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With the surge in the demand for online services and multimedia applications, the traffic on the underlying network infrastructure has escalated (multi-folded) in recent years. To meet the strict latency requirements, Software-defined Networking (SDN) provides flexible network control (and possible intelligence) that can act as an enabler for application-oriented service industry. However, the crippling gap between the business needs and the network delivery potential necessitates the underlying network to constantly (and consistently) adapt, protect, and inform across all strands of the service-oriented landscape. Intent-based network has emerged as a recent solution to the cover the above gap by capturing business intent and thereafter activating and assuring it networkwide. Motivated from these facts, in this article, an Intent-based network control framework has been designed over the SDN architecture for data dissemination in the vehicular edge computing ecosystem. In this framework, a tensor-based mechanism is used to reduce the dimensionality of the incoming elephant-like traffic and then classifying the specific-attribute data traffic according to the defined priority requirement of the underlying applications. Here, the network policies are configured using the intent-based controller according to the application requirement and then forwarded to the SDN controller to enable intelligent data dissemination (through an optimal route) at the data plane. Convolution Neural Network is used to train the flow table to allocate the route dynamically for the classified traffic queues. The proposed framework has been evaluated through extensive simulations and the results supports the claims in terms of the quality of service requirements. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) has escalated the movement of big data across moving vehicles which create a huge burden on the network infrastructure. In IoV environment, effective handling of streaming data has to face various challenges like; traffic monitoring, flow management, re-configuration and security. Software-defined networks (SDN) provides improved flexibility, and centralized control of the network to overcome (almost) the above-mentioned challenges. However, it can lead to an easy target (node or controller) for malicious agents. So, to detect the anomalous behaviour of the nodes in the IoV environment, a hybrid approach using probabilistic data structures is proposed which works in the following phases. In phase I, a traffic monitoring scheme using Count-Min-Sketch is designed to identify the suspicious nodes. In phase II, to detect an anomaly, a Bloom filter-based control scheme is used for signature verification of suspicious nodes. In phase III, a Quotient filter is used for fast and efficient storage of malicious nodes. In phase IV, to detect the super points (malicious hosts that are connected to a large number of destinations), a Hyperloglog counter is used to measure the cardinality of each flow passing through the switches. The proposed scheme has been evaluated in a simulated environment. The results obtained depict that the proposed scheme is faster, accurate, and efficient concerning detection ratio and false-positive ratio. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) provides an efficient way of managing traffic load by shifting complex and rigid computing tasks to the centralized controller. It reduces the burden on the switches, task of which is to perform the routing based upon the rule–action pair. However, the flow table storage capacity of switches is limited. It may have to face performance bottlenecks, which, in turn, can cause serious security breaches and performance degradation. Hence, in this article, BloomStore, which is a dynamic bloom-filter-based secure rule-space management scheme in SDN, is proposed. BloomStore handles the data traffic dynamically by managing network resources. A twofold security check is used for secure data transfer using double hashing, i.e., two independent hash functions are used to generate $k$ hash functions. Moreover, partitioned hashing is proposed to have insertion and query in a bucket of bloom array. The result analysis demonstrates that BloomStore outperforms its competing variants with respect to various performance parameters. [ABSTRACT FROM AUTHOR]

Bounds on the frequency domain behavior of electrically small antennas are adapted to assess the time-domain distortion or fidelity achievable by simple linear time-invariant (LTI) systems. Rigorous expressions for a TM01 spherical shell are used as a direct analog to the well-known Chu limit on an antenna’s bandwidth-efficiency product. These expressions are shown to agree with results obtained using narrowband single resonance approximations, allowing for the analysis of arbitrary small dipole-like structures through bounds on their single-frequency Q-factor and efficiency. The resulting expressions are used as a basis for assessing the performance gains of electrically small non-LTI (e.g., direct antenna modulation) transmitters for which Q-factor and impedance bandwidth are not defined but which can be analyzed directly in the time domain via distortion. [ABSTRACT FROM AUTHOR]

There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance. [ABSTRACT FROM AUTHOR]

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time. [ABSTRACT FROM AUTHOR]

