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The objective of this chapter is to discuss data dissemination in vehicular networks in detail. We concentrate essentially on network-layer and application-layer protocols, which are often discussed and developed as a single protocol above the respective access technologies. The key objective is to achieve information exchange between any two vehicles, from one vehicle to all neighboring ones, from one vehicle to infrastructure components, from infrastructure to one or all neighboring vehicles, and dissemination from a vehicle to all those that are interested in the content.
We start by looking at ad-hoc routing protocols that were suggested in the early days of vehicular networks. Having identified their limitations, we explore alternatives, starting with geographic routing and geocast as communication primitives, and then exploring one of the most promising domains in the scope of vehicular networks: beaconing or one-hop broadcasting. In this framework, this chapter details the basic principles underlying such inter-vehicle communication (IVC) approaches, namely unicast, local broadcast, anycast, and multicast communication principles as used in vehicular networks. We of course investigate the current state of the standardization efforts, primarily focusing on the European Telecommunications Standards Institute (ETSI) standardization towards cooperative awareness messages (CAMs) and decentralized environmental notification messages (DENMs) as well as the ETSI GeoNetworking initiative. Furthermore, we explore options for exploiting available infrastructure such as roadside units (RSUs) or even parked vehicles to provide access to some backbone network or to help spread information among the vehicles. We conclude this chapter with a discussion of delay/disruption-tolerant network (DTN) approaches and the use of concepts known from Internet-based peer-to-peer networks.
This chapter is organized as follows.
• Ad-hoc routing (Section 5.1) – We start by exploring classical mobile ad-hoc network (MANET) routing algorithms in detail, because some basic knowledge of ad-hoc routing is needed in order to understand the more sophisticated vehicular ad-hoc network (VANET) routing options. In this section, we also discuss the applicability of MANET routing to VANETs as well as the specific challenges resulting from the underlying mobility pattern and delay constraints for vehicular safety applications.
The wireless technology multimedia sensor network (WMSN) is a multidisciplinary technology. Researchers from diverse fields such as wireless and ad-hoc sensor networks, multimedia, distributed signal processing, control theory, and embedded systems have contributed new ideas to innovate real-life applications. Advances in embedded electronics and MEMS have resulted in greater processing power per unit size of the sensor nodes in such a way that today it is possible to bring them together in a more powerful, complex, and compact manner. With the increased processing power, it is now able to capture and process complex multimedia data such as video, audio, and images. For example, with the help of new sensing technologies, it is possible to provide proper images and audio-visual feeds in different surveillance applications, in addition to the scalar parameters of environment. The easy availability of inexpensive miniaturized hardware such as cameras and microphones, the advancements in distributed signal processing, and multimedia source coding allow much richer sampling of the environment than traditional WSNs. This trend opened up new opportunities for protocol designers to propose effective and energy-efficient solutions of the existing problems [1–62].
Network applications
Applications of WMSNs introduce new multimedia features in addition to the general capabilities of WSNs, to enrich them in terms of monitoring. Along with enhancing the existing sensor network applications, WMSNs use various multimedia-specific ideas to pioneer new opportunities in this field. We classify these applications of WMSNs into several categories and explain them briefly in this chapter.
This paper presents a novel deformable mobile robot with five degrees of freedom (DOFs). The robot contains two equivalent expandable triangular platforms connected by three equivalent chains. Each platform is a regular triangle with a single DOF. Each chain consists of two links and three joints (one spherical joint at the middle of a chain, and one revolute joint at each end of the chain). Through kinematic and locomotion mode analysis, the robot exhibits three motion modes: worm-like, self-crossing, and rolling modes. The worm-like and self-crossing modes can be used for narrow passages (e.g., pipelines, holes, and caves). The rolling mode has three different directions at the initial state. By switching between these, the robot can operate on rough ground. To verify the locomotion modes and functionality of the robot, the results of a series of experiments performed on a manufactured prototype are reported.
Wireless sensor networks (WSNs) exhibit an “autocratic” operational policy with minimal human intervention. So, such networks must be self-configurable to maintain their autonomy. The requisitions of WSN-specific applications are quenched by temporal cooperation and coordination among the sensor nodes. Naturally, these nodes are expected to perpetuate a healthy intra-network infrastructure. However, they are power constrained with a bounded communication range and low computational ability. Hence, issues related to network infrastructure should be dynamically handled with efficacy.
