CAM: Congestion Avoidance and Mitigation in Wireless Sensor Networks

Summary Congestion in WSN increases the energy dissipation rates of sensor nodes as well as the loss of packets and thereby hinders fair and reliable event detection. We find that one of the key reasons of congestion in WSN is allowing sensing nodes to transfer as many packets as possible. This is due to the use of CSMA/CA that gives opportunistic medium access control. In this paper, we propose an energy efficient congestion avoidance protocol that includes source count based hierarchical and load adaptive medium access control and weighted round robin packet forwarding. We also propose in-node fair packet scheduling to achieve fair event detection. The results of simulation show our scheme exhibits more than 90% delivery ratio even under bursty traffic condition which is good enough for reliable event perception. Key words: Sensor Network, Congestion Avoidance, Hierarchical Medium Access, Fair Event Detection. 1. Introduction Wireless Sensor Networks (WSNs) are densely deployed for a wide range of applications in the military, health, environment, agriculture and smart office domain. These networks deliver numerous types of traffic, from simple periodic reports to unpredictable bursts of packets triggered by sensed events. Therefore, congestion happens due to contention caused by concurrent transmissions, buffer overflows and time varying wireless channel condition [1][2][3]. As WSN is a multi-hop network, congestion taking place at a single node may diffuse to the whole network and degrades its performance drastically [4]. Congestion causes many folds of drawbacks: (i) it increases energy dissipation rates of sensor nodes, (ii) it causes a lot of packet loss, which in turn diminish the network throughput, and (iii) it hinders fair event detections and reliable data transmissions. Therefore, congestion avoidance and fair packet delivery become a crucial research issue for the practical realization of WSN based envisioned applications.

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.

Rare events detection is one of the main applications in Wireless Sensor Networks (WSN) and is currently a central concern of a vast literature. Compressed Sensing (CS) theory has been proved to be quite adapted to this objective. Although this is not the first work on applying CS to sparse events detection in WSN, it is the first to highly justify the validity of the targets detection and counting problem formulation. In order to enhance the CS recovery capacity in WSN, this work considers an approach based on a coherence reduction of the sensing matrix premised on the transmitted power control (PC). Simulation results prove that, under the constraint of equal power consumption, the detection and counting performance is improved when the proposed power control scheme is employed compared to the case without PC.

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.

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.

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%.

For reliable event detection in wireless sensor networks, a sink requires collective information reported by sensor nodes sensing an event. In other words, the sink has to receive a certain amount of data dealing with the event for reliable event detection. Existing studies propose quantity-based event reliability protocols that improve event reliability by increasing the number of data successfully transmitted by controlling transport process. These studies present diverse schemes to successfully transmit as many reporting data as possible such as diversification of routing path for congestion avoidance and control of data reporting frequency rate for transport error compensation. However, since it frequently happens that wireless sensor networks with restricted capacity cannot deliver more than required amount of data due to data collision and congestion, the quantity-based event reliability protocols have limitation to raise the possibility of event detection. In this paper, we proposes a Quality-based Event Reliability Protocol (QERP) utilizing a property that the reporting data from sensor nodes are different in the contribution degree (CD) for event detection according to their environmental conditions. QERP selects sensor nodes to send their reporting data according to CD, and differentially transports them by CD-based buffer management and load balancing. Simulation results show that QERP detect more reliably and energy-efficiently an event than quantity-based event reliability protocols.

With the advancement of technology in micro-electronics and wireless communication, small miniature devices called sensor nodes can be used to perform various tasks by forming themselves in to wireless sensor networks. In Wireless Sensor Networks(WSN), event detection is one of the main requirements for most of the applications. An event can be a simple event or a combination of two or more simple events (Composite Event). Detecting and reporting an event desired by the application (user) inspite of stringent constraints of sensor nodes like low energy, low bandwidth, frequent failures etc., is one of the main challenges in WSN. This can be achieved with less uncertainty and masking failures by considering collaboration among sensor nodes. We propose a framework for distributed event detection using collaboration in WSN. The framework consists of two protocols that build a tree by using a communication model similar to the Publish-Subscribe paradigm. This framework is a part of Component Oriented Middleware for Sensor networks (COMiS). In COMiS framework, components are loaded as and when required based on the application semantics. If collaboration is considered, the goal of the application can be easily accomplished even in case of failures of sensors and low energy of nodes.

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.

