1622745
Replication-based efficient data delivery scheme (red) for delay/fault-tolerant mobile sensor network (DFT-MSN)
The delay/fault-tolerant mobile sensor network (DFT-MSN) has been proposed recently for pervasive information gathering. DFT-MSN distinguishes itself from conventional sensor networks by several unique characteristics such as sensor mobility, loose connectivity, and delay/fault tolerability. The mainstream approaches for sensor networking/communication cannot be applied in DFT-MSN directly. In this paper we propose a replication-based efficient data delivery (RED) scheme based on erasure coding technology tailored for DFT-MSN. RED consists of two key components for data transmission and message management, respectively. The former makes decision on when and where to transmit data messages according to the delivery probability. The latter decides the optimal erasure coding parameters (including the number of data blocks and the needed redundancy) based on its current delivery probability, in order to achieve the desired data delivery ratio while minimizing overhead at the same time. Extensive simulation has been carried out for performance evaluation. Compared with other approaches, the proposed RED data delivery scheme achieves high message delivery ratio with low transmission overhead and data management complexity
17279570
Protocol Design and Optimization for Delay/Fault-Tolerant Mobile Sensor Networks
While extensive studies have been carried out in the past several years for many sensor applications, they cannot be applied to the network with extremely low and intermittent connectivity, dubbed the delay/fault-tolerant mobile sensor network (DFT-MSN). Without end-to-end connections due to sparse network density and sensor node mobility, routing in DFT-MSN becomes localized and ties closely to medium access control, which naturally calls for merging Layer 3 and Layer 2 protocols in order to reduce overhead and improve network efficiency. DFT-MSN is fundamentally an opportunistic network, where the communication links exist only with certain probabilities and become the scarcest resource. At the same time, the sensor nodes in DFT-MSN have very limited battery power like those in other sensor networks. Clearly, there is a tradeoff between link utilization and energy efficiency. To address this tradeoff, we develop a cross-layer data delivery protocol for DFT-MSN, which includes two phases, i.e., the asynchronous phase and the synchronous phase. In the first phase, the sender contacts its neighbors to identify a set of appropriate receivers. Since no central control exists, the communication in the first phase is contention-based. In the second phase, the sender gains channel control and multicasts its data message to the receivers. Furthermore, several optimization issues in these two phases are identified, with solutions provided to reduce the collision probability and to balance between link utilization and energy efficiency. Our results show that the proposed cross-layer data delivery protocol for DFT-MSN achieves a high message delivery ratio with low energy consumption and an acceptable delay.
5788616
On Mining Group Patterns of Mobile Users
In this paper, we present a group pattern mining approach to derive the grouping information of mobile device users based on the spatio-temporal distances among them. Group patterns of users are determined by a distance threshold and a minimum duration. To discover group patterns, we propose the AGP and VG-growth algorithms that are derived from the Apriori and FP-growth algorithms respectively. We further evaluate the efficiencies of these two algorithms using synthetically generated user movement data.
961760
Revisiting Colored Networks and Privacy Preserving Censorship
Reliable networks are obviously an important aspect of critical information infrastructures. Dolev-Dwork-Waarts-Yung linked research on reliable point-to-point networks with privacy and authenticity. In their threat model the adversary can only take over a number of nodes bounded by a threshold k. Hirt-Maurer introduced the concept of an adversary structure (i.e. the complement of an access structure). Kumar-Goundan-Srinathan-Rangan and Desmedt-Wang-Burmester generalized Dolev-Dwork-Waarts-Yung scenarios to the case of a general adversary structure. 
 
Burmester-Desmedt introduced a special adversary structure, now called a color based adversary structure. Their argument in favor of their model is that using automated attacks (such as worms), a vulnerability can be exploited on all computers in the network running the same platform (color). In their model the adversary can control all nodes that use up to k different platforms (or colors). 
 
We will demonstrate one of the limitations of their model. Although the family of color based adversary structures has a trivial representation which size grows polynomial in the size of the graph, we will demonstrate in this paper that deciding reliability issues and security issues are co-NP-complete. 
 
In most societies censorship is common. Indeed, for centuries it has often been viewed by authorities as an essential security tool. We apply the computational complexity result to study censorship. Authorities may require network designers to demonstrate the capability to censor the internet. We present a zero-knowledge interactive proof for the case of a color based adversary structure.
16439255
Privacy Aware Market Basket Data Set Generation: A Feasible Approach for Inverse Frequent Set Mining
Association rule mining has received a lot of attention in the data mining community and several algorithms were proposed to improve the performance of association rule or frequent itemset mining. The IBM Almaden synthetic data generator has been commonly used for performance evaluation. One recent work shows that the data generated is not good enough for benchmarking as it has very different characteristics from real-world data sets. Hence there is a great need to use real-world data sets as benchmarks. However, organizations hesitate to provide their data due to privacy concerns. Recent work on privacy preserving association rule mining addresses this issue by modifying real data sets to hide sensitive or private rules. However, modifying individual values in real data may impact on other, non-sensitive rules. In this paper, we propose a feasible solution to the NPcomplete problem of inverse frequent set mining. Since solving this problem by linear programming techniques is very computationally prohibitive, we apply graph-theoretical results to divide the original itemsets into components that preserve maximum likelihood estimation. We then use iterative proportional fitting method to each component. The technique is experimentally evaluated with two real data sets and one synthetic data set. The results show that our approach is effective and efficient for reconstructing market basket data set from a given set of frequent itemsets while preserving sensitive information.
18778381
Block truncation coding with adaptive decimation and interpolation
A modification of block truncation coding (BTC) is proposed in this paper. The modification is with a novel adaptive decimation and interpolation method, and predictive entropy coding of the quantization data. The decimation algorithm is designed based on the directional gradients of image blocks so as (1) to preserve the edge information, (2) to enable the interpolator utilize the already reconstructed data in decoder and (3) to better predict the mean value of a block. The quantization data is not directly coded by the two reconstruction levels, but by the block mean and the difference between the block mean and the lower reconstruction level, to further reduce the bit-rate. Compared to other interpolative methods, the new decimation and interpolation method substantially improves the image quality and removes the annoying blocky artifact at the same time. The proposed scheme, which has low computational complexity, is shown to have comparable or better performance to/than BTC methods combined with vector quantization/discrete cosine transform and the most recently BTC modification that utilize the inter-block correlation.
2617111
Efficient Algorithms for Traffic Grooming in SONET/WDM Networks
In SONET/WDM optical networks, a wavelength channel is shared by multiple low-rate traffic demands. The multiplexing is known as traffic grooming and carried out by SONET add-drop multiplexers (SADM). A key optimization problem in traffic grooming is to minimize the number of SADMs. This optimization problem is challenging and NP-hard even for unidirectional SONET/WDM rings (UPSR) with symmetric unitary traffic demands. In this paper, we give a linear time heuristic algorithm for this NP-hard problem. Empirical results show that the algorithm outperforms previous algorithms. The algorithm uses the minimum number of wavelengths, which are also precious resources in optical networks. An important subclass of the symmetric unitary traffic pattern is the regular traffic pattern, where each network node appears in exactly r symmetric demands. The regular traffic pattern is a generalization of the well known all-to-all traffic pattern, in which r = n - 1 for a network of n nodes. We prove that the optimization problem remains NP-hard for the regular traffic pattern on the UPSR. We also propose an algorithm for this problem with a better upper bound on the number of used SADMs than previous algorithms. This algorithm always uses the minimum number of wavelengths as well