Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

On eliminating packet droppers in MANET: A modular solution

In this paper we deal with misbehaving nodes in mobile ad hoc networks (MANETs) that drop packets supposed to be relayed, whose purpose may be either saving their resources or launching a DoS attack. We propose a new solution to monitor, detect, and safely isolate such misbehaving nodes, structured around five modules: (i) The monitor, responsible for controlling the forwarding of packets, (ii) the detector, which is in charge of detecting the misbehaving of monitored nodes, (iii) the isolator, basically responsible for isolating misbehaving nodes detected by the detector, (iv) the investigator, which investigates accusations before testifying when the node has not enough experience with the accused, and (v) finally the witness module that responds to witness requests of the isolator. These modules are based on new approaches, aiming at improving the efficiency in detecting and isolating misbehaving nodes with a minimum overhead. We describe these modules in details, and their interactions as well. We also mathematically analyze our solution and assess its performance by simulation, and compare it with the watchdog, which is a monitoring technique employed by almost all the current solutions.     

Mining of High-Utility Patterns in Big IoT-based Databases

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster.

     

Delay-efficient MAC protocol with traffic differentiation and run-time parameter adaptation for energy-constrained wireless sensor networks

This paper presents an asynchronous cascading wake-up MAC protocol for heterogeneous traffic gathering in low-power wireless sensor networks. It jointly considers energy/delay optimization and switches between two modes, according to the traffic type and delay requirements. The first mode is high duty cycle, where energy is traded-off for a reduced latency in presence of realtime traffic (RT). The second mode is low duty cycle, which is used for non-realtime traffic and gives more priority to energy saving. The proposed protocol, DuoMAC, has many features. First, it quietly adjusts the wake-up of a node according to (1) its parent’s wake-up time and, (2) its estimated load. Second, it incorporates a service differentiation through an improved contention window adaptation to meet delay requirements. A comprehensive analysis is provided in the paper to investigate the effectiveness of the proposed protocol in comparison with some state-of-the-art energy-delay efficient duty-cycled MAC protocols, namely DMAC, LL-MAC, and Diff-MAC. The network lifetime and the maximum end-to-end packet latency are adequately modeled, and numerically analyzed. The results show that LL-MAC has the best performance in terms of energy saving, while DuoMAC outperforms all the protocols in terms of delay reduction. To balance the delay/energy objectives, a runtime parameter adaptation mechanism has been integrated to DuoMAC. The mechanism relies on a constrained optimization problem with energy minimization in the objective function, constrained by the delay required for RT. The proposed protocol has been implemented on real motes using MicaZ and TinyOS. Experimental results show that the protocol clearly outperforms LL-MAC in terms of latency reduction, and more importantly, that the runtime parameter adaptation provides additional reduction of the latency while further decreasing the energy cost.     

Struggling against selfishness and black hole attacks in MANETs

Since mobile ad hoc networks (MANETs) are infrastructureless and multi‐hop by nature, transmitting packets from any node to another usually relies on services provided by intermediate nodes. This reliance introduces a new vulnerability; one node could launch a Black Hole DoS attack by participating in the routing protocol and including itself in routes, then simply dropping packets it receives to forward. Another motivation for dropping packets in self‐organized MANETs is resource preservation. Some solutions for detecting and isolating packet droppers have been recently proposed, but almost all of them employ the promiscuous mode monitoring approach (watchdog (WD)) which suffers from many problems, especially when employing the power control technique. In this paper we propose a novel monitoring approach that overcomes some WD's shortcomings, and improves the efficiency in detection. To overcome false detections due to nodes mobility and channel conditions we propose a Bayesian technique for the judgment, allowing node redemption before judgment. Finally, we suggest a social‐based approach for the detection approval and isolation of guilty nodes. We analyze our solution and asses its performance by simulation. The results illustrate a large improvement of our monitoring solution in detection versus the WD, and an efficiency through our judgment and isolation techniques as well. Copyright © 2007 John Wiley & Sons, Ltd.     

