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We propose Agora, the first blockchain-based data marketplace that enables multiple privacy-concerned parties to get compensated for contributing and exchanging data, without relying on a trusted third party during the exchange. Agora achieves data privacy, output verifiability, and atomicity of payments by leveraging cryptographic techniques, and is designed as a decentralized application via smart contracts. Particularly, data generators provide encrypted data to data brokers who use a functional secret key to learn nothing but the output of a specific, agreed upon, function over the raw data. Data consumers can purchase decrypted outputs from the brokers, accompanied by corresponding proofs of correctness. We implement a working prototype of Agora on Ethereum and experimentally evaluate its performance and deployment costs. As a core building block of Agora, we propose a new functional encryption scheme with additional public parameters that operate as a trust anchor for verifying decrypted results.

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users’ context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme’s security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users’ context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme’s security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme. IEEE

Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR. © 2021 Association for Computing Machinery.

We propose Agora, the first privacy-aware data marketplace that enables parties to get compensated for contributing data, without relying on a trusted third party. We leverage cryptographic techniques to achieve three security properties: (i) data privacy—raw data remain private except for a function output, (ii) output verifiability—the output is proven to be correct, and (iii) atomicity of payments—parties cannot avoid paying for provided services. Agora is designed as a decentralized blockchain application via smart contracts. We implement a prototype on Ethereum and evaluate its performance in terms of computation overhead and monetary cost.

Peer-to-peer (P2P) networks utilize centralized entities (trackers) to assist peers in finding and exchanging information. Although modern P2P protocols are now trackerless and their function relies on distributed hash tables (DHTs), centralized entities are still needed to build file indices (indexing) and assist users in joining DHT swarms (bootstrapping). Although the functionality of these centralized entities are limited, every peer in the network is expected to trust them to function as expected (e.g. to correctly index new files). In this work, a new approach for designing and building decentralized online applications is proposed by introducing DIBDApp. The approach combines blockchain, smart contracts and BitTorrent for building up a combined technology that permits to create decentralized applications that do not require any assistance from centralized entities. DIBDApp is a software library composed of Ethereum smart contracts and an API to the BitTorrent protocol that fully decentralizes indexing, bootstrapping and file storing. DIBDApp enables any peer to seamlessly connect to the designed smart contracts via the Web3J protocol. Extensive experimentation on the Rinkeby Ethereum testnet shows that applications built using the DIBDApp library can perform the same operations as in traditional back-end architectures with a gas cost of a few USD cents.

Recent advances in machine and deep learning allow for enhanced retail analytics by applying object detection techniques. However, existing approaches either require laborious installation processes to function or lack precision when the customers turn their back in the installed cameras. In this paper, we present EyeShopper, an innovative system that tracks the gaze of shoppers when facing away from the camera and provides insights about their behavior in physical stores. EyeShopper is readily deployable in existing surveillance systems and robust against low-resolution video inputs. At the same time, its accuracy is comparable to state-of-the-art gaze estimation frameworks that require high-resolution and continuous video inputs to function. Furthermore, EyeShopper is more robust than state-of-the-art gaze tracking techniques for back head images. Extensive evaluation with different real video datasets and a synthetic dataset we produced shows that EyeShopper estimates with high accuracy the gaze of customers.

Information acquisition through crowdsensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires efficient information elicitation mechanisms to guarantee that the collected data are accurately acquired and reported. Such mechanisms can be implemented via smart contracts on blockchain to enable privacy and trust. In this work we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based crowdsensing queries without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We identify six necessary characteristics for information elicitation mechanisms to be applicable in spontaneous location-based settings and implement an existing state-of-the-art mechanism using smart contracts. Additionally, as location information is exogenous to these mechanisms, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We examine the performance of Orthos on Rinkeby Ethereum testnet and conduct experiments with live audience.

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (e.g., crowdsensing). More sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices and operates via two consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze the security of Mneme and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

The increasing complexity of smartphone applications and services necessitate high battery consumption, but the growth of smartphones' battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper, we advance on previous ideas, proposing and implementing a Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, its better security, and its low battery consumption characteristics. We design a new NFC communication protocol that overcomes the limitations of the default NFC protocol; removing the need for constant user interaction, the one-way communication restraint, and the limit on low data size transfer. We present experimental results of the energy consumption and the time duration of computationally and data intensive representative applications: (i) RSA key generation and encryption, (ii) gaming/puzzles, (iii) face detection, (iv) media download from the Internet, and (v) data transferring between the mobile and the cloudlet. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low-power NFC interface and the benefits of offloading.

