HCIFR: Hierarchical Clustering and Iterative Filtering Routing Algorithm for Wireless Sensor Networks

The hierarchical clustering and iterative filtering algorithms are combined to form an energy efficient routing algorithm which supports in improved performance, efficient routing at the time of link failure, collusion robust and secure data aggregation. The idea of combining these two algorithms which may lead to improved performance. Initially clusters are formed by neighborhood. The cluster is a combination of one clusterhead, two deputy clusterheads and cluster members. This system uses a Hierarchical clustering algorithm for efficient data transmission to their clusterhead by cluster members. The clusterhead aggregate the collected data and check for trustworthiness. The data is aggregated by clusterhead using the iterative filtering algorithm and resistant to collusion attacks. Simulation results depict the average energy consumption, throughput, packet drops and packet delivery under the influence of proposed algorithm

Secure and Energy Efficient Data Aggregation Technique for Cluster Based Wireless Sensor Network

In the past few years secure transmission of data along with efficiency is a serious issue for wireless sensor networks (WSNs).Clustering is a powerful and convenient way to enhance performance of the WSNs system. In this project work, a secure transmission of data for cluster-based WSNs (CWSNs) is studied, where the clusters are formed dynamically and infrequently. Basically protocols for CWSNs, called SET-IBS (Identity-Based digital Signature)scheme and SET-IBOOS (Identity-Based Online / Offline digital Signature)scheme, correspondingly. In SET-IBS, security relies on the hardness of the Dill-Hellman difficulty in the pairing area. Data aggregation is the process of abbreviation and combining sensor data in order to reduce the amount of data transmission in the network. This paper investigates the relationship between security and data aggregation process in wireless sensor networks. In this paper propose SET-IBS and data aggregation techniques for secure and efficient data transmission. For energy consumption using DRINA algorithm. DRINA means Data Routing for In-Network Aggregation, that has some key aspects such as high aggregation rate, a reduced number of messages for setting up a routing

Improving IF Algorithm for Data Aggregation Techniques in Wireless Sensor Networks

In Wireless Sensor Network (WSN), fact from different sensor nodes is collected at assembling node, which is typically complete via modest procedures such as averaging as inadequate computational power and energy resources. Though such collections is identified to be extremely susceptible to node compromising attacks. These approaches are extremely prone to attacks as WSN are typically lacking interfere resilient hardware. Thus, purpose of veracity of facts and prestige of sensor nodes is critical for wireless sensor networks. Therefore, imminent gatherer nodes will be proficient of accomplishment additional cultivated data aggregation algorithms, so creating WSN little unresisting, as the performance of actual low power processors affectedly increases. Iterative filtering algorithms embrace inordinate capacity for such a resolution. The way of allocated the matching mass elements to information delivered by each source, such iterative algorithms concurrently assemble facts from several roots and deliver entrust valuation of these roots. Though suggestively extra substantial against collusion attacks beside the modest averaging techniques, are quiet vulnerable to a different cultivated attack familiarize. The existing literature is surveyed in this paper to have a study of iterative filtering techniques and a detailed comparison is provided. At the end of this paper new technique of improved iterative filtering is proposed with the help of literature survey and drawbacks found in the literature

