# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 2 Related Work  
Smart cities require different varieties of networks to support multidimensional applications widely supported by IoT setups. Trustful communication between different varieties of applications is a dire need to avoid malicious nodes. That's why, it is in highly researched area these days and there is a lot of research on trustful management in different areas of the communication field. In [20], authors emphasized the tradeoff between benefits and risks in trustful communication and developed a multi-aspect and adaptive trust-based situation-aware access control framework and named it \"MATS\". The framework is based on semantic web technologies and game theory by considering dynamic network situations by carefully balancing efficiency and simplicity. The authors validate the performance of their framework with experimental results. In [21], authors proposed a trust mechanism for Unmanned Aerial Vehicles (UAVs) and IoTs in vehicular network scenarios. The proposed framework emphasizes two ways: Firstly a deadline deadline-aware data is collected in collaboration with mobile vehicles and UAVs, and secondly an active and verifiable trust approach is used by considering the security and privacy of the system. Finally, a trajectory optimization algorithm is proposed to collect maximum baseline data to decide on trustful agreements. The authors claimed that their proposed framework improved the data accuracy by 33.34% on average with 35% reduced cost and 58.32% reduced delay. In [22], authors addressed the trustful data needs in online dating networks and emphasized harmful attacks such as creating fake accounts. The authors proposed a trust-aware framework to detect malicious users by developing a user trust model and applied a data balancing technique to find out the malicious nodes continuously send data and disturb the system. The authors evaluated the performance of their proposed scheme through extensive experiments and claimed that their proposed model improves the data precision up to 59.16% more form the other baseline algorithms. In [23], authors focused on big data problems and proposed a MapReduce-based framework in big data processing tasks by initially quantifying and sensitiing the data and trust values. Depending upon this received data, the authors formulated the MapReduce scheduling problem with the bipartite matching problem by considering the maximum weights. The problem became the NP-hard problem and is tackled by sharing the slot nodes within a computing node by allocating the same level of trust share to each slicing network user. After tackling this NP-hard problem, an efficient heuristic-based algorithm is proposed. Authors claimed that their proposed framework achieved 94.7% of the optimal solution obtained through exhaustive search in a big data environment. In [24], [PERSON] et al. addressed the fundamental issues faced in online property-based trust attestation due to virtual network involvement. The authors proposed a model to eliminate the virtual network problems faced during boot and run time. The authors defined simple rules in order to identify legitimate property data users. The authors claimed that their proposed model helps in developing trustworthy applications. The authors implemented a prototype of a trust management platform by using an open source MANO platform and claimed that their proposed platform slightly reduced the performance of the network over a virtual network function in a dynamic environment. In [25], [PERSON] et al. focused on an IoT-based healthcare system and exploited the evidence theory to a decentralized service-oriented trust management model in it. Malicious attacks on healthcare systems are avoided by measuring the evidence distance by rewarding the healthcare service providers and punishing the malicious users. The authors proposed a two-fold system model by providing trust in healthcare services and trust in recommendations.