This study examines ESP32-based static and dynamic load-balancing algorithms to enhance defence networks’ Wi-Fi range and traffic control. Real-time Wi-Fi scanning assessed the performance of these algorithms, covering response time, throughput, network latency, jitter, and packet loss. The static algorithm demonstrated a 5% lower average response time and 3% higher throughput than the dynamic algorithm, leading to significant improvements in jitter (from 1.01 ms to 0.80 ms) and packet loss rate (from 1.50% to 0.88%). On the other hand, dynamic load balancing reduced Access Points (APs) overload by 20% during peak periods, enhancing network stability and resource utilisation, which is crucial for defence operations. These findings highlight the contribution of ESP32-based load balancing, offering a practical solution to optimise defence network performance by enhancing throughput scalability and AP resource efficiency. The study provides essential insights into managing signal variability, congestion, and disruptions, offering valuable guidance for defence and security professionals in optimising wireless network infrastructure.

In this paper, we raise five questions about the Internet, which are: What are the positive and negative effects of broadband Internet? Which type of Internet access is preferable? Which one of them needs more attention? How much and what kind of developments affect the Internet? What solutions are there to improve the current situation? and find solutions for them. First, fourteen advantages and positive effects of broadband Internet in every corner of life are presented. Second, fifty criteria are extracted for comparing wired and wireless broadband Internet. The third task, they are compared by describing the characteristics of optical fiber-based fixed and fifth-generation-based fixed/mobile broadband Internet access. Fourth, issues that directly and/or indirectly affect the global development of broadband Internet are outlined. The fifth task elicits twenty items on balanced sustainable development and strategic planning of broadband Internet. Finally, we propose ten solutions to improve the current state of global broadband Internet, reduce the digital divide in the world, and decrease the negative social effects of broadband Internet.

Besides cryptocurrencies, DLTs may be also exploited in enterprise systems operated by a consortium of organizations. Their interaction takes usually place on a permissioned blockchain network that holds a set of data to be queried frequently. In this scope, the main problem of DLTs is their unsuitability for a fast service of complex queries on those data. In order to solve this issue, many proposals dump the ledger contents onto databases that, because of their own goals and design, are already optimized for the execution of those queries. Unfortunately, many of those proposals assume that the data to be queried consist in only a block or (cryptocurrency-related) transaction history. However, those organization consortiums commonly store other structured business-related information in the DLT, and there is an evident lack of support for querying that other kind of structured data. To remedy those problems, DELTA synchronizes, with minimal overhead, the DLT state into a database, providing: (1) a modular architecture with event-based handling of DLT updates that supports different DLTs and databases, (2) a transparent management, since DLT end users do not need to learn or use any new API in order to handle that synchronization (i.e., those users still rely on the original interface provided by their chosen DLT), (3) the efficient execution of complex queries on those structured data. Thus, DELTA reduces query times up to five orders of magnitude, depending on the DLT and the database, compared to queries directed to the ledger nodes.

This paper presents Deeper, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on-chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. Deeper solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook-based smart contract that adds our custom functionality to Uniswap V4. Experiments on historical prices show that for a batch consisting of 8 trading pairs, Deeper enhances liquidity by over 2.6–5.9×. The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, Liquidity Providers should be cautious of certain risks and pitfalls, which are described. Overall, Deeper enables the creation of fair markets for low trading volume token pairs.

Financial services that are not only related to crypto-currencies but rely on blockchain for security and integrity are jointly referred to as Decentralized Fiance (DeFi) and are evolving rapidly. Given their novel applications of DLT and sophistical economical designs, the distinction between DeFi services and understanding the involved risk are often complex. This paper systematically studies the major classes of DeFi protocols, including risk and security. The selection of DeFi categories is based on a quantitative approach, covering over 80% of total value locked (TVL) in DeFi. Further, a structured methodology is provided to differentiate between DeFi protocols based on the algorithmic design and blockchain-network architecture. The findings indicate that every DeFi protocol falls into one of the three classes of DeFi algorithms: liquidity pool, synthetic asset or aggregator protocol. This work concludes with the risk analysis that is derived from the DeFi protocol, underlying tokens and agents. Certain DeFi assets, such as crypto-backed stablecoins, liquid staking tokens, and wrapped tokens of bridges, are synthetic assets, similar to derivatives in traditional finance, and bore similar risk exposure.