Topology is a vital aspect of WSNs that needs attention for both network and fault management. In this chapter, we focus on two aspects of topology: (a) topology management, and (b) topology control.
Topology management is the process of deriving a simple graph of node connectivity by determining the inter-nodal links and virtual relationships for efficient operations within a network contour. Topology management aims at conserving the energy of the nodes and consequently extending the lifetime of the network with parallel maintenance of network connectivity.
Topology control of a WSN is a measure of the degree of network coverage and internode connectivity. Topology management and control might appear analogous. However, these two aspects are distinct, and so is their categorization, which we will discuss in this chapter.
Communication between vehicles (and between vehicles and available infrastructure) is the topic of this chapter. Essentially, we give a basic introduction to inter-vehicle communication (IVC) identifying the key concepts in the safety as well as in the non-safety application domains. For this, we derive all the relevant communication concepts and solutions for those applications and, most importantly, identify the requirements such as maximum delays, minimum dissemination range, or minimum data rates. Furthermore, we present an overview on the possible communication paradigms, such as whether information is to be exchanged without the help of any available infrastructure or whether infrastructure elements – roadside units (RSUs), parked vehicles, or even widely deployed cellular networks – can be used for the information exchange.
The scope of this chapter is to introduce IVC as an active research field. The main motivation is to become familiar with the field to a level that helps one to understanding the fundamental concepts and their limitations. All the communication principles outlined will be studied in greater detail in the following chapters.
This chapter is organized as follows.
• Applications (Section 3.1) – In this section, we introduce the field starting with typical applications for IVC. We will show that the scope and character of these applications vary widely, which complicates the development of common and generalized IVC protocols.
• Requirements and components (Section 3.2) – Starting from knowledge about IVC applications, we derive requirements on IVC solutions and study metrics to assess their effectiveness. In a second part, we introduce all the communication entities involved and possible mechanisms for information exchange.
• Concepts for inter-vehicle communication (Section 3.3) – This section can be regarded as the main part of this chapter. We broadly study all the communication principles and protocols that have been considered for IVC. This overview explains why the different protocols have been studied and what their main advantages and disadvantages are.
• Fundamental limits (Section 3.4) – We conclude this chapter by discussing fundamental limits for IVC.
Wireless sensor networks (WSNs) have fascinated both the research and development communities. Applications of WSNs have mushroomed in both civilian and military domains. The growth of wireless sensor networks was originally motivated by military applications; however, WSNs are now used in all kinds of civilian and industrial applications [1–20]. Currently, the overwhelming majority of WSNs measure scalar tangible phenomena such as humidity, pressure, temperature, movement, and pollutants. Typically, most WSNs are built for delay-tolerant and low-bandwidth applications. Hence, most research efforts have concentrated on this latter paradigm, which is often called terrestrial sensor networks. In any WSN application the main challenge is the lifetime of the network. A WSN structure includes a gateway that offers wireless connectivity back to the wired/fixed network.
In nearly all applications of the WSNs, they are used to monitor certain physical processes. They are deployed for measuring the temperature, air pressure, radiation of the human body, chemical reactions, movement of objects, vitals of the body, and so on. By doing this we can recover some important information of the boundaries that are often called edges. To keep track of the edge of a physical development, the recognition of a boundary is imperative. The issue of discovering the boundary is considered to be the first step for resolving the edge detection. In digital processing there are many methods for identifying the edge; however, they are not easy to implement in the WSNs’ environment since the sensor nodes are not equally spaced like pixels, and because of limited computational power and memory [3–6].
Wireless sensor networks (WSNs) use the same medium (which is the air) for wireless transmission that the nodes in a wireless local area network use. In order for nodes in a local area network to communicate properly, standard access protocols like IEEE 802.11, IEEE 802.15.4, and ZigBee, are available. However, these and other protocols cannot be directly applied to the wireless sensor area networks. The major difference is that, unlike the devices participating in local area networks, sensors are equipped with a small source of power (usually a battery), which drains very quickly. In addition, sensor nodes have limited resources of memory and computational power. Hence, there is a need to design new protocols for medium access control (MAC). This chapter investigates some of the major MAC protocols available [1–7].
