Despite its advantages for privacy-preserving data-driven modeling, federated learning is vulnerable to privacy breaches, as demonstrated by recent attacks on its privacy properties. It has been proven that sharing the weights alone is insufficient to protect the underlying data. In this work, we provide a solution for sharing the aggregated weights with a central server while safeguarding the privacy of individual client weights. Our solution introduces a decentralized committee election mechanism, eliminating the need for a trusted party. The election phase is based on verifiable random functions (VRFs), whereas the aggregation phase is based on Elliptic curve cryptography and multi-party secret-sharing schemes. Our experimental results show that our solution outperforms the proposed solutions in terms of communication and computation costs. Overall, our approach offers a robust solution for privacy-preserving federated learning without compromising its accuracy and without relying on a third party.

In online auctions, fraudulent behaviours such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behaviour-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviours, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.

Runtime-verification of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of runtime-verification in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Although several runtime protection solutions have been proposed, they do not discuss the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose an efficient Smart contract Runtime Protection framework, called SRP, that minimizes the onchain burden of runtime-verification by integrating an off-chain mechanism with onchain contract execution. The proposed hybrid architecture is designed to protect already-deployed smart contracts from attacks in real-time while maintaining the throughput of the underlying blockchain. We first present SRP from a design perspective proposing a protocol customized for off-chain runtime-verification interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local instance of the Ethereum network. Our empirical and experimental results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads.

Abstract The licensed band is crowded and suffers from immense mobile data traffic growth, which exceeded 58 exabytes per month in 5 years. Meanwhile, a significant portion of the unlicensed band is underutilized and not coordinated efficiently. Experiments in some urban areas of the world have shown that only 5% of the unlicensed 5 GHz band is being used. 5G NR‐U technology supports 5G networks in the unlicensed band to alleviate the traffic congestion and boosts 5G networks capacity. Different heterogeneous network access technologies already use the unlicensed band. Consequently, 5G NR‐U networks will operate in the proximity of the other coexisting networks, such as WiFi networks in the 5 GHz and 6 GHz bands. In such environments, assessing the shared spectrum becomes challenging and necessitates adequate protocols to identify idle slots for successful transmissions. Cooperative Spectrum Sensing (CSS) improves the spectrum assessment process, as the decision about the spectrum state is rendered based on the local decisions of multiple sensing nodes. CSS is exploited by integrating it with Blockchain technology to design a decentralized cooperative spectrum management system called: Blockchain‐Based Cooperative Spectrum Management (BCSM). The system is attributed to ameliorating 5G NR‐U awareness about the neighboring WiFi networks traffic in the unlicensed band. An algorithm is designed for performing distributed cooperative spectrum assessment between the 5G NR‐U base stations to profile the WiFi networks traffic in their proximity. To ensure fairness based on the effort expended in assessing the spectrum, a priority‐based algorithm is designed for spectrum access scheduling. A proof‐of‐concept is implemented using private Ethereum Blockchain and NS3 simulator. Finally, the system's accuracy is evaluated empirically along with theoretical security analysis.

Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation. Many studies are exploring the use of deep learning (DL) algorithms for localization. DL's ability to extract features and to classify autonomously makes it an attractive solution for fingerprint-based localization. These solutions require frequent retraining of DL models with vast amounts of measurements. Although crowdsourcing is an excellent way to gather immense amounts of data, it jeopardizes the privacy of participants, as it requires to collect labeled data at a centralized server. Recently, federated learning has emerged as a practical concept in solving the privacy preservation issue of crowdsourcing participants by performing model training at the edge devices in a decentralized manner; the participants do not expose their data anymore to a centralized server. This paper presents a novel method utilizing federated learning to improve the accuracy of RSS fingerprint-based localization while preserving the privacy of the crowdsourcing participants. Employing federated learning allows ensuring preserving the privacy of user data while enabling an adequate localization performance with experimental data captured in real-world settings. The proposed method improved localization accuracy by 1.8 meters when used as a booster for centralized learning and achieved satisfactory localization accuracy when used standalone.

