Smartphones enable understanding human behavior with activity recognition to support people’s daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people’s daily lives, and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and ∼ 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.

With today's technologies, the possibility to generate highly realistic visual fakes is within everyone's reach, leading to major threats in terms of misinformation and data trustworthiness. This holds in particular for synthetically generated faces, which are able to deceive even the most experienced observers, and can be exploited to create fake digital identities with synthetic facial attributes, to be used on social networks and online services. In response to this threat, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem “in the wild”, more realistic image databases are needed to train specialised detectors. In this work, we present TrueFace, a first dataset of social-media-processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram. The dataset is used to validate a ResNet-based image classification model addressing the discrimination of synthetic-vs-real faces in both presocial and post-social scenarios. The results demonstrate that even detectors with extremely high performance on non-shared images struggle to retain their accuracy on images from social media, while fine-tuning with shared images strongly mitigates such performance issues.

Container technology has become a very popular choice for easing and managing the deployment of cloud applications and services. Container orchestration systems such as Kubernetes can automate to a large extent the deployment, scaling, and operations for containers across clusters of nodes, reducing human errors and saving cost and time. Designed with "traditional" cloud environments in mind (i.e., large datacenters with close-by machines connected by high-speed networks), systems like Kubernetes present some limitations in geo-distributed environments where computational workloads are moved to the edges of the network, close to where data is being generated/consumed. In geo-distributed environments, moving around containers, either to follow moving data sources/sinks or due to unpredictable changes in the network substrate, is a rather common operation. We present MyceDrive, a stateful resource migration solution natively integrated with the Kubernetes orchestrator. We show that geo-distributed Kubernetes pod migration is feasible while remaining fully transparent to the migrated application as well as its clients, while reducing downtimes up to 7x compared to state-of-the-art solutions.

Abstract Security and privacy of information transmitted among the devices involved in an Internet of Things (IoT) network represent relevant issues in IoT contexts. Guaranteeing effective control and supervising access permissions to IoT applications is a complex task, mainly due to resources' heterogeneity and scalability requirements. The design and development of highly customizable access control policies, along with an efficient mechanism for ensuring that the rules applied by the IoT platform are not tampered with or violated, will undoubtedly have a significant impact on the diffusion of IoT‐based solutions. In such a direction, the article proposes the integration of a permissioned blockchain within an honest‐but‐curious (i.e., not trusted) IoT distributed middleware layer, which aims to guarantee the correct management of access to resources by the interested parties. The result is a robust and lightweight system, able to manage the data produced by IoT devices, support relevant security features, such as integrity and confidentiality, and resist different kinds of attacks. The use of blockchain will ensure the tamper‐resistance and synchronization of the distributed system, where various stakeholders own applications and IoT platforms. The methodology and the proposed architecture are validated employing a test‐bed.

Container migration is an essential functionality in large-scale geo-distributed platforms such as fog computing infrastructures. Contrary to migration within a single data center, long-distance migration requires that the container's disk state should be migrated together with the container itself. However, this state may be arbitrarily large, so its transfer may create long periods of unavailability for the container. We propose to exploit the layered structure provided by the OverlayFS file system to transparently snapshot the volumes' contents and transfer them prior to the actual container migration. We implemented this mechanism within Kubernetes. Our evaluations based on a real fog computing test-bed show that our techniques reduce the container's downtime during migration by a factor 4 compared to a baseline with no volume checkpoint.

This paper introduces a data model for representing humans-chatbots interactions. Despite there are many models that allow representing the usage and the behaviour of bots, a service that can store information from any conversational agent regardless the architecture is still missing. With this work, we introduce a general-purpose data model to store both messages and logs. To succeed, we raise the level of abstraction of the other analyzed models and we focused on the core logic of chatbots: conversations and interactions with the users

Summary Fog computing is an emerging paradigm in the Internet of Things (IoT) space, consisting of a middle computation layer, sitting between IoT devices and Cloud servers. Fog computing provides additional computing, storage, and networking resources in close proximity to where data is being generated and/or consumed. As the Fog layer has direct access to data streams generated by IoT devices and responses/commands sent from the Cloud, it is in a critical position in terms of security of the entire IoT system. Currently, there is no specific tool or methodology for analysing the security of Fog computing systems in a comprehensive way. Generic security evaluation procedures applicable to most information technology products are time consuming, costly, and badly suited to the Fog context. In this article, we introduce a methodology for evaluating the security of Fog computing systems in a systematic way. We also apply our methodology to a generic Fog computing system, showcasing how it can be purposefully used by security analysts and system designers.

