Compressive Sensing Based Distributed Data Storage for Mobile Crowdsensing

In the modern era, we need to deploy several functionalities either on central cloud servers or edge cloud servers to provide Internet of Things (IoT)-based services via a wide-area network. Typically a huge database and several non-real-time functions are deployed on the central cloud servers. On the other hand, real-time functions are deployed on edge cloud servers. For delay-sensitive services, this approach has an issue in completing the analysis with the latest information when the delay between the central and edge cloud servers is large. In this paper, we propose an allocation scheme of database and applications, which minimizes the delay from the latest update of the database to analyze the real-time data from IoT devices. In the proposed scheme, the delay is minimized considering two constraints of each server, which are maximum accommodating capacity of the IoT devices and whether the database function can be deployed. Numerical results observe that the proposed scheme reduces the delay of analysis compared to the conventional scheme. These results indicate that the proposed scheme can configure a low-delay network for delay-sensitive IoT services with data analysis.

The number of Internet of Things (IoT) devices has exponentially increased, creating an explosion of data that requires sophisticated processing and analysis techniques. When it comes to meeting the demands of long duration and narrow band of the things' Internet applications, traditional cloud computing solutions may encounter difficulties. To overcome these issues, fog computing has developed into a workable concept for extending nube services all the way to the system's edge. The development of a fog computing architecture for the analysis of data from the internet of things is the topic of this study. Our system's three primary parts are fog nodes, edge devices, and a central cloud server. Sensors and edge devices of the Internet of Things (IoT) are in charge of local preprocessing and data collection. In between network edge devices and the cloud server, fog nodes act as intermediaries. Their actions have reduced the volume of raw data sent to the cloud for processing and archiving. One cloud server manages all aspects of data analysis, storage, and archiving. In order to show how effective and efficient our architecture is, Our approach was supported by data gathered from a variety of Internet-connected devices, and by lowering the amount of data transferred to the cloud, we were able to considerably lower lag and the use of black band. The network's core fog nodes also offered the processing capacity required to carry out analysis relatively instantly. The advantages of the board devices, given that a large number of Internet of Things applications require real-time or almost real-time data processing, this architecture stands out because to its capacity to lower latency, save bandwidth, and improve system efficiency.

In contrast to the traditional cloud-based IoT structure, which imposes high computation and storage demands on the central cloud server, edge computing can process data at the network edge. In this paper, we present an edge computing data storage protocol employing the blockchain and the group signature, where the blockchain works as the trusted third party, offering a convenient platform for the data storage and protection, and the group signature is utilized for privacy identity information protection. Unlike the common group signature scheme, we propose a notion of \emph{simplified group signature}, which removes the traceable property to improve the computational efficiency. In edge computing, the edge devices and the end devices can easily set up a long-term trust relationship through the cloud services, so the traceability may be of little utility in this scenario. In our protocol, we also design a new data validation scheme that has a proof size of only <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$O(1)$</tex></formula> .

City Internet-of-Things (IoT) applications are becoming increasingly complicated and thus require large amounts of computational resources and strict latency requirements. Mobile cloud computing (MCC) is an effective way to alleviate the limitation of computation capacity by offloading complex tasks from mobile devices (MDs) to central clouds. Besides, mobile-edge computing (MEC) is a promising technology to reduce latency during data transmission and save energy by providing services in a timely manner. However, it is still difficult to solve the task offloading challenges in heterogeneous cloud computing environments, where edge clouds and central clouds work collaboratively to satisfy the requirements of city IoT applications. In this article, we consider the heterogeneity of edge and central cloud servers in the offloading destination selection. To jointly optimize the system utility and the bandwidth allocation for each MD, we establish a hybrid offloading model, including the collaboration of MCC and MEC. A distributed deep learning-driven task offloading (DDTO) algorithm is proposed to generate near-optimal offloading decisions over the MDs, edge cloud server, and central cloud server. Experimental results demonstrate the accuracy of the DDTO algorithm, which can effectively and efficiently generate near-optimal offloading decisions in the edge and cloud computing environments. Furthermore, it achieves high performance and greatly reduces the computational complexity when compared with other offloading schemes that neglect the collaboration of heterogeneous clouds. More precisely, the DDTO scheme can improve computational performance by 63%, compared with the local-only scheme.

Enhanced multi-objective gorilla troops optimizer for real-time multi-user dependent tasks offloading in edge-cloud computing

The intersection of security and sustainability within wireless sensor networks (WSNs) underscores pivotal factors such as energy efficiency, resource optimization, energy waste reduction, and the sustained integrity of network infrastructure. This interplay ensures that deployments are not just efficient but also ecologically sound. WSNs comprise autonomously dispersed sensors linked to battery-powered devices, facilitating wireless data transmission. The optimization of WSNs through Fog and Edge Computing signifies a paradigm shift, diminishing reliance on central cloud servers. This adaptive strategy enhances WSN efficiency across diverse environmental conditions by streamlining data transmission to centralized cloud servers. In cryptographic systems, conventional approaches reliant on mathematical algorithms to secure communication channels encounter vulnerabilities. Quantum cryptography presents a more robust alternative to conventional methods, while post-quantum cryptography (PQC) employs algorithms resilient to both traditional and quantum threats. This chapter introduces a novel approach for mutual authentication and generating session keys in communications between WSN nodes. We use Super singular Hyperelliptic Curve Cryptography (HECC) with a small size by exchange key Diffie-Hellman (DH) to improve security in IoT and WSN. This method provides a promising mix of quantum resistance and integration into conventional approaches.

Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing

This paper proposes a power-saving method for time series environment monitoring wireless sensor networks (WSNs) system using the compressed sensing technique. The data reconstruction of compressed sensing is complex, but the compression process itself is simple. While sensor nodes have limited resources, servers have abundant resources. Therefore, the sensor nodes compress the environmental data measured in time series, and the server reconstructs the compressed environmental data. In the environmental data collection phase, the sensor nodes transmit environmental measurement data using compressed sensing to save energy at the sensor nodes. The server reconstructs the received environmental data. This study develops the ZigBee WSN based time series indoor environment data collection system. Then we investigate the impact of compressed sensing technology on WSNs. The experimental results show that for sensors with dynamically varying sleep periods, when the compression ratio is set to 20% or less, the power consumption is reduced by 20% with a decision coefficient of 0.7 or higher, confirming the effectiveness of the proposed method.

With the popularity of the smart phones embedded in a large number of sensors, mobile crowd sensing (MCS) applications have developed rapidly. These applications often require the participants to collect the location-related data. However, this process faces the risk of the privacy leakage. The existing anonymous and cloaking methods can still analyze the user's identity and sensing locations by obtaining enough data with spatiotemporal correlation. In addition, the existing encryption methods not only take a lot of computation cost but also need to assume a full trusted sensing platform. In order to preserve the users' privacy, we need to separate the user's true identity and the sensing location. We propose a privacy-preserving method based on server-aided (or cloud-assisted) reverse oblivious transfer (ROT) protocol containing a cloud server which can compute the results of the encrypted sensing data to avoid the full trust in the sensing platform and enhance the computing efficiency in MCS. It overcomes the derivation of the malicious users in the standard malicious model through constructing the coefficients of the polynomial. Finally, the analysis demonstrates that the proposed method possesses the most efficient computing time, communication overhead and storage overhead comparing with other methods and can achieve the quality-privacy tradeoff optimization.

Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

In recent days with the tremendous increase of unstructured data, cloud storage technology gets more attention and better development. All the user’s data is totally stored in the centralized cloud servers rather than in their own location. Traditional privacy protection methods are usually based on encryption and decryption technology, but those methods don’t resist attack from the internal or external attacker of cloud server. In order to solve this problem, we try to apply fog computing and data replication technique for providing more security for the sensitive data. The proposed techniques can both take full advantage of cloud storage and protect the privacy of data. However, appropriate replica placement in a large-scale, dynamically scalable and totally virtualized data centers is much more complicated. In order to develop a cost-effective availability, minimize the response time of applications and make load balancing for cloud storage, a new replica placement like Efficient Dynamic Replication Algorithm (EDRA) is proposed. Here we divide data into different parts. And then place the data blocks in a replicated manner in local machine and fog server and cloud server in order to protect the privacy. By conducting various experiments on our current approach the theoretical and experimental evaluation has been validated, which is really a powerful supplement to existing cloud storage scheme.

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes.

With the rapid development of cloud servers, storing data on cloud servers has become a popular option. However, cloud servers are centralized. Storing data on centralized cloud servers may involve some risks. For example, the data access pattern may be revealed when accessing data on cloud servers. Therefore, protecting a user’s patterns has become a crucial concern. Oblivious RAM (ORAM) is a candidate solution to hide the data access pattern. However, it inherently induces some overhead of accessing data, and many blockchain-based applications also do not consider the access pattern leakage issues. In this paper, we address these issues above by proposing a decentralized database system with oblivious access in a (parallel) smart contract model. The interactions of oblivious access are asymmetric where the smart contract side is expected to put much effort into computation. The proposed system slightly reduces the overhead of ORAM and overcomes the issues stemming from the centralization of servers. The main techniques are to use the garbled circuits to reduce the cost of communication and to combine with the parallel smart contract model to (conceptually) improve the performance of smart contract execution on the blockchain.

Virtual tube storage scheme for supporting mobile sink groups in wireless sensor networks

Vehicular ad hoc networks (VANETs) are projected to be an integral component in intelligent transportation systems, poised to support road safety services via the Vehicle to Vehicle (V2V) and Vehicle to Roadside (V2R) units communication. With the evolution of technology and the growth in the number of smart vehicles, traditional VANETs face technical challenges in deployment and management due to less scalability and poor connectivity. Current smart vehicles are identified, authenticated, and connected through central cloud servers. This model will have limited scalability as the technology becomes pervasive, and the cloud servers will remain a single point of failure that can disrupt the entire network. Therefore, we need a secure distributed system to reduce the network traffic rate. In this paper, we propose a blockchain-based distributed message exchange system that will handle the exchange of safety and periodic beacon messages among vehicles. Since blockchain is characterized as being a decentralized and non-tampering system. We considered saving the safety messages only in the blockchain as they occur less than the periodic messages and they are more important. We propose to implement the blockchain per country to reduce the number of nodes/vehicles joining the network. We also reduce the block body size by using the Kademlia Distributed Hash Table (DHT) to broadcast the beacon messages. Experimental evaluation shows that the system can protect a V2V network against different attack types, such as sybil attack and alteration attack with TPR more than 95%. The experiments also show that the block body size is reduced by a factor of 1:5, which helps in broadcasting the data faster.