Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach

A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select action Trust or Mistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select action Trust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose action Trust as their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism

Community-Based Security for the Internet of Things

With more and more devices becoming connectable to the internet, the number of services but also a lot of threats increases dramatically. Security is often a secondary matter behind functionality and comfort, but the problem has already been recognized. Still, with many IoT devices being deployed already, security will come step-by-step and through updates, patches and new versions of apps and IoT software. While these updates can be safely retrieved from app stores, the problems kick in via jailbroken devices and with the variety of untrusted sources arising on the internet. Since hacking is typically a community effort? these days, security could be a community goal too. The challenges are manifold, and one reason for weak or absent security on IoT devices is their weak computational power. In this chapter, we discuss a community based security mechanism in which devices mutually aid each other in secure software management. We discuss game-theoretic methods of community formation and light-weight cryptographic means to accomplish authentic software deployment inside the IoT device community

Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio

Socially Trusted Collaborative Edge Computing in Ultra Dense Networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author

Credibility-Based Binary Feedback Model for Grid Resource Planning

In commercial grids, Grid Service Providers (GSPs) can improve their profitability by maintaining the lowest possible amount of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve this goal, they require an estimate of the demand for their service, but collecting demand data is costly and difficult. In this paper we develop an approach to building a proxy for demand, which we call a value profile. To construct a value profile, we use binary feedback from a collection of heterogeneous clients. We show that this can be used as a proxy for a demand function that represents a client’s willingness-to-pay for grid resources. As with all binary feedback systems, clients may require incentives to provide feedback and deterrents to selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. We use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. Finally, we use game theory to study how cooperation can emerge in this community of clients and GSPs

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201