Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks

There has been a tremendous growth in the field of wireless sensor networks (WSNs) in recent years, which is reflected in various applications. As the use of WSN applications increases, providing security to WSNs becomes a leading issue. This is complex due to the unique features of WSNs. This paper proposes a trust-based intrusion detection that uses multi-attribute trust metrics to improve detection accuracy. It uses an enhanced distributive trust calculation algorithm that involves monitoring neighbouring nodes and trust calculation using the trust metrics message success rate (MSR), elapsed time at node (ETN), correctness (CS) and fairness (FS). In addition to the normal communication-based trust property MSR, this paper uses effective parameters like ETN, which focuses on data and address modification attacks in an effective manner, and two social-interaction-based parameters CS and FS, which address trust-related attacks effectively. Simulation results show that the proposed method has higher performance and provides more security in terms of detection accuracy and false alarm rate.     

Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to sensor node fault detection. Recurrent neural networks (NNs) are used to model a sensor node, the node's dynamics, and interconnections with other sensor network nodes. An NN modeling approach is used for sensor node identification and fault detection in WSNs. The input to the NN is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type NN. The input to the NN and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme.     

Controlled deployments for wireless sensor networks

In wireless sensor networks (WSNs), the operation of sensor nodes has to rely on a limited supply of energy (such as batteries). To support long lifetime operation of WSNs, an energy-efficient way of sensor deployment and operation of the WSNs is necessary. A new controlled layer deployment (CLD) protocol to guarantee coverage and energy efficiency for a sensor network is proposed. CLD outperforms previous similar protocols in that it can achieve the same performances and guarantee full area coverage and connection using a smaller number of sensors. It can also ameliorate the 'cascading problem' that reduces the whole network lifetime. Finally, analysis and simulation results show that CLD can use fewer sensor nodes for coverage and also increases the lifetime of the sensor network when compared with the probing environment and adapting sleeping (PEAS) protocol.     

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

An IoT-based wireless sensor network (WSN) comprises many small sensors to collect the data and share it with the central repositories. These sensors are battery-driven and resource-restrained devices that consume most of the energy in sensing or collecting the data and transmitting it. During data sharing, security is an important concern in such networks as they are prone to many threats, of which the deadliest is the wormhole attack. These attacks are launched without acquiring the vital information of the network and they highly compromise the communication, security, and performance of the network. In the IoT-based network environment, its mitigation becomes more challenging because of the low resource availability in the sensing devices. We have performed an extensive literature study of the existing techniques against the wormhole attack and categorised them according to their methodology. The analysis of literature has motivated our research. In this paper, we developed the ESWI technique for detecting the wormhole attack while improving the performance and security. This algorithm has been designed to be simple and less complicated to avoid the overheads and the drainage of energy in its operation. The simulation results of our technique show competitive results for the detection rate and packet delivery ratio. It also gives an increased throughput, a decreased end-to-end delay, and a much-reduced consumption of energy.     

Distributed inference in wireless sensor networks

Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.     

无线传感器网络中基于信誉的恶意节点检测

提出一种基于信誉的恶意节点检测方法——RMDMN,在分簇的网络结构基础上,对节点的行为属性(如丢包率、转发率、位置匹配信息等)和网络攻击进行建模,结合阈值比较法动态地更新节点信誉值并进行恶意节点判断.实验仿真显示,该方法具有一定的恶意节点检测能力.     

Voronoi-based relay placement scheme for wireless sensor networks

Energy consumption is a crucially important issue in battery-driven wireless sensor networks (WSNs). In most sensor networks, the sensors near the data collector (i.e. the sink) become drained more quickly than those elsewhere in the network since they are required to relay all of the data collected in the network to the sink. Therefore more balanced data paths to the sink should be established in order to extend the lifetime of the sensor network. Accordingly, a novel relay deployment scheme for WSNs based on the Voronoi diagram is proposed. The proposed scheme is applicable to both two-dimensional and three-dimensional network topologies and establishes effective routing paths that balance the traffic load within the sensor network and alleviate the burden on the sensors around the sink. Simulation results indicate that the number of relays deployed in the proposed scheme is similar to that deployed in the predetermined location scheme and is significantly less than that deployed in the minimum set cover scheme. Furthermore, the lifetime of the sensor network containing relay nodes deployed using the current scheme is longer than that achieved using either the predetermined location scheme or the minimum set cover scheme.     