In the digital age, where the Internet connects things across the globe and individuals are constantly online, data security and privacy are becoming key drivers (and barriers) of change for adoption of innovative solutions. Traditional approaches, whereby communication links are secured by means of encryption, and access control is run in a static way by a centralized authority, are showing their limits when applied to massive-scale, interconnected and distributed systems. Regulations, while still fragmented, are moving to adapt to changes in technology and society, with the aim to protect confidential information by governments, businesses, and individual citizens. In this landscape, proper mechanisms should be defined to allow a strict control over the data life-cycle and to guarantee the privacy and the application of specific regulations on personal information's disclosure, usage and access. Sticky policies represent one approach to improve owners' control over their data. In such an approach, machine-readable policies are attached to data. They are called 'sticky' in that they travel together with data, as data travels across multiple administrative domains. In this article we survey the state-of-the-art in sticky policies, discussing limitations, open issues, applications and research challenges, with a specific focus on their applicability to Internet of Things, cloud computing, and Content Centric Networking.

Security and privacy represent critical issues for a wide adoption of Internet of Things (IoT) technologies both by industries and people in their every-day life. Besides, the complexity of an IoT system's management resides in the presence of heterogeneous devices, which communicate by means of different protocols and provide information belonging to various application domains. Hence, adequate policies must be correctly distributed and applied to the information made available by the IoT network to secure the data themselves and to regulate the access to the managed resources over the whole IoT system. Policies mainly involve the access to resources and are usually established by system administrators in accordance with the rules of each specific domain. Since IoT concerns multiple application fields and often wide areas, a centralized solution which manages all the required policies would not be neither efficient nor scalable. Therefore, in this paper, a distributed middleware overlying the IoT network is proposed and integrated with a synchronization system for guaranteeing the correct distribution, update, and application of the policies across the entire IoT environment in real-time. Such a distribution and synchronization system has been developed within a policy enforcement framework. The presented solution has been validated by means of a simple yet real prototype; the analyzed metrics regard delay, overhead and robustness of the proposed enforcement and synchronization framework.

Optimal stochastic control of delay tolerant networks is studied in this paper. First, the structure of optimal two-hop forwarding policies is derived. In order to be implemented, such policies require knowledge of certain global system parameters such as the number of mobiles or the rate of contacts between mobiles. But, such parameters could be unknown at system design time or may even change over time. In order to address this problem, adaptive policies are designed that combine estimation and control: based on stochastic approximation techniques, such policies are proved to achieve optimal performance in spite of lack of global information. Furthermore, the paper studies interactions that may occur in the presence of several DTNs which compete for the access to a gateway node. The latter problem is formulated as a cost-coupled stochastic game and a unique Nash equilibrium is found. Such equilibrium corresponds to the system configuration in which each DTN adopts the optimal forwarding policy determined for the single network problem.

Social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy. A new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted such behavior exceeds some threshold. In this paper we study the question of whether the emergence of threshold policies may be modeled as a result of some rational process which would describe the behavior of non-cooperative rational members of some social network. We focus on situations in which individuals take the decision whether to access or not some content, based on the number of views that the content has. Our analysis aims at understanding not only the behavior of individuals, but also the way in which information about the quality of a given content can be deduced from view counts when only part of the viewers that access the content are informed about its quality. In this paper we present a game formulation for the behavior of individuals using a meanfield model: the number of individuals is approximated by a continuum of atomless players and for which the Wardrop equilibrium is the solution concept. We derive conditions on the problem's parameters that result indeed in the emergence of threshold equilibria policies. But we also identify some parameters in which other structures are obtained for the equilibrium behavior of individuals.

Energy efficiency is acknowledged as a pivotal issue for a sustainable development of wireless networking technologies. Traditionally, most works in the area focused on the user equipment, where battery duration represents a key asset. However, as the smartphone and tablet revolution fuels a massive deployment of wireless networks, often in the form of WiFi hotspots, more and more attention is expected to be devoted to the energy-efficient management of wireless access infrastructure. These networks tend to be dense and over-provisioned, which in time leads to significant energy wastage in off-peak conditions. In this paper, we present MORFEO a flexible energy-saving decision algorithm to tune the energy consumption of a wireless infrastructure to the actual network conditions in terms of both user density and traffic patterns. Experimental results from a real-life deployment shows that our solution can deliver significant energy savings with minimal degradation in terms of the quality of service provided.

