A mechanical metamaterial with reprogrammable logical functions

Embedding mechanical logic into soft robotics, microelectromechanical systems (MEMS), and robotic materials can greatly improve their functional capacity. However, such logical functions are usually pre-programmed and can hardly be altered during in-life service, limiting their applications under varying working conditions. Here, we propose a reprogrammable mechanological metamaterial (ReMM). Logical computing is achieved by imposing sequential excitations. The system can be initialized and reprogrammed via selectively imposing and releasing the excitations. Realization of universal combinatorial logic and sequential logic (memory) is demonstrated experimentally and numerically. The fabrication scalability of the system is also discussed. We expect the ReMM can serve as a platform for constructing reusable and multifunctional mechanical systems with strong computation and information processing capability.

Interoperable Multi-Blockchain Platform Based on Integrated REST APIs for Reliable Tourism Management

The tourism industry can significantly benefit from the blockchain since its implementation can build trust among stakeholders and improve customer satisfaction. However, most of the existing tourism-specified blockchain platforms are single-chains that provide business support for enterprises without guaranteeing transaction information privacy. Besides, these platforms are specified to a single use case and lack interoperability with other platforms to support heterogenous tourism services. This paper aims to address this issue by introducing a multi-chain architecture that utilizes multiple blockchains to enhance processing capability and provide various business services for the tourism industry. The proposed multi-chain architecture improves the interoperability between the activities in different chains by providing functional requirements in practical applications and supports the inter-ledger application. In addition, the private blockchain will be made available to allow users to access the network through central authorization. It also increases the transaction processing capability by distributing multiple tasks across the chains for large-scale applications. To demonstrate the usability and efficiency of the developed approach, a case study on hotel booking is conducted using the blockchain frameworks Winding Tree and Hyperledger Fabric. A comprehensive evaluation experiment is conducted, and the results show the significance of the proposed system.

A QoS-aware Fuzzy-based System for Assessment of Edge Computing Resources in SDN-VANETs

Vehicular Ad hoc Networks (VANETs) face resource management challenges due to their dynamic network topology and massive amount of data generated by the ever-rising number of vehicles. In this paper, we implement a Fuzzy-based System for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC), in which we consider two models (FS-ANVPC1 and FS-ANVPC2) to assess the available edge computing resources in Software Defined-VANETs. The proposed system determines the processing capability of each neighboring vehicle, and based on the final value, it can be decided whether the edge layer can be used by the vehicles in need of additional resources. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the vehicle in need, while FS-ANVPC2 includes in addition the quality of service of the communication link among vehicles. We evaluate the proposed system by computer simulations. From the evaluation results, we see that FS-ANVPC2 shows better results than FS-ANVPC1 although its complexity is higher.

AccelSDP: A Reconfigurable Accelerator for Software Data Plane Based on FPGA SmartNIC

Software-defined networking (SDN) has attracted much attention since it was proposed. The architecture of the SDN data plane is also evolving. To support the flexibility of the data plane, the software implementation approach is adopted. The software data plane of SDN is commonly implemented on a commercial off-the-shelf (COTS) server, executing an entire processing logic on a commodity CPU. With sharp increases in network capacity, CPU-based packet processing is overwhelmed. However, completely implementing the data plane on hardware weakens the flexibility. Therefore, hybrid implementation where a hardware device is adopted as the accelerator is proposed to balance the performance and flexibility. We propose an FPGA SmartNIC-based reconfigurable accelerator to offload some of the operation-intensive packet processing functions from the software data plane to reconfigurable hardware, thus improving the overall data plane performance while retaining flexibility. The accelerated software data plane has a powerful line-rate packet processing capability and flexible programmability at 100 Gbps and higher throughput. We offloaded a cached-rule table to the proposed accelerator and tested its performance with 100 GbE traffic. Compared with the software implementation, the evaluation result shows that the throughput can achieve a 600% improvement when processing small packets and a 100% increase in large packet processing, and the latency can be reduced by about 20× and 100×, respectively, when processing small packets and large packets.

Investigation into the effect of data reduction in offloadable task for distributed IoT-fog-cloud computing

With the increasing level of IoT applications, computation offloading is now undoubtedly vital because of the IoT devices limitation of processing capability and energy. Computation offloading involves moving data from IoT devices to another processing layer with higher processing capability. However, the size of data offloaded is directly proportional to the delay incurred by the offloading. Therefore, introducing data reduction technique to reduce the offloadable data minimizes delay resulting from the offloading method. In this paper, two main strategies are proposed to address the enormous data volume that result to computation offloading delay. First, IoT Canonical Polyadic Decomposition for Deep Learning Algorithm is proposed. The main purpose of this strategy is to downsize the IoT offloadable data. In the study, the Kaggle-cat-and-dog dataset was used to evaluate the impact of the proposed data compression. The proposed method downsizes the data significantly and can reduce the delay due to network traffic. Secondly, Rank Accuracy Estimation Model is proposed for determining the Rank-1 value. The result of the proposed method proves that the proposed methods are better in terms of data compression compared to distributed deep learning layers. This method can be applied in smart city, vehicular networks, and telemedicine etc.

A fuzzy-based approach for resource management in SDN-VANETs: Effect of trustworthiness on assessment of available edge computing resources

Vehicular Ad hoc Networks (VANETs) aim to improve the efficiency and safety of transportation systems by enabling communication between vehicles and roadside units, without relying on a central infrastructure. However, since there is a tremendous amount of data and significant number of resources to be dealt with, data and resource management become their major issues. Cloud, Fog and Edge computing, together with Software Defined Networking (SDN) are anticipated to provide flexibility, scalability and intelligence in VANETs while leveraging distributed processing environment. In this paper, we consider this architecture and implement and compare two Fuzzy-based Systems for Assessment of Neighboring Vehicles Processing Capability (FS-ANVPC1 and FS-ANVPC2) to determine the processing capability of neighboring vehicles in Software Defined Vehicular Ad hoc Networks (SDN-VANETs). The computational, networking and storage resources of vehicles comprise the Edge Computing resources in a layered Cloud-Fog-Edge architecture. A vehicle which needs additional resources to complete certain tasks and process various data can use the resources of the neighboring vehicles if the requirements to realize such operations are fulfilled. The proposed systems are used to assess the processing capability of each neighboring vehicle and based on the final value, it can be determined whether the edge layer can be used by the vehicles in need. FS-ANVPC1 takes into consideration the available resources of the neighboring vehicles and the predicted contact duration between them and the present vehicle, while FS-ANVPC2 includes in addition the vehicles trustworthiness value. Our systems take also into account the neighboring vehicles’ willingness to share their resources and determine the processing capability for each neighbor. We evaluate the proposed systems by computer simulations. The evaluation results show that FS-ANVPC1 decides that helpful neighboring vehicles are the ones that are predicted to be within the vehicle communication range for a while and have medium/large amount of available resources. FS-ANVPC2 considers the same neighboring vehicles as helpful neighbors only if they have at least a moderate trustworthiness value ( VT = 0.5). When VT is higher, FS-ANVPC2 takes into consideration also neighbors with less available resources.