Exploiting the power of multiplicity: a holistic survey of network-layer multipath

The Internet is inherently a multipath network: For an underlying network with only a single path, connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity, through which a diverse collection of paths is resource pooled as a single resource, to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities, promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault tolerance (through the use of multiple paths in backup/redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be multipath, including the use of multipath technology in data center computing; the ready availability of multiple heterogeneous radio interfaces in wireless (such as Wi-Fi and cellular) in wireless devices; ubiquity of mobile devices that are multihomed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as multipath TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely, the control plane problem of how to compute and select the routes and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work

Anomaly detection in unknown environments using wireless sensor networks

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance. The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications

GCP: Gossip-based Code Propagation for Large-scale Mobile Wireless Sensor Networks

Wireless sensor networks (WSN) have recently received an increasing interest. They are now expected to be deployed for long periods of time, thus requiring software updates. Updating the software code automatically on a huge number of sensors is a tremendous task, as ''by hand'' updates can obviously not be considered, especially when all participating sensors are embedded on mobile entities. In this paper, we investigate an approach to automatically update software in mobile sensor-based application when no localization mechanism is available. We leverage the peer-to-peer cooperation paradigm to achieve a good trade-off between reliability and scalability of code propagation. More specifically, we present the design and evaluation of GCP ({\emph Gossip-based Code Propagation}), a distributed software update algorithm for mobile wireless sensor networks. GCP relies on two different mechanisms (piggy-backing and forwarding control) to improve significantly the load balance without sacrificing on the propagation speed. We compare GCP against traditional dissemination approaches. Simulation results based on both synthetic and realistic workloads show that GCP achieves a good convergence speed while balancing the load evenly between sensors

Efficient energy, cost reduction, and QoS based routing protocol for wireless sensor networks

Recent developments and widespread in wireless sensor network have led to many routing protocols, many of these protocols consider the efficiency of energy as the ultimate factor to maximize the WSN lifetime. The quality of Service (QoS) requirements for different applications of wireless sensor networks has posed additional challenges. Imaging and data transmission needs both QoS aware routing and energy to ensure the efficient use of sensors. In this paper, we propose an Efficient, Energy-Aware, Least Cost, (ECQSR) quality of service routing protocol for sensor networks which can run efficiently with best-effort traffic processing. The protocol aims to maximize the lifetime of the network out of balancing energy consumption across multiple nodes, by using the concept of service differentiation, finding lower cost by finding the shortest path using nearest neighbor algorithm (NN), also put certain constraints on the delay of the path for real-time data from where link cost that captures energy nodes reserve, energy of the transmission, error rate and other parameters. The results show that the proposed protocol improves the network lifetime and low power consumption

