Clustering provides an effective way to prolong the lifetime of wireless sensor networks. One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network. Another is the mode of inter-cluster communication. In this paper, an energy-balanced unequal clustering (EBUC) protocol is proposed and evaluated. By using the particle swarm optimization (PSO) algorithm, EBUC partitions all nodes into clusters of unequal size, in which the clusters closer to the base station have smaller size. The cluster heads of these clusters can preserve some more energy for the inter-cluster relay traffic and the ‘hot-spots’ problem can be avoided. For inter-cluster communication, EBUC adopts an energy-aware multihop routing to reduce the energy consumption of the cluster heads. Simulation results demonstrate that the protocol can efficiently decrease the dead speed of the nodes and prolong the network lifetime.
Network coding, which exploits the broadcast nature of wireless medium, is an effective way to improve network performance in wireless multi-hop networks, but the first practical wireless network coding system COPE cannot actively detect a route with more coding opportunities and limit the coding structure within two-hop regions. An on-demand coding-aware routing scheme (OCAR) for wireless Mesh networks is proposed to overcome the limitations specified above by actively detecting a route with more coding opportunities along the entire route rather than within two-hop regions. Utilizing more coding opportunities tends to route multiple flows ‘close to each other’ while avoiding interference requires routing multiple flows ‘away from each other’. OCAR achieves a tradeoff by adopting as routing metric in route discovery, which is not only coding-aware but also considers both inter and intra flow interference. Simulation results show that, compared with Ad-hoc on-demand distance vecfor routing (AODV) and AODV+COPE, OCAR can find more coding opportunities, thus effectively increase network throughput, reduce end to end delay and alleviate network congestion.
This paper reviews multi-channel media access control (MAC) protocols based on IEEE 802.11 in wireless Mesh networks (WMNs). Several key issues in multi-channel IEEE 802.11-based WMNs are introduced and typical solutions proposed in recent years are classified and discussed in detail. The experiments are performed by network simulator version 2 (NS2) to evaluate four representative algorithms compared with traditional IEEE 802.11. Simulation results indicate that using multiple channels can substantially improve the performance of WMNs in single-hop scenario and each node equipped with multiple interfaces can substantially improve the performance of WMNs in multi-hop scenario.
Energy-efficient communication is an important requirement for mobile relay networks due to the limited battery power of user terminals. This paper considers energy-efficient relaying schemes through selection of mobile relays in cooperative cellular systems with asymmetric traffic. The total energy consumption per information bit of the battery-powered terminals, i.e., the mobile station (MS) and the relay, is derived in theory. In the joint uplink and downlink relay selection (JUDRS) scheme we proposed, the relay which minimizes the total energy consumption is selected. Additionally, the energy-efficient cooperation regions are investigated, and the optimal relay location is found for cooperative cellular systems with asymmetric traffic. The results reveal that the MS-relay and the relay-base station (BS) channels have different influence over relay selection decisions for optimal energy-efficiency. Information theoretic analysis of the diversity-multiplexing tradeoff (DMT) demonstrates that the proposed scheme achieves full spatial diversity in the quantity of cooperating terminals in this network. Finally, numerical results further confirm a significant energy efficiency gain of the proposed algorithm comparing to the previous best worse channel selection and best harmonic mean selection algorithms.
With the broad implementations of the electronic business and government applications, robust system security and strong privacy protection have become essential requirements for remote user authentication schemes. Recently, Chen et al. showed that Wang et al.’s scheme is vulnerable to the user impersonation attack and parallel session attack, and proposed an enhanced version to overcome the identified security flaws. In this paper, however, we show that Chen et al.’s scheme still cannot achieve the claimed security goals and report its following problems: (1) It suffers from the offline password guessing attack, key compromise impersonation attack and known key attack; (2) It fails to provide forward secrecy; (3) It is not easily repairable. As our main contribution, a robust dynamic ID-based scheme based on non-tamper resistance assumption of the smart cards is presented to cope with the aforementioned defects, while preserving the merits of different related schemes. The analysis demonstrates that our scheme meets all the proposed criteria and eliminates several grave security threats that are difficult to be tackled at the same time in previous scholarship.