The proliferation of renewable energy systems and high-capacity batteries has enabled customers to trade their excess energy on the market in a peer-to-peer manner through the smart grid. At the same time, electric vehicles (EVs) are enjoying widespread acceptance, leading to a higher demand for charging stations. In this paper, we propose a system where energy traders and EV owners collectively work to satisfy the energy demands of EVs. Specifically, energy traders make bids to EV owners who, in turn, reserve their preferred charging station for a specific period of time. To protect the privacy of EV owners, we also introduce an anonymous payment system that cannot link individual owners to specific charging locations. Finally, to guarantee the security and transparency of the entire system, we store all transactions on a consortium blockchain that is managed by the energy traders and the financial institutions that support the anonymous payment system. Our experimental results indicate that the overhead of the cryptographic operations involved in the major transactions is low, in terms of both computational and communication cost.

This contribution presents a solution to improve the performances of micro unmanned aerial vehicles (UAVs) by increasing their missions coverage in terms of distance and time. This is achieved by letting the UAV ride existing land public transport such as the city bus throughout the route to its mission location. Indeed, due to their limited battery capacity, micro-UAVs flying time is restrained, which affects their mission and coverage performances. In this paper, we propose to leverage the use of public transport infrastructure, such as city buses, to carry the UAVs whenever it is possible in order to minimize their flight energy consumption. For this purpose, a generic scheduling framework to efficiently cover spatially and temporally distributed events in a geographical area of interest over a long period of time is proposed. By considering the public transport schedule table, a mixed integer linear programming problem (MILP) aiming at minimizing the total energy consumption of the UAVs is formulated while accomplishing all the pre-scheduled missions. The proposed proactive UAV scheduling framework optimizes the UAV trips according to the mission occurrence and the schedule table of the buses. The obtained results demonstrate the effectiveness of the collaboration between the UAVs and the land transport to improve the overall UAV missions' performances in terms of distance coverage.

Recently, Autonomous Vehicles (AVs) have gained extensive attention from both academia and industry. AVs are a complex system composed of many subsystems, making them a typical target for attackers. Therefore, the firmware of the different subsystems needs to be updated to the latest version by the manufacturer to fix bugs and introduce new features, e.g., using security patches. In this paper, we propose a distributed firmware update scheme for the AVs' subsystems, leveraging blockchain and smart contract technology. A consortium blockchain made of different AVs manufacturers is used to ensure the authenticity and integrity of firmware updates. Instead of depending on centralized third parties to distribute the new updates, we enable AVs, namely distributors, to participate in the distribution process and we take advantage of their mobility to guarantee high availability and fast delivery of the updates. To incentivize AVs to distribute the updates, a reward system is established that maintains a credit reputation for each distributor account in the blockchain. A zero-knowledge proof protocol is used to exchange the update in return for a proof of distribution in a trustless environment. Moreover, we use attribute-based encryption (ABE) scheme to ensure that only authorized AVs will be able to download and use a new update. Our analysis indicates that the additional cryptography primitives and exchanged transactions do not affect the operation of the AVs network. Also, our security analysis demonstrates that our scheme is efficient and secure against different attacks.

Localization services are very critical for many applications of wireless networks. The use of the received signal strength (RSS) is quite popular in the localization process. RSS-based methodologies fall into two categories based on whether the RSS measurements are used to estimate distance or simply to infer relative proximity to certain anchors. In this paper, we analytically derive the probability of error in the distance and proximity estimations caused by the non-monotonicity of RSS, under most popular radio propagation models. We further analyze the localization error for both area-based and multilateration algorithms which use the proximity and the distance information, respectively, as a basis to estimate the position. The analytical results show the advantage of using RSS for proximity over distance estimation. The probability of wrong proximity estimation is found to be much smaller than that of distance. The comparison between area-based and multilateration under various settings shows that area-based localization is more robust than multilateration in minimizing location estimation errors particularly when the radio propagation cannot be accurately modeled.

