Network Life Research Articles

Potential use of district heating networks and the prospects for the advancements within urban areas of Nottingham as a case study

Buildings in urban areas significantly contribute to global energy consumption, necessitating solutions to reduce energy demands and mitigate environmental impacts. This study investigates district heating networks in urban areas, using Nottingham as a case study. A literature review addresses key barriers to decarbonising building energy in the UK, focusing on: (1) Information, engagement, and behaviour, (2) Strategy, policy, and regulation, (3) Infrastructure, (4) Supply chain and skills, and (5) Distribution of costs and impacts. The study examines the network's location, current processes, energy performance, and potential future impacts. Heat mapping and energy flow assessments identify areas for energy savings, guiding system optimisation. The findings suggest that increasing the energy output from the heat station to the network can enhance performance. The analysis highlights the potential of a life cycle perspective for district heating networks, proposing a framework that considers all stages from material sourcing to system utilisation. This approach helps assess environmental impacts, including greenhouse gas emissions and resource depletion, identifying opportunities to improve resource efficiency and reduce environmental impacts. By evaluating current sustainability goals and regulatory compliance, the study provides insights into the environmental and social implications of the district heating network's operations, pinpointing areas for improvement. It emphasises the need for continuous optimisation and innovation throughout the network's life cycle. The potential of a full life cycle assessment (LCA) approach is highlighted to evaluate four main future advancements: (1) heating network with added energy storage, (2) advanced system operations through model predictive control (MPC) for demand prediction, (3) general network improvements, and (4) network expansion. Overall, this study aims to enhance the sustainability, efficiency, and resilience of district heating networks, contributing to the transition towards sustainable energy systems while ensuring environmental and economic viability over time.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

An energy‐efficient Chebyshev fire hawks optimization algorithm for energy balancing in sensor‐enabled Internet of Things

SummarySensor‐enabled systems have been used successfully in agricultural, healthcare, commercial, and military application domains. Recently, there has been significant interest in the intelligent applications of sensor‐enabled technologies, particularly in the domains of smart grid, Internet of Vehicles (IoV), body area networks, and the Internet of Things (IoT). In recent research, various protocols and algorithm are developed for effective energy‐efficient routing and energy balancing. These existing models have some issues like high energy consumption and minimum network life time. In order to overcome these existing issues, a novel cluster head selection and routing mechanism in a wireless sensor network (WSN) environment is proposed. The clustering process has been formed by an enhanced Taylor kernel fuzzy C‐means algorithm (TKFC‐means). The cluster head in the group of sensor nodes has been identified based on energy and distance calculation. Finally, the routing has been performed by a novel energy‐efficient Chebyshev fire hawks optimization‐based routing protocol to route data to the edge server, which helps to balance the energy effectively. This protocol takes into account various factors, including distance, cost, residual energy, load, temperature, latency, and overall energy. The proposed model can obtain a throughput value of 82 Mbps for the sensor nodes at 500 and an end‐to‐end delay of 3.6 at 500 sensor nodes. The packet delivery ratio and loss ratio attain 96.4% and 2.7%, respectively, with 500 sensor nodes in the proposed approach. The proposed method consumes 0.45 mJ of energy with 500 nodes. From this analysis, the proposed model can obtain better results than the existing compared models.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

Energy efficient scheme for improving network lifetime using BAT algorithm in wireless sensor network

SummaryWireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently.

Wireless optimization for sensor networks using IoT-based clustering and routing algorithms.

