Smart hospital emergency system via mobile-based requesting services

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study

Norm-based and commitment-driven agentification of the Internet of Things

There are no doubts that the Internet-of-Things (IoT) has conquered the ICT industry to the extent that many governments and organizations are already rolling out many anywhere,anytime online services that IoT sustains. However, like any emerging and disruptive technology, multiple obstacles are slowing down IoT practical adoption including the passive nature and privacy invasion of things. This paper examines how to empower things with necessary capabilities that would make them proactive and responsive. This means things can, for instance reach out to collaborative peers, (un)form dynamic communities when necessary, avoid malicious peers, and be “questioned” for their actions. To achieve such empowerment, this paper presents an approach for agentifying things using norms along with commitments that operationalize these norms. Both norms and commitments are specialized into social (i.e., application independent) and business (i.e., application dependent), respectively. Being proactive, things could violate commitments at run-time, which needs to be detected through monitoring. In this paper, thing agentification is illustrated with a case study about missing children and demonstrated with a testbed that uses different IoT-related technologies such as Eclipse Mosquitto broker and Message Queuing Telemetry Transport protocol. Some experiments conducted upon this testbed are also discussed

Cognitive computing meets the internet of things

Abstract: This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today’s things are confined into a data-supplier role, which deprives them from being the technology of choice for smart applications development. Cognitive computing is about reasoning, learning, explaining, acting, etc. In this paper, cognitive things’ features include functional and non-functional restrictions along with a 3 stage operation cycle that takes into account these restrictions during reasoning, adaptation, and learning. Some implementation details about cognitive things are included in this paper based on a water pipe case-study

Trust-based management in IoT federations

This paper presents a trust-based evolutionary game model for managing Internet-of-Things (IoT) federations. The model adopts trust-based payoff to either reward or penalize things based on the behaviors they expose. The model also resorts to monitoring these behaviors to ensure that the share of untrustworthy things in a federation does not hinder the good functioning of trustworthy things in this federation. The trust scores are obtained using direct experience with things and feedback from other things and are integrated into game strategies. These strategies capture the dynamic nature of federations since the population of trustworthy versus untrustworthy things changes over time with the aim of retaining the trustworthy ones. To demonstrate the technical doability of the game strategies along with rewarding/penalizing things, a set of experiments were carried out and results were benchmarked as per the existing literature. The results show a better mitigation of attacks such as bad-mouthing and ballot-stuffing on trustworthy things

Cognitive Computing Meets the Internet of Things

This paper discusses the blend of cognitive computing with the Internet-of-Things that should result into developing cognitive things. Today\u27s things are confined into a data-supplier role, which d ..

DDoS-FOCUS:A Distributed DoS Attacks Mitigation using Deep Learning Approach for a Secure IoT Network

The fast growth of the Internet of Things devices and communication protocols poses equal opportunities for lifestyle-boosting services and pools for cyber attacks. Usually, IoT network attackers gain access to a large number of IoT (e.g., things and fog nodes) by exploiting their vulnerabilities to set up attack armies, then attacking other devices/nodes in the IoT network. The Distributed Denial of Service (DDoS) flooding-attacks are prominent attacks on IoT. DDoS concerns security professionals due to its nature in forming sophisticated attacks that can be bandwidth-busting. DDoS can cause unplanned IoT-services outages, hence requiring prompt and efficient DDoS mitigation. In this paper, we propose a DDoS-FOCUS; a solution to mitigate DDoS attacks on fog nodes. The solution encompasses a machine learning model implanted at fog nodes to detect DDoS attackers. A hybrid deep learning model was developed using Conventional Neural Network and Bidirectional LSTM (CNN-BiLSTM) to mitigate future DDoS attacks. A preliminary test of the proposed model produced an accuracy of 99.8% in detecting DDoS attacks

Cloud-edge coupling to mitigate execution failures

This paper examines the doability of cloud-edge coupling to mitigate execution failures and hence, achieve business process continuity. These failures are the result of disruptions that impact the cycles of consuming cloud resources and/or edge resources. Cloud/Edge resources are subject to restrictions like limitedness and non-shareability that increase the complexity of resuming execution operations to the extent that some of these operations could be halted, which means failures. To mitigate failures, cloud and edge resources are synchronized using messages allowing proper consumption of these resources. A Microsoft Azure-based testbed simulating cloud-edge coupling is also presented in the paper

An IoT Application Business-Model on Top of Cloud and Fog Nodes

This paper discusses the design of a business model dedicated for IoT applications that would be deployed on top of cloud and fog resources. This business model features 2 constructs, flow (specialized into data and collaboration) and placement (specialized into processing and storage). On the one hand, the flow construct is about who sends what and to whom, who collaborates with whom, and what restrictions exist on what to send, to whom to send, and with whom to collaborate. On the other hand, the placement construct is about what and how to fragment, where to store, and what restrictions exist on what and how to fragment, and where to store. The paper also discusses the development of a system built-upon a deep learning model that recommends how the different flows and placements should be formed. These recommendations consider the technical capabilities of cloud and fog resources as well as the networking topology connecting these resources to things

Thing Artifact-based Design of IoT Ecosystems

This paper sheds light on the complexity of designing Internet-of-Things (IoT) ecosystems where a high number of things reside and thus, must collaborate despite their reduced size, restricted connectivity, and constrained storage limi- tations. To address this complexity, a novel concept referred to as thing artifact is devised abstracting the roles that things play in an IoT ecosystem. The abstraction focuses on 3 cross-cutting aspects namely, functionality in term of what to perform, lifecycle in term of how to behave, and interaction flow in term of with whom to exchange. Building upon the concept of data artifact commonly used in data-driven business applications design, thing artifacts en- gage in relations with peers to coordinate their individual behaviors and hence, avoid conflicts that could result from the quality of exchanged data. Putting functionality, lifecycle, interaction flow, and relation together contributes to ab- stracting IoT ecosystems design. A system implementing a thing artifact-based IoT ecosystem along with some experiments are presented in the paper as well