Wireless Sensor Networks are often deployed in a dense and random manner which is considered as the easiest way to cover a large region of interest. However, this kind of deployment cannot guarantee complete sensing coverage of large areas and both uncovered regions (coverage holes) or regions covered by more than one sensor (redundant sensors) arise. Therefore, it is mandatory to perform a second post-deployment operation to improve the coverage. In this paper, we consider the use of multi-robots to relocate redundant sensors and cover the holes. The main goal of this work is to develop a new path planning algorithm, based on Travelling Salesman Problem (TSP), to enable robots to efficiently relocate sensor nodes and cover the holes. We propose a heuristic algorithm to solve the problem of relocation by defining an on optimal path that comprises redundant sensors and holes where the robots have to follows this path to relocate the redundant sensors in the holes. Simulation results clearly demonstrate the effectiveness of our algorithm, as compared to existing solution, in term of the distance traveled by robots and the average relocation time.

Wireless sensor networks have become an attractive choice for many monitoring applications in an unattended setups; some of these applications such as pipeline, railroad and highways monitoring require the sensors to be placed in a linear topology. These sensors need to be placed as far apart while still maintaining a minimum required coverage in order to minimize the required node count and reduce cost. In this paper, we present three optimization models for determining the node density that varies in the objective. The first opts to achieve a desired level of detection fidelity while minimizing the number of deployed sensors. The second model considers the scenario with a constrained node count and determines the position of the available nodes such that the coverage is maximized. In the third model, we strive to minimize the number of deployed nodes when the desired fidelity is not uniform and some locations require higher coverage than others. The proposed optimization formulations are generic in nature and can be applied to any sensor coverage model. The three optimization models are validated through implementation using an attenuated disc coverage model where the detectability of a sensor is inversely proportional to the distance.

Area-based localization is a simple and efficient approach, where each node estimates its position based on proximity information to some special nodes with known location, called anchors. Based on the anchors' coordinates, each node first determines its residence area and then approximates its position as the centroid of that area. Therefore, the accuracy of the estimated position depends on the size of the residence area; the smaller the residence area is, the better the accuracy is likely to be. Because the size of the residence area mainly depends on the number and the positions of anchor nodes, their deployment should be carefully considered in order to achieve a better accuracy while minimizing the cost. For this purpose, in this paper we conduct a theoretical study on anchor placement for a very popular area based localization approach. We determine the optimal anchor placement pattern for increased accuracy and how to achieve a particular accuracy goal with the least anchor count. Our analytical results are further validated through simulation.

Area-based localization algorithms use only the position of some reference nodes, called anchors, to estimate the residence area of the remaining nodes. Existing algorithms use a triangle, a ring or a circle as the geometric shape that defines the node's residence area. However, existing algorithms suffer from two major problems: (1) in some cases, they might make wrong decisions about a node presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error and high ratio of localizable nodes. In this paper, we overcome these shortcomings by introducing a new approach for determining the node's residence area that is geometrically shaped as a half-symmetric lens. A novel half symmetric lens based localization algorithm (HSL) is proposed. HSL yields smaller residence areas, and consequently, better location accuracy than contemporary schemes. HSL further employs Voronoi diagram in order to boost the percentage of localizable nodes. The performance of HSL is validated through mathematical analysis, extensive simulations experiments and prototype implementation. The validation results confirm that HSL achieves better location accuracy and higher ratio of localizable nodes compared to competing algorithms.