Several novel metrics have been proposed in recent literature in order to study the relative importance of nodes in complex networks. Among those, k-coreness has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-coreness of a network, a process also known as k-core decomposition. This technique 1) allows the decomposition, over a set of connected machines, of very large graphs, when size does not allow storing and processing them on a single host, and 2) enables the runtime computation of k-cores in “live” distributed systems. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world data sets is provided.

Maximizing system throughput and bandwidth utilization while guaranteeing fairness among users is a core technical challenge in the design of next generation cellular networks. In this context, fractional frequency reuse (FFR) is a helpful tool if it is flexible in terms of resource and power allocation as well as scheduling. Complementary to this tool, a well-chosen association of users to base station sectors can yet be another important factor, particularly when users are distributed unevenly in space. Here we propose a load balancing scheme that combines flexible FFR with an unrestricted association of users to sectors or base stations, thereby effectively increasing the user data rates in the dense regions of the network. In a first step, we perform virtual joint power allocation and scheduling so as to determine the best sectors for maximizing the throughput for a given user. In a second step, users are actually associated to the best sectors, which are not necessarily the ones providing the strongest signal. Simulation results show that our proposed mechanism provides fairness by load balancing while still maintaining the throughput over all users.

The signalling subsystem is the most expensive and complex element in cellular networks. Today's networks use signalling mechanisms whose design builds on more than 20 years of the operational expertise. Despite this, the signalling subsystem of all mobile network standards remains vulnerable to failures of equipment and to sharp increases in offered load caused by unanticipated traffic patterns. In this work we analyse the technical vulnerability of paging - a key signalling mechanism that is responsible for notifying mobile terminals of downlink service requests. Our assessment considers the operation of Long-Term Evolution (LTE) networks and in particular the performance of the paging mechanism on the radio interface. We implemented the paging procedure in a simulator, and we propose and verify a mathematical model of the system behaviour. The proposed mathematical model effectively captures the non-zero threshold of paging failure probability; it is scrupulously precise for the paging loads below the nonzero threshold and delivers a close approximation with the increasing paging load. The mathematical model can be applied to optimise the performance of the paging mechanism and to devise techniques able to overcome its technical vulnerability.

TCP congestion control has a critical impact on the Internet stability and performance. In relatively recent times, a number of novel TCP congestion control variants, such as TCP BIC, CUBIC and Compound, started to be deployed in modern operating systems. While risks and benefits of these recent developments are subject of debate, the research community needs to monitor the extent to which each of these novel TCP congestion control variants is actually used in regulating Internet traffic, and this information, due to the complex, diverse, and decentralized nature of the Internet, is very difficult to be obtained. As a first step towards the collection of this important piece of information, we have developed a tool, presented in this paper, which allows us to identify the TCP variants employed in monitored TCP sessions. Our approach consists in passively monitoring packets and ACKs exchanged in TCP sessions, estimating the evolution of congestion window, and matching a set of features against those typical of the various TCP variants considered. Our identification tool has been validated trough emulation and real Internet experiments, showing promising results in the identification of New Reno, BIC, Cubic and Compound TCP variants.

Multi-hop wireless networks are emerging as a viable alternative for building access networks in areas where conventional solutions (cellular, fiber) are neither feasible nor attractive from an economical standpoint. The management of such networks represents an overly complex task because of the time-varying nature of the radio channel, the mobility of users and the presence of adaptive, self-configuration features. Various solutions are currently being researched, whereby network management functionalities are performed autonomously at the network nodes themselves. Such approaches require a monitoring framework able to bring network-level information to the relevant decision points in an effective and robust manner. In this paper, we present a distributed network monitoring framework, specifically developed for wireless multi-hop networks. The system architecture and the protocols are presented together with results obtained using a prototypical implementation over a real-world testbed. Experimental results show that the framework generates a limited amount of traffic, and that the system is able to consistently recover from node failures.

Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabling the run-time computation of k-cores in "live" distributed systems and (ii) allowing the decomposition, over a set of connected machines, of very large graphs, that cannot be hosted in a single machine. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world graphs is provided.

We show how the TUNEGreen real-time energy consumption monitoring capabilities can be used to understand how and where power is consumed in a simple star-shaped network based on IEEE 802.11. Different traffic patterns will be generated in order to demonstrate the correlation between traffic and energy consumption.

