Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

/pdf/medical-cyber-physical-systems-a-survey-44o0y3ph99.pdf

Medical cyber-physical systems: A survey.

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

Every year, mining industry sees huge losses in terms of human lives and valuable infrastructure due to accidents and disasters. Besides other measures, effective monitoring and control can greatly reduce the risks of such incidents. Wireless sensor networks (WSNs) are increasingly being used for such applications. This paper proposes a WSN-based system, which is capable of detecting and identifying events of interest (with 90% success rate) and localization of miners (2–4 m) and roof falls (10–12 m). A comprehensive integrated system covering a range of aspects from radio frequency propagation, communication protocol with latency, and energy–efficiency tradeoff and autonomous event detection is presented. The results show a lower path loss for 433 MHz operating frequency compared to 868 MHz. Moreover, a novel energy-efficient hybrid communication protocol using both periodic and aperiodic modes of communication while adhering to low latency requirement for emergency situations is proposed and implemented. Finally, for intelligent processing of gathered data, a spatio-temporal and attribute-correlated event detection mechanism suitable for the highly unreliable mine environment is described.

A WSN for Monitoring and Event Reporting in Underground Mine Environments

The rapid proliferation of smart mobile devices has significantly enhanced the popularization of the cyber-physical social network, where users actively publish data with sensitive information. Adversaries can easily obtain these data and launch continuous attacks to breach privacy. However, existing works only focus on either location privacy or identity privacy with a static adversary. This results in privacy leakage and possible further damage. Motivated by this, we propose a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary. We study the privacy protection problem as the tradeoff between the users aiming at maximizing data utility with high-level privacy protection while adversaries possessing the opposite goal. We first establish a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game. To acquire the best strategy for users, we employ a modified state-action-reward-state-action reinforcement learning algorithm. Iteration times decrease because of cardinality reduction from $n$ to 2, which accelerates the convergence process. Our extensive experiments on real-world data sets demonstrate the efficiency and feasibility of the propose method.

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

Link prediction is an important task in data mining, which has widespread applications in social network research. Given a social network, the objective of this task is to predict future links which have not yet observed in the current state of the network. Owing to its importance, the link prediction task has received substantial attention from researchers in diverse disciplines; thus, a large number of methodologies for solving this problem have been proposed in recent decades. However, existing literatures lack a current and comprehensive analysis of existing link prediction methodologies. Couple of survey articles on link prediction are available, but they are out-dated as numerous link prediction methods have been proposed after these articles have been published. In this paper, we provide a systematic analysis of existing link prediction methodologies. Our analysis is comprehensive, it covers the earliest scoring-based methodologies and extends up to the most recent methodologies which are based on deep learning methods. We also categorize the link prediction methods based on their technical approach, and discuss the strength and weakness of various methods.

A systemic analysis of link prediction in social network

While privacy preserving mechanisms, such as hiding one’s friends list, may be available to withhold personal information on online social networking sites, it is not obvious whether to which degree a user’s social behavior renders such an attempt futile. In this paper, we study the impact of additional interaction information on the inference of links between nodes in partially covert networks. This investigation is based on the assumption that interaction might be a proxy for connectivity patterns in online social networks. For this purpose, we use data collected from 586 Facebook profiles consisting of friendship ties (conceptualized as the network) and comments on wall posts (serving as interaction information) by a total of 64 000 users. The link-inference problem is formulated as a binary classification problem using a comprehensive set of features and multiple supervised learning algorithms. Our results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved.

Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

/pdf/an-energy-efficient-cooperative-hierarchical-mimo-clustering-3jk8e3cg5b.pdf

An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.

/pdf/real-time-detection-of-content-polluters-in-partially-1mst6sej7d.pdf

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

In this paper, we present a user study conducted on students of a local university in Pakistan and collected a corpus of Roman Urdu text messages. We were interested in forms and functions of Roman Urdu text messages. To this end, we collected a mobile phone usage dataset. The data consists of 116 users and 346, 455 text messages. Roman Urdu text, is the most widely adopted style of writing text messages in Pakistan. Our user study leads to interesting results, for instance, we were able to quantitatively show that a number of words are written using more than one spelling; most participants of our study were not comfortable in English and hence they write their text messages in Roman Urdu; and the choice of language adopted by the participants sometimes varies according to who the message is being sent. Moreover we found that many young students send text messages(SMS) of intimate nature.

Roman-txt: forms and functions of roman urdu texting

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.

Mehwish Nasim

Papers

Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

Roman-txt: forms and functions of roman urdu texting

Pachinko Prediction: A Bayesian method for event prediction from social media data