170 research outputs found
Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN
Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR
Performance assessment of real-time data management on wireless sensor networks
Technological advances in recent years have allowed the maturity of Wireless Sensor Networks
(WSNs), which aim at performing environmental monitoring and data collection. This sort of
network is composed of hundreds, thousands or probably even millions of tiny smart computers
known as wireless sensor nodes, which may be battery powered, equipped with sensors, a radio
transceiver, a Central Processing Unit (CPU) and some memory. However due to the small size and
the requirements of low-cost nodes, these sensor node resources such as processing power, storage
and especially energy are very limited.
Once the sensors perform their measurements from the environment, the problem of data
storing and querying arises. In fact, the sensors have restricted storage capacity and the on-going
interaction between sensors and environment results huge amounts of data. Techniques for data
storage and query in WSN can be based on either external storage or local storage. The external
storage, called warehousing approach, is a centralized system on which the data gathered by the
sensors are periodically sent to a central database server where user queries are processed. The
local storage, in the other hand called distributed approach, exploits the capabilities of sensors
calculation and the sensors act as local databases. The data is stored in a central database server
and in the devices themselves, enabling one to query both.
The WSNs are used in a wide variety of applications, which may perform certain operations on
collected sensor data. However, for certain applications, such as real-time applications, the sensor
data must closely reflect the current state of the targeted environment. However, the environment
changes constantly and the data is collected in discreet moments of time. As such, the collected
data has a temporal validity, and as time advances, it becomes less accurate, until it does not
reflect the state of the environment any longer. Thus, these applications must query and analyze
the data in a bounded time in order to make decisions and to react efficiently, such as industrial
automation, aviation, sensors network, and so on. In this context, the design of efficient real-time
data management solutions is necessary to deal with both time constraints and energy consumption.
This thesis studies the real-time data management techniques for WSNs. It particularly it focuses
on the study of the challenges in handling real-time data storage and query for WSNs and on the
efficient real-time data management solutions for WSNs.
First, the main specifications of real-time data management are identified and the available
real-time data management solutions for WSNs in the literature are presented. Secondly, in order to
provide an energy-efficient real-time data management solution, the techniques used to manage
data and queries in WSNs based on the distributed paradigm are deeply studied. In fact, many
research works argue that the distributed approach is the most energy-efficient way of managing
data and queries in WSNs, instead of performing the warehousing. In addition, this approach can provide quasi real-time query processing because the most current data will be retrieved from the
network.
Thirdly, based on these two studies and considering the complexity of developing, testing, and
debugging this kind of complex system, a model for a simulation framework of the real-time
databases management on WSN that uses a distributed approach and its implementation are
proposed. This will help to explore various solutions of real-time database techniques on WSNs
before deployment for economizing money and time. Moreover, one may improve the proposed
model by adding the simulation of protocols or place part of this simulator on another available
simulator. For validating the model, a case study considering real-time constraints as well as energy
constraints is discussed.
Fourth, a new architecture that combines statistical modeling techniques with the distributed
approach and a query processing algorithm to optimize the real-time user query processing are
proposed. This combination allows performing a query processing algorithm based on admission
control that uses the error tolerance and the probabilistic confidence interval as admission
parameters. The experiments based on real world data sets as well as synthetic data sets
demonstrate that the proposed solution optimizes the real-time query processing to save more
energy while meeting low latency.Fundação para a Ciência e Tecnologi
Integrated Circuits and Systems for Smart Sensory Applications
Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware
Towards Integrated Sensing and Communications for 6G: A Standardization Perspective
The radio communication division of the International Telecommunication Union
(ITU-R) has recently adopted Integrated Sensing and Communication (ISAC) among
the key usage scenarios for IMT-2030/6G. ISAC is envisioned to play a vital
role in the upcoming wireless generation standards. In this work, we bring
together several paramount and innovative aspects of ISAC technology from a
global 6G standardization perspective, including both industrial and academic
progress. Specifically, this article provides 6G requirements and ISAC-enabled
vision, including various aspects of 6G standardization, benefits of ISAC
co-existence, and integration challenges. Moreover, we present key enabling
technologies, including intelligent metasurface-aided ISAC, as well as
Orthogonal Time Frequency Space (OTFS) waveform design and interference
management for ISAC. Finally, future aspects are discussed to open various
research opportunities and challenges on the ISAC technology towards 6G
wireless communications.Comment: 7 pages, 5 figure
A Unified Wormhole Attack Detection Framework for Mobile Ad hoc Networks
The Internet is experiencing an evolution towards a ubiquitous network paradigm, via the so-called internet-of-things (IoT), where small wireless computing devices like sensors and actuators are integrated into daily activities. Simultaneously, infrastructure-less systems such as mobile ad hoc networks (MANET) are gaining popularity since they provide the possibility for devices in wireless sensor networks or vehicular ad hoc networks to share measured and monitored information without having to be connected to a base station. While MANETs offer many advantages, including self-configurability and application in rural areas which lack network infrastructure, they also present major challenges especially in regard to routing security. In a highly dynamic MANET, where nodes arbitrarily join and leave the network, it is difficult to ensure that nodes are trustworthy for multi-hop routing. Wormhole attacks belong to most severe routing threats because they are able to disrupt a major part of the network traffic, while concomitantly being extremely difficult to detect.
This thesis presents a new unified wormhole attack detection framework which is effective for all known wormhole types, alongside incurring low false positive rates, network loads and computational time, for a variety of diverse MANET scenarios. The framework makes three original technical contributions: i) a new accurate wormhole detection algorithm based on packet traversal time and hop count analysis (TTHCA) which identifies infected routes, ii) an enhanced, dynamic traversal time per hop analysis (TTpHA) detection model which is adaptable to node radio range fluctuations, and iii) a method for automatically detecting time measurement tampering in both TTHCA and TTpHA.
The thesis findings indicate that this new wormhole detection framework provides significant performance improvements compared to other existing solutions by accurately, efficiently and robustly detecting all wormhole variants under a wide range of network conditions
Internet of Things From Hype to Reality
The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions
A Learning-based Approach to Exploiting Sensing Diversity in Performance Critical Sensor Networks
Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting and classifying events while maximizing system lifetime. to meet high accuracy requirements and maximize system lifetime, we must address sensing diversity: sensing capability differences among both heterogeneous and homogeneous sensors in a specific deployment. Existing approaches either ignore sensing diversity entirely and assume all sensors have similar capabilities or attempt to overcome sensing diversity through calibration. Instead, we use machine learning to take advantage of sensing differences among heterogeneous sensors to provide high accuracy and energy savings for performance critical applications.;In this dissertation, we provide five major contributions that exploit the nuances of specific sensor deployments to increase application performance. First, we demonstrate that by using machine learning for event detection, we can explore the sensing capability of a specific deployment and use only the most capable sensors to meet user accuracy requirements. Second, we expand our diversity exploiting approach to detect multiple events using a distributed manner. Third, we address sensing diversity in body sensor networks, providing a practical, user friendly solution for activity recognition. Fourth, we further increase accuracy and energy savings in body sensor networks by sharing sensing resources among neighboring body sensor networks. Lastly, we provide a learning-based approach for forwarding event detection decisions to data sinks in an environment with mobile sensor nodes
HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing
制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584
Collaborative Networking: The Integration of Collaborative Communication into WSN-routing
According to the Collaborative Communication (CC) techniques, a group of sensor nodes modify their carrier phases, so that their signals are received by the destination synchronously to gain higher level of reliability and flexibility. In this research, CC is fused into networking approaches to extend its scalability as well
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