Although the primary function of distributed ledgers is to store data related to users' interactions, their capabilities allow them to offer more sophisticated functionalities. Advances in blockchain technologies introduced smart contracts, software programs that define immutable rules as functions stored on the blockchain and can be executed on demand. Smart contracts can interact not only with users but also with each other via message exchange. We compare existing smart contract interactions, and develop an architecture for asynchronous state consensus, a novel type of smart contract interaction required in applications but had rarely been addressed. The proposed architecture is composed of two types of smart contracts, ie, Custodian and Client. Client smart contracts serve as network participants reaching a particular consensus collectively by forming a cluster and issuing votes towards a final state agreement. Custodian smart contracts serve as the arbiters that aggregate and calculate voting results as the finalized state consensus that is shared across the network. To test the feasibility of our proposal, we conduct experiments on the consensus reaching latency and the scalability under different network configurations with standardized Amazon Web Service instances. Lastly, we discuss the robustness our proposal concerning Byzantine Fault tolerance and list possible applications. In the gaming industry, an ERC721 smart contract does not allow contrasting structural features between individual tokens, yet only minor value‐level differences. The proposed solution can address the need for character diversity that characters can be created and attached to a gaming smart contract after deployment, which enables fine distinction between characters. The proposal can also achieve sharing states across smart contracts, such as the jackpot, which renovates the flexibility of blockchain gaming.

Although the primary function of distributed ledgers is to store data related to users' interactions, their capabilities allow them to offer more sophisticated functionalities. Advances in blockchain technologies introduced smart contracts, software programs that define immutable rules as functions stored on the blockchain and can be executed on demand. Smart contracts can interact not only with users but also with each other via message exchange. We compare existing smart contract interactions, and develop an architecture for asynchronous state consensus, a novel type of smart contract interaction required in applications but had rarely been addressed. The proposed architecture is composed of two types of smart contracts, ie, Custodian and Client. Client smart contracts serve as network participants reaching a particular consensus collectively by forming a cluster and issuing votes towards a final state agreement. Custodian smart contracts serve as the arbiters that aggregate and calculate voting results as the finalized state consensus that is shared across the network. To test the feasibility of our proposal, we conduct experiments on the consensus reaching latency and the scalability under different network configurations with standardized Amazon Web Service instances. Lastly, we discuss the robustness our proposal concerning Byzantine Fault tolerance and list possible applications. In the gaming industry, an ERC721 smart contract does not allow contrasting structural features between individual tokens, yet only minor value‐level differences. The proposed solution can address the need for character diversity that characters can be created and attached to a gaming smart contract after deployment, which enables fine distinction between characters. The proposal can also achieve sharing states across smart contracts, such as the jackpot, which renovates the flexibility of blockchain gaming.

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.

Although many children are born with congenital limb malformation, contemporary functional artificial hands are costly and are not meant to be adapted to growing hand. In this work, we develop a low cost, adaptable and personalizable system of an artificial prosthetic hand accompanied with hardware and software modules. Our solution consists of (i) a consumer grade electromyography (EMG) recording hardware, (ii) a mobile companion device empowered by deep learning classification algorithms, (iii) an cloud component for offloading computations, and (iv) mechanical 3D printed arm operated by the embedded hardware. We focus on the flexibility of the designed system making it more affordable than the alternatives. We use 3D printed materials and open-source software thus enabling the community to contribute and improve the system. In this paper, we describe the proposed system and its components and present the experiments we conducted in order to show the feasibility and applicability of our approach. Extended experimentation shows that our proposal is energy efficient and has high accuracy.

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent's history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars. © 2019 IEEE.

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.

Although many children are born with congenital limb malformation, contemporary functional artificial hands are costly and are not meant to be adapted to growing hand. In this work, we develop a low cost, adaptable and personalizable system of an artificial prosthetic hand accompanied with hardware and software modules. Our solution consists of (i) a consumer grade electromyography (EMG) recording hardware, (ii) a mobile companion device empowered by deep learning classification algorithms, (iii) an cloud component for offloading computations, and (iv) mechanical 3D printed arm operated by the embedded hardware. We focus on the flexibility of the designed system making it more affordable than the alternatives. We use 3D printed materials and open-source software thus enabling the community to contribute and improve the system. In this paper, we describe the proposed system and its components and present the experiments we conducted in order to show the feasibility and applicability of our approach. Extended experimentation shows that our proposal is energy efficient and has high accuracy.

