What’s in it for me? Incentive-compatible route coordination of crowdsourced resources

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

Incentive-compatible route coordination of crowdsourced resources

Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments

Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

Backpressure meets taxes: Faithful data collection in stochastic mobile phone sensing systems

The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS. To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly (around 100%) while confirming that our system achieves incentive compatibility, individual rationality, and server profitability

A Taxonomy on Misbehaving Nodes in Delay Tolerant Networks

Delay Tolerant Networks (DTNs) are type of Intermittently Connected Networks (ICNs) featured by long delay, intermittent connectivity, asymmetric data rates and high error rates. DTNs have been primarily developed for InterPlanetary Networks (IPNs), however, have shown promising potential in challenged networks i.e. DakNet, ZebraNet, KioskNet and WiderNet. Due to unique nature of intermittent connectivity and long delay, DTNs face challenges in routing, key management, privacy, fragmentation and misbehaving nodes. Here, misbehaving nodes i.e. malicious and selfish nodes launch various attacks including flood, packet drop and fake packets attack, inevitably overuse scarce resources (e.g., buffer and bandwidth) in DTNs. The focus of this survey is on a review of misbehaving node attacks, and detection algorithms. We firstly classify various of attacks depending on the type of misbehaving nodes. Then, detection algorithms for these misbehaving nodes are categorized depending on preventive and detective based features. The panoramic view on misbehaving nodes and detection algorithms are further analyzed, evaluated mathematically through a number of performance metrics. Future directions guiding this topic are also presented