586 research outputs found

    Provably Secure Decisions based on Potentially Malicious Information

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    There are various security-critical decisions routinely made, on the basis of information provided by peers: routing messages, user reports, sensor data, navigational information, blockchain updates, etc. Jury theorems were proposed in sociology to make decisions based on information from peers, which assume peers may be mistaken with some probability. We focus on attackers in a system, which manifest as peers that strategically report fake information to manipulate decision making. We define the property of robustness: a lower bound probability of deciding correctly, regardless of what information attackers provide. When peers are independently selected, we propose an optimal, robust decision mechanism called Most Probable Realisation (MPR). When peer collusion affects source selection, we prove that generally it is NP-hard to find an optimal decision scheme. We propose multiple heuristic decision schemes that can achieve optimality for some collusion scenarios

    Trust Management of Tiny Federated Learning in Internet of Unmanned Aerial Vehicles

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    Lightweight training and distributed tiny data storage in local model will lead to the severe challenge of convergence for tiny federated learning (FL). Achieving fast convergence in tiny FL is crucial for many emerging applications in Internet of Unmanned Aerial Vehicles (IUAVs) networks. Excessive information exchange between UAVs and IoT devices could lead to security risks and data breaches, while insufficient information can slow down the learning process and negatively system performance experience due to significant computational and communication constraints in tiny FL hardware system. This paper proposes a trusting, low latency, and energy-efficient tiny wireless FL framework with blockchain (TBWFL) for IUAV systems. We develop a quantifiable model to determine the trustworthiness of IoT devices in IUAV networks. This model incorporates the time spent in communication, computation, and block production with a decay function in each round of FL at the UAVs. Then it combines the trust information from different UAVs, considering their credibility of trust recommendation. We formulate the TBWFL as an optimization problem that balances trustworthiness, learning speed, and energy consumption for IoT devices with diverse computing and energy capabilities. We decompose the complex optimization problem into three sub-problems for improved local accuracy, fast learning, trust verification, and energy efficiency of IoT devices. Our extensive experiments show that TBWFL offers higher trustworthiness, faster convergence, and lower energy consumption than the existing state-of-the-art FL scheme

    Climate Change and Critical Agrarian Studies

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    Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty

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    In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computation-intensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI

    Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

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    Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models

    Implementation design of energy trading monitoring application for blockchain technology-based wheeling cases

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    One obstacle to the energy industry’s tendency toward adopting renewable energy is the requirement for a monitoring system for energy transactions based on microgrids in the wheeling scheme (shared use of utility networks). The quantity of transaction expenses for each operational generator is not monitored in any case. In this project, a mobile phone application is developed and maintained to track the total amount of fees paid and received by all wheeling parties and the amount of electricity produced by the microgrid. In the wheeling case system research, the number of transaction costs, such as network rental fees, loss costs, and profit margins, must be pretty calculated for all wheeling participants. The approach created in this study uses a blockchain system to execute transactions, and transactions can only take place if the wheeling actor and the generator have an existing contract. The application of energy trading is the main contribution of this research. The created application may track energy transfers and track how many fees each wheeling actor is required to receive or pay. Using a security system to monitor wheeling transactions will make energy trades transparent

    STV+Reductions: Towards Practical Verification of Strategic Ability Using Model Reductions

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    We present a substantially expanded version of our tool STV for strategy synthesis and verification of strategic abilities. The new version adds user-definable models and support for model reduction through partial order reduction and checking for bisimulation
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