Fast distributed multi-hop relative time synchronization protocol and estimators for wireless sensor networks

The challenging problem of time synchronization in wireless sensor networks is considered in this paper, where a new distributed protocol is proposed for both local and multi-hop synchronization. The receiver-to-receiver paradigm is used, which has the advantage of reducing the time-critical-path and thus improving the accuracy compared to common sender-to-receiver protocols. The protocol is fully distributed and does not rely on any fixed reference. The role of the reference is divided amongst all nodes, while timestamp exchange is integrated with synchronization signals (beacons). This enables fast acquisition of timestamps that are used as samples to estimate relative synchronization parameters. An appropriate model is used to derive maximum likelihood estimators (MLE) and the Cramer-Rao lower bounds (CRLB) for both the offset-only, and the joint offset/skew estimation. The model permits to directly estimating relative parameters without using or referring to a reference’ clock. The proposed protocol is extended to multi-hop environment, where local synchronization is performed proactively and the resulted estimates are transferred to the intermediate/end-point nodes on-demand, i.e. as soon as a multi-hop communication that needs synchronization is initiated. On-demand synchronization is targeted for multi-hop synchronization instead of the always-on global synchronization model, which avoids periodic and continuous propagation of synchronization signals beyond a single-hop. Extension of local MLE estimators is proposed to derive relative multi-hop estimators. The protocol is compared by simulation to some state-of-the-art protocols, and results show much faster convergence of the proposed protocol. The difference has been on the order of more than twice compared to CS-MNS, more than ten times compared to RBS, and more than twenty times compared to TPSN. Results also show scalability of the proposed protocol concerning the multi-hop synchronization. The error does not exceed few microseconds for as much as 10 hops in R4Syn, while in CS-MNS, and TPSN, it reaches few tens of microseconds. Implementation and tests of the protocol on real sensor motes confirm microsecond level precision even in multi-hop scenarios, and high stability (long lifetime) of the skew/offset model.     

A new framework for metaheuristic-based frequent itemset mining

This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining (FIM), which considers intrinsic features of the FIM problem. The framework, called META-GD, can be used to steer any metaheuristics-based FIM approach. Without loss of generality, three metaheuristics are considered in this paper, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO). This allows to derive three approaches, named GA-GD, PSO-GD, and BSO-GD, respectively. An extensive experimental evaluation on medium and large database instances reveal that PSO-GD outperforms state-of-the-art metaheuristic-based approaches in terms of runtime and solution quality.     

BOD‐LEACH: broadcasting over duty‐cycled radio using LEACH clustering for delay/power efficient dissimilation in wireless sensor networks

Broadcasting delay‐sensitive information over a duty‐cycled wireless sensor network is considered, and a cluster‐based protocol is proposed. The proposed protocol, namely Broadcast over Duty‐Cycle and LEACH (BOD‐LEACH), takes advantage of the LEACH's energy‐efficient clustering. This approach shifts the total burden of energy consumption of a single cluster head by rotating the cluster‐head role among all nodes in the network. It also permits the ordinary (member) nodes in a cluster to turn off their radios whenever they enter inactive TDMA slots. However, LEACH does not consider broadcast messages, and the member nodes scheduling is established as a sequence of Time Division Multiple Access (TDMA) without any common active period. A broadcast message should then be postponed to the next TDMA schedule and transmitted in a sequence of unicast messages, which is inefficient in terms of latency, bandwidth occupation, and power consumption. The proposed protocol adds new common static and dynamic broadcast periods to support and accelerate broadcasting. The dynamic periods are scheduled following the past arrivals of messages and using a Markov chain model. To our knowledge, this work is the first that proposes the use of clustering to perform simultaneous local broadcasts at several clusters. This reduces broadcast latency and ensures scalability. The protocol has been simulated, numerically analyzed, and compared with LEACH. The results show clear improvement over LEACH with regard to broadcast latency, at a low energy cost. Copyright © 2013 John Wiley & Sons, Ltd.