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The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the automatic identification system (AIS) data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.

Mobile networks are facing unprecedented challenges due to the traits of large scale, heterogeneity, and high mobility. Fortunately, the emergence of fog computing offers surprisingly perfect solutions considering the features of consumer proximity, wide-spread geographical distribution, and elastic resource sharing. In this paper, we propose a novel mobile networking framework based on fog computing which outperforms others in resilience. Our scheme is constituted of two parts: the personalized customization mobility management (MM) and the market-driven resource management (RM). The former provides a dynamically customized MM framework for any specific mobile node to optimize the handoff performance according to its traffic and mobility traits; the latter makes room for economic tussles to find out the competitive service providers offering a high level of service quality at sound prices. Synergistically, our proposed MM and RM schemes can holistically support a full-fledged resilient mobile network, which has been practically corroborated by numerical experiments.

Abstract Background Global maritime trade plays an important role in the modern transportation industry. It brings significant economic profit along with bioinvasion risk. Species translocate and establish in a non-native area through ballast water and biofouling. Aiming at aquatic bioinvasion issue, people proposed various suggestions for bioinvasion management. Nonetheless, these suggestions only focus on the chance of a port been affected but ignore the port’s ability to further spread the invaded species. Results To tackle the issues of the existing work, we propose a biosecurity triggering mechanism, where the bioinvasion risk of a port is estimated according to both the invaded risk of a port and its power of being a stepping-stone. To compute the invaded risk, we utilize the automatic identification system data, the ballast water data and marine environmental data. According to the invaded risk of ports, we construct a species invasion network (SIN). The incoming bioinvasion risk is derived from invaded risk data while the invasion risk spreading capability of each port is evaluated by s-core decomposition of SIN. Conclusions We illustrate 100 ports in the world that have the highest bioinvasion risk when the invaded risk and stepping-stone bioinvasion risk are equally treated. There are two bioinvasion risk intensive regions, namely the Western Europe (including the Western European margin and the Mediterranean) and the Asia-Pacific, which are just the region with a high growth rate of non-indigenous species and the area that has been identified as a source for many of non-indigenous species discovered elsewhere (especially the Asian clam, which is assumed to be the most invasive species worldwide).

Mobile Cloud Computing (MCC) is getting growing interest due to its wide applicability in variety of social, industrial, and commercial mobile applications. Mobile and smart devices can share complex computational operations with Cloud Service Providers (CSPs). It also provides storage, access polices enforcement, and security operations. In many cases, CSP requires services from crowd contributors CCs for data collection, sharing, and mobile application support. It requires trust management for CCs to guard against malicious CCs and ensure security and privacy of data. However, end users or data requesters also demand reliable security solutions for sharing their data or accessing data from unknown CCs. To ensure strong security, mobile devices are not computationally feasible to perform complex cryptographic operations for desired privacy. To resolve these issues, we propose Reputation-aware Trust and Privacy Preservation scheme for MCC. In first phase, we deal with the trust management by utilizing reputation aware selection of CCs and leverage cloud services where CSP maintains trust score for CCs and data requesters. In second phase, we manage privacy preservation by using our proposed Anonymous Secure-shell Ciphertextpolicy Attribute Based Encryption (AS-CABE). We have also proposed a hybrid policy tree mechanism for dynamic attribute selection used for security solutions and key management. Next, an anonymous secure shell is maintained between the CCs and the crowd servers to ensure registration after approval from trusted authority. In the similar vein, we propose outsourced encryption and decryption mechanism for mobiles that further utilize encryption and decryption service providers for complex operations. To the best of our knowledge, we are the first one to deal with the trust issues of data requester and privacy concerns of CCs and users both at the same time. After that, we have presented the security analysis to analyze AS-CABE against security attacks. Finally, the results are presented that ensure the supremacy of our proposed scheme as compared to counterparts in terms of reputation score, storage, computation, trust, resilience, encryption, and decryption time.

Social networking has become part of our life in recent years, allowing users to converse and connect with people sharing similar interests in real world. However, networking via the social media suffers from serious privacy issues, and one of which is profile attribute leakage in friend discovery. While existing studies mainly focus on leveraging rich cryptographic algorithms to prevent privacy leak, we propose a novel perturbation-based private profile matching mechanism by mixing the private data with random noise to preserve privacy in this paper. In this paper, we consider the case where the profiles are fine-grained, meaning that each attribute is associated with a user-specific numerical value to indicate the level of interest. By carefully tuning the amount of information owned by each party, we guarantee that privacy is effectively preserved while the matching result of users' profiles can be cooperatively obtained. We first give an introduction to a basic scheme, then detail two improved ones by, respectively, taking collusion attack and verifiability into consideration. As no expensive encryption algorithms get involved, our methods are computationally efficient; thus they are more practical for real-world applications. Theoretical security analysis as well as comparison-based simulation studies are carried out to evaluate the performance of our designs.

The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.

As a promising paradigm, mobile crowdsensing exerts the potential of widespread sensors embedded in mobile devices. The greedy nature of workers brings the problem of low-quality sensing data, which poses threats to the overall performance of a crowdsensing system. Existing works often tackle this problem with additional function components. In this paper, we systematically formulate the problem into a crowdsensing interaction process between a requestor and a worker, which can be modeled by two types of iterated games with different strategy spaces. Considering that the low-quality data submitted by the workers can reduce the requestor’s payoff and further decrease the global income, we turn to controlling the social welfare in the games. To that aim, we take advantage of zero-determinant strategy, based on which we propose two social welfare control mechanisms under both game models. Specifically, we consider the requestor as the controller of the games and, with proper parameter settings for the to-be-adopted zero-determinant strategy, social welfare can be optimized to the desired level no matter what strategy the worker adopts. Simulation results demonstrate that the requestor can achieve the maximized social welfare and keep it stable by using our proposed mechanisms.

In a context sensing system in which a sensor-equipped mobile phone runs an unreliable context-aware application, the application can infer the user’s contexts, based on which it provides personalized services. However, the application may sell the user’s contexts to some malicious adversaries to earn extra profits, which will hinder its widespread use. In the real world, the actions of the user, the application and the adversary in the context sensing system affect each other, so that their payoffs are constrained mutually. To figure out under which conditions they behave well (the user releases, the application does not leak and the adversary does not retrieve the context), we take advantage of game theory to analyze the context sensing system. We use the extensive form game and the repeated game, respectively, to analyze two typical scenarios, single interaction and multiple interaction among three players, from which Nash equilibriums and cooperation conditions are obtained. Our results show that the reputation mechanism for the context-sensing system in the former scenario is crucial to privacy preservation, so is the extent to which the participants are concerned about future payoffs in the latter one.