This paper studies the distributed energy efficient resource management in the Internet of Things (IoT). Wireless communication networks support the IoT without limitation of distance and location, which significantly impels its development. We study the communication channel and energy management in the wireless communication network supported IoT to improve the ability of connection, communication, share and collaboration, by using the game theory and distributed learning algorithm. First, we formulate an energy efficient neighbor collaborative game model and prove that the proposed game is an exact potential game. Second, we design a distributed energy efficient channel selection learning algorithm to obtain the global optimum in a distributed manner. We prove that the proposed algorithm will asymptotically converge to the global optimum with geometric speed. Finally, we make the simulations to verify the theoretic analysis and the performance of proposed algorithm.

In this paper, we deal with the problem of M2M gateways' network selection for different types of M2M traffic in heterogeneous wireless networks. Based on the difference in traffic's quality of service (QoS) requirements, the M2M traffic produced by various applications is mainly classified as two categories: flexible traffic and rigid traffic. Then, game theory is adopted to solve the problem of network-channel selection with the coexistence of flexible and rigid traffic, named as flexible network access (FNA). We prove the formulated discrete game is a potential game. The existence and feasibility of the Nash equilibrium (NE) of the proposed game are also analyzed. Then, an iterative algorithm based on optimal reaction criterion and a distributed algorithm with limited feedback based on learning automata are presented to obtain the NE of the proposed game. In simulations, the proposed iterative algorithm can achieve a near optimal sum utility of whole network with low complexity compared to the exhaustive search. In addition, the simulation results show that our proposed algorithms outperform existing methods in terms of sum utility and load balance.

In this paper, we investigate the differential game theoretic approach for distributed dynamic cooperative power control in cognitive radio ad hoc networks (CRANETs). First, a payoff function is defined by taking into consideration the tradeoff between the stock of accumulated power interference to the primary networks and the dynamic regulation of the transmit power of secondary users (SUs). Specifically, the payoff function not only reflects the tradeoff between the requirement for quickly finding the stable available spectrum opportunities and the need for better channel conditions, but also reveals the impact of the differentiated types of data traffic on the demand of transmission quality. Then the dynamic power control problem is modeled as a differential game model. Moreover, we convert the differential game model into a dynamic programming problem to obtain a set of optimal strategies of SUs under the condition of the grand coalition. A distributed dynamic cooperative power control algorithm is developed to dynamically adjust the transmit power of SUs under grand coalition. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithm for efficient power control in CRANETs.

This paper is concerned with the problem of joint resource allocation and user-pairing in virtual MIMO SC-FDMA systems to improve service quality of experience (QoE). No-reference logarithmic model is introduced to quantify service experience for each user and the objective is to maximize sum of all user's mean of score (MOS). We firstly formulate the optimal problem into an S-dimensional (S-D) assignment problem. Then, to solve this problem, the modified Lagrangian relaxation algorithm is deduced to obtain the suboptimal result of joint user-paring and subchannel allocation. The merits of this solution are as follows. First, the gap between its results and the global optimal one can be quantified and controlled by balancing the complexity and accuracy, which merit the other suboptimal algorithms do not have. Secondly, it has the polynomial computational complexity and the worst case complexity is O(3LN³), where L is the maximum iteration time and N is the number of subchannels. Simulations also prove that our proposed algorithm can effectively improve quality of experience and the gap between our proposed and the optimal algorithms can be controlled below 8%.

The dynamic nature of opportunistic networks results in long delays, low rates of success for deliveries, etc. As such user experience is limited, and the further development of opportunistic networks is constrained. This paper proposes a prediction-based routing method for opportunistic networks (PB-OppNet). Firstly, using an ARIMA model, PB-OppNet describes the historical contact information between a node pair as a time series to predict the average encounter time interval of the node pair. Secondly, using an optimal stopping rule, PB-OppNet obtains a threshold for encounter time intervals as forwarding utility. Based on this threshold, a node can easily make decisions of stopping observing, or delivering messages when potential forwarding nodes enter its communication range. It can also report different encounter time intervals to the destination node. With the threshold, PB-OppNet can achieve a better compromise of forwarding utility and waiting delay, so that delivery delay is minimized. The simulation experiment result presented here shows that PB-OppNet is better than existing methods in prediction accuracy for links, delivery delays, delivery success rates, etc.

