Typically, a firm's objectives include establishing consumer confidence, preserving its brand image, and developing a profitable business strategy. Consumers now place greater emphasis on the sustainability and transparency of their purchases. Given environmental and economic limitations, firms are often compelled to implement sustainable production methods. This is especially a struggle for small- and medium-sized enterprises (SMEs) with new technology as it can increase their risk of failure. This has led to a problem for consumers, who must cross-check the sustainability-related claims of firms they buy from. This is challenging because of limited process trace data and restricted enforcement capabilities. This study expands the knowledge of product sustainability and transparency. It aids our understanding of how SMEs develop prototypes and pilot solutions using blockchain and the Internet of Things (IoTs). To fulfill this aim, we discuss the experiences of three SMEs in two design domains: home decor and fashion goods. During the prototype and pilot phases, we used an iterative approach to match the firms' solution specifications and ensured that they did not replace but instead complemented the SME's legacy systems. Our pilot solutions supported SMEs to achieve their desired sustainability goals. Users could see the amount of recycled plastic used and the total C O 2 reduction after scanning a QR code. Upon scanning a QR code for a particular item, its origins and associated geographical details are displayed. The implementation of transparency involved monitoring the mobile application's popularity, where more than 10,000 clicks were documented for each product upon its introduction.

Network-based applications rely on the underlying network infrastructure to reliably forward packets between nodes. The way packets are forwarded has a significant impact on service quality. Therefore, it is important to gain a better understanding of data packet routes. To obtain detailed information about network paths, continuous and long-term packet analysis is required. To achieve this, we present our open-source framework HiPerConTracer 3.0 for large-scale IP trace analysis. It performs Ping and Traceroute measurements to provide detailed insights into packet routes and packet timing by tracing routes between senders and receivers in public and private networks. Particularly, it runs its own measurements, without need to obtain data, or cooperation from, the underlying network service providers or remote server owners. Our tool supports large-scale data collection, storage, and post-processing stages. It supports easy-to-understand route visualization, round-trip time measurements, and hop counts. A proof-of-concept analysis revealed that packet route lengths can change drastically when traveling through unexpected countries, regions, and network operators.

The number of large language models for code generation is rising. However, comprehensive evaluations that focus on reliability and security remain sparse. This study evaluated the Python language code quality generated by five large language models. They are GPT-4-Turbo, DeepSeek-Coder-33B-Instruct, Gemini Pro 1.0, Codex and CodeLLama70 b -Instruct. The evaluation considered three diverse application domains with varying prompt lengths for fair comparison. We found GPT-4-Turbo generated (on average) 4.5% more secure code than a Python code developer with three years of experience.

Blockchain offers decentralization and data security in a trustless environment. Transaction details are logged to trace a transaction trail. Blockchain has multiple components that are crucial to overall performance. One such component is the consensus model. It is responsible for network scaling, and blockchain suffers from scaling problems. It is also important for the growth and existence of the network to satisfy the transaction proposers and validators. To maintain the network, incentives are given to validators in a digital asset form, commonly using cryptocurrency. A lack of appropriate incentives can lead to suboptimal network performance, preventing networks from reaching their full potential. This paper introduces a noncooperative game theory-based incentive model to improve network performance while enhancing the scaling feature of a permissioned blockchain network. The model is generic and focuses on the incentive structure of the network. The proposed model is based on five design goals. They are generability, scalability, energy awareness, fairness, and dynamism. The proposed model is not a consensus model but a complement to a suitable voting-based consensus model. An extensive simulation campaign was conducted to demonstrate the effectiveness of the proposed model.

Cloud computing is now a ‘go-to’ platform for running various types of application. A wide spectrum of users needing software and hardware resources have embraced the cloud. Following the cloud business model, one is either a cloud-based service provider or a cloud service (hardware and software) user. There is a continuous evolution in the cloud ecosystem to support the ever-changing features of user applications. The cloud ecosystem has added a new resource delegation model to accommodate such new requirements. This new resource delegation model is known as fog. Both, cloud and fog platforms, employ complex hardware and software to provide better run-time support for applications. This paper presents a message-based privacy-aware application execution zone management framework for the cloud/fog platform. The framework aims to create a static execution zone based on the performance of the user application and the data privacy requirements. It also enables the user to set the resource termination conditions. Here, we have prototyped the proposed framework and showed how the proposed approach works with message structure and experiments with a P4 switch. We also present how the prototype could be implemented on a cloud/fog platform.

Cloud computing makes complex processing an off-premise activity by offering software- and hardware-based services using standard security protocols over the Internet. It has been seen that the cloud is not ideal for latency-sensitive applications. Thanks to the current growth of network communication and infrastructure, fog adds a computing resource delegation model between the user and the cloud. Fog aims to improve latency-sensitive applications support. Here, we propose one unified, proactive resource orchestration framework from a cloud/fog service provider perspective. The framework consists of a predictor and a resource allocator module. Users subscribe to these resources to execute their applications. The framework is modular and does not require application-specific information. A service provider can customise each module. We have presented the framework prototype by showing each module's simulated performance results using the parameters of our cloud/fog research testbed.

