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Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.

Modern algorithms for mining frequent itemsets face noteworthy deterioration of performance when minimum support tends to decrease, especially for sparse datasets. Long-tailed itemsets, frequent itemsets found at lower minimum support, are significant for present-day applications such as recommender systems. In this study, we have developed a novel power set based method named as HARnessing the Power of Power sets (HARPP) for mining frequent itemsets. HARPP iteratively generates power sets to make combinations of overlapping varying-sized subsets of I, where I is a set of items in a large database. Intrinsic feature of creating power sets along with the use of set data structure ensures the agility of HARPP because most of its operations take constant running time. Without storing it entirely in memory, HARPP scans the dataset only once and mines frequent itemsets on the fly. In contrast to state-of-the-art, efficiency of HARPP increases with decrease in minimum support that makes it a viable technique for mining long-tailed itemsets. Performance study shows that HARPP is efficient and scalable, and is faster up to two orders of magnitude than FP-Growth algorithm at lower minimum support particularly when datasets are sparse.

Edit distance has applications in many domains such as bioinformatics, spell checking, plagiarism checking, query optimization, speech recognition, and data mining. Traditionally, edit distance is computed by dynamic programming based sequential solution which becomes infeasible for large problems. In this paper, we introduce NvPD, a novel algorithm for parallel edit distance computation by resolving dependencies in the conventional dynamic programming based solution. We also establish the correctness of modified dependencies. NvPD exhibits certain characteristics such as balanced workload among processors, less synchronization overhead, maximum utilization of resources and it can exploit spatial locality. It requires $$\min (m,n)$$ steps to complete as compared to diagonal based approach that completes in $$\max (m,n)$$ . Experimental evaluation using variety of random and real life data sets over shared memory multi-core systems and graphic processing units (GPUs) show that NvPD outperforms state-of-the-art parallel edit distance algorithms.

Confidentiality and authentication are primary concerns of RFID (Radio Frequency IDentification) based identification systems. Low cost, non sight-line scanning capability, unique identification and functional haste make RFID systems much more suitable for supply chain management and IoT networks, compare to barcodes and magnetic tapes. Since, RFID tags can be scanned from a greater distance over a radio channel which is easily accessible for adversaries, therefore numerous Ultralightweight Mutual Authentication Protocols (UMAPs) have been proposed to ensure the security of the RFID systems in a cost effective way. These UMAPs mainly involve low cost bitwise logical ( $$Triangular$$ ) operations such as $$XOR, AND OR$$ etc., usually these logical operations offer poor diffusion properties and hence result in disclosure of the concealed secrets. Almost all of the previous UMAPs have certain pitfalls and reported to be susceptible to Denial of Service, traceability, full disclosure and cloning attacks. In this paper, we propose a new UMAP that is resilient to structured and unstructured cryptanalysis techniques. The proposed Ultralightweight Resilient Mutual Authentication Protocol (URMAP) avoids all the pitfalls and unbalanced operators in its design that were the main reasons of failure of previous UMAPs. The incorporation of ultralightweight primitives; $$Per{\text{-}}XOR$$ ( $${P}_{x})$$ and $$Inverse Per{\text{-}}XOR$$ $$({ P}_{x}^{-1})$$ makes the protocol messages more resistant to all types of adversaries. The security and privacy capabilities of URMAP are evaluated through formal analysis. The results of GNY Logic analysis prove that the proposed protocol is resilient to all possible attack scenarios thus ensuring optimal confidentiality, integrity and availability services.

With the rapid development of mobile network and devices, spatial crowdsourcing (SC) has recently attracted much attention. For the improvement of quality of service (QoS) in spatial crowdsourcing platforms, existing works usually adopt the many-to-one strategy - assigning multiple workers as a team for each published task. However, such an allocation scheme fails to consider team characteristics which can strongly affect the QoS for some experience-sensitive collaborative tasks. In this paper, we jointly consider two team characteristics to further improve the QoS: Diversity, which is the union of experiences within a team and Affinity, which is how efficiently team members collaborate. Inspired by these two characteristics, we study an important problem, namely, Affinitive Diversity-Aware Spatial Crowdsourcing (ADA-SC), which aims to find an allocation scheme, such that each team satisfies the affinity requirement of the corresponding task and maximizes the team diversity under budget and spatial constraints. Since ADA-SC is proven to be NP-hard by reduction from the set cover problem with nonlinear constraints, we propose two submodular approximation algorithms with pruning strategies for two single-task scenarios. Then a greedy-based algorithm is designed for the multi-task scenario. Extensive experiments on real and synthetic data verify the effectiveness of our proposed methods.

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.

