In crowdsourcing systems, requesters publish tasks, and interested workers provide answers to get rewards. Worker anonymity motivates participation since it protects their privacy. Anonymity with unlinkability is an enhanced version of anonymity because it makes it impossible to “link” workers across the tasks they participate in. Another core feature of crowdsourcing systems is worker quality which expresses a worker's trustworthiness and quantifies their historical performance. In this work, we present AVeCQ, the first crowdsourcing system that reconciles these properties, achieving enhanced anonymity and verifiable worker quality updates. AVeCQ relies on a suite of cryptographic tools, such as zero-knowledge proofs, to (i) guarantee workers’ privacy, (ii) prove the correctness of worker quality scores and task answers, and (iii) commensurate payments. AVeCQ is developed modularly, where requesters and workers communicate over a platform that supports pseudonymity, information logging, and payments. To compare AVeCQ with the state-ofthe-art, we prototype it over Ethereum. AVeCQ outperforms the state-of-the-art in three popular crowdsourcing tasks (image annotation, average review, and Gallup polls). E.g., for an Average Review task with 5 choices and 128 workers AVeCQ is 40% faster (including computing and verifying necessary proofs, and blockchain transaction processing overheads) with the task's requester consuming 87% fewer gas.

Parallel Split Learning (SL) allows resource-constrained devices that cannot participate in Federated Learning (FL) to train deep neural networks (NNs) by splitting the NN model into parts. In particular, such devices (clients) may offload the processing task of the largest model part to a computationally powerful helper, and multiple helpers may be employed and work in parallel. In hybrid federated and split learning (HFSL), on the other hand, devices can participate in the training process through any of the two protocols (SL and FL), depending on the system's characteristics. This could considerably reduce the maximum training time over all clients (makespan), especially in highly heterogeneous scenarios. In this paper, we study the joint problem of the training protocol selection, client-helper assignments, and scheduling decisions, to minimize the training makespan. We prove this problem is NP-hard and propose two solution methods: one based on the decomposition of the problem by leveraging its inherent symmetry, and a second fully scalable one. Through numerical evaluations using our testbed's measurements, we build a solution strategy comprising these methods. Moreover, this strategy finds a near-optimal solution and achieves a shorter makespan than the baseline schemes by up to 71%.

Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper. In parallel SL, multiple helpers can process model parts of one or more clients, thus, considerably reducing the maximum training time over all clients (makespan). In this paper, we focus on orchestrating the workflow of this operation, which is critical in highly heterogeneous systems, as our experiments show. In particular, we formulate the joint problem of client-helper assignments and scheduling decisions with the goal of minimizing the training makespan, and we prove that it is NPhard. We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable. A wealth of numerical evaluations using our testbed’s measurements allow us to build a solution strategy comprising these methods. Moreover, we show that this strategy finds a near-optimal solution, and achieves a shorter makespan than the baseline scheme by up to 52.3%.

Usability challenges and social acceptance of textual input in a context of extended realities (XR) motivate the research of novel input modalities. We investigate the fusion of inertial measurement unit (IMU) control and surface electromyography (sEMG) gesture recognition applied to text entry using a QWERTY-layout virtual keyboard. We design, implement, and evaluate the proposed multi-modal solution named MyoKey. The user can select characters with a combination of arm movements and hand gestures. MyoKey employs a lightweight convolutional neural network classifier that can be deployed on a mobile device with insignificant inference time. We demonstrate the practicality of interruption-free text entry with MyoKey, by recruiting 12 participants and by testing three sets of grasp micro-gestures in three scenarios: empty hand text input, tripod grasp (e.g., pen), and a cylindrical grasp (e.g., umbrella). With MyoKey, users achieve an average text entry rate of 9.33 words per minute (WPM), 8.76 WPM, and 8.35 WPM for the freehand, tripod grasp, and cylindrical grasp conditions, respectively.

In Federated learning (FL) systems, a centralized entity (server), instead of access to the training data, has access to model parameter updates computed by each participant independently and based solely on their samples. Unfortunately, FL is susceptible to model poisoning attacks, in which malicious or malfunctioning entities share polluted updates that can compromise the model’s accuracy. In this study, we propose FedClean, an FL mechanism that is robust to model poisoning attacks. The accuracy of the models trained with the assistance of FedClean is close to the one where malicious entities do not participate.