This paper proposes rate-maximized (MR) joint subcarrier pairing (SP) and power allocation (PA) (MR-SP&PA), a novel scheme for maximizing the weighted sum rate of the orthogonal-frequency-division multiplexing (OFDM) relaying system with a decode-and-forward (DF) relay. MR-SP&PA is based on the joint optimization of both SP and power allocation with total power constraint, and formulated as a mixed integer programming problem in the paper. The programming problem is then transformed to a convex optimization problem by using continuous relaxation, and solved in the Lagrangian dual domain. Simulation results show that MR-SP&PA can maximize the weighted sum rate under total power constraint and outperform equal power allocation (EPA) and proportion power allocation (PCG).
One of the key issues for radio resources management is network selection strategy in heterogeneous scenarios. In order to provide ubiquitous service, the paper puts forward a network selection algorithm based on multiple attribute decision making (MADM) and group decision making (GDM). Firstly, the proposed algorithm acquires attribute weights’ vectors of the subjective and objective decision makers based on MADM, and then the two attribute weights’ vectors are synthesized to be a new attribute weights’ vector by using GDM. Considering that the results of GDM should be reasonable and convincible, the criterion of consistency is adopted for judging the compatibility of group judgments. More specifically, the algorithm takes into account not only objective attributes of networks but also the preference of subscribers and traffic class. Hence it guarantees that the subscribers can not select the networks with poor performance depending on their preference. The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide subscribers with satisfactory quality of service (QoS).
Since the year of 2006, peer-to-peer (P2P) streaming media service has been developing rapidly, the user scale and income scale achieve synchronous growth. However, while people enjoying the benefits of the distributed resources, a great deal of network bandwidth is consumed at the same time. Research on P2P streaming traffic characteristics and identification is essential to Internet service providers (ISPs) in terms of network planning and resource allocation. In this paper, we introduce the current common P2P traffic detection technology, and analyze the payload length distribution and payload length pattern in one flow of four popular P2P streaming media applications. Combining with the deep flow inspection and machine learning algorithm, a nearly real-time identification approach for P2P streaming media is proposed. The experiments proved that this approach can achieve a high accuracy with low false positives.
Recently, echo state networks (ESN) have aroused a lot of interest in their nonlinear dynamic system modeling capabilities. In a classical ESN, its dynamic reservoir (DR) has a sparse and random topology, but the performance of ESN with its DR taking another kind of topology is still unknown. So based on complex network theory, three new ESNs are proposed and investigated in this paper. The small-world topology, scale-free topology and the mixed topology of small-world effect and scale-free feature are considered in these new ESNs. We studied the relationship between DR architecture and prediction capability. In our simulation experiments, we used two widely used time series to test the prediction performance among the new ESNs and classical ESN, and used the independent identically distributed (i.i.d) time series to analyze the short-term memory (STM) capability. We answer the following questions: What are the differences of these ESNs in the prediction performance? Can the spectral radius of the internal weights matrix be wider? What is the short-term memory capability? The experimental results show that the proposed new ESNs have better prediction performance, wider spectral radius and almost the same STM capacity as classical ESN’s.
An effective text representation scheme dominates the performance of text categorization system. However, based on the assumption of independent terms, the traditional schemes which tediously use term frequency (TF) and document frequency (DF) are insufficient for capturing enough information of a document and result in poor performance. To overcome this limitation, we investigate exploring the relationships between different terms of the same class tendency and the way of measuring the importance of a repetitive term in a document. In this paper, a group of novel term weighting factors are proposed to enhance the category contribution for each term. Then, based on a novel strategy of generating passages from document, we present two schemes, the weighted co-contributions of different terms corresponding to the class tendency and the weighted co-contributions for each term in different passages, to achieve improvements on text representation. The prior scheme works in a dimensionality reduction mode while the second one runs in the conventional way. By employing the support vector machine (SVM) classifier, experiments on four benchmark corpora show that the proposed schemes could achieve a consistent better performance than the conventional methods in both efficiency and accuracy. Further analysis also confirms some promising directions for the future works.
Threshold signature plays an important role to distribute the power of a single authority in modern electronic society. In order to add functions and improve efficiency of threshold signatures, a multi-policy threshold signature scheme with distinguished signing authorities is proposed. In the scheme two groups can sign and verify each other, so the scheme is two-way signing and verifying. Moreover, the threshold values of the two groups can change with the security classification of the signing document, every discretionary signatory only signs a small part of the document instead of the whole one, so the bandwidth of data transmission for group signature construction can be reduced and the size of group signature is equivalent to that of any individual signature.