Attack classification schema for smart city WSNs

Peer-reviewedUrban areas around the world are populating their streets with wireless sensor networks (WSNs) in order to feed incipient smart city IT systems with metropolitan data. In the future smart cities, WSN technology will have a massive presence in the streets, and the operation of municipal services will be based to a great extent on data gathered with this technology. However, from an information security point of view, WSNs can have failures and can be the target of many different types of attacks. Therefore, this raises concerns about the reliability of this technology in a smart city context. Traditionally, security measures in WSNs have been proposed to protect specific protocols in an environment with total control of a single network. This approach is not valid for smart cities, as multiple external providers deploy a plethora of WSNs with different security requirements. Hence, a new security perspective needs to be adopted to protect WSNs in smart cities. Considering security issues related to the deployment of WSNs as a main data source in smart cities, in this article, we propose an intrusion detection framework and an attack classification schema to assist smart city administrators to delimit the most plausible attacks and to point out the components and providers affected by incidents. We demonstrate the use of the classification schema providing a proof of concept based on a simulated selective forwarding attack affecting a parking and a sound WSN.Las zonas urbanas de todo el mundo están poblando sus calles con redes de sensores inalámbricos (WSN) para alimentar sistemas informáticos de incipientes ciudades inteligentes con datos metropolitanos. En las futuras ciudades inteligentes, la tecnología WSN tendrá una presencia masiva en las calles, y la operación de los servicios municipales se basará en gran medida en los datos recopilados con esta tecnología. Sin embargo, desde un punto de vista de seguridad de la información, las WSN pueden tener fallos y pueden ser el objetivo de muchos tipos diferentes de ataques. Por lo tanto, esto plantea preocupaciones sobre la fiabilidad de esta tecnología en un contexto de ciudad inteligente. Tradicionalmente, se han propuesto medidas de seguridad en WSNs para proteger protocolos específicos en un entorno con control total de una sola red. Este enfoque no es válido para ciudades inteligentes, ya que múltiples proveedores externos implementan una gran cantidad de WSN con diferentes requisitos de seguridad. Por lo tanto, se debe adoptar una nueva perspectiva de seguridad para proteger las WSNs en ciudades inteligentes. En este artículo proponemos un marco de detección de intrusiones y un esquema de clasificación de ataques para ayudar a los administradores de ciudades inteligentes a delimitar los ataques más plausibles y señalar los componentes y los proveedores afectados por incidentes. Demostramos el uso del esquema de clasificación proporcionando una prueba de concepto basada en un ataque simulado de reenvío selectivo que afecta a un estacionamiento y un sonido WSN.Les zones urbanes de tot el món estan poblant els seus carrers amb xarxes de sensors sense fils (WSN) per alimentar sistemes informàtics d'incipients ciutats intel·ligents amb dades metropolitans. A les futures ciutats intel·ligents, la tecnologia WSN tindrà una presència massiva als carrers, i l'operació dels serveis municipals es basarà en gran mesura en les dades recopilades amb aquesta tecnologia. No obstant això, des d'un punt de vista de seguretat de la informació, les WSN poden tenir errors i poden ser l'objectiu de molts tipus diferents d'atacs. Per tant, això planteja preocupacions sobre la fiabilitat d'aquesta tecnologia en un context de ciutat intel·ligent. Tradicionalment, s'han proposat mesures de seguretat en xarxes de sensors sense fils per protegir protocols específics en un entorn amb control total d'una sola xarxa. Aquest enfocament no és vàlid per a ciutats intel·ligents, ja que múltiples proveïdors externs implementen una gran quantitat de WSN amb diferents requisits de seguretat. Per tant, s'ha d'adoptar una nova perspectiva de seguretat per protegir les WSNs en ciutats intel·ligents. En aquest article proposem un marc de detecció d'intrusions i un esquema de classificació d'atacs per ajudar els administradors de ciutats intel·ligents a delimitar els atacs més plausibles i assenyalar els components i els proveïdors afectats per incidents. Demostrem l'ús de l'esquema de classificació proporcionant una prova de concepte basada en un atac simulat de reenviament selectiu que afecta un estacionament i un so WSN

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs

Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates Machine Learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the WSN Dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary, it achieves an accuracy rate of 99.78% and in multiclass, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.Comment: International Journal of Information Security, Springer Journal - Q1, Scopus, ISI, SCIE, IF: 3.2 - Accepted on Jan 17, 202

Detecting Packet Droppers and Modifiers in Wireless Sensor Network

Wireless networks are widely used because these are very easy to install. However, there are various security issues and problems while deploying it. Two most important issues are Packet modification and dropping. These are the common attacks that can be generated by an attacker to disrupt communication in wireless sensor networks. Many schemes have been proposed to reduce or tolerate such attacks but very few can effectively and efficiently identify the intruders. This paper proposed a simple and an effective scheme, which can identify misbehaving nodes that drop or modify packets. Heuristic ranking algorithm is been used to identify the bad nodes. The alert message will be forwarded to all the users in the network if any misbehaving action occurred, so that no message will reach the misbehaved node and the node will be blocked