Content-centric networking (CCN) introduces a paradigm shift from a host centric to an information centric communication model for future Internet architectures. It supports the retrieval of a particular content regardless of the physical location of the content. Content caching and content delivery networks are the most popular approaches to deal with the inherent issues of content delivery on the Internet that are caused by its design. Moreover, intermittently connected mobile environments or disruptive networks present a significant challenge to CCN deployment. In this paper, we consider the possibility of using mobile users in improving the efficiency of content delivery. Mobile users are producing a significant fraction of the total Internet traffic, and modern mobile devices have enough storage to cache the downloaded content that may interest other mobile users for a short period too. We present an analytical model of the CCN framework that integrates a delay tolerant networking architecture into the native CCN, and we present large-scale simulation results. Caching on mobile devices can improve the content retrieval time by more than 50%, while the fraction of the requests that are delivered from other mobile devices can be more than 75% in many cases.

A set of cooperation-enforcing mechanisms can be utilized depending on the requirements and the type of application, while the users of the crowdsensing application can impose their requests on the server and receive the results.

This chapter presents D2D ecosystems and listed some popular applications. It peovides the importance of hidden market design in the sharing of the users’ resources without the need for their intervention, and discusses the types of the interactions between the mobile users. D2D ecosystems are challenging due to their unpredictability, which is caused by users’ mobility; the limited, compared with conventional computer, computational resources and battery; and the incentives required to motivate mobile users to participate. Synchronized systems guarantee data coherence and integrity. In a synchronized reputation system for D2D ecosystems, all the mobile devices should assign the same trust scores to the rest of the participating devices. Reputation is defined as the opinion that someone has about someone or something, or how much respect or admiration someone or something receives, based on past behavior or character. Reputation systems allow users to rate each other in communities in order to build trust through reputation.

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users' privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.

The multitude of data generated by sensors available on users' mobile devices, combined with advances in machine learning techniques, support context-aware services in recognizing the current situation of a user (i.e., physical context) and optimizing the system's personalization features. However, context-awareness performances mainly depend on the accuracy of the context inference process, which is strictly tied to the availability of large-scale and labeled datasets. In this work, we present a framework developed to collect datasets containing heterogeneous sensing data derived from personal mobile devices.The framework has been used by 3 voluntary users for two weeks, generating a dataset with more than 36K samples and 1331 features. We also propose a lightweight approach to model the user context able to efficiently perform the entire reasoning process on the user mobile device. To this aim, we used six dimensionality reduction techniques in order to optimize the context classification. Experimental results on the generated dataset show that we achieve a 10x speed up and a feature reduction of more than 90% while keeping the accuracy loss less than 3%. © Copyright held by the owner/author(s).

Although the primary role of decentralized ledgers, such as blockchains, in cryptocurrencies, is to store data related to interactions between users to establish trust within incognizant parties, their capabilities allow them to offer more sophisticated functionalities. Smart contracts are decentralized rules that are stored on the blockchain and are executed on demand. Furthermore, smart contracts can interact with each other via message exchange to access data that are stored on them and to call each others' methods. In this paper, we propose a two-level hierarchical architecture that is composed of two types of smart contracts: custodian and client. A custodian contract can deploy on-demand client contract, access their data and call their methods to perform specific updates. Moreover, we develop a framework to allow client contracts to share common variables among all or partial group of the contracts, which may only be mutated by its creator, custodian contracts.We measure the performance of our proposal by developing the proposed contracts and deploying them on three popular testnets. © 2018 Association for Computing Machinery.