As the number and capabilities of mobile devices is rapidly increasing, new challenges arise in the way services are currently designed. Users are seeking for more complex and advanced functionalities able to satisfy their increasing requirements. As a consequence, in ubiquitous environments, a different way to design services has to be introduced in order to guarantee services always up to date in a transparent and efficient way. In this paper, we present and analyze a framework for distributed cooperative service evolution in a wireless nomadic environment. In particular, we assume a disconnected network architecture, where users' mobility is exploited to achieve a scalable behavior, and communication is based on localized peer-to-peer interactions among neighboring nodes. Service management is achieved by introducing autonomic services, whose operations are based on a distributed evolution process, which draws tools and concepts from evolutionary computation (and genetic algorithms in particular). The latter relies on the concept of recombination , i.e., the exchange of information among service users, which collaborate to enhance their fitness , defined as the ability of the actual service to fulfill user's requirements. We introduce a general framework for analyzing service recombination policies and exploit results from martingales theory to study their convergence properties.

The technology world is experiencing the emergence of a generation of massive-scale networked digital systems, performing complex operations that are partially out of the control of the systems' designers. This is the case, e.g., of pervasive computing environments, characterized by the proliferation of embedded electronic devices with communication and computing capabilities, able to network themselves forming a “digital halo” surrounding the user in its daily activities. In this paper, we advocate the use of bottom-up design patterns, able to build on results in complex systems science and systems biology, for devising new solutions for designing and managing such environments. We also present a possible application to a novel cyberphysical ecosystem, that we call the Energy Web, arising from the combination of innovative information and communication technologies with distributed renewable energy sources.

We study in this paper optimal stochastic control issues in delay tolerant networks. We first derive the structure of optimal 2-hop forwarding policies. In order to be implemented, such policies require the knowledge of some system parameters such as the number of mobiles or the rate of contacts between mobiles, but these could be unknown at system design time or may change over time. To address this problem, we design adaptive policies combining estimation and control that achieve optimal performance in spite of the lack of information. We then study interactions that may occur in the presence of several competing classes of mobiles and formulate this as a cost-coupled stochastic game. We show that this game has a unique Nash equilibrium such that each class adopts the optimal forwarding policy determined for the single class problem.

On behalf of the Technical Programme Committee, we are glad to welcome you to the 7th edition Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

Wireless mesh networks are emerging as a promising access network architecture. In this work, we report on an extensive measurement campaign performed over an experimental WiFi-based mesh network, deployed in an office building environment. The measurements are focused on the performance of HTTP traffic. Results demonstrate the existence of an unfairness issue, whereby longer sessions achieve better performance than shorter ones. This is known to be potentially harmful in terms of mean session delay and user-perceived quality of service. Potential countermeasures are presented and discussed.

Distributed estimation of global parameters in intermittently connected mobile environments is a challenging problem. In this paper, we introduce a set of methods, based on gossip techniques and population protocols, for performing such task. The applicability of such techniques to various environments, characterized by different mobility patterns, is evaluated through numerical simulations and discussed extensively. Guidelines are provided to help practitioners choosing the right method for their specific application problem.

In this paper we present airtime deficit round robin (ADRR) a novel scheduling algorithm for IEEE 802.11-based wireless mesh networks. ADRR enhances the deficit round robin scheduling discipline by taking into account the channel quality experienced by the transmitting node. The devised algorithm addresses the IEEE 802.11 performance anomaly, preventing a node which experiences poor channel conditions from monopolizing the wireless medium lowering the performance of the whole system. The proposed approach combines link scheduling with measurable routing metrics typically available in WMNs. Results show the ability of the ADRR scheduler to achieve performance isolation among links characterized by heterogeneous channel conditions. The proposed solution has been implemented and tested over an IEEE 802.11-based wireless mesh network. Source code has been released under a BSD License making it fully available to the research community.

In this paper, we first present an analytical procedure for the computation of the node isolation probability and coverage in an ad hoc network in the presence of channel randomness, with applications to shadowing and fading phenomena. These results are used to obtain an estimate of the connectivity features for very dense networks. Stochastic orderings are used to show the beneficial impact of lognormal shadowing and the negative impact of Rayleigh fading. The impact of SIMO/MISO and MIMO schemes is also addressed, showing how channel diversity can be exploited to enhance network coverage and connectivity.