Wireless sensor networks (WSN) are among the most prominent current technologies. Its popularity has skyrocketed because of its capacity to operate in difficult situations. The WSN market encompasses various industries, including building automation, security networks, healthcare systems, logistics, and military operations. Therefore, increasing the energy efficiency of these networks is of utmost importance. Hierarchical topology, which typically uses a clustering methodology, is one of the most well-known methods for WSN energy optimization. To achieve energy efficiency in WSN, hierarchical topology low-energy adaptive clustering hierarchy (LEACH) was first introduced, and this served as the foundation. However, conventional LEACH has several limitations, which have led to extensive research into improving LEACH's efficacy in its current form. The use of particular algorithms and strategies to enhance the functionality of the conventional LEACH protocol forms the basis of ongoing efforts. Utilizing this enhanced LEACH, performance in terms of throughput and network life may be enhanced by concentrating on elements such as cluster head formation and transmission energy consumption. The enhanced LEACH algorithm demonstrates significant improvements in both throughput and network lifetime compared with conventional LEACH. Through rigorous experimentation, it was found that the enhanced algorithm increases the throughput by 25% on average, which is attributed to its dynamic clustering and optimized routing strategies. Furthermore, the network lifetime is extended by approximately 30%, primarily because of enhanced energy efficiency through adaptive clustering and transmission power control.

A Novel Secure and Energy-efficient Routing Method for the Agricultural Internet of Things Using Whale Optimization Algorithm

The Internet of Things (IoT) is an all-encompassing system that tracks and monitors real-world activities by gathering, handling, and interpreting data from IoT equipment. It has successfully been applied in several fields, particularly smart agriculture since there is a high demand for high-quality foodstuffs worldwide. It is essential to develop new agricultural production schemes to meet these demands. The heterogeneity of IoT devices makes security essential for IoT communication. Also, IoT devices are restricted in terms of processing, memory, and power capacities. Therefore, energy is a key factor in extending the life of an agricultural IoT network. This study presented a novel energy-aware and secure routing scheme using the Whale Optimization Algorithm (WOA) for IoT, referred to as SRWOA. The simulation results indicate that SRWOA uniformly distributes energy consumption in IoT and maximizes the packet delivery ratio.

Genetic and Environmental Influences on Perceived Stress in South Korean Twins.

Researchers have proposed that culture significantly influences perceived stress (PS). To date, however, twin studies on PS have been conducted mostly in western, individualistic cultures, which demonstrate that PS due to controllable (personal) life events is more heritable than PS due to uncontrollable (network) life events. This study aimed to investigate genetic and environmental influences on PS in South Korean twins. South Korea practices a dominant collectivist culture. In total, 1372 twin individuals (mean age = 22.4 ± 2.5 years) completed an online survey on PS, which consisted of the scales, Friendship, Academic Stress, Future Career, Family Conflicts, and Family Financial Difficulties (FFD). Friendship, Academic Stress, and Future Career can be considered PS due to personal life events, and Family Conflict and FFD, PS due to network life events. The general sex-limitation model-fitting analysis revealed the absence of qualitative or quantitative sex differences in genetic and environmental influences. Specifically, additive genetic influences were predominant for Friendship (63%), Academic Stress (67%), and Future Career (57%) for both sexes, with the remaining variance attributable to nonshared environmental influences. In contrast, shared environmental influences were largest for Family Conflict (47% for both genders) and FFD (64% for males, 63% for females) with no significant genetic effects. Despite known cultural differences in the means and variances of PS, South Korean twins exhibited significant genetic effects in PS due to personal life events and large shared environmental effects in PS due to network life events, which is similar to western samples.

Research on WSN clustering algorithm of firefly algorithm to optimize k-means

Aiming at the problem that uneven clustering of wireless sensor networks leads to large energy consumption differences between clusters and short network lifetime, this paper proposes a clustering routing algorithm of WSN based on the Firefly algorithm to optimize k-means (FAKMC). This paper mainly studies the clustering stage of the network, adopts the k-means algorithm for node partition, and uses the improved FA to optimize the sensitive problem of the initial clustering centre of k-means. Simulation results show that, compared to LEACH and k-means, the clustering result proposed in this paper is more uniform and can extend the network life cycle.

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH). Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time.