The proliferation of computationally capable mobile devices gave birth to device-to-device (D2D) ecosystems where devices communicate, exchange resources and assist each other with the execution of tasks to extend their battery lifetime and improve users’ quality of experience. The effectiveness of such collaborations depends on the characteristics of the selected devices and the willingness of their owners to help. Thus, mobile applications need to adopt mechanisms that motivate collaboration and compensate those who share their resources. Research has proposed incentive schemes and reputation-based mechanisms designed to operate via centralized authorities. In this thesis, we design and analyze decentralized mechanisms to facilitate the exchange of resources, reputation, and credit, and to maintain a ledger in D2D ecosystems. For resource exchange, we introduce FlopCoin, a cryptocurrency operating as an incentive scheme for computation offloading and maintained by Internet-connected users called miners. We show how FlopCoin rewards collaborating devices and sidelines selfish ones. For reputation exchange between mobile users, we present OPENRP, a lightweight and scalable middleware that provides a unified interface to applications. OPENRP updates the reputation of participating peers based on their interactions and chooses the best peers with which to collaborate. The traffic generated by applications using OPENRP is lower compared to benchmark strategies. For credit exchange in D2D ecosystems, we propose LocalCoin, a cryptocurrency that requires minimal computational resources and works with off-the-shelf mobile devices. We prove that under the assumption of a sufficient number of mobile users and adequately selected tuning parameters the probability of double spending converges to zero. Finally, to maintain a decentralized ledger in D2D ecosystems, we design Mneme and propose a consensus protocol that is based on users’ context. We prove that the proposed protoc

This chapter presents that crowdsensing users have a particular behavior regarding their location and confidence in the mobile crowdsensing application. It focuses on task overhead performance and user altruistic behavior, respectively, to provide a better understanding of users’ behavior in these mobile crowd–based scenarios. The capabilities of modern smart devices (SD) provide new possibilities for more refined and precise studies in various areas due to their ability to contribute to crowd computing and networking procedures. Mobile SDs can contribute to the analysis of human behavior and natural phenomena such as pollution, CO2 levels, traffic, and collective mobility patterns. SDs also has wireless capabilities that allow them to transfer the collected data to each other or to upload them to cloud servers. The most important factor contributing to the success of data transmission applications running on crowd-assisted ecosystems is the goodwill of network users to compute other users’ tasks.

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users' privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.

Although the primary role of decentralized ledgers, such as blockchains, in cryptocurrencies, is to store data related to interactions between users to establish trust within incognizant parties, their capabilities allow them to offer more sophisticated functionalities. Smart contracts are decentralized rules that are stored on the blockchain and are executed on demand. Furthermore, smart contracts can interact with each other via message exchange to access data that are stored on them and to call each others' methods. In this paper, we propose a two-level hierarchical architecture that is composed of two types of smart contracts: custodian and client. A custodian contract can deploy on-demand client contract, access their data and call their methods to perform specific updates. Moreover, we develop a framework to allow client contracts to share common variables among all or partial group of the contracts, which may only be mutated by its creator, custodian contracts. We measure the performance of our proposal by developing the proposed contracts and deploying them on three popular testnets.

This chapter presents D2D ecosystems and listed some popular applications. It peovides the importance of hidden market design in the sharing of the users’ resources without the need for their intervention, and discusses the types of the interactions between the mobile users. D2D ecosystems are challenging due to their unpredictability, which is caused by users’ mobility; the limited, compared with conventional computer, computational resources and battery; and the incentives required to motivate mobile users to participate. Synchronized systems guarantee data coherence and integrity. In a synchronized reputation system for D2D ecosystems, all the mobile devices should assign the same trust scores to the rest of the participating devices. Reputation is defined as the opinion that someone has about someone or something, or how much respect or admiration someone or something receives, based on past behavior or character. Reputation systems allow users to rate each other in communities in order to build trust through reputation.

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.

During the last years, researche'rs have proposed solutions to help smartphones improve execution time and reduce energy consumption by offloading heavy tasks to remote entities. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices, giving birth to the Device-to-Device offloading paradigm. None of these strategies, though, offers any mechanism that considers selfish users and, most importantly, that motivates and defrays the participating devices who spend their resources. In this paper, we address these problems and propose the design of a framework that integrates an incentive scheme and a reputation mechanism. Our proposal follows the principles of the Hidden Market Design approach, which allows users to specify the amount of resources they are willing to sacrifice when participating in the offloading system. The underlying algorithm, that users are not aware of, is based on a truthful auction strategy and a peer-to-peer reputation exchange scheme. Extensive simulations on real traces depict how our designed mechanism achieves higher offloading rate and produces less traffic compared to three benchmark algorithms. Finally, we show how collaborating devices get rewarded for their contribution, while selfish ones get sidelined by others.