This paper deals with the problem of supporting sink mobility in a wireless sensor network. It focuses on a specific scenario, where mobile nodes act themselves as both end-users, when querying the WSN for specific information, and as sinks, when gathering the queried data. Due to the mobility of users and to the topology of the network, sinks experience frequent disconnections from the WSN. A system architecture, and the related protocols, capable of supporting the envisioned application domain has been proposed. The performance of the proposed system architecture has been evaluated by means of simulative studies, together with addressing the impact of mobility, deployment geometry and routing parameters.

This paper deals with a system level design solution to support information gathering in the presence of a mobile querying node that experiences frequent disconnections from a Wireless Sensor Network (WSN), an application scenario that embraces the area of Intelligent Transportation Systems (ITS). The proposed scheme basically relies on the network capability of autonomously adapting to sink mobility in order to properly deliver the requested data. The system is defined as far as its functional elements and related communications protocols, together with validating its effectiveness by means of a simulative study. In particular, the performance in terms of delivering latency and packet delivery ratio has been investigated for several network topologies, architecture and mobility pattern, always highlighting a remarkable quality of provided services together with robustness with regard to operative conditions.

Recently, research on disconnected networks has been fostered by several studies on delay-tolerant networks, which are designed in order to sustain disconnected operations. We focus on the emerging notion of connectivity which exists in such networks, where the message exchange between nodes is enforced by leveraging storage capabilities at intermediate relays, with the aim of achieving connectivity over time. The problem, under the constraint of intermittent connectivity, is hence to devise efficient mechanisms for message delivery, and evaluate the performance thereof. In this paper, we introduce a graph-based model able to capture the evolution of the connectivity properties of such systems over time. We show that, for most networks of interest, such connectivity graphs can be modeled as Erdos-Renyi random graphs. Furthermore, we show that, under a uniformity assumption, the time taken for the connectivity graph to become connected scales as Theta((n log n)/lambda) with the number of nodes in the network. Hence we found that, using epidemic routing techniques, message delay is O((n log2n)/(lambda log log n)). The model is validated by numerical simulations and by a comparison with the connectivity patterns emerging from real experiments.

In this work, we introduce a framework to let forwarding schemes evolve in order to adapt to changing and a priori unknown environments. The framework is inspired by genetic algorithms: at each node a genotype describes the forwarding scheme used, a selection process fosters the diffusion of the fittest genotypes in the system and new genotypes are created by combining existing ones or applying random changes. A case study implementation is presented and its performance evaluated via numerical simulations.

This demonstration presents a data-centric opportunistic dissemination platform. The proposed platform achieves system wide delivery of data exploiting opportunistic communications among mobile nodes. The prototype is developed over java-enabled smartphones and relies on Bluetooth connectivity for achieving proximity communications. The platform is described in all its functional components, together with the details of its software implementation.

In this work, we present a multi-resolution data management scheme for efficiently storing and diffusing information, which is originating from sensors and augmenting mobile applications. The proposed mechanism exploits the discrete wavelet transform properties for providing a location-dependent data management scheme, which alleviates the constraints on the storage resources needed for running context-aware mobile applications. The data management scheme has been implemented and evaluated by means of numerical simulations.

The emergence of future pervasive ICT environments, characterized by the massive proliferation of embedded electronic devices with communication and computing capabilities, are posing enormous challenges to existing approaches to networking, service provisioning and system management. In this paper, we make a case to go for biology as possible source of novel, revolutionary design patterns suitable to large-scale highly dynamic ICT systems. We also present a case study, the EU- funded BIONETS project, which tackles the increased complexity of large-scale ubiquitous systems by applying an evolutionary framework to the provisioning of pervasive services.

The three articles in this special section focus on wireless technologies in factory and industrial automation. Further papers on this topic will appear in the August issue of IEEE Transactions on Industrial Informatics.

The three papers in this special section focus on wireless technologies in factory and industrial automation. The papers which appear in this second part cover everything from protocol design and evaluation to the design and assessment of system-level solutions for wireless sensor networks in industrial automation.

