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The widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (ESSs) (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the ESSs is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). In this paper, we investigated the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimizing the probability of service outage and energy wastage).

Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers’ evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees’ reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship’s indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.

This paper proposes a method to assess the security of an $n$ device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called the Associated RNN (ARNN). For each of the $n$ devices or IP addresses in the IoT network, two distinct neurons of the ARNN advocate opposite views: compromised or not compromised. The fully interconnected $2n$ neuron ARNN structure of paired neurons learns offline from ground truth data. Thus rather than requiring a separate attack detector at each network node, the ARNN offers a single overall attack detector that observes the incoming traffic at each node, learns about the interdependencies between network nodes, and formulates a recommendation for each device or IP address in an IoT network. The ARNN weight initialization and learning algorithm are discussed, and the ARNN performance is evaluated using real attack data, and compared against several learning and testing techniques. Results are obtained both for off-line learning with ground truth data, and for on-line incremental learning using a simplified average metric measured from incoming packet traffic. Comparisons with the best state-of-the-art techniques show that the ARNN significantly outperforms previously known approaches.

A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.

The Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device’s battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.

Abstract Background Increased digitalization of healthcare comes along with the cost of cybercrime proliferation. This results to patients’ and healthcare providers' skepticism to adopt Health Information Technologies (HIT). In Europe, this shortcoming hampers efficient cross-border health data exchange, which requires a holistic, secure and interoperable framework. This study aimed to provide the foundations for designing a secure and interoperable toolkit for cross-border health data exchange within the European Union (EU), conducted in the scope of the KONFIDO project. Particularly, we present our user requirements engineering methodology and the obtained results, driving the technical design of the KONFIDO toolkit. Methods Our methodology relied on four pillars: (a) a gap analysis study, reviewing a range of relevant projects/initiatives, technologies as well as cybersecurity strategies for HIT interoperability and cybersecurity; (b) the definition of user scenarios with major focus on cross-border health data exchange in the three pilot countries of the project; (c) a user requirements elicitation phase containing a threat analysis of the business processes entailed in the user scenarios, and (d) surveying and discussing with key stakeholders, aiming to validate the obtained outcomes and identify barriers and facilitators for HIT adoption linked with cybersecurity and interoperability. Results According to the gap analysis outcomes, full adherence with information security standards is currently not universally met. Sustainability plans shall be defined for adapting existing/evolving frameworks to the state-of-the-art. Overall, lack of integration in a holistic security approach was clearly identified. For each user scenario, we concluded with a comprehensive workflow, highlighting challenges and open issues for their application in our pilot sites. The threat analysis resulted in a set of 30 user goals in total, documented in detail. Finally, indicative barriers of HIT acceptance include lack of awareness regarding HIT risks and legislations, lack of a security-oriented culture and management commitment, as well as usability constraints, while important facilitators concern the adoption of standards and current efforts for a common EU legislation framework. Conclusions Our study provides important insights to address secure and interoperable health data exchange, while our methodological framework constitutes a paradigm for investigating diverse cybersecurity-related risks in the health sector.

The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%.

It has been recently observed that Software Defined Networks (SDN) can change the paths of different connections in the network at a relatively frequent pace to improve the overall network performance, including delay and packet loss, or to respond to other needs such as security. These changes mean that a network that SDN controls will seldom operate in steady state; rather, the network may often be in transient mode, especially when the network is heavily loaded and path changes are critically important. Hence, we propose a transient analysis of such networks to better understand how frequent changes in paths and the switches’ workloads may affect multi-hop networks’ performance. Since conventional queueing models are difficult to solve for transient behaviour and simulations take excessive computation time due to the need for statistical accuracy, we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show that network optimization should consider the transient effects of SDN and that transients need to be included in the design of algorithms for SDN controllers that optimize network performance.