In this study, three different methods of teaching the long jump were evaluated. One hundred and thirty-one students of fifth and sixth grade (Mage = 11.4 ± 0.47 years) were randomly assigned into three groups. The first intervention group (INT) followed a blended learning approach using interactive learning activity software, the second intervention group (INTVF) followed the same method with an added direct video feedback system and the control group (CON) was taught the traditional approach. A pre-post 2D kinematic analysis of the students’ body segments’ position was conducted. Α two-way mixed ANOVA analysis showed an improvement in all three groups. However, the INTVF group performed significantly better regarding leg joints angles, trunk inclination, and body mass center parameters at take-off, compared to the INT and CON groups. In conclusion, teaching using blended learning, new technologies and direct video feedback seems to support students in developing a more efficient take-off technique in long jump.

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 (DLs) to function. DLs, 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 DLs are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyze Mneme, a DAG-based DL that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: 1) Proof of Context (PoC) and 2) 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 DLs: 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 proliferation of resourceful mobile devices that store rich, multidimensional and privacy-sensitive user data motivate federated learning, a paradigm that enables mobile devices to produce a machine-learning model without sharing their data. However, the majority of the existing federated frameworks follow a centralized approach. In this work, we introduce IPLS, a fully decentralized federated learning framework that is partially based on the interplanetary file system (IPFS). By using IPLS and connecting into the corresponding private IPFS network, any party can initiate the training process of a machine-learning model or join an ongoing training process that has been started by another party. IPLS scales with the number of participants, is robust against intermittent connectivity and dynamic participant departures/arrivals, requires minimal resources and guarantees that the accuracy of the trained model quickly converges to that of a centralized federated learning framework with a negligible accuracy drop of less than 10/00.

Data protection regulatory policies, such as the European Union's General Data Protection Regulation (GDPR), force website operators to request users' consent before collecting any personal information revealed through their web browsing. Website operators, motivated by the potential value of the collected personal data, employ various methods when designing consent notices (e.g., dark patterns) in order to convince users to allow the collection of as much of their personal data as possible. In this paper, we design and conduct a user study where 1100 MTurk workers interact with eight different designs of cookie consent notices. We show that the nudging designs used in the different cookie consent notices have a large effect on the choices user make. Our results show that color-based nudging bars can significantly impact the participants' decisions to change the default cookie settings, despite using dark patterns. Also, in contrast to previous works, we report that users who do not use ad-blocking software are less likely to modify default cookie settings. Our findings demonstrate the importance of nudged interfaces and the effects orthogonal nudging techniques can have on users' choices.

Incremental and quantitative improvements of two-way interactions with e x tended 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.

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.

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.

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.

Summary 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.

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. Via the implemented framework, parts of mobile applications can be offloaded to other mobile devices or cloudlets equipped with an NFC reader. 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. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. 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 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.

In some applicable scenarios, such as community patrolling, mobile nodes are restricted to move only in their own communities. Exploiting the meetings of the nodes within the same community and the nodes within the neighboring communities, a delay tolerant network (DTN) can provide communication between any two nodes. In this paper, two analytical models based on stochastic reward nets (SRNs) are proposed to evaluate the performance of the epidemic content retrieval in such multi-community DTNs. Performance measures computed by the proposed models are the average retrieval delay and the average number of transmissions. The monolithic SRN model proposed in the first step is not scalable, in terms of the number of communities and nodes, due to the state space explosion in the underlying Markov chain. In order to solve the scalability problem of the monolithic model, an approximate model based on the folding technique is presented which allows us to evaluate the performance of large-scale DTNs. In order to cross-validate the results obtained from the proposed models, we extend the ONE simulator to support our network model. The analytic-numeric results indicate that both models have good accuracy, and the folded model reduces the state space highly, achieving good scalability without any significant loss of accuracy.

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.

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.

Mobile augmented reality (MAR) applications are gaining popularity due to the wide adoption of mobile and especially wearable devices. Such devices often present limited hardware capabilities while MAR applications often rely on computationally intensive computer vision algorithms with extreme latency requirements. To compensate for the lack of computing power, offloading data processing to a distant machine is often desired. However, if this process introduces new constrains in the application, especially in terms of latency and bandwidth. If current network infrastructures are not ready for such traffic, we envision that future wireless networks such as 5G will rapidly be saturated by resource hungry MAR applications. Moreover, due to the high variance of wireless networks, MAR applications should not rely only on the evolution of infrastructures. In this article, we analyze MAR applications and justify their need for accessing external infrastructure. After a review of the existing network infrastructures and protocols, we define guidelines for future real-time and multimedia transport protocols, with a focus on MAR offloading.