Distributed K-coverage self-location estimation scheme based on Voronoi diagram

The success of many wireless sensor network (WSN) applications, such as moving target tracking or environment monitoring, is dependent upon achieving 'k-coverage' of the sensed area, that is every point in the surveillance area is monitored by at least k sensors. This study presents a novel distributed self-location estimation scheme based on a Voronoi diagram to achieve k-coverage in a WSN with mobile nodes. The simulation results show that the proposed scheme effectively to perform k-coverage within the sensing field and fast convergent to fulfil more than 88% k-coverage ratio following three movements for the minimal required sensor deployment.     

Transmission Control under Multi-Service Disciplines in Wireless Sensor Networks

The wireless sensor network (WSN), as the terminal data acquisition system of the 5G network, has attracted attention due to advantages such as low cost and easy deployment. Its development is mainly restricted by energy. The traditional transmission control scheme is not suitable for WSNs due to the significant information interaction. A switchable transmission control scheme for WSNs based on a queuing game (SQGTC) is proposed to improve network performance. Considering that sensor nodes compete for the resources of sink nodes to realize data transmission, the competitive relationship between nodes is described from the perspective of a game. Different types of sensor node requests require a sink node to provide different service disciplines. Mathematical models of social welfare are established for a sink node under the service disciplines of first-come, first-served (FCFS), egalitarian processor sharing (EPS), and shortest service first (SSF). The optimal service strategies are obtained by maximizing social welfare. The sensor nodes provide the expected benefits and satisfy the service requirements of the requests, and the sink node switches the transmission control strategy for the service. Simulation results show that the proposed scheme improves the data transmission efficiency of WSNs and achieves the optimal allocation of resources.     

An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided using energy-harvesting or energy-scavenging techniques with employing huge, and expensive devices. However, the high price of energy-harvesting devices hinders the use of this technology in large and dense networks, as such networks require multiple WMCs to improve the quality of service to the sensor nodes. To solve this problem, multiple power banks can be employed instead of utilizing WMCs. Furthermore, the long waiting time of critical sensor nodes located outside the charging range of the energy transmitters is another limitation of the previous works. However, the sensor nodes are equipped with radio frequency (RF) technology, which allows them to exchange energy wirelessly. Consequently, critical sensor nodes located outside the charging range of the WMC can easily receive energy from neighboring nodes. Therefore, in this paper, an energy-efficient and cost-effective wireless power transmission (ECWPT) scheme is presented to improve the network lifetime and performance in forest fire detection-based systems. Simulation results exhibit that ECWPT scheme achieves improved network performance in terms of computational time (12.6%); network throughput (60.7%); data delivery ratio (20.9%); and network overhead (35%) as compared to previous related schemes. In conclusion, the proposed scheme significantly improves network energy efficiency for WSN.     

Diagnosing Anomalies and Identifying Faulty Nodes in Sensor Networks

In this paper, an anomaly detection approach that fuses data gathered from different nodes in a distributed sensor network is proposed and evaluated. The emphasis of this work is placed on the data integrity and accuracy problem caused by compromised or malfunctioning nodes. The proposed approach utilizes and applies Principal Component Analysis simultaneously on multiple metrics received from various sensors. One of the key features of the proposed approach is that it provides an integrated methodology of taking into consideration and combining effectively correlated sensor data, in a distributed fashion, in order to reveal anomalies that span through a number of neighboring sensors. Furthermore, it allows the integration of results from neighboring network areas to detect correlated anomalies/attacks that involve multiple groups of nodes. The efficiency and effectiveness of the proposed approach is demonstrated for a real use case that utilizes meteorological data collected from a distributed set of sensor nodes     

EIAS: An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

Wireless sensor networks (WSNs) are the major contributors to big data acquisition. The authenticity and integrity of the data are two most important basic requirements for various services based on big data. Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs. However, the process of data acquisitions in WSNs are in open environments, data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence, such as coalition attack. Aimed to provide data authenticity and integrity protection for WSNs, an efficient and secure identity-based aggregate signature scheme (EIAS) is proposed in this paper. Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks. The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.     