Medium access control in wireless networks
There exists a very good set of standard protocols for wired and wireless area networks, which are proven to operate efficiently. There are many reasons for not using the wired and traditional wireless MAC protocols for WSNs. The standard protocols in wired local area networks use the well-known carrier sense multiple access with collision detection (CSMA/CD) scheme for individual stations to access the medium. This protocol, the IEEE 802.3, is known as Ethernet [1–46]. In this scheme, each station senses a medium for a random amount of time. If no activity is detected it starts its own transmission. If it detects any activity in the medium it defers its transmission until the activity ceases. If it senses a collision of packets from different nodes in the networks it backs off a random exponential amount of time and then starts contending for transmission using the backoff exponential algorithm. Slotted ALOHA networks traditionally use a time division multiple access (TDMA) scheme [9–10]. In this scheme, the time is divided into an equal number of slots such that a node is allowed to transmit only in its allotted slot. Here, the disadvantage is that a node is allowed to transmit if and only if it owns the slot; otherwise it has to listen to the medium for the data intended for it. Therefore, if the node has no data to send the bandwidth is simply wasted because there will be no activity for the amount of slot time allotted to a node with no data.
Radio access technologies like WiFi, IEEE 802.11p, and LTE form the basis of any communication stack, and the choice of technology heavily influences application performance. They are the topic of this chapter.
In general, two families of radio access technologies can be differentiated: those based on cellular networks and those based on short-range radio. Traditionally, these two families were conceptually vastly different. Cellular networks relied on central coordination, whereas short-range radio operated in a fully distributed fashion. Cellular networks used licensed spectrum, whereas short-range radio had to make do with unlicensed spectrum. These differentiations are no longer strictly true. Cellular networks are slowly moving towards (some) distributed control, while short-range radio, particularly for inter-vehicle communication (IVC), is profitting from infrastructure support and central services. Further, short-range radio for IVC can now rely on allocated dedicated spectrum. A new trend further blends licensed and unlicensed spectrum into spectrum that has primary users, which can access the spectrum with absolute priority, but one also allows its white spaces to be filled by non-primary users.
We will take a detailed look at the concepts and the underlying principles of representative radio access technologies from both families, always with a focus on their use in vehicular networks.
This chapter is organized as follows.
• Cellular networks (Section 4.1) – We start by following the evolution of the Third Generation Partnership Project (3GPP) family of cellular networks: from GSM, via UMTS, to LTE, with a perspective on future technologies. We will always be considering both halves of a cellular network, that is, the air interface and the radio access network (RAN), as well as the core network.
• Short-range radio technologies (Section 4.2) – We then turn towards a classical short-range radio technology, following the evolution of IEEE 802.11 wireless LAN (WLAN) and its many extensions. We discuss one extension in particular detail: IEEE 802.11p, which extended WLAN for use in vehicular networks. Lastly, we discuss efforts building on WLAN to provide a complete IVC protocol suite.
In the concurrency theory, various semantic equivalences on transition systems are based on traces decorated with some additional observations, generally referred to as decorated traces. Using the generalized powerset construction, recently introduced by a subset of the authors (Silva et al.2010 FSTTCS. LIPIcs8 272–283), we give a coalgebraic presentation of decorated trace semantics. The latter include ready, failure, (complete) trace, possible futures, ready trace and failure trace semantics for labelled transition systems, and ready, (maximal) failure and (maximal) trace semantics for generative probabilistic systems. This yields a uniform notion of minimal representatives for the various decorated trace equivalences, in terms of final Moore automata. As a consequence, proofs of decorated trace equivalence can be given by coinduction, using different types of (Moore-) bisimulation (up-to context).
In this paper, we propose and examine a force-resisting balance control strategy for a walking biped robot under the application of a sudden unknown, continuous force. We assume that the external force is acting on the pelvis of a walking biped robot and that the external force in the z-direction is negligible compared to the external forces in the x- and y-directions. The main control strategy involves moving the zero moment point (ZMP) of the walking robot to the center of the robot's sole resisting the externally applied force. This strategy is divided into three steps. The first step is to detect an abnormal situation in which an unknown continuous force is applied by examining the position of the ZMP. The second step is to move the ZMP of the robot to the center of the sole resisting the external force. The third step is to have the biped robot convert from single support phase (SSP) to double support phase (DSP) for an increased force-resisting capability. Computer simulations and experiments of the proposed methods are performed to benchmark the suggested control strategy.
