Computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

In the past few decades, the prevalence of Internet of Things (IoT) applications has brought both opportunities and challenges of different categories. One of the main concerns nowadays is semantic interoperability. Although plenty of augmented ontologies and resource description frameworks have been designed to achieve semantic interoperation, there is still no sustainable solution to interconnect the increasing number of heterogeneous devices and corresponding data. Aiming at this problem, this article presents a dataspaces model utilizing distributed approaches to represent semantic information as a sustainable solution for IoT semantic interoperability. In this model, an attention-based entity embedding approach is designed to convert IoT entities into low-dimensional dense vectors, then calculations, including entities, relations, etc., can be further conducted. Consequently, the entity embeddings could reflect the semantic information of Class equivalences among entities. To recognize entity relations, the generated entity vectors and the arithmetic results of two entities are combined as input features. By feeding the input features to a well-trained Tensor-train modified DNN, the relation between two entities could be recognized. Finally, experiments are conducted on two data sets to evaluate the proposed approaches. Results indicate that the generated entity vectors could effectively reflect the semantic similarity between entities. More importantly, while achieving parameter compression and better generalizing ability, the relation recognition approach improves the relation recognition accuracy on new entities compared with the state-of-art models.

In recent years, blockchain technology has been widely applied in the Internet of Things (IoT) field, where devices located in different blockchains need to interact with each other cross-chain. Existing cross-chain models have high-implementation complexity, long response times, and are difficult to apply in IoT scenarios. In this article, we propose keyword-based embedded smart contract cross-chain interoperability and collaboration model. This model identifies the status of smart contracts through the keywords of smart contracts, completes cross-chain operations, and achieves smart contract collaboration and asset exchange among devices in different blockchains. To find matching collaborative contracts, contract-specific keywords will be set, including asset amounts and types. In the cross-chain asset exchange scenario of the model, the assets sent by the sender are frozen in the contract. When the contract is successfully executed, the assets will be sent to the receiver, otherwise returned to the sender. In the cross-chain contract collaboration scenario, we identify the number of contract interactions, and when the contract collaboration is completed, the data will be retained, otherwise it will be invalidated. We also use an embedded smart contract method to further optimize IoT application scenarios. This method embeds the steps of deploying smart contracts into the invoked transaction, enabling the deployment and invocation of smart contracts through a single transaction. The experiments show that the keyword-based embedded contract method is 47% faster and 49% less costly compared to notary schemes solutions in cross-chain scenarios.

In recent years, the blockchain technology has been widely used in the Internet of Things (IoT). One of the major concerns is how to adopt a smart contract to process data from IoT devices flexibly. While plenty of smart contract-based methods can be used, smart contracts are required to be deployed previously. This requires an additional step (to deploy a smart contract) and makes a smart contract separate from its data (transactions to trigger its interface), which generates limitations in IoT scenarios. In this article, we developed an approach to embed the smart contract and its data into the same transaction, eliminating the need for a predeployment step. Data is employed as parameters to invoke the interface of a smart contract, and the smart contract is used to process the data inside the transaction. With this method, a smart contract does not need other transactions from the user. Results indicate that the proposed method can eliminate the requirement of a separately deployed smart contract, saving costs, and waiting time for the predeployment.

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.