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, by proposing and implementing the first known Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, with its better security, and its low battery consumption. We design a new NFC communication protocol that overcomes the limitations of the default 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 two computationally intensive representative applications: (i) RSA key generation and encryption, and (ii) gaming/puzzles. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. 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.

We develop Hyperion a Wearable Augmented Reality (WAR) system based on Google Glass to access text information in the ambient environment. Hyperion is able to retrieve text content from users' current view and deliver the content to them in different ways according to their context. We design four work modalities for different situations that mobile users encounter in their daily activities. In addition, user interaction interfaces are provided to adapt to different application scenarios. Although Google Glass may be constrained by its poor computational capabilities and its limited battery capacity, we utilize code-level offloading to companion mobile devices to improve the runtime performance and the sustainability of WAR applications. System experiments show that Hyperion improves users ability to be aware of text information around them. Our prototype indicates promising potential of converging WAR technology and wearable devices such as Google Glass to improve people's daily activities.

The proliferation of mobile devices combined with advances in the area of low-power wireless communication, such as Wi-Fi Direct and Bluetooth 4.0, gave rise to a new computation paradigm known as Device-to-Device (D2D) offloading. In this scenario, devices collaborate with each other using short wireless links to create ad-hoc P2P networks for distributed task execution. Experiments on human movement, a non-negligible factor in the D2D context, have shown that people move in group or meet frequently, which suggests that D2D is possible. In this work, we examine the case of parallel compression of smartphone recorded videos with the help of nearby devices. First, we present a mathematical formulation of the problem that optimizes the compression time on the number of nearby helping devices, and show that the problem can be mapped as a water-filling problem. Then, we present real results of the compression time and energy when the compression is performed on one device and when it is parallelized among collaborating devices. To obtain these results, we implemented an Android application that is able to detect nearby devices, connect with them using Wi-Fi Direct, send video chunks for compression, receive and merge compressed chunks into one full compressed video.

The wide spread of smart mobile devices such as tablets and phones makes mobile crowdsensing a viable approach for collecting data and monitoring phenomena of common interest. Smart devices can sense and compute their surroundings and contribute to mechanisms that examine social and collective behaviours. Crowdsensing offers a feasible alternative to exchange and compute sensing tasks and data between devices. Due to the limited resources (i.e., battery, processing power, memory) of smart mobile devices, the cooperation and hence, the performance of the mobile crowdsensing applications may be affected. We empirically show that collective incentives, such as trust (social ties) among participants, and resources availability can boost the performance of mobile crowdsensing applications. This collective incentive together with the existing cooperation enforcing mechanisms, can enhance the cooperation of the participants and incentify them to cooperate in social based mobile crowdsensing applications.

We introduce ReadMe, a real-time recommendation system (RS) and an online algorithm for Mobile Augmented Reality (MAR) ecosystems. A MAR ecosystem is the one that contains mobile users and virtual objects. The role of ReadMe is to detect and present the most suitable virtual objects on the mobile user's screen. The selection of the proper virtual objects depends on the mobile users' context. We consider the user's context as a set of variables that can be either drawn directly by user's device or can be inferred by it or can be collected in collaboration with other mobile devices.

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.

During the last years, researchers have proposed solutions to help smartphones offload heavy tasks to remote entities in order to improve execution time and reduce energy consumption. Lately, inspired by the promising results of message forwarding in opportunistic networks, many researchers have proposed strategies for task offloading towards nearby mobile devices. None of these strategies, though, proposes 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.

Advances in device-to-device (D2D) ecosystems have brought on mobile applications that utilise nearby mobile devices in order to improve users' quality of experience (QoE). The interactions between the mobile devices have to be transparent to the end users and can be of many services - opportunistic networking, traffic offloading, computation offloading, cooperative streaming and P2P based k-anonymity location privacy service, to name a few. Whenever mobile users are willing to “ask for help” from their neighbours, they need to make non trivial decisions in order to maximise their utility. Current motivation approaches for mobile users that participate in such environments are of two types: (i) credit-based and (ii) reputation-based. These approaches rely either on centralised authorities or require prohibitively many messages or require tamper resistant security modules. In this paper we propose a trust-based approach that does not require synchronisation between the mobile users. Moreover, we present the three-way tradeoff between, consistency, message exchange and awareness and we conclude that our approach can provide first-rate data to neighbour selection mechanisms for D2D ecosystems with much less overhead.