Distributed Location and Trust Based Replica Detection in Wireless Sensor Networks

A Wireless Sensor network (WSN) consists of large number of low cost low power sensor nodes. The nature of wireless sensor networks makes them very attractive to attackers. One of the most popular and serious attacks in wireless sensor networks is sink hole attack and most existing protocols to defend against this attack used cryptographic methods with keys. In sink hole attack a sensor node will have a lot of false neighbors. Wireless Sensor Network has a dynamic topology, intermittent connectivity, and resource constrained device nodes. Researchers over the past years have encouraged the use of mobile agent to overcome these challenges. The proposed scheme is to defend against sink hole attacks using mobile agents. Mobile agent is a program segment which is self controlling. They navigate from node to node not only transmitting data but also doing computation. They are an effective paradigm for distributed applications, and especially attractive in a dynamic network environment. A routing algorithm with multiple constraints is proposed based on mobile agents. It uses mobile agents to collect information of all mobile sensor nodes to make every node aware of the entire network so that a valid node will not listen the cheating information from malicious or compromised node which leads to sink hole attack. The significant feature of the proposed mechanism is that it does not need any encryption or decryption mechanism to detect the sink hole attack. This mechanism does not require more energy than normal routing protocols like AODV. Here we implement a simulation-based model of our solution to recover from a Sinkhole attack in a Wireless Sensor Network. The mobile agents were developed using the Aglet.

Outlier detection is an important task for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). Outliers in sensed data may be caused due to compromised or malfunctioning sensor nodes. In this paper, we propose a centralized and a distributed approach based on the principal component analysis (PCA) for outlier detection in WSNs. In the distributed approach, we partition the network into multiple groups of sensor nodes. Each group has a group head and several member nodes. Every member node uses a fixed- width clustering algorithm and sends a description of its local sensed data to the group head. The group head then applies a distributed PCA to establish a global normal pattern and detect outliers. This pattern is periodi- cally updated using weighted coefficients. We compare the performance of the centralized and distributed approaches based on the real sensed data collected by 54 Mica2Dot sensors deployed in Intel Berkeley Re- search Lab. The experimental results show that the distributed approach reduces both communication over- head and energy consumption, while achieving comparable accuracy.

Event detection in wireless sensor networks is a sophisticated method for processing sampled data directly on the sensor nodes, thereby reducing the need for multihop communication with the base station of the network. In contrast to application-agnostic compression or aggregation techniques, event detection pushes application-level knowledge into the network. In-network event detection - especially the distributed form involving multiple sensor nodes - has thus an exceptional potential to increase energy efficiency, thus prolonging the lifetime of the network. In this article, we summarize recently proposed system architectures and algorithms employed for event detection in wireless sensor networks. On the example of the AVS-Extrem platform, we illustrate how energy-efficient event detection can be implemented through a combination of custom hardware design and distributed event detection algorithms. We then continue to present a brief evaluation of the detection accuracy and the energy consumption that is achievable by current systems.

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

Data reliability in Wireless Sensor Networks (WSNs) has a substantial influence on their smooth functioning and resource limitations. In a WSN, the data aggregated from clustered sensor nodes are forwarded to the base station for analysis. Anomaly Detection (AD) focuses on detecting outlier data to ensure consistency during data aggregation. As WSNs have critical resource limitations concerning energy consumption and sensor node lifetime, AD is supposed to provide data integrity with minimum energy consumption, which has been an active research problem. Hence, researchers are striving for methods to improve the accuracy of data handled with a concern on the constraints of WSNs. This paper introduces a Feed-forward Autoencoder Neural Network (FANN) model to detect abnormal instances with improved accuracy and reduced energy consumption. The proposed model also acts as a False Positive Reducer intending to reduce false alarms. It has been compared with the other dominant unsupervised algorithms over robustness and other significant metrics with real-time datasets. Relatively, our proposed model yields an improved accuracy with fewer false alarms thereby supporting a sustainable WSN.

Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

Wireless Sensor Networks (WSNs) are essential for real-time monitoring and control, but sensor faults can compromise data integrity and reduce the reliability of decisions. Supervised fault detection techniques typically require large amounts of labeled data, which are often unavailable in real-world scenarios. This paper proposes a data-efficient, unsupervised fault detection method that combines an LSTM-Attention Autoencoder with a dynamic thresholding mechanism. The model captures temporal patterns in normal sensor behavior and detects anomalies through context-aware reconstruction error analysis. Unlike conventional static thresholds, the dynamic approach adapts to changing sensor conditions, enhancing robustness in noisy or drifting environments. Experimental results on a synthetic dataset generated from real temperature sensor readings show that the proposed method achieves an F1-score of 98.90% and a recall of 99.24%, outperforming baseline methods including plain autoencoders, LSTM autoencoders, isolation forest, and one-class SVM. These findings demonstrate the model's effectiveness for real-time, label-free fault detection in resource-constrained WSNs.