With the exponential growth of technologies such as IoT, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart citiy environment in recent years. Thus, there is a need to develop sophisticated techniques that can efficiently process such huge volumes of data. One of the important components of smart cities, ITS, has led to many applications, including surveillance, infotainment, real-time traffic monitoring, and so on. However, its security, performance, and availability are major concerns facing the research community. The existing solutions, such as cellular networks, RSUs, and mobile cloud computing, are far from perfect because these are highly dependent on centralized architecture and bear the cost of additional infrastructure deployment. Also, the conventional methods of data processing are not capable of handling dynamic and scalable data efficiently. To mitigate these issues, this article proposes an advanced vehicular communication technique where RSUs are proposed to be replaced by edge computing platforms. Then secure V2V and V2E communication is designed using the Quotient filter, a probabilistic data structure. In summary, a smart security framework for VANETs equipped with edge computing nodes and 5G technology has been designed to enhance the capabilities of communication and computation in the modern smart city environment. It has been experimentally demonstrated that use of edge nodes as an intermediate interface between vehicle and cloud reduces access latency and avoids congestion in the backbone network, which allows quick decisions to be made based on the traffic scenario in the geographical location of the vehicles. The proposed scheme outperforms the conventional vehicular models by providing an energy-efficient secure system with minimum delay. [ABSTRACT FROM AUTHOR]

Over the last few years, we have witnessed an exponential increase in the computing and storage capabilities of smart devices that has led to the popularity of an emerging technology called edge computing. Compared to the traditional cloud-computing- based infrastructure, computing and storage facilities are available near end users in edge computing. Moreover, with the widespread popularity of unmanned aerial vehicles (UAVs), huge amounts of information will be shared between edge devices and UAVs in the coming years. In this scenario, traffic surveillance using UAVs and edge computing devices is expected to become an integral part of the next generation intelligent transportation systems. However, surveillance in ITS requires uninterrupted data sharing, cooperative decision making, and stabilized network formation. Edge computing supports data processing and analysis closer to the deployed machines (i.e., the sources of the data). Instead of simply storing data and missing the opportunity to capitalize on it, edge devices can analyze data to gain insights before acting on them. Transferring data from the vehicle to the edge for real-time analysis can be facilitated by the use of UAVs, which can act as intermediate aerial nodes between the vehicles and edge nodes. However, as the communication between UAVs and edge devices is generally done using an open channel, there is a high risk of information leakage in this environment. Keeping our focus on all these issues, in this article, we propose a data-driven transportation optimization model where cyber-threat detection in smart vehicles is done using a probabilistic data structure (PDS)- based approach. A triple Bloom filter PDS- based scheduling technique for load balancing is initially used to host the real-time data coming from different vehicles, and then to distribute/collect the data to/from edges in a manner that minimizes the computational effort. The results obtained show that the proposed system requires comparatively less computational time and storage for load sharing, authentication, encryption, and decryption of data in the considered edge-computing-based smart transportation framework. [ABSTRACT FROM AUTHOR]

We investigate the effect of the micromagnetic exchange tensor on the magnetization switching in bi-layer rectangular dots. The micromagnetic model adopted in this work is used to calculate the in-plane and out-of-plane hysteresis loops based on the fundamental magnetic parameters of hcp CoCrPt for a wide range of elemental compositions corresponding to magnetically hard and semi-hard cases. The exchange interaction term in micromagnetic energy functional is constructed to capture the results of the first principles calculations that show large difference between the in-plane and out-of-plane constants of the micromagnetic exchange tensor for hcp Co. In our calculations we compare two cases for in-plane and perpendicular exchange constants: (a) A\Vert = A\bot and (b) A\Vert = 0.5\ A\bot, and find that the athermal coercive force for 30\,\times\,30 nm bi-layer rectangular dots differs for these two cases. Further, we find that the magnetization switching changes from curling mode in the case (a) to the vortex formation in the case (b). We also find that these differences in magnetization switching become more pronounced if interface exchange between hard and soft layers is increased to the bulk value. [ABSTRACT FROM AUTHOR]

We investigate the effect of crystal symmetry driven tensor form of micromagnetic exchange stiffness A on switching properties of L10 FePt composite nano-structures. The exchange interaction term in micromagnetic energy functional have been constructed to capture the results of the first principle calculations for magnetic excitations in [001] and [100] directions. We find that out-of-plane (OP) exchange component (A\bot) is about two times weaker as compared to the in-plane (IP) component (A\Vert). We study the effect of this difference between principal axis elements of A tensor on the M-H loops using micromagnetic simulations. We compare two cases for IP and OP exchange constants: (a) A\bot = A\Vert (limited case) and (b) A\bot = 0.5\ A\Vert (based on ab-initio calculations), and find that the athermal coercive force for 30\,\times\,30 nm bi-layer rectangular dots differs for these two cases. Further, we find that the coercive force difference between two cases decreases as the external field goes from OP to IP. [ABSTRACT FROM AUTHOR]