Common software vulnerabilities can result in severe security breaches, financial losses, and reputation deterioration and require research effort to improve software security. The acceleration of the software production cycle, limited testing resources, and the lack of security expertise among programmers require the identification of efficient software vulnerability predictors to highlight the system components on which testing should be focused. Although static code analyzers are often used to improve software quality together with machine learning and data mining for software vulnerability prediction, the work regarding the selection and evaluation of different types of relevant vulnerability features is still limited. Thus, in this paper, we examine features generated by SonarQube and CCCC tools, to identify those that can be used for software vulnerability prediction. We investigate the suitability of thirty-three different features to train thirteen distinct machine learning algorithms to design vulnerability predictors and identify the most relevant features that should be used for training. Our evaluation is based on a comprehensive feature selection process based on the correlation analysis of the features, together with four well-known feature selection techniques. Our experiments, using a large publicly available dataset, facilitate the evaluation and result in the identification of small, but efficient sets of features for software vulnerability prediction.

Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the reliability of the application. Since checkpointing introduces additional computation time and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a cost function that includes the execution time and the energy. Expressions for both the program’s energy consumption and execution time are derived as a function of the failure probability per instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear combination of the average energy consumption and execution time of the program, in terms of the classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy consumption is also derived. The results are illustrated with numerical examples regarding programs of various lengths and showing the relation between the checkpoint interval that minimizes energy consumption and execution time, and the one that minimizes a weighted sum of the two. In addition, our results are applied to a popular software benchmark, and posted on a publicly accessible web site, together with the optimization software that we have developed.

We investigate the cooperation among energy prosumers (unified energy provider and consumer) through the energy packet network (EPN) paradigm, which represents both the flow of work that requires energy, and the flow of energy itself, in terms of discrete units. This paper details a stochastic model of EPNs, which is inspired from a branch of queuing theory called G-networks. The model allows us to compute the equilibrium state of a system that includes energy storage units, energy transmission networks, and energy consumers, together with the intermittent energy sources. The model is then used to show how the flow of work and energy in the system can be optimized for certain utility functions that consider both the needs of the consumers, and the desire to maintain some reserve energy for potential future needs.

Emergency navigation algorithms for evacuees in confined spaces typically treat all evacuees in a homogeneous manner, using a common metric to select the best exit paths. In this paper, we present a quality of service (QoS) driven routing algorithm to cater to the needs of different types of evacuees based on age, mobility, and level of resistance to fatigue and hazard. Spatial information regarding the location and the spread of hazards is also integrated into the routing metrics to avoid situations where evacuees may be directed toward hazardous zones. Furthermore, rather than persisting with a single decision algorithm during an entire evacuation process, we suggest that evacuees may adapt their course of action with regard to their ongoing physical condition and environment. A widely tested routing protocol known as the cognitive packet network with random neural networks and reinforcement learning are employed to collect information and provide advice to evacuees, and is beneficial in emergency navigation owing to its low computational complexity and its ability to handle multiple QoS metrics in its search for safe exit paths. The simulation results indicate that the proposed algorithm, which is sensitive to the needs of evacuees, produces better results than the use of a single metric. Simulations also show that the use of dynamic grouping to adjust the evacuees' category, and routing algorithms that have regard for their on-going health conditions and mobility, can achieve higher survival rates.

We consider a wireless sensor node that gathers energy through harvesting and reaps data through sensing. The node has a wireless transmitter that sends out a data packet whenever there is at least one “energy packet” and one “data packet”, where an energy packet represents the amount of accumulated energy at the node that can allow the transmission of a data packet. We show that such a system is unstable when both the energy storage space and the data backlog buffer approach infinity, and we obtain the stable stationary solution when both buffers are finite. We then show that if a single energy packet is not sufficient to transmit a data packet, there are conditions under which the system is stable, and we provide the explicit expression for the joint probability distribution of the number of energy and data packets in the system. Since the two flows of energy and data can be viewed as flows that are instantaneously synchronised, this paper also provides a mathematical analysis of a fundamental problem in computer science related to the stability of the “join” synchronisation primitive.

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.

