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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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs)-based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large-scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running the bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network, a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of being a spiking neural network. This is demonstrated on a number of real-world classification datasets. [ABSTRACT FROM AUTHOR]

The need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of software-defined networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed. [ABSTRACT FROM AUTHOR]

Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users. [ABSTRACT FROM AUTHOR]

We investigate how the flow of energy and the flow of jobs in a service system can be used to minimize the average response time to jobs that arrive according to random arrival processes at the servers. An interconnected system of workstations (WSs) and energy storage (ES) units that are fed with randomly arriving harvested energy is analyzed by means of the energy packet network (EPN) model. The system state is discretized and uses discrete units to represent the backlog of jobs at the WSs and the amount of energy that is available at the ES units. An energy packet (EP), which is the unit of energy, can be used to process one or more jobs at a WS, and an EP can also be expended to move a job from one WS to another one. The system is modeled as a probabilistic network that has a product-form solution for the equilibrium probability distribution of system state. The EPN model is used to solve two problems related to using the flow of energy and jobs in a multiserver system, so as to minimize the average response time experienced by the jobs that arrive at the system. [ABSTRACT FROM AUTHOR]

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing. [ABSTRACT FROM AUTHOR]

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs’ storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs’ caching utilities and MNO’s caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results. [ABSTRACT FROM AUTHOR]

The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things (IoT) has penetrated many aspects of the physical world to realize different applications. Through IoT, these applications generate, exchange, aggregate, and analyze a vast amount of security-critical and privacy- sensitive data, which makes them attractive targets of attacks. Therefore, it is rather necessary for IoT systems to be equipped with the ability to resist security and privacy risks when fulfilling the desired functional requirements and services. To achieve these goals, there are many new challenges for IoT to implement privacy preserving data manipulation. First, data analysts need to process privacy-sensitive data to extract the expected information without privacy disclosure. In addition, many privacy related factors, including privacy valuation and risk assessment, affect sensitive and private data trading between data owners and requesters. Moreover, the data owners' security behavior also plays an important role in privacy protection in IoT applications. Concerning these issues, this article introduces and surveys privacy preserving techniques in the processes of data aggregation, trading, and analysis: the balance between data analysis and privacy preservation from the data analysts' perspective, secure data trading from the perspective of data owners and requesters, and secure private data aggregation from the data owners' perspective. [ABSTRACT FROM AUTHOR]

Web users are not assured that the results provided by Web search engines or recommender systems are either exhaustive, or even the most relevant to their search needs. Businesses that provide services through the web have a commercial interest to rank higher on results or recommendations to attract more customers, while Web search engines and recommender systems make a profit based on the advertisers that can offer a higher payment for their advertisements. Thus we propose a neuro-computing application that addresses these issues from the perspective of end-users. We present an Intelligent Search Assistant (ISA) that acts as an interface between the user and different search engines. Using the Random Neural Network as a learning engine that operates with learned by gradient descent and reinforcement learning, the ISA learns how to reorder results and present them to the end-user, according to the perceived relevance of the results for the end-user's benefit. We also present a new relevance metric, which combines both relevance and rank, to validate and compare the performance of our proposed solution against other Web search engines and recommender systems. The resulting rank relevance provided by different Web search engines, metasearch engines, academic databases and recommender systems are compared with the ISA, which appears to outperform commercial search tools. [ABSTRACT FROM AUTHOR]