Radio Frequency IDentification (RFID) is one of the most promising identification schemes in the field of pervasive systems. Unique identification and non-line of sight capabilities make RFID systems more protuberant than its contending systems. As RFID systems incorporate wireless channel, there are some allied security threats and apprehensions to the systems from malicious adversaries. In order to make the system reliable and secure, numerous Ultralightweight Mutual Authentication Protocols (UMAPs) have been proposed which involve only simple bitwise logical operations (AND, XOR & OR etc.) in their designs. However, almost all of the previously proposed UMAPs are reported to be vulnerable against various security attacks (Desynchronization and Full disclosure attacks etc.). In this paper, we propose a new pseudo-Kasami code based Mutual Authentication Protocol (KMAP). The proposed protocol, KMAP, avoids unbalanced logical operations (OR, AND) and introduces a new Ultralightweight primitive: pseudo-Kasami code (Kc). The newly proposed primitive (pseudo-Kasami code) enhances the diffusion properties of the protocol messages and makes hamming weight of the secrets unpredictable and irreversible. The security analysis illustrates that the KMAP provides excellent protocol functionalities and is also highly resistive against all possible attacks. The performance evaluation shows that the KMAP requires fewer resources on the tag in terms of on-chip memory, communication cost and computational operations.

Named data networking (NDN) is an emerging technology. It was designed to eliminate the dependency of IP addresses in the hourglass model. Mobility is a key concern of the modern Internet architecture, even though the NDN architecture has solved the consumer mobility. That is, the consumer can rerequest the desired data contents, while the producer mobility remains as an issue in the NDN architecture. This paper focuses on the issue of producer mobility and proposes the cluster-based device mobility management scheme, which uses the cluster heads to solve the producer mobility issue in NDN. In the proposed scheme, a cluster head has all information of its attached devices. A cluster head updates the routes, when a device moves to the new access router by sending all the attachment information. The proposed scheme is evaluated and compared with the existing scheme by using the ndnSIM simulation. From the results, we see that the proposed scheme can decrease the numbers of interest packets in the network, compared with the existing scheme.

In optical burst-switched (OBS) networks, packets are aggregated into bursts that are sent an offset time after the corresponding control packet. The process of aggregating high and low-priority traffic in the same burst is known as composite burst assembly, where high-priority and low-priority packets are placed at the head and the tail end of the burst, respectively. Such composite bursts are beneficial in order to resolve the contention by dropping low-priority packets when preemptive minimum overlap channel (P-MOC) scheduling algorithms are used. For non-preemptive minimum overlap channel with void-filling (NP-MOC-VF) scheduling algorithm, we propose that high-priority packets should be placed in the middle of a burst and low-priority packets at the head and tail ends of a burst. That allows dropping of low-priority packets from the head and the tail of a burst when it contends with two other bursts where the head of the burst contends with one burst and the tail contends with another. The simulation results show that high-priority traffic faces reduced loss at the cost of increased loss of low-priority traffic. In this paper, the effect of varying the proportion of high-priority packets in the middle of the burst has been studied.

We present performance evaluation of an optical packet/burst switch with internal fiber rings. Such a switch consists of multiple fabrics linked together by direct interconnections and optical delay lines. Having more number of optical delay lines and direct interconnections per fabric pair reduces contention and results in a reduced blocking probability. However, due to the size and cost of optical delay lines, it is desirable to minimize their number for a specific blocking probability. We propose an analytical model for the switch in order to study its scalability and packet/burst blocking probability. The switch is then dimensioned for given values of blocking probability and a given range of input load. Simulation results concerning switch scalability by taking into account the most relevant physical effects are also shown. We then consider the minimal practical switch size and discuss how the switch can be dimensioned with a combination of delay lines and direct interconnections to achieve our target of a certain blocking probability and range of input load using a minimum number of fiber delay lines.

Optical burst switching is a technique for transmitting several packets, through an optical transport network, by assembling them in large data containers, known as bursts. These bursts can be of variable length and are separated from their headers. The header, known as control packet, is sent through the network using a separate control channel, to reserve resources for the data burst that follows a short while later. This process demands efficient contention resolution, which can be done by optical buffering, wavelength conversion, deflection routing or burst dropping. The first three techniques, however, require additional resources in the network and/or nodes. In case additional resources are not available, or are scarce, it is beneficial to resolve contention using the burst dropping scheme. In this technique, whenever the transmission time of two or more bursts overlaps, one of the bursts needs to be dropped. In most of the cases, however, this overlap will only be partial, and it is more beneficial to drop only that part of the burst which overlaps with the other burst. This technique is known as segmentation-based dropping. This paper looks at burst dropping techniques in detail and compares their performance in terms of the burst blocking probabilities. Results show that segmentation-based dropping technique offers a significant improvement over the simple burst dropping technique.