One of the most promising use of Wireless Sensor Networks is in the field of the event warning applications. However, depending on the communication scheme adopted, the amount of the exchanged data may be huge, with large energy consumption. In this paper we study the efficiency of two approaches to composite event detection in Wireless Sensor Networks. The first solution approaches the problem by collecting all the available information in a single sensor that performs the real event detection. If the area covered by the network is wide this approach may result inefficient. In the second solution, the sensors may exchange among themselves the sensed data and execute locally the event detection, with the advantage of propagating the sensed data only in a smaller area.

Due to the major operating restrictions, ensuring security is the fundamental problem of Wireless Sensor Networks (WSNs). Because of their inadequate security mechanisms, WSNs are indeed a simple point for malware (worms, viruses, malicious code, etc.). According to the epidemic nature of worm propagation, it is critical to develop a worm defense mechanism in the network. This concept aims to establish novel malware detection in WSN that consists of several phases: “(i) Preprocessing, (ii) feature extraction, as well as (iii) detection”. At first, the input data is subjected for preprocessing phase. Then, the feature extraction takes place, in which principal component analysis (PCA), improved linear discriminant analysis (LDA), and autoencoder-based characteristics are retrieved. Moreover, the retrieved characteristics are subjected to the detection phase. The detection is performed employing combined shallow learning and DL. Further, the shallow learning includes decision tree (DT), logistic regression (LR), and Naive Bayes (NB); the deep learning (DL) includes deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Here, the DT output is given to the DNN, LR output is subjected to CNN, and the NB output is given to the RNN, respectively. Eventually, the DNN, CNN, and RNN outputs are averaged to generate a successful outcome. The combination can be thought of as an Ensemble classifier. The weight of the RNN is optimally tuned through the Self Improved Shark Smell Optimization with Opposition Learning (SISSOOL) model to improve detection precision and accuracy. Lastly, the outcomes of the suggested approach are computed in terms of different measures.

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.

Wireless sensor networks have emerged as a significant source for the study and analysis of data from the environment. These networks are deployed in harsh and inaccessible environments with the purpose of monitoring their respective surroundings, and generating observed readings, for delivery to a centralised entity, for further data analysis. Sensor nodes are tiny devices with limited available resources for performing all their sensory operations, and be sustained for their entire lifetime. Applications of wireless sensor networks such as battlefield monitoring, bushfire monitoring and surveillance, are mission-critical in nature. The timeliness and accuracy in the delivery of the sensory data affects several mitigation efforts that may be launched upon successful detection of a particular event in the environment. Therefore, it is essential to protect such networks from malicious attacks, that may be launched by the adversary-class, with the intent of causing loss to the network operations. Distributed Denial of Service(DDoS) attacks are defined as attacks launched from multiple ends of a wireless sensor network towards a set of legitimate sensor nodes, with the intent of exhausting their limited energy resources. These attacks can significantly affect the performance of the network, and eventually lead to complete compromise of all sensor nodes of the network. The consequences of such an attack, if left undetected, can be catastrophic to the operations of the entire network.

Quantity-based event reliability protocols have been proposed for reliable event detection in wireless sensor networks. They support the event reliability by achieving the desired number of data packets successfully transmitted from sensor nodes sensing an event to a sink by controlling the transport process. However, since many data collisions and buffer overflows frequently happen due to data congestions on limited data delivery paths from an event to a sink, the quantity-based event reliability protocols are hard to achieve the desired number due to lost data packets. Thus, this letter proposes a Quality-basect Event Reliability Protocol (QERP) utilizing a property that the data packets from sensor nodes have different Contribution Degree (CD) values for event detection according to their environmental conditions. QERP selects sensor nodes to forward their data packets according to CD, and differentially transports the data packets by CD-based buffer management and load balancing.

We develop a new class of event detection algorithms in Wireless Sensor Networks where the sensors are randomly deployed spatially. We formulate the detection problem as a binary hypothesis testing problem and design the optimal decision rules for two scenarios, namely the Poisson Point Process and Binomial Point Process random deployments. To calculate the intractable marginal likelihood density, we develop three types of series expansion methods which are based on an Askey-orthogonal polynomials. In addition, we develop a novel framework to provide guidance on which series expansion is most suitable (i.e., most accurate) to use for different system parameters. Extensive Monte Carlo simulations are carried out to illustrate the benefits of this framework as well as the quality of the series expansion methods, and the impacts that different parameters have on detection performance via the Receiver Operating Curves (ROC).