them to the base station (BS). During the chaining phase, since two-thirds of the nodes are dead and the residual energy is insufficient for clustering, the remaining nodes perform multihop routing to create chaining until the data are transmitted to the BS. This enriches the energy efficiency and the network lifespan, as found in both the theoretical and simulation analyses.

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.

Towards improved clustering and routing protocol for wireless sensor networks

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

CBPR: A Cluster-Based Backpressure Routing for the Internet of Things

The concept of the Internet of Things (IoT)-based Wireless Sensor Network (WSN) is rapidly gaining wide-spread recognition and acceptance in our day-to-day lives. Nowadays, the application of the IoT sensor nodes in various domains of endeavors such as health-care, smart homes, industrial and production sectors, control networks, and in many other fields has continued to increase steadily. In IoT-based WSN, sensor nodes dynamically join the internet and collaborate to accomplish a mission by sensing and collecting event data from the application field. The sensor nodes thus forward the collected information to the sink nodes or to the nearest base station for further transmission. However, one of the significant drawbacks of the IoT-based WSN networks is that the battery life of the sensor nodes is often short-lived due to the energy-limited nature of the electronic sensors, resulting in the network’s short lifetime. Thus, prolonging the lifetime duration of the sensor nodes becomes a fundamental task. Whether the battery life of a sensor node is extended for a reasonable length of time or depleted in a moment depends mainly on the energy efficiency of the underlying routing protocol. Therefore, the issue of network lifetime can be fundamentally addressed by implementing an efficient and robust energy-aware routing protocol for sustainable and prolonged network operation time in a WSN based IoT network scenario. In this paper, a cluster-based Backpressure routing (CBPR) scheme has been proposed, which targets to prolong the network lifetime and enhance the data transmission reliability using energy load-balancing mechanism. For every cluster of the sensor node, the CBPR scheme elects a cluster head which has the highest energy level and the shortest distance to the sink node. The proposed CBPR routing scheme further utilizes a very robust data aggregation algorithm to checkmate and prevent the circulation of redundant data packets in the network while also exploiting the Backpressure scheduling machine for data packets queueing and for route selection, which allows it to select the next-hop sensor node based on the queue length value of the sensor nodes. Extensive simulations have been performed to evaluate the efficiency of the proposed CBPR routing scheme, which was compared with that of other well-known routing schemes such as the Information Fusion Based Role Assignment and Data Routing for In-Network Aggregation in terms of throughput, energy consumption, and packet delivery ratio.

Chain‐routing scheme with compressive sensing‐based data acquisition for Internet of Things‐based wireless sensor networks

The emerging Internet of Things (IoT)-based systems that integrate diverse types of sensors, mobiles and other technologies to physical world is becoming increasingly popular for use in wide varieties of applications. Compressive sensing (CS)-based information acquisition and in-network compression provide an effective method for accurate data recovery at the base station (BS) with reduced cost of communication. In this study, how CS can be combined with routing protocols for gathering data in IoT-based Wireless Sensor Networks (WSNs) in an energy-efficient manner are examined. A novel chain-routing scheme with CS based data acquisition is introduced that includes the following new algorithms: (1) Seed Estimation Algorithm (SEA) to find the best measurement matrix by selecting the best-estimated seed, (2) Chain Construction Algorithm (CCA) to organise the network nodes during transmitting and receiving process, (3) Compression approach with reduced consumption of energy that improves the lifetime of the network by minimising the local data traffic, and (4) Reconstruction Algorithm (RA) that reconstructs the original data with minimum reconstruction error. Here, extensive simulation and analysis results prove the performance of our proposed method in improving the network lifetime 35% better than the ECST algorithm and 93% better than the PEGASIS algorithm. In addition, the proposed reconstruction algorithm exceeds the other reconstruction algorithms performance.

Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

Energy Optimised Security against Wormhole Attack in IoT-Based Wireless Sensor Networks

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

Minimizing the quantity of transmitted data and protecting the devices from the adversary environment are the main challenges hindering the advancement of Internet of Things (IoT) network. Based on the theorem of unique prime factorization that any natural number can be expressed as the product of powers of prime numbers, we introduce a new scheme in this paper for secure and efficient gathering of the IoT data. The proposed scheme consists of three phases: Setup, Secure data gathering, and Data reconstruction. In the Setup phase, the properties of the prime numbers to create an efficient clustering and routing tree algorithm are utilized to reduce the power consumption during data transmission process. The Secure data gathering phase includes secure intra and inter communication procedures. During secure intra-communication procedure, the Compressive Sensing (CS) based method of data compression and encryption is used. Moreover, the prime number properties and the Bezier curve function are used to solve the CS problems. In the secure inter-communication procedure, the security performance of the IoT based Wireless Sensor Networks (WSNs) is improved by combining Private and Public key algorithms with the consideration of the power limitation of IoT devices. Finally, the proposed scheme introduces an adept reconstruction algorithm which is a hybrid algorithm of Bees and Genetic Algorithm to successfully restore the actual sensor data from the compressed samples, which improves the reconstruction process.