We present a novel direction based shortest path search algorithm to guide evacuees during an emergency. It uses opportunistic communications (oppcomms) with low-cost wearable mobile nodes that can exchange packets at close range of a few to some tens of meters without help of an infrastructure. The algorithm seeks the shortest path to exits which are safest with regard to a hazard, and is integrated into an autonomous Emergency Support System (ESS) to guide evacuees in a built environment. The algorithm proposed that ESSs are evaluated with the DBES (Distributed Building Evacuation Simulator) by simulating a shopping centre where fire is spreading. The results show that the directional path finding algorithm can offer significant improvements for the evacuees.

Cyber-physical systems that include human beings and vehicles in a built environment, such as a building or a city, together with sensor networks and decision support systems have attracted much attention. In emergencies, which also include mobile searchers and rescuers, the interactions among civilians and the environment become much more diverse, and the complexity of the emergency response also becomes much greater. This paper surveys current research on sensor-assisted evacuation and rescue systems and discusses the related research issues concerning communication protocols for sensor networks, as well as several other important issues, such as the integrated asynchronous control of large-scale emergency response systems, knowledge discovery for rescue and prototyping platforms. Then, we suggest directions for further research.

Energy saving in networks has traditionally focussed on reducing battery consumption through smart wireless network design. Recently, researchers have turned their attention to the energy cost and carbon emissions of the backbone network that both fixed and mobile communications depend on, proposing primarily mechanisms that turn equipments OFF or put them into deep sleep. This is an effective way of saving energy, provided that the nodes can return to working condition quickly, but it introduces increased delays and packet losses that directly affect the quality of communication experienced by the users. Here we investigate the associated tradeoffs between power consumption and quality of service in backbone networks that employ deep sleep energy savings. We examine these tradeoffs by conducting experiments on a real PC-based network topology, where nodes are put into deep sleep at random times and intervals, resulting in a continuously changing network with reduced total power consumption. The average power consumption, the packet loss and the average delay of this network are examined with respect to the average value of the ON rate and the ON/OFF cycle of the nodes.

An attractive advantage of mobile networks is that their users can gain easy access to different services. In some cases, equivalent services could be fulfilled by different providers, which brings the question of how to rationally select the best provider among all possibilities. In this paper, we investigate an answer to this question from both quality-of-service (QoS) and energy perspectives by formulating an optimisation problem. We illustrate the theoretical results with examples from experimental measurements of the resulting energy and performance.

Cyber-Physical-Human Systems (CPHS) combine sensing, communication and control to obtain desirable outcomes in physical environments for human beings, such as buildings or vehicles. A particularly important application area is emergency management. While recent work on the design and optimisation of emergency management schemes has relied essentially on discrete event simulation, which is challenged by the substantial amount of programming or reprogramming of the simulation tools and by the scalability and the computing time needed to obtain useful performance estimates, this paper proposes an approach that offers fast estimates based on graph models and probability models. We show that graph models can offer insight into the critical areas in an emergency evacuation and that they can suggest locations where sensor systems are particularly important and may require hardening. On the other hand, we also show that analytical models based on queueing theory can provide useful estimates of evacuation times and for routing optimisation. The results are illustrated with regard to the evacuation of a real-life building.

Abstract: Queueing networks are used to model the performance of the Internet, of manufacturing and job-shop systems, supply chains, and other networked systems in transportation or emergency management. Composed of service stations where customers receive service, and then move to another service station till they leave the network, queueing networks are based on probabilistic assumptions concerning service times and customer movement that represent the variability of system workloads. Subject to restrictive assumptions regarding external arrivals, Markovian movement of customers, and service time distributions, such networks can be solved efficiently with “product form solutions” that reduce the need for software simulators requiring lengthy computations. G-networks generalise these models to include the effect of “signals” that re-route customer traffic, or negative customers that reject service requests, and also have a convenient product form solution. This paper extends G-networks by including a new type of signal, that we call an “Adder”, which probabilistically changes the queue length at the service center that it visits, acting as a load regulator. We show that this generalisation of G-networks has a product form solution.