Performance Evaluation and Cyberattack Mitigation in a Blockchain-Enabled Peer-to-Peer Energy Trading Framework.

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.

A Novel Blockchain-Based Healthcare System Design and Performance Benchmarking on a Multi-Hosted Testbed.

As a result of the proliferation of digital and network technologies in all facets of modern society, including the healthcare systems, the widespread adoption of Electronic Healthcare Records (EHRs) has become the norm. At the same time, Blockchain has been widely accepted as a potent solution for addressing security issues in any untrusted, distributed, decentralized application and has thus seen a slew of works on Blockchain-enabled EHRs. However, most such prototypes ignore the performance aspects of proposed designs. In this paper, a prototype for a Blockchain-based EHR has been presented that employs smart contracts with Hyperledger Fabric 2.0, which also provides a unified performance analysis with Hyperledger Caliper 0.4.2. The additional contribution of this paper lies in the use of a multi-hosted testbed for the performance analysis in addition to far more realistic Gossip-based traffic scenario analysis with Tcpdump tools. Moreover, the prototype is tested for performance with superior transaction ordering schemes such as Kafka and RAFT, unlike other literature that mostly uses SOLO for the purpose, which accounts for superior fault tolerance. All of these additional unique features make the performance evaluation presented herein much more realistic and hence adds hugely to the credibility of the results obtained. The proposed framework within the multi-host instances continues to behave more successfully with high throughput, low latency, and low utilization of resources for opening, querying, and transferring transactions into a healthcare Blockchain network. The results obtained in various rounds of evaluation demonstrate the superiority of the proposed framework.

JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs.

Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.

Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition.

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.

GaitRec-Net: A Deep Neural Network for Gait Disorder Detection Using Ground Reaction Force.

Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability. In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait. This article presents an artificial intelligence-based comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force. The used dataset comprised GRF measurements from different patients. The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. A deep learning-based architecture GaitRec-Net is proposed for this classification. The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach. Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification. As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.