Energy Efficient Handover Management in Cluster Based Wireless Sensor Network

Wireless sensors are compact-size, low power, inexpensive devices which are capable to measure local environmental conditions or other parameters such as temperature, acceleration, and forward such information to a sink for proper processing. Wireless sensor networks (WSNs) have been under development by both academic and industrial societies for a while. By moving toward applications such as the area of medical care and disaster response mobility in wireless sensor networks has attracted a lot of attentions. In energy constraint sensor network, mobility handling introduces unique challenges in aspects like resource management, coverage, routing protocols, security, etc. This paper, proposes an energy-efficient mobility-aware MAC protocol to handle node handover among different clusters. The simulation-based experiments show that the proposed protocol has better performance compared to the existing S-MAC method

Energy Efficient Handover Management in Cluster Based Wireless Sensor Network

Wireless sensors are compact-size, low power, inexpensive devices which are capable to measure local environmental conditions or other parameters such as temperature, acceleration, and forward such information to a sink for proper processing. Wireless sensor networks (WSNs) have been under development by both academic and industrial societies for a while. By moving toward applications such as the area of medical care and disaster response mobility in wireless sensor networks has attracted a lot of attentions. In energy constraint sensor network, mobility handling introduces unique challenges in aspects like resource management, coverage, routing protocols, security, etc. This paper, proposes an energy-efficient mobility-aware MAC protocol to handle node handover among different clusters. The simulation-based experiments show that the proposed protocol has better performance compared to the existing S-MAC method

A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event's location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments

A security and privacy scheme based on node and message authentication and trust in fog-enabled VANET

Security and privacy are the most important concerns related to vehicular ad hoc network (VANET), as it is an open-access and self-organized network. The presence of ‘selfish’ nodes distributed in the network are taken into account as an important challenge and as a security threat in VANET. A selfish node is a legitimate vehicle node which tries to achieve the most benefit from the network by broadcasting wrong information. An efficient and proper security model can be useful to tackle advances from attackers, as well as selfish nodes. In this study, a privacy-preserving node and message authentication scheme, along with a trust model was developed. The proposed node authentication ensures the legitimacy of the vehicle nodes, whereas the message authentication was developed to ensure the message's integrity. To deal with selfish nodes, an experience-based trust model was also designed. Additionally, to fulfill the privacy-preserving aspect, the mapping of each vehicle was performed using a different pseudo-identity. In this paper, fog nodes instead of road-side units (RSUs), were distributed along the roadside. This was mainly because of the fact that fog computing reduces latency, and results in increased throughput. Security analysis indicated that our scheme met the VANETs' security requirements. In addition, the performance analysis showed that the proposed scheme had a lower communication and computation overhead, compared to the other related works. Monte-Carlo simulation results were applied to estimate the false-positive rates (FPR), which also proved the validity of the proposed security scheme

Game Theory-Based Cooperation for Underwater Acoustic Sensor Networks: Taxonomy, Review, Research Challenges and Directions.

Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted

A hybrid prediction model for energy-efficient data collection in wireless sensor networks

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count

Breaking Barriers in Sentiment Analysis and Text Emotion Detection: Toward a Unified Assessment Framework

Sentiment analysis (SA) and text emotion detection (TED) are two computer techniques used to analyze text. SA categorizes text into positive, negative, or neutral opinions, while TED can identify a wide array of emotional states, allowing an automated agent to respond appropriately. These techniques can be helpful in areas such as employee and customer management, online support, and customer loyalty, where identifying human emotions is crucial. Among other approaches, research has been conducted using machine learning (ML) algorithms, and labeled datasets have been created to train these models. Current state-of-the-art research for supervised ML algorithms reports good performance for TED (approximately 80% accuracy) and even better results for SA (above 90%). After conducting an extensive review of 30 surveys, the primary objective of this manuscript is to point out that most of these articles (94%) focus heavily on comparing the applied computational methods (the algorithm). At the same time, relatively diminished attention is paid to three other critical factors, namely the selection of an appropriate emotion model (mentioned only in 23% of cases), the corpora utilized for training (30%), and the data source employed during analysis and evaluation (20%). The lack of standardization across these essential elements presents a significant challenge when performing meaningful performance comparisons among algorithms. Consequently, the absence of a unified framework for comparison hampers the practical implementation of SA and TED techniques within mission-critical scenarios within real-world mission-critical scenarios

LC-IDS: Loci-Constellation-Based Intrusion Detection for Reconfigurable Wireless Networks

Detection accuracy of current machine-learning approaches to intrusion detection depends heavily on feature engineering and dimensionality-reduction techniques (e.g., variational autoencoder) applied to large datasets. For many use cases, a tradeoff between detection performance and resource requirements must be considered. In this paper, we propose Loci-Constellation-based Intrusion Detection System (LC-IDS), a general framework for network intrusion detection (detection of already known and previously unknown routing attacks) for reconfigurable wireless networks (e.g., vehicular ad hoc networks, unmanned aerial vehicle networks). We introduce the concept of ‘attack-constellation’, which allows us to represent all the relevant information for intrusion detection (misuse detection and anomaly detection) on a latent 2-dimensional space that arises naturally by considering the temporal structure of the input data. The attack/anomaly-detection performance of LC-IDS is analyzed through simulations in a wide range of network conditions. We show that for all the analyzed network scenarios, we can detect known attacks, with a good detection accuracy, and anomalies with low false positive rates. We show the flexibility and scalability of LC-IDS that allow us to consider a dynamic number of neighboring nodes and routing attacks in the ‘attack-constellation’ in a distributed fashion and with low computational requirements