Crowdsourcing has emerged as a promising computing paradigm that utilizes human intelligence to achieve complex tasks, but it encounters several security and trust issues. Blockchain is a potential technology that can resolve most of these issues, however, it is difficult to find an appropriate consensus protocol applicable to crowdsourcing systems. Therefore, this work presents a Trust and Transactions Chain (TTC) consensus protocol built upon blockchain technology. It selects a trusted leader and validators considering a trust model which depends on deposit ratio, block generation and validation rate, and waiting rate. The TTC protocol addresses the main challenge of ensuring correctness related to critical systems of crowdsourcing which has extreme significance as their failure can result in disastrous consequences. This work is primarily focused on fraud-resilience avoiding double-spending attack. It also deals with sybil and eclipse attacks. Model checking is exploited because it is effective and automatic to conduct formal verification. The TTC protocol is formally modeled utilizing Communicating Sequential Programs, and the fraud-resilience property is specified using Linear Temporal Logic. The verification of the model is done using Process Analysis Toolkit that takes the formal model and specified properties as input to inspect the properties’ satisfaction or violation. The results of the formal verification are analyzed with respect to the verification time and the number of visited states.

In migrating and upgrading an Ethereum smart contract, it is necessary to transfer both the code as well as the stored data. Various methods attempt to migrate or upgrade a smart contract, but they are mostly manual, error-prone, and applicable only before deployment. Further, they have challenges in extracting the storage state of complex mapping data structures along with their keys. In this work, we present Smartmuv as an automatic source-code-based static analysis tool to analyze and extract the state from the storage-trie of smart contracts. Based on the abstract syntax tree and the control flow graphs of the Solidity source code, the tool analyzes each state variable including mapping types along the inheritance hierarchy. It also provides the upgrade algorithm that initializes the extracted state in the constructor of new smart contract. Smartmuv safely approximates the origin of the keys used in the mapping to extract values and has been able to extract the mapping state of 23,673 smart contracts with 95.7% overall precision. Moreover, we also validate the Smartmuv’s extracted state with the third-party tool Etherscan.

The performance of densely-deployed low-power wide-area networks (LPWANs) can significantly deteriorate due to packets collisions, and one of the main reasons for that is the rule-based PHY layer transmission parameters assignment algorithms. LoRaWAN is a leading LPWAN technology where LoRa serves as the physical layer. Here, we propose and evaluate a deep reinforcement learning (DRL)-based PHY layer transmission parameter assignment algorithm for LoRaWAN. Our algorithm ensures fewer collisions and better network performance compared to the existing state-of-the-art PHY layer transmission parameter assignment algorithms for LoRaWAN. Our algorithm outperforms the state of the art learning-based technique achieving up to 500% improvement of PDR in some cases.

For maintaining the quality of software updates to complex software products (e.g. Windows 7 OS), an extensive, broad level regression testing is conducted whenever releasing new code fixes or updates. Despite the huge cost and investment in the test infrastructure to execute these massive tests, the developer of the code fix has to wait for the regression test failures to be reported after checkin. These regression tests typically run way later from the code editing stage and consequently the developer has no test impact visibility while introducing the code changes at compile time or before checkin. We argue that it is valuable and practically feasible to tailor the entire development/testing process to provide valuable and actionable test feedback at the development/compilation stage as well. With this goal, this paper explores a system model that provides a near real-time test feedback based on regression tests while the code change is under development or as soon as it becomes compilable. OnSpot system dynamically overlays the results of tests on relevant source code lines in the development environment; thereby highlighting test failures akin to syntax failures enabling quicker correction and re-run at compile time rather than late when the damage is already done. We evaluate OnSpot system with the security fixes in Windows 7 while considering various factors like test feedback time, coverage ratio. We found out that on average nearly 40% of the automated Windows 7 regression test collateral could run under 30 seconds providing same level of coverage; thereby making OnSpot approach practically feasible and manageable during compile time

CEJVM is a cluster enabled Java Virtual Machine, which executes in a distributed fashion among collaborating nodes of a dedicated cluster. It extends Java's multithreading mechanism to the parallel computing paradigm by transparent migration of independent application threads modeled in master worker paradigm. The goal is to obtain improved performance for computationally-intensive multi-threaded Java programs without modifying traditional JVM code, Java language or compiler implementation. Deploying a master worker relationship among the nodes in the cluster, CEJVM monitors, packs, transports and resurrects the Java threads on "Master JVM" and helper "Worker JVMs". Currently, we have created a prototype that runs pure Java applications on the local Ethernet based cluster of Win2K computers. We have achieved appreciable speedup for an ANN training program written in Java (with independent threads) on CEJVM without any modification to the program's source code.