Several recent studies have addressed the suitability of current wireless technologies for industrial communications which, frequently, are requested to cope with severe timing constraints. Most of the analysis that have lately appeared in the literature have focused on the performance offered by the lower layers of the communication stack. However, in order to obtain a complete picture, it is important to analyze how this gets coupled with the features of higher layer protocols typically employed by industrial communication applications. In this paper, we investigate the performance obtained by an application layer protocol, derived from those currently employed by wired fieldbuses, running over COTS devices based on two popular wireless communication standards, namely, IEEE 802.15.4 and IEEE 802.11. In particular, we present a mapping of the application service elements onto the services offered by the mentioned wireless systems and discuss some possible design choices. A prototype of the application layer protocol is then implemented for each considered wireless technology and the performance figures evaluated by means of experimental measurements.

Current wireless technologies offer a viable solution for implementing communication systems capable of satisfying the severe timing requirements often required for operations at the lowest levels of factory automation systems. Most of the contributions appeared in the literature on this subject aimed at investigating the suitability to industrial applications of some wireless communication protocols. In this paper, we address the problem from a different perspective. Namely, we are interested in characterizing the application layer performance of a field-bus protocol running over an IEEE 802.11g-based wireless LAN. We provide a preliminary Java-based implementation of such application protocol, and test it on laptops running a Linux operating system and off-the-shelf 802.11 cards. The performance of the application layer protocol are extensively tested on an experimental system characterized by environmental conditions similar to those of industrial installations.

In this paper, mechanisms to enhance the performance of TCP Westwood+ in the presence of a large Bandwidth-Delay Product are studied. In particular, the employment of a logarithmic function for congestion window increase in the absence of packet losses is proposed. Extensive numerical simulations, carried out by using ns-2, show that the use of such logarithmic function can lead to relevant performance improvements with respect to standard Westwood+ protocol.

In this paper, we analyze the performance of a family of opportunistic forwarding schemes (the K-copy relaying strategies) over disconnected wireless networks. We introduce a classification of mobility models based on their dynamic properties, and characterize the M2(marks-memory less) class. Statistical tools are combined with numerical simulations to show that some of the most used mobility models in the literature fall within the M2class. A mathematical framework is provided for evaluating the performance of opportunistic forwarding schemes in the presence of M2mobility, and it is shown that the finiteness of the mean time necessary to deliver a message depends only on the mobility characteristics and not on the relaying protocol specification

In this paper we investigate the limiting properties, in terms of capacity and delay, of an ad hoc network employing a topology-transparent scheduling scheme. In particular, we focus on Time-Spread Multiple Access (TSMA) protocols, which are able to offer, in a distributed fashion, a deterministic upper bound on the access delay. The analysis is based on some asymptotic (focusing) properties of geometric random graphs. The analytical framework is applied to both static and mobile networks. The obtained results are compared with the results present in the literature for the case of an optimum (centralized) scheduling scheme.

Wireless communication systems are rapidly becoming a viable solution for employment at the lowest level of factory automation systems, usually referred to as either "device" or "field" level, where the requested performance may be rather critical in terms of both transmission time and reliability. In this paper, we deal with the use of wireless networks at the device level. Specifically, after an analysis of the communication requirements, we introduce a general profile of a wireless fieldbus. Both the physical and data link layers are taken directly from existing wireless local area networks and wireless personal area networks standards, whereas the application layer is derived from the most popular wired fieldbuses. We discuss implementation issues related to two models of application layer protocols and present performance results obtained through numerical simulations. We also address some important aspects related to data security and power consumption.

We study a fixed point formalisation of the well known analysis of Bianchi. We provide a significant simplification and generalisation of the analysis. In this more general framework, the fixed point solution and performance measures resulting from it are studied. Uniqueness of the fixed point is established. Simple and general throughput formulas are provided. It is shown that the throughput of any flow will be bounded by the one with the smallest transmission rate. The aggregate throughput is bounded by the reciprocal of the harmonic mean of the transmission rates. In an asymptotic regime with a large number of nodes, explicit formulas for the collision probability, the aggregate attempt rate and the aggregate throughput are provided. The results from the analysis are compared with ns2 simulations, and also with an exact Markov model of the back-off process. It is shown how the saturated network analysis can be used to obtain TCP transfer throughputs in some cases.

In this paper, we present an analytical procedure for the computation of the node isolation probability in an ad hoc network in the presence of channel randomness, with applications to shadowing and fading phenomena. Such a probability coincides with the complement of the coverage probability, given that nodes are distributed according to a Poisson point process. These results are used to obtain an estimate of the connectivity features for very dense networks. For the case of superimposed lognormal shadowing and Rayleigh fading, the connectivity improvements achievable by means of diversity schemes are investigated.