A Novel Lie Hypergraph Based Lifetime Enhancement Routing Protocol for Environmental Monitoring in Wireless Sensor Networks

Wireless sensor network (WSN) is a rapid surging technology promising many fruitful innovations for the user community. The use of WSNs for continuous monitoring of environmental factors in severe situations, such as detection of volcanoes, forest fires, floods, and so on. Despite WSN’s various potential applications, energy and deployment are two major concerns influencing the network life span. The primary requirement is to monitor node energy consumption in order to improve network performance. Many approaches are proposed in recent times for energy efficiency, this article introduces an associated hypergraph with Lie algebra of upper triangular matrices for energy efficient clustering and routing. Initially, the sensor nodes are formulated as a hypergraph, and each hyperedge is treated as a cluster, from which cluster-head (CH) selection is exploited by minimum hypergraph transversal. The routing decision is employed by constructing the upper triangular matrix (UTM), and Lie commutators of the UTM Lie algebra identifies the best relay nodes for data forwarding. The performance of the proposed work is evaluated using simulation results which shows the effectiveness of the scheme over compared protocols in terms of, number of alive nodes, energy consumption and number of packets received in BS.

Sensorineural hearing loss in patients with type 2 diabetes mellitus: a hospital based cross sectional study

Background: One of the most important of the five senses in human beings is hearing. It plays an important role in the development of speech, cognitive and communication skills. The decrease in length, quality of life and depletion of social networks can be caused by hearing impairment. Therefore, the sense of hearing, perception of sound and its biological purposes, can’t be lightly dismissed. Diabetes mellitus is a common metabolic disorder that causes impairment in various systems of the body. One of the complications of diabetes mellitus is hearing impairment and tinnitus which in turn leads to decreased quality of life in affected individuals. Methods: 75 type 2 diabetes mellitus patients age group of 35 -55 yrs with or without aural symptoms (deafness, tinnitus) were taken into the study. Results: prevalence of sensorineural hearing loss (SNHL) was found 60%. Prevalence of SNHL is lowest among 35-40 age group (12.5%) and highest among 51-55 age group (80.5%). Prevalence of SNHL among the diabetic patients was calculated and it was noted to be 36.8%, 60% and 74% with the duration of diabetes ≤5 yrs, 6-10 yrs, ≥11 yrs. respectively. Conclusions: SNHL is prevalent in 60% of type II diabetic patients.

Cluster-Based Cross Layer-Cross Domain Routing Model with DNN-Based Energy Prediction

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node’s energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods.

Network Ideological and Political Education System for College Students Based on Multimedia Service Architecture

Today, the network society has become an integral part of human society, and the network life marked by digitization, informatization and networking has become a new state of existence; a new society marked by interactivity, virtuality and innovation A mode of operation has been created. A network, in simple terms, is to connect, and can be called a computer network. Such highest degree of social informatization in my country. It is a new idea designed by making full use of computer networks, multimedia technology and modern communication technology and closely combining the cognitive hotspots of contemporary college students. way of political education. In response to the above problems, this article uses deep learning technology to develop an intelligent education system based on detection technology. The system takes identifying whether the standard is met as the main goal, and gives comprehensive evaluation and improvement suggestions for the quality of action completion. It gets rid of the constraints of the venue.

Energy aware and secure routing for hierarchical cluster through trust evaluation