Ensuring area coverage is one of the key requirements of wireless sensor networks (WSNs). When nodes are randomly placed in the area of interest, redundancy is often provisioned in order to lower the probability of having voids, where part of the area is not within the detection range of any sensor. To extend the lifetime of the network, a duty cycle mechanism is often applied in which only a subset of the nodes are activated at a certain time while the other nodes switch to low-power mode. The set of active nodes are changed over time in order to balance the load on the individual sensors. The selection of active nodes is subject to meeting the coverage requirement. Assessing the coverage of a sensor is based on knowing its position. However, localization schemes usually yield a margin of errors which diminishes the coverage fidelity. Conservative approaches for mitigating the position inaccuracy assume the worst-case error across the network and end up activating excessive number of nodes and reduces the network lifetime. In this paper, we present an approach for estimating a bound on the maximum error for the position of each sensor and propose a distributed algorithm for achieving high fidelity coverage while engaging only a subset of the sensors. The simulation results confirm the performance advantages of our approach.

This paper focuses on data aggregation scheduling problem in wireless sensor networks (WSNs), to minimize time latency. Prior works on this problem have adopted a structured approach, in which a tree-based structure is used as an input for the scheduling algorithm. As the scheduling performance mainly depends on the supplied aggregation tree, such an approach cannot guarantee optimal performance. To address this problem, we propose approaches based on Semi-structured Topology (DAS-ST) and Unstructured Topology (DAS-UT). The approaches are based on two key design features, which are: (1) simultaneous execution of aggregation tree construction and scheduling, and (2) parent selection criteria that maximize the choices of parents for each node and maximize time slot reuse. We prove that the latency of DAS-ST is upper-bounded by ([2π/arccos(1/1+ϵ)]+4)R+Δ-4, where R is the network radius, Δ is the maximum node degree, and 0.05 <; ϵ ≤ 1. Simulations results show that DAS-UT outperforms DAS-ST and four competitive state-of-the-art aggregation scheduling algorithms in terms of latency and network lifetime.

The area-based localization algorithms use only the location information of some reference nodes, called anchors, to give the residence area of the remaining nodes. The current algorithms use triangle, ring or circle as a geometric shape to determine the sensors' residence area. Existing works suffer from two major problems: (1) in some cases, they might issue wrong decisions about nodes' presence inside a given area, or (2) they require high anchor density to achieve a low location estimation error. In this paper, we deal with the localization problem by introducing a new way to determine the sensors' residence area which shows a better accuracy than the existing algorithms. Our new localization algorithm, called HSL (Half Symmetric Lens based localization algorithm for WSN), is based on the geometric shape of half-symmetric lens. We also uses the Voronoi diagram in HSL to mitigate the problem of unlocalizable sensor nodes. Finally, we conduct extensive simulations to evaluate the performance of HSL. Simulation results show that HSL has better locatable ratio and location accuracy compared to representative state-of-the-art area-based algorithms.

In wireless sensor networks, reliability represents a design goal of a primary concern. To build a comprehensive reliable system, it is essential to consider node failures and intruder attacks as unavoidable phenomena. In this paper, we present a new intrusion-fault tolerant routing scheme offering a high level of reliability through a secure multi-path communication topology. Unlike existing intrusion-fault tolerant solutions, our protocol is based on a distributed and in-network verification scheme, which does not require any referring to the base station. Furthermore, it employs a new multi-path selection scheme seeking to enhance the tolerance of the network and conserve the energy of sensors. Extensive simulations with Tiny OS showed that our approach improves the overall Mean Time To Failure (MTTF) while conserving the energy resources of sensors.

Energy is a scarce resource in Wireless Sensor Networks. Some studies show that more than 70% of energy is consumed in data transmission. Since most of the time, the sensed information is redundant due to geographically collocated sensors, most of this energy can be saved through data aggregation. Furthermore, data aggregation improves bandwidth usage. Unfortunately, while aggregation eliminates redundancy, it makes data integrity verification more complicated since the received data is unique. In this paper, we present a new protocol that provides secure aggregation for wireless sensor networks. Our protocol is based on a two hops verification mechanism of data integrity. Our solution is essentially different from existing solutions in that it does not require referring to the base station for verifying and detecting faulty aggregated readings, thus providing a totally distributed scheme to guarantee data integrity. We carried out simulations using TinyOS environment. Simulation results show that the proposed protocol yields significant savings in energy consumption while preserving data integrity.