Lithium-ion power battery grouping: A multisource data fusion based clustering approach and distributed deployment

Consistence of lithium-ion power battery significantly affects the life and safety of battery modules and packs. To improve the consistence, battery grouping is employed, assembling batteries with similar electrochemical characteristics to make up modules and packs. Therefore, grouping process boils down to unsupervised clustering problem. Current used grouping approaches include two aspects, static characteristics based and dynamic based. However, there are three problems. First, the common problem is underutilization of multi-source data. Second, for the static characteristics based, there is grouping failure over time. Third, for the dynamic characteristics based, there is high computational complexity. To solve these problems, we propose a distributed multisource data fusion based battery grouping approach. The proposed approach designs an effective network structure for multisource data fusion, and a self supervised scheme for feature extraction from both static and dynamic multisource data. We apply our approach on real battery modules and test state of health (SOH) after charging-discharging cycles. Experimental results indicate that the proposed scheme can increase SOH of modules by 3.89%, and reduce the inconsistence by 68.4%. Meanwhile, with the distributed deployment the time cost is reduced by 87.9% than the centralized scheme.

BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest

The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

Support for Adaptive and Distributed Deployment of CEP Continuous Queries for the IoMT

The current dissemination of IoT increases the deployment of stream processing solutions for monitoring and controlling elements of the real-world. One of those solutions is Complex Event Processing (CEP), and to handle the high volume, velocity and volatility of data streams from IoT sensors the CEP pipeline should be distributed, preferably having CEP operators both in the cloud/cluster and in edge devices. In this paper, we present a model for a distributed CEP platform and an implementation of this model called Global CEP Manager (GCM). GCM is a service of the ContextNet middleware that supports the deployment and dynamic rearrangement of CEP queries to CEP engines executing in the cloud and in M-Hubs, that are ContextNet’s mobile edge devices.

Research on Multinode Collaborative Computing Offloading Algorithm Based on Minimization of Energy Consumption

Mobile edge computing (MEC) nodes are deployed at positions close to users to address excessive latency and converging flows. Nevertheless, the distributed deployment of MEC nodes and offload of computational tasks among several nodes consume additional energy. Accordingly, how to reduce the energy consumption of edge computing networks while satisfying latency and quality of service (QoS) demands has become an important challenge that hinders the application of MEC. This paper built a local-edge-cloud edge computing network and proposes a multinode collaborative computing offloading algorithm. It can be applied to smart homes, realize the development of green channels, and support local users of Internet of Things (IoT) to decompose computational tasks and offload them to multiple MEC or cloud nodes. The simulation analysis reveals that the new local-edge-cloud edge computing offload method not only reduces network energy consumption more effectively compared with traditional computing offload methods but also ensures the implementation of more data samples.