With the rapid growth of technology advancement in wireless communication have resulted in interconnection of large number of devices covering wide range of application services. Wireless sensor network (WSN) have become popular and provide various benefits, including low-power smart computation, compact size, self organizing and ability to route via routing protocols. WSN covers a wide range of application services, including home automation, smart cities, fire detection, unmanned aerial vehicle (UAV), and object tacking. Sensor nodes are tiny, battery operated, and suffer from fundamental restrictions such as available bandwidth, memory space, power restrictions, and limited resources. Due to their communication characteristics and working environment, WSN faces energy optimization and secure data communication challenges to increase network lifetime and defend against malicious nodes. To overcome energy scarcity and consumption issues, clustering methods have proven smart and effective approaches to reduce energy consumption and extend network life. Low energy adaptive clustering hierarchy (LEACH), well-known clustering method, enhances energy efficiency by selecting cluster head (CH) on a rotation basis by employing random integer technique. To extend network stability period, extensions of LEACH was proposed. LEACH and its extensions mainly focus on optimal selection of CH and fail to concentrate on secure communication. Integration of blockchain and real-time message content validation (RMVC) in LEACH was modelled to detect malicious node through secure hash algorithm. However, existing scheme had complex cryptographic functions and higher computation overhead. In this paper, we present energy aware and secure routing (EASR) for hierarchical cluster based WSN. Existing LEACH is modified and an improved threshold value is adapted to select secure optimal CH and trust based routing model which combines direct and indirect trust opinion values to resist routing attacks for establishing trustworthy and reliable routes towards base station.

The unique role of meaning in life in the relationships between trait awe, subjective well-being, and prosocial tendency: A network analysis.

Although individuals with higher trait awe (the tendency to experience awe) are known to be happier and more prosocial, there is limited understanding of the mechanisms underlying these complex relationships. This study uses network analysis to explore dimension-level relationships between trait awe, meaning in life, subjective well-being (SWB), and prosocial tendency in a joint network and to explore the bridging role of meaning in life in the network. A total of 538 adults (53.2% females; Mage = 19.86 ± 1.51) completed the survey. The network revealed unique and intricate connections between the dimensions of trait awe, meaning in life (i.e., the presence of and the search for meaning, abbreviated as POM and SFM), subjective happiness and life satisfaction (SWB), and prosocial tendency (i.e., willingness to donate money and volunteer time). Trait awe exhibited direct links to subjective happiness, life satisfaction, and prosocial tendency to donate money. Moreover, through POM and SFM, trait awe also exhibited indirect links to each dimension of SWB and prosocial tendency. Within the global network, POM was further identified as acting as a bridge node with the highest bridge strength and closeness, indicating that POM could efficiently transmit influences within the entire network. These findings highlight the distinct contributions of meaning in life to understanding the relationships between trait awe, SWB, and prosocial tendency, and provide valuable insights for improving SWB and fostering prosocial tendencies.

Node Localization Using Modified Wild Horse Optimization and Energy Efficient Secured Routing Protocol for IoT Based Wireless Sensor Networks

Internet of Things (IoT) devices are being utilized extensively as a result of the development of information and communication technology. Wireless sensor networks (WSNs), which are formed of inexpensive smart devices for information collection, plays a vital function in the establishment of the IoT. These smart devices are not without limitations, though, whether it comes to processing, memory, computing, and energy usage. In addition to these limitations, the core difficulties facing WSN include node localization, reliability, and data security during transmission in a dangerous environment from hostile nodes. The important and difficult problem for researchers to solve in order to improve network longevity, reliability, scalability, connectivity, throughput is accurate localization and routing. To improve the network period and data trustworthiness, this study intends to design an Energy-Efficient and Secure Routing protocol (EESR) and node localization centered on modified wild horse optimization (LMWHO) for intrusion avoidance in IoT utilizing WSN. Initially the suggested protocol bases its creation of various energy-efficient clusters on the inherent characteristics of nodes. Secondly, the base station (BS) and cluster head are able to reliably and securely share sensory data according to the (k,n) threshold-based Shamir secret sharing method. And finally, node localization centered on modified wild horse optimization (EESR-LMWHO), where the fitness function was formed by the development of residual energy and distance estimate. The suggested EESR-LMWHO utilizes less energy and prolongs the life of wireless networks. Lastly, the simulations are run to evaluate the suggested method's efficiency. The suggested approach, according to the experiments, approximates the location of the unknown node, offers a minimal localization error, and is a lightweight way to deal with intrusions caused by hostile nodes.