Efficient monitoring approach for reputation system-based trust-aware routing in wireless sensor networks

Trust-aware routing in wireless sensor networks (WSNs) is a crucial problem that has drawn the attention of researchers. The motivation for tackling this problem arises directly from the highly constrained nature of a WSN and its easy exposure to insecure conditions. In this regard, reputation-based solutions are used to provide trust-aware routing. However, this approach requires that a node needs to continuously monitor its environment to detect misbehaviour events. This is considered to be a costly operation for WSN nodes because of its resource scarcity. Here, the authors propose a reputation system-based solution for trust-aware routing, which implements a new monitoring strategy called an efficient monitoring procedure in a reputation system (EMPIRE). EMPIRE is a probabilistic and distributed monitoring methodology that tries to reduce the monitoring activities per node while maintaining the ability to detect attacks at a satisfactory level. The proposed procedure has been evaluated using the Monte Carlo simulation. New evaluation methodologies are introduced to test and explore the efficiency of our proposed procedure. Simulation results of the reputation system show that reducing monitoring activities with EMPIRE does not have a significant impact on system performance in terms of security.     

A statistical analysis on the leak detection performance of underground and overground pipelines with wireless sensor networks through the maximum likelihood ratio test

Leaks in pipelines of the oil and gas industry are an economic and environmental problem that needs to be detected early and effectively. Wireless sensor networks (WSNs) have been researched as one of those technologies to be used in the remote monitoring of pipeline infrastructure. The idea of using tiny sensor nodes on pipelines seemingly provides industries with effective and reliable real-time monitoring, and better coverage density per area. The benefits are apparent in the deployment of WSNs for pipeline monitoring. However, what really lacks is an actual comparison in the detection performance between deployment in overground pipelines and underground pipelines. Extensive research has been going on the use of wireless underground sensor networks for a number of applications. This paper attempts to provide a statistical insight on the concepts of leak detection performance of WSNs when deployed on overground and underground pipelines. The approach in the study employs the hypothesis testing problem to formulate a solution on the detection plan. Through the hypothesis test, the maximum likelihood ratio scheme is used to provide an optimal performance analysis of the detection idea. The test also takes into consideration the signal to noise ratio performance of the two settings of underground and overground and is crucial in bringing up a conjecture on the performance of detection. As would be shown in the paper, thresholds, determined by probability, are the key in ensuring a good detecting performance for the WSN.     

Effective metric for detecting distributed denial-of-service attacks based on information divergence

In information theory, the relative entropy (or information divergence or information distance) quantifies the difference between information flows with various probability distributions. In this study, the authors first resolve the asymmetric property of Re′nyi divergence and Kullback?Leibler divergence and convert the divergence measures into proper metrics. Then the authors propose an effective metric to detect distributed denial-of-service attacks effectively using the Re′nyi divergence to measure the difference between legitimate flows and attack flows in a network. With the proposed metric, the authors can obtain the optimal detection sensitivity and the optimal information distance between attack flows and legitimate flows by adjusting the order?s value of the Re′nyi divergence. The experimental results show that the proposed metric can clearly enlarge the adjudication distance, therefore it not only can detect attacks early but also can reduce the false positive rate sharply compared with the use of the traditional Kullback?Leibler divergence and distance approaches.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.     

LWDSA: light-weight digital signature algorithm for wireless sensor networks

The essential security mechanism in wireless sensor networks (WSNs) is authentication, where nodes can authenticate each other before transmitting a valid data to a sink. There are a number of public key authentication procedures available for WSN in recent years. Due to constraints in WSN environment there is a need for light-weight authentication procedure that consumes less power during computation. This proposed work aims at developing a light-weight authentication protocol using MBLAKE2b with elliptic curve digital signature algorithm (ECDSA). The proposed protocol is also tested using the protocol verification tool Scyther and found to be secure in all claims and roles. This proposed algorithm increases the network life time and reduces the computation time, which is essential for the constrained environment like WSNs.     

Smart-City-based Data Fusion Algorithm for Internet of Things

Increasingly, Wireless Sensor Networks (WSNs) are contributing enormous amounts of data. Since the recent deployments of wireless sensor networks in Smart City infrastructures, significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation. Using wireless sensor nodes, a Smart City environment may now be shown for the benefit of residents. The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society, including the elderly and others. Weak, Quality of Service (QoS) and poor data performance are common problems in WSNs, caused by the data fusion method, where a small amount of bad data can significantly impact the total fusion outcome. In our proposed research, a WSN multi-sensor data fusion technique employing fuzzy logic for event detection. Using the new proposed Algorithm, sensor nodes will collect less repeated data, and redundant data will be used to increase the data's overall reliability. The network's fusion delay problem is investigated, and a minimum fusion delay approach is provided based on the nodes’ fusion waiting time. The proposed algorithm performs well in fusion, according to the results of the experiment. As a result of these discoveries, It is concluded that the algorithm describe here is effective and dependable instrument with a wide range of applications.     

Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks

Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.