Prospectiva de seguridad de las redes de sensores inalámbricos

En las Redes de Sensores Inalámbricos (WSN), los nodos son vulnerables a los ataques de seguridad porque están instalados en un entorno difícil, con energía y memoria limitadas, baja capacidad de procesamiento y transmisión de difusión media; por lo tanto, identificar las amenazas, los retos y las soluciones de seguridad y privacidad es un tema candente hoy en día. En este artículo se analizan los trabajos de investigación que se han realizado sobre los mecanismos de seguridad para la protección de las WSN frente a amenazas y ataques, así como las tendencias que surgen en otros países junto con futuras líneas de investigación. Desde el punto de vista metodológico, este análisis se muestra a través de la visualización y estudio de trabajos indexados en bases de datos como IEEE, ACM, Scopus y Springer, con un rango de 7 años como ventana de observación, desde 2013 hasta 2019. Se obtuvieron un total de 4.728 publicaciones, con un alto índice de colaboración entre China e India. La investigación planteó desarrollos, como avances en los principios de seguridad y mecanismos de defensa, que han llevado al diseño de contramedidas en la detección de intrusiones. Por último, los resultados muestran el interés de la comunidad científica y empresarial por el uso de la inteligencia artificial y el aprendizaje automático (ML) para optimizar las medidas de rendimiento.In Wireless Sensor Networks (WSN), nodes are vulnerable to security attacks because they are installed in a harsh environment with limited power and memory, low processing power, and medium broadcast transmission. Therefore, identifying threats, challenges, and solutions of security and privacy is a talking topic today. This article analyzes the research work that has been carried out on the security mechanisms for the protection of WSN against threats and attacks, as well as the trends that emerge in other countries combined with future research lines. From the methodological point of view, this analysis is shown through the visualization and study of works indexed in databases such as IEEE, ACM, Scopus, and Springer, with a range of 7 years as an observation window, from 2013 to 2019. A total of 4,728 publications were obtained, with a high rate of collaboration between China and India. The research raised developments, such as advances in security principles and defense mechanisms, which have led to the design of countermeasures in intrusion detection. Finally, the results show the interest of the scientific and business community in the use of artificial intelligence and machine learning (ML) to optimize performance measurements

Maximizing Network Lifetime using Fuzzy Based Secure Data Aggregation Protocol (FSDAP) in a Wireless Sensor Networks

Secure Data Aggregation in Wireless Senor Networks (WSNs) is a challenging issue. The various protocols has been suggested in the recent past such as EDIT[13], ADA[8], TSDA[9], SEDAN[10]. These protocols effectively meet the constraints of WSNs. In this paper, we have proposed a Fuzzy Based Secure Data Aggregation protocol (FSDAP) which is an efficient localized protocol. The FSDAP protocol is designed with three phases. The first phase selects Aggregator Node using ANS algorithm. An ANS algorithm involves two steps to elect an Aggregator Node in the clustered network. In first step, the cluster head is selected based on the Euclidean distance and in second step, the cluster head is selected based on the fuzzy product and fuzzy value (α). Then, in second phase, a selected AN eliminates data redundancy sensed by all sensor nodes within the cluster. Finally, in third phase, the FSDAP protocol effectively detects malicious node and provides secure data transmission path. Thus, the proposed protocol, FSDAP utilizes the node’s resource parameter uniformly, which in turn improves Network Lifetime, maximizes Throughput Rate, maximizes Packet Delivery Ratio and minimizes End-to-End Delay. The FSDAP is simulated using the NS2 simulator and compared with centroid algorithms Fuzzy C-Means and K-Means algorithm and a secure aggregation protocol implemented using SAR (Secure Aware Ad hoc Routing). The time complexity of FSDAP protocol is O(m2n)

Robust reputation independence in ranking systems for multiple sensitive attributes

Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as users’ discrimination. A notable example is represented by reputation-based ranking systems, a class of systems that rely on users’ reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes. To safeguard that a given sensitive user’s attribute does not systematically affect the reputation of that user, prior work has operationalized a reputation independence constraint on this class of systems. In this paper, we uncover that guaranteeing reputation independence for a single sensitive attribute is not enough. When mitigating biases based on one sensitive attribute (e.g., gender), the final ranking might still be biased against certain demographic groups formed based on another attribute (e.g., age). Hence, we propose a novel approach to introduce reputation independence for multiple sensitive attributes simultaneously. We then analyze the extent to which our approach impacts on discrimination and other important properties of the ranking system, such as its quality and robustness against attacks. Experiments on two real-world datasets show that our approach leads to less biased rankings with respect to multiple users’ sensitive attributes, without affecting the system’s quality and robustness

Trust Modeling in Wireless Sensor Networks: State of the Art

Wireless sensor networks (WSNs) is the backbone of the new generation of internet of things (IoT). WSNs are growing rapidly and security threats are increasingly growing as well. Trust computing plays a crucial role in WSN security modeling. In WSN node trust is important to keep the network safe and operational. This paper presents the state-of-theart techniques in WSN Trust modeling. Comparison and analysis of most recent solutions were conducted. Direction and trends of current and future research approaches are also presented