Abstract Applications running in a large and complex manycore system can significantly benefit from adopting the dataflow model of computation. In a dataflow execution environment, a thread can run only if all its required inputs are available. While the potential benefits are large, it is not trivial to improve resource utilization and energy efficiency by focusing on dataflow thread execution models (i.e., the ways specifying how the threads adhering to a dataflow model of computation execute on a given compute/communication architecture). This paper proposes and implements a hardware-software co-design-based dataflow threads management framework. It works at the Network-on-Chip (NoC) level and consists of three stages. The first stage focuses on a fast and effective thread distribution policy. The next stage proposes an approach that adds reconfigurability to a 2D mesh NoC via customized instructions to manage the dataflow thread distribution. Finally, a 2D mesh and ring-based hybrid NoC is proposed for better scalability and higher performance. This work can be considered a primary reference framework from which extensions can be carried out.

In today's complex Internet platform, online users need help to protect their online identity. Only sometimes, websites are very transparent about how user data will be collected, stored and processed by them. Sometimes Internet entities collect more online user information than required. These entities often share user identity-related data with third parties without consent. Existing traditional identity schemes need to be improved to stop and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen the preservation of online users’ digital identity due to its decentralised nature and robust data security features. In this paper, we proposed and implemented a generic blockchain-IoT-based self-sovereign identity management framework called ChainDiscipline. We have demonstrated the framework's operability and functionality by implementing healthcare and smart home data management-based use cases.

In today’s communication-centric world, users generate and exchange a massive amount of data. The Internet helps user data to travel from one part of the world to another, via a complex set of network systems. These systems are intelligent, heterogeneous, and non-transparent to users. This paper presents an extensive, trace-driven study of user data traffic covering five years of observations, six large ISPs, 22 different autonomous systems, and a total of 12 countries. This work aims to make users aware of how their data travels in the Internet, as the interests of ISPs majorly influence the data traffic path. Although data traffic should prefer to travel through countries that share land borders, we found that the shortest land distance between the two countries does not impact data path selection.1

The shared mobility concept is seen as disruptive and transformative for the automotive industry. Shared mobility is changing the way we choose our travel mode, from just owning a car to e-hailing, car-sharing, and other relevant mobility solutions. There is a growing interest of car manufacturers (original equipment manufacturers or OEMs) in car-sharing as an expansion strategy. Similarly, blockchain technology is seen as another disruptive technology, which can potentially change how the data is stored and accessible via its immutable, transparent, and trustworthy features. Motivated by these two current trends, this paper aims to explore how blockchain and IoT technologies together can drive shared mobility forward. We have presented a high-level architecture for a blockchain-IoT-based platform for promoting shared mobility combining car-sharing and car-leasing. We also demonstrated a prototype implemented from the OEM’s point of view by developing a blockchain-IoT-based platform streamlining car-sharing and leasing processes by taking into consideration of primary stakeholders (such as OEMs, a peer-to-peer car-sharing provider, leasing company and insurance provider as well as public authorities). This work also demonstrates that the design of such an integrated platform depends on the right balance between the key design principles (such as security and privacy, authenticity, traceability and reliability, scalability, and interoperability) in the context of car-sharing platforms.

Cloud computing has revolutionised the development and deployment of applications by running them cost-effectively in remote data centres. With the increasing need for mobility and micro-services, particularly with the emerging 5G mobile broadband networks, there is also a strong demand for mobile edge computing (MEC). It enables applications to run in small cloud systems in close proximity to the user in order to minimise latencies. Both cloud computing and MEC have their own advantages and disadvantages. Combining these two computing paradigms in a unified multi-cloud platform has the potential of obtaining the best of both worlds. However, a comprehensive study is needed to evaluate the performance gains and the overheads imposed by this combination to real-world cloud applications. In this paper, we introduce a baseline performance evaluation in order to identify the fallacies and pitfalls of combining multiple cloud systems and MEC into a unified MEC-multi-cloud platform. For this purpose, we analyze the basic, application-independent performance metrics of average round-trip time (RTT) and average application payload throughput in a setup consisting of two private and one public cloud systems. This baseline performance analysis confirms the feasibility of MEC-multi-cloud and provides guidelines for designing an autonomic resource provisioning solution in terms of an extension proposed to our existing Melodic middleware platform for multi-cloud applications.

Cyber-Physical Systems (CPSs) are widely necessary for many applications that require interactions with the humans and the physical environment. A CPS integrates a set of hardware-software components to distribute, execute and manage its operations. The AXIOM project (Agile, eXtensible, fast I/O Module) aims at developing a hardware-software platform for CPS such that i) it can use an easy parallel programming model and ii) it can easily scale-up the performance by adding multiple boards (e.g., 1 to 10 boards can run in parallel). AXIOM supports task-based programming model based on OmpSs and leverage a high-speed, inexpensive communication interface called AXIOM-Link. Another key aspect is that the board provides programmable logic (FPGA) to accelerate portions of an application. We are using smart video surveillance, and smart home living applications to drive our design.