In heterogeneous ultra-dense networks (HetUDNs), the software-defined wireless network (SDWN) separates resource management from geo-distributed resources belonging to different service providers. A centralized SDWN controller can manage the entire network globally. In this paper, we focus on mobile traffic offloading and resource allocation in SDWN-based HetUDNs, constituted of different macro base stations and small-cell base stations (SBSs). We explore a scenario where SBSs’ capacities are available, but their offloading performance is unknown to the SDWN controller: this is the information asymmetric case. To address this asymmetry, incentivized traffic offloading contracts are designed to encourage each SBS to select the contract that achieves its own maximum utility. The characteristics of large numbers of SBSs in HetUDNs are aggregated in an analytical model, allowing us to select the SBS types that provide the off-loading, based on different contracts which offer rationality and incentive compatibility to different SBS types. This leads to a closed-form expression for selecting the SBS types involved, and we prove the monotonicity and incentive compatibility of the resulting contracts. The effectiveness and efficiency of the proposed contract-based traffic offloading mechanism, and its overall system performance, are validated using simulations. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on green pervasive and ubiquitous computing. These computing systems are a promising paradigm in human?s daily life, which enables computation, storage, and communications services to appear everywhere and anytime. It requires seamless integration and interaction of heterogeneous and complex systems, ranging from a variety of local smart yet resource-constrained devices to remote powerful cloud platforms. Such a pervasive ecosystem not only depends on performance-oriented system infrastructures and ubiquitous computing/networking technologies, but also faces critical energy-efficiency issues, exemplified by controlling the tremendous power consumption, electricity cost, and carbon-emission of large-scale datacenter systems, as well as prolonging the limited battery lifetime of mobile devices in broad Internet applications. Therefore, new energy aware architectures, emerging systems-of-systems and related techniques, such as mobile-cloud computing, virtualization, and scheduling of renewable energy resources, are increasingly drawing development efforts from both academia and industry, in order to shape an environmentally sustainable ?Green? world for the next decade. Due to the challenging open issues, green pervasive and ubiquitous systems deserve academic attentions from the interdisciplinary aspects of information systems, computing and networking optimizations, energy control, and engineering technologies. [ABSTRACT FROM PUBLISHER]

This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source–destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some $2\times 10^6$ measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α = 1 , this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α = 1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α ∈ (0 , 2) \ { 1 } and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Health data exchange is a major challenge due to the sensitive information and the privacy issues entailed. Considering the European context, in which health data must be exchanged between different European Union (EU) Member States, each having a different national regulatory framework as well as different national healthcare structures, the challenge appears even greater. Europe has tried to address this challenge via the epSOS ("Smart Open Services for European Patients") project in 2008, a European large-scale pilot on cross-border sharing of specific health data and services. The adoption of the framework is an ongoing activity, with most Member States planning its implementation by 2020. Yet, this framework is quite generic and leaves a wide space to each EU Member State regarding the definition of roles, processes, workflows and especially the specific integration with the National Infrastructures for eHealth. The aim of this paper is to present the current landscape of the evolving eHealth infrastructure for cross-border health data exchange in Europe, as a result of past and ongoing initiatives, and illustrate challenges, open issues and limitations through a specific case study describing how Italy is approaching its adoption and accommodates the identified barriers. To this end, the paper discusses ethical, regulatory and organizational issues, also focusing on technical aspects, such as interoperability and cybersecurity. Regarding cybersecurity aspects per se, we present the approach of the KONFIDO EU-funded project, which aims to reinforce trust and security in European cross-border health data exchange by leveraging novel approaches and cutting-edge technologies, such as homomorphic encryption, photonic Physical Unclonable Functions (p-PUF), a Security Information and Event Management (SIEM) system, and blockchain-based auditing. In particular, we explain how KONFIDO will test its outcomes through a dedicated pilot based on a realistic scenario, in which Italy is involved in health data exchange with other European countries. [ABSTRACT FROM AUTHOR]

The frequency, destruction and costs of natural and human-made disasters in modern highly-populated societies have resulted in research on emergency evacuation and wayfinding, which has drawn considerable attention. The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and computing dynamic reactions that mitigate or prevent the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to share ongoing research results, identify the research gaps and address the need for future research. We present a comprehensive review of research on emergency evacuation and wayfinding, focusing on the algorithmic and system design aspects. Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy consumption by ICT equipment that operates the emergency management infrastructure. [ABSTRACT FROM AUTHOR]