In this paper, we prove some embedding theorems for LTL (linear-time temporal logic) and its variants: viz. some generalisations, extensions and fragments of LTL. Using these embedding theorems, we give uniform proofs of the completeness, cut-elimination and/or decidability theorems for LTL and its variants. The proposed embedding theorems clarify the relationships between some LTL-variations (for example, LTL, a dynamic topological logic, a fixpoint logic, a spatial logic, Prior's logic, Davies' logic and an NP-complete LTL) and some traditional logics (for example, classical logic, intuitionistic logic and infinitary logic).
We give a short and elementary proof of the Schröder–Simpson Theorem, which states that the space of all continuous maps from a given space X to the non-negative reals with their Scott topology is the cone-theoretic dual of the probabilistic powerdomain on X.
How do humans judge the creativeness of an artwork or other artifact? This article suggests that such judgments are based on the pleasures of an aesthetic experience, which can be modeled as a mathematical product of psychological arousal and appraisal. The arousal stems from surprise, and is computed as a marginal entropy using information theory. The appraisal assigns meaning, by which the surprise is resolved, and is computed as a posterior probability using Bayesian theory. This model is tested by obtaining human ratings of surprise, meaning, and creativeness for artifacts in a domain of advertising design. The empirical results show that humans do judge creativeness as a product of surprise and meaning, consistent with the computational model of arousal and appraisal. Implications of the model are discussed with respect to advancing artificial intelligence in the arts as well as improving the computational evaluation of creativity in engineering and design.
Retrenchment is a flexible model evolution formalism that compensates for the limitations imposed by specific formulations of refinement. Its refinement-like proof obligations feature additional predicates for accommodating design data describing the model change. The best results are obtained when refinement and retrenchment cooperate, the paradigmatic scheme for this being the commuting square or tower, in which ‘horizontal retrenchment rungs’ commute with ‘vertical refinement columns’ to navigate through a much more extensive design space than permitted by refinement alone. In practice, the navigation is accomplished through ‘square completion’ constructions, and we present and prove a full suite of square completion theorems.
In this paper we introduce stable systems of inclusions, which feature chosen arrows A ↪ B to capture the notion that A is a subobject of B, and proposes them as an alternative context to stable systems of monics to discuss partiality. A category C equipped with such a system $\mathscr{I}$, called an i-category, is shown to give rise to an associated category ∂(C,$\mathscr{I}$) of partial maps, which is a split restriction category whose restriction monics are inclusions. This association is the object part of a 2-equivalence between such inclusively split restriction categories and i-categories. $\mathscr{I}$ determines a stable system of monics $\mathscr{I}$+ on C, and, conversely, a stable system of monics $\mathscr{M}$ on C yields an i-category (C[$\mathscr{M}$],$\mathscr{M}$+), giving a 2-adjunction between i-categories and m-categories. The category of partial maps Par(C,$\mathscr{M}$) is isomorphic to the full subcategory of ∂(C[$\mathscr{M}$],$\mathscr{M}$+) comprising the objects of C, and ∂(C,$\mathscr{I}$) ≅ Par(C,$\mathscr{I}$+).
Continuous lattices were characterised by Martín Escardó as precisely those objects that are Kan-injective with respect to a certain class of morphisms. In this paper we study Kan-injectivity in general categories enriched in posets. As an example, ω-CPO's are precisely the posets that are Kan-injective with respect to the embeddings ω ↪ ω + 1 and 0 ↪ 1.
For every class $\mathcal{H}$ of morphisms, we study the subcategory of all objects that are Kan-injective with respect to $\mathcal{H}$ and all morphisms preserving Kan extensions. For categories such as Top0 and Pos, we prove that whenever $\mathcal{H}$ is a set of morphisms, the above subcategory is monadic, and the monad it creates is a Kock–Zöberlein monad. However, this does not generalise to proper classes, and we present a class of continuous mappings in Top0 for which Kan-injectivity does not yield a monadic category.
We developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superior F measure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.