Secure data collection and critical data transmission technique in mobile sink wireless sensor networks

In Mobile sink wireless sensor networks (MSWSN) Sensor nodes are low cost tiny devices with limited storage,computational capability and power except the sink node. Mobile sink has no resource limitation. It has wide range of application in the real world problem like military and civilian domain etc. The nodes in the network are unattended and unprotected so energy efficient and security are two major issues of sensor network. The sensors have limited battery power and low computational capability, requires a security mechanism that must be energy efficient. In this proposed system model mobile sink traverse the network to collect the data. Here we proposed energy efficient secure data collection techniques with mobile sink wireless sensor networks based on symmetric key cryptography. In proposed data collection technique mobile sink traverse network and collect data from one hop neighbors. Proposed cryptosystem is time based as after each fixed amount of time sink generates a large prime number. Using the prime number all nodes in the network update their key to avoid replay attack keep. Data collection MSWSN is three step process. At each new position mobile sink broadcast a beacon frame to alert the static sensors about its presence, secondly sensors send their sensed data towards sink node and finaly mobile sink broad cast another beacon frame to stop the data transmission by sensors. Sensor authenticate the mobile sink with the shared key concept, if it finds that sink is the legitimate node then sensor encrypt their data and transmit it to the sink. A static sensor sense some critical information and sink is not within its range, that that time sensor needs to transmit its data towards sink immediately. It cannot wait till sink come to its range. For that we proved an existing protocol Sensor Protocol for Information via Negation (SPIN) is efficient for critical data transmission to the mobile sink. Then we make it as the secured protocol by using symmetric key cryptography. Here we use the previous assumption to make it as the secure protocol. All the simulation has been carried out with NS 2.34. This thesis is supported bythe literature survey in the area of Mobile Sink Wireless Sensor Networks to make it complete

Towards efficient and lightweight security architecture for big sensing data streams

University of Technology Sydney. Faculty of Engineering and Information Technology.A large number of mission critical applications from disaster management to health monitoring are contributing to the Internet of Things (IoT) by deploying a number of smart sensing devices in a heterogeneous environment. Resource constrained sensing devices are being used widely to build and deploy self-organising wireless sensor networks for a variety of critical applications. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. The key requirement of such applications is the need for near real-time stream data processing in large scale sensing networks. This trend gives birth to an area called big sensing data streams. One of the key problems in big data is to ensure end-to-end security where a Data Stream Manager (DSM) must always verify the security of the data before executing a query to ensure data security (i.e., confidentiality, integrity, authenticity, availability and freshness) as the medium of communication is untrusted. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to ensure the trustworthiness of collected data so that any decisions are made on the correct data, followed by protecting the data streams from information leakage and unauthorised access. This thesis considers end-to-end means from source sensors to cloud data centre. Although some security issues are not new, the situation is aggravated due to the features of the five Vs of big sensing data streams: Volume, Velocity, Variety, Veracity and Value. Therefore, it is still a significant challenge to achieve data security in big sensing data streams. Providing data security for big sensing data streams in the context of near real time analytics is a challenging problem. This thesis mainly investigates the problems and security issues of big sensing data streams from the perspectives of efficient and lightweight processing. The big data streams computing advantages including real-time processing in efficient and lightweight fashion are exploited to address the problem, aiming at gaining high scalability and effectiveness. Specifically, the thesis examines three major properties in the lifecycle of security in big data streams environments. The three properties include authenticity, integrity and confidentiality also known as the AIC triad, which is different to CIA triad used in general data security. Accordingly, a lightweight security framework is proposed to maintain data integrity and a selective encryption technique to maintain data confidentiality over big sensing data streams. These solutions provide data security from source sensing devices to the processing layer of cloud data centre. The thesis also explore a further proposal on a lattice based information flow control model to protect data against information leakage and unauthorised access after performing the security verification at DSM. By integrating the access control model, this thesis provides an end-to-end security of big sensing data streams i.e. source sensing device to the cloud data centre processing layer. This thesis demonstrates that our solutions not only strengthen the data security but also significantly improve the performance and efficiency of big sensing data streams compared with existing approaches

PAAL : a framework based on authentication, aggregation, and local differential privacy for internet of multimedia things

Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture to manage end and edge devices, preserve the privacy of end-devices and data, and protect the underlying network from external attacks. The proposed framework has three layers. In the first layer, the underlying network is partitioned into multiple clusters to manage end-devices and level-one edge devices (LOEDs). In the second layer, the LOEDs apply an efficient aggregation technique to reduce the volumes of generated data and preserve the privacy of end-devices. The privacy of sensitive information in aggregated data is protected through a local differential privacy-based technique. In the last layer, the mobile sinks are registered with a level-two edge device via a handshaking mechanism to protect the underlying network from external threats. Experimental results show that the proposed framework performs better as compared to existing frameworks in terms of managing the nodes, preserving the privacy of end-devices and sensitive information, and protecting the underlying network. © 2014 IEEE

Blockchain and Federated Learning-enabled Distributed Secure and Privacy-preserving Computing Architecture for IoT Network

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A Traffic-Aware Approach for Enabling Unmanned Aerial Vehicles (UAVs) in Smart City Scenarios

In smart cities, vehicular applications require high computation capabilities and low-latency communication. Edge computing offers promising solutions for addressing these requirements because of several features, such as geo-distribution, mobility, low latency, heterogeneity, and support for real-time interactions. To employ network edges, existing fixed roadside units can be equipped with edge computing servers. Nevertheless, there are situations where additional infrastructure units are required to handle temporary high traffic loads during public events, unexpected weather conditions, or extreme traffic congestion. In such cases, the use of flying roadside units are carried by unmanned aerial vehicles (UAVs), which provide the required infrastructure for supporting traffic applications and improving the quality of service. UAVs can be dynamically deployed to act as mobile edges in accordance with traffic events and congestion conditions. The key benefits of this dynamic approach include: 1) the potential for characterizing the environmental requirements online and performing the deployment accordingly, and 2) the ability to move to another location when necessary. We propose a traffic-aware method for enabling the deployment of UAVs in vehicular environments. Simulation results show that our proposed method can achieve full network coverage under different scenarios without extra communication overhead or delay

SECBlock-IIoT : A Secure Blockchain-enabled Edge Computing Framework for Industrial Internet of Things

This work was supported by a National Research Foundation of Korea (NRF) grant funded by Korean Government (MSIT) (No. 2021R1A2C2014333).Postprin