Isometric projection (IsoProjection) is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality. The non-orthogonality also distorts the metric structure embedded in the data. This paper proposes a new method called orthogonal isometric projection (O-IsoProjection), which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection. Similar to IsoProjection, O-IsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space, and then obtains the projections by preserving such a graph structure. Different from IsoProjection, O-IsoProjection requires the basis vectors to be orthogonal, and the orthogonal basis vectors can be calculated by iterative way. Experimental results on ORL and Yale databases show that O-IsoProjection has better recognition rate for face recognition than Eigenface, Fisherface and IsoProjection.
In this paper, we focus on the resource scheduling in the downlink of long term evolution advanced (LTE-A) assuming equal power allocation among subcarriers. Considering the backward compatibility, the LTE-A system serves LTE-A and long term evolution (LTE) users together with carrier aggregation (CA) technology. When CA is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint scheduling (JS) and independent scheduling (INS) are two resource scheduling schemes. JS is optimal in performance but with high complexity. Whereas INS is applied, the LTE users will acquire few resources because they can not support CA technology. And the system fairness is disappointing. In order to improve the system fairness without bringing high complexity to the system, an improved proportional fair (PF) scheduling algorithm base on INS is proposed. In this algorithm, we design a weigh factor which is related with the number of the carriers and the percentage of LTE users. Simulation result shows that the proposed algorithm can effectively enhance the throughput of LTE users and improve the system fairness.
An adaptive fuzzy Q-learning (AFQL) based on fuzzy inference systems (FIS) is proposed. The FIS realized by a normalized radial basis function (NRBF) neural network is used to approach Q-value function, whose input is composed of state and action. The rules of FIS are created incrementally according to the novelty of each element of the pair of state-action. Moreover the premise part and consequent part of the FIS are updated using extended Kalman filter (EKF). The action that impacts on environment is the one with maximum output of FIS in the current state and generated through optimization method. Simulation results in the wall-following task of mobile robots and the inverted pendulum balancing problem demonstrate that the superiority and applicability of the proposed AFQL method.
This article puts forward a new scheme to control message redundancy efficiently in delay tolerant mobile Ad-hoc networks (MANET). The class of networks generally lacks end-to-end connectivity. In order to improve the efficiency that messages are delivered successfully, multiple message copies routing protocols are usually used, but the network load is increased due to a large number of message redundancies. In the study, by using counter method, every node adds an encounter counter based on epidemic routing scheme. The counter records the number which the node encounters other nodes with the same message copy. If the counter of a node reaches the installed threshold, the node removes the copy. Theoretical analysis gives a lower bound of threshold in delay tolerant MANET. According to the lower bound of threshold, a rational threshold is installed in real environment. With proposed scheme message copies decrease obviously and are removed completely finally. The successful delivery efficiency is still the same as epidemic routing and the redundant copies are efficiently controlled to a relatively low level. Computer simulations give the variation of message copies concerning different thresholds in fast and slow mobility scenes.
The problem to improve the performance of resisting geometric attacks in digital watermarking is addressed in this paper. Based on the optimized support vector regression (SVR), a zero-bit watermarking algorithm is presented. The proposed algorithm encrypts the watermarking image by using composite chaos with large key space and capacity against prediction, which can strengthen the safety of the proposed algorithm. By using the relationship between Tchebichef moment invariants of detected image and watermarking characteristics, the SVR training model optimized by composite chaos enhances the ability of resisting geometric attacks. Performance analysis and simulations demonstrate that the proposed algorithm herein possesses better security and stronger robustness than some similar methods.
This paper proposes a simple and discriminative framework, using graphical model and 3D geometry to understand the diversity of urban scenes with varying viewpoints. Our algorithm constructs a conditional random field (CRF) network using over-segmented superpixels and learns the appearance model from different set of features for specific classes of our interest. Also, we introduce a training algorithm to learn a model for edge potential among these superpixel areas based on their feature difference. The proposed algorithm gives competitive and visually pleasing results for urban scene segmentation. We show the inference from our trained network improves the class labeling performance compared to the result when using the appearance model solely.
Privacy is an important issue in electronic voting. The concept of ‘full privacy’ in electronic voting was firstly proposed, not only the privacy of voters is concerned, but also the candidates’. Privacy preserving electronic election architecture without any trusted third party is presented and a general technique for k¬-out-of-m election based on distributed ElGamal encryption and mix-match is also provided. The voters can compute the result by themselves without disclosing their will and the vote of the losing candidates. Moreover, whether the vote of winner candidate is more than a half can be verified directly. This scheme satisfies ‘vote and go’ pattern and achieves full privacy. The correctness and security are also analyzed.