The nature of wireless sensor networks (WSNs) makes them very vulnerable to adversary's malicious attacks. Therefore, network security is an important issue to WSNs. Due to the constraints of WSN, intrusion detection in WSNs is a challengeable task. In this paper, we present a novel intrusion detection mechanism for WSNs, which is composed of a secure data communication algorithm and an intrusion detection algorithm. The major contribution of this paper is that we propose an original secure mechanism to defend WSNs against malicious attacks by using the information generated during data communication. The approach is able to protect the data communication in a WSN even if some sensor nodes are compromised by adversary. The proposed approach is easy to be implemented and performed in resource-constrained WSN. We also evaluate the proposed approach by a simulation experiment and analyze the simulation results in detail.

Widely used computer systems have always become a target for different type of attacks. With the specifications of easiness in deploying and setting up low cost and low power consumption systems, Wireless Sensor Networks (WSN) have also become a specific target for attackers. The WSNs, which consist of sensor nodes that sends information they perceive and forward to a base station/sink, cannot be directly protected by the traditional security precautions because of low resources of nodes, such as memory and computing capability. Hence, it is tried to find out different methods for intrusion detection in WSNs. This paper surveys frameworks for intrusion detection in WSNs by using a swarm optimization algorithm as ant colony approach.

How to sense and monitor the environment with high quality is an important research subject in the Internet of Things (IOT). This paper deals with the important issue of the balance between the quality of target detection and lifetime in wireless sensor networks. Two target-monitoring schemes are proposed. One scheme is Target Detection with Sensing Frequency K (TDSFK), which distributes the sensing time that currently is only on a portion of the sensing period into the entire sensing period. That is, the sensing frequency increases from 1 to K. The other scheme is Target Detection with Adjustable Sensing Frequency (TDASF), which adjusts the sensing frequency on those nodes that have residual energy. The simulation results show that the TDASF scheme can improve the network lifetime by more than 17.4% and can reduce the weighted detection delay by more than 101.6%.

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

Wireless Sensor Networks (WSNs) have grown to become one of the most promising and interesting fields over the past few years. WSNs are wireless networks consisting of distributed sensor nodes which cooperatively monitor physical or environmental conditions. A sensor node is a tiny and simple device with limited computational resources. Sensor nodes are randomly and densely deployed in a sensed environment. WSN is designed to detect events or phenomena, and collect and return sensed data to the user. WSNs have been used inmany applications such as battlefield surveillance, trafficmonitoring, health-care, environment monitoring, etc. Some basic features of sensor networks are (Ilyas & Mahgoub, 2005):

Wireless Sensor Network (WSN) is characterized with limited battery power and limited computation capability. Sensor nodes which produce a data set that is different from their counter parts are called Outlier nodes. For example, in a particular room, if we expect temperature of 25°C and if we receive a temperature reading of 73°C. This is called outlier data and the node which produces this data is called outliers nodes. We are considering a system where user is interested only in outlier data. We assumed that outlier data is generated due to the ambiance parameter. We also assumed that instead of sending all the sensed data, nodes are required to send the outlier data in response to a query from the base station. In this paper we found that using traditional routing protocol for communications is not an optimum solution. Traditional routing protocols consume more memory and battery power in the route-request process. We have also suggested that use of "Random Walk on Random Graph Technique" reduces the overhead of transmitting packets. We have proved this with analysis and simulation. We argue that with random walk on random graph mechanism, energy consumption is minimized and numbers of packets flooded in the network is very less.

In this paper, a binary hypothesis distributed detection problem in correlated wireless sensor networks with cooperative relays deployment is considered. In particular, the effect of correlation between sensor nodes is modeled and analyzed in Rayleigh flat fading channels in order to explore the natural tradeoffs between the number of sensor/relay nodes and the detection error performance in the network. Specifically, two communication protocols are utilized; in protocol I, each sensor node communicates its observation directly to the fusion center while in protocol II, amplify-and-forward (AF) cooperative relays are deployed and a fewer number of sensors is used. Based on the theoretical analysis and simulations, it is revealed that employing less sensor nodes and instead deploying relay nodes results in significant performance gains under strict network power constraint It is concluded that with cooperative distributed detection and exploitation of spatial diversity, better detection error performance is achieved as well as reduction in the required number of sensor nodes.

Intrusion detection in wireless sensor networks is crucial for ensuring network security. This study focuses on the problem of estimating the number of barriers necessary for effective intrusion detection in WSNs. The aim is to make accurate predictions to improve security optimization in WSNs. To this end, various regression models (Linear Regression, Ridge and Lasso Regression, Random Forest, Support Vector and Gradient Boosting) were applied on a dataset including parameters such as field size, sensing range, transmission range, and the number of sensor nodes. The performance of the models was evaluated with metrics such as R2, RMSE, MAE, and MSE, and validated with 5-fold cross-validation. The results show that the Linear Regression model achieved the best performance with the lowest error values (RMSE 0.0181, MAE 0.0136, and MSE 0.0003), followed closely by Ridge Regression. These findings highlight the effectiveness of simple linear models in accurately predicting barrier requirements, supporting the optimization of WSN security systems