Blockchain Mechanism and Symmetric Encryption in A Wireless Sensor Network.

The Internet of Things (IoT) paradigm allows the connection and exchange of information between millions of smart devices. This paradigm grows and develops exponentially as do the risks and attacks on IoT infrastructures. Security, privacy, reliability, and autonomy are the most important requirements in IoT Systems. If these issues are not guaranteed, the IoT system could be susceptible to malicious users and malicious use. In centralized IoT systems, attacks and risks are greater, especially when data is transmitted between devices and shared with other organizations. To avoid these types of situations, this work presents a decentralized system that guarantees the autonomy and security of an IoT system. The proposed methodology helps to protect data integrity and availability based on the security advantages provided by blockchain and the use of cryptographic tools. The accuracy of the proposed methodology was measured on a temperature and humidity sensing IoT-based Wireless Sensor Network (WSN). The obtained results prove that the proposal fulfils the main requirements of an IoT system. It is autonomous, secure to share and send information between devices and users, has privacy, it is reliable, and the information is available in the infrastructure. Furthermore, this research demonstrates that the proposal is less susceptible to the most frequent attacks against IoT systems, such as linking attack, man in the middle, and Distributed Denial of Service (DDoS) attack.

An Efficient Compressive Sensing Routing Scheme for Internet of Things Based Wireless Sensor Networks

Internet of Things (IoT) integrates diverse types of sensors, mobiles and other technologies to physical world and IoT technology is used in a wide range of applications. Compressive sensing based in-network compression is an efficient technique to reduce communication cost and accurately recover sensory data at the base station. In this paper, we investigate how compressive sensing can be combined with routing protocols for energy efficient data gathering in IoT-based wireless sensor networks. We propose a new compressive sensing routing scheme that includes the following new algorithms: (1) seed estimation algorithm to find the best measurement matrix by selecting the best-estimated seed, (2) chain construction algorithm to organize the network nodes during transmitting and receiving process, (3) compression approach to reduce the energy consumption and prolong the network lifetime by reducing the local data traffic, and (4) reconstruction algorithm to reconstruct the original data with minimum reconstruction error. The simulation results reveal that the proposed scheme outperforms existing baseline algorithms in terms of energy consumption, network lifetime and reconstruction error.

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture.

Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

IoT Based Wireless Sensor Network for Power Quality Control in Smart Grid

The IoT based Power management system requires data from the feeder in the grid. Sufficient power supply with demand is a significant challenge for several countries around the world. RaPID growing demand for power supply requires power quality enhancement to get higher reliability in the smart grid. This smart power system sensor equipped that measures grid power capacity and update to the organization on a consistent schedule. Energy supplies to the specific region indicated by power install capacity in the grid; use a global system for mobile communications (GSM) messaging service to notify customers of power generation and power supply time. The IoT based wireless Sensor network (WSN) is a revolutionary system for smart monitoring. In this article propose a system demonstrate for the progress and implementation of WSN-based communication systems for smart monitoring and automated control in the electric grid. This work allows for the improvement of grid sharing for maintaining power quality. The dynamic controller has controlled the event of Power quality problem and voltage rise. Appropriate systems and controllers have been demonstrated and analyzed for control performance of a monitoring system in the smart grid.

Robust intrusion detection system based on fuzzy C means clustering scheme implemented in IoT-based wireless sensor networks

Research in wireless networks and communication protocols have reached new heights in recent times with the advent of state-of-the-art communication techniques and gadgets. They have found widespread utility in recent times especially with the advent of cloud computing and internet of things (IoT). IoTs offer immense potential in today's scenario aiding in smart computing and automation of things focused to making life of consumers easier and simpler. However, security of information transmitted and received over wireless networks is an essential concern to preserve privacy and authenticity of information. This research issue is however quite complex as it involves security of bulk information quite private and confidential to be delivered in a wireless medium prone to hackers. This research article has investigated the various issues related to security of IoT-based wireless sensor networks and proposed a fuzzy-based clustering algorithm to effectively detect an intrusion and make the information secure.