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

This paper introduces a full duplex single secondary user multiple-input multiple-output (FD-SSU-MIMO) cognitive radio network, where secondary user (SU) opportunistically accesses the authorized spectrum unoccupied by primary user (PU) and transmits data based on FD-MIMO mode. Then we study the network achievable average sum-rate maximization problem under sum transmit power budget constraint at SU communication nodes. In order to solve the trade-off problem between SU's sensing time and data transmission time based on opportunistic spectrum access (OSA) and the power allocation problem based on FD-MIMO transmit mode, we propose a simple trisection algorithm to obtain the optimal sensing time and apply an alternating optimization (AO) algorithm to tackle the FD-MIMO based network achievable sum-rate maximization problem. Simulation results show that our proposed sensing time optimization and AO-based optimal power allocation strategies obtain a higher achievable average sum-rate than sequential convex approximations for matrix-variable programming (SCAMP)-based power allocation for the FD transmission mode, as well as equal power allocation for the half duplex (HD) transmission mode.

In this paper, performance analysis of full-duplex (FD) relay selection under decode-and-forward (DF) relaying mode is carried out by taking into account several critical factors, including the distributions of the received signal-to-noise ratio (SNR) and the outage probability of wireless links. The tradeoff between the FD and half-duplex (HD) modes for relay selection techniques is also analyzed, where the former suffers from the impact of residual self-interference, but the latter requires more channel resources than the former (i.e., two orthogonal channels are required). Furthermore, the impact of optimal power allocation (OPA) on the proposed relay-selection scheme is analyzed. Particularly, the exact closed-form expressions for outage probability of the proposed scheme over Rayleigh fading channels are derived, followed by validating the proposed analysis using simulation. Numerical results show that the proposed FD based scheme outperforms the HD based scheme by more than 4 dB in terms of coding gain, provided that the residual self-interference level in the FD mode can be substantially suppressed to the level that is below the noise power.

Currently Energy-Saving (ES) methods in cellular networks could be improved, as compensation method for irregular Base Station (BS) deployment is not effective, most regional ES algorithm is complex, and performance decline caused by ES action is not evaluated well. To resolve above issues, a low-complexity energy efficient BS cooperation mechanism for Long Time Evolution (LTE) networks is proposed. The mechanism firstly models the ES optimization problem with coverage, resource, power and Quality of Service (QoS) constraints. To resolve the problem with low complexity, it is decomposed into two sub-problems: BS Mode Determination (BMD) problem and User Association Optimization (UAO) problem. To resolve BMD, regional dynamic multi-stage algorithms with BS cooperation pair taking account of load and geographic topology is analyzed. And then a distributed heuristic algorithm guaranteeing user QoS is adopted to resolve UAO. The mechanism is simulated under four LTE scenarios. Comparing to other algorithms, results show that the mechanism can obtain better energy efficiency with acceptable coverage, throughput, and QoS performance.

Since the decentralized structure and the blindness of a large-scale wireless network make it difficult to collect the real-time channel state or other information from random distributed relays, a fundamental question is whether it is feasible to perform the relay selection without this knowledge. In this paper, a Slotted ALOHA based Greedy Relay Selection (SAGRS) scheme is presented. The proposed scheme allows the relays satisfying the user's minimum transmission request to compete for selection by randomly accessing the channel through the slotted ALOHA protocol without the need for the information collection procedure. Moreover, a greedy selection mechanism is introduced with which a user can wait for an even better relay when a suitable one is successfully stored. The optimal access probability of a relay is determined through the utilization of the available relay region, a geographical region consisting of all the relays that satisfy the minimum transmission demand of the user. The average number of the selection slots and the failure probability of the scheme are analyzed in this paper. By simulations, the validation and the effectiveness of the SAGRS scheme are confirmed. With a balance between the selection slots and the instantaneous rate of the selected relay, the proposed scheme outperforms other random access selection schemes.

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

Exact average bit error probability (BEP) expressions for mobile-relay-based mobile-to-mobile (M2M) cooperative networks with incremental decode-and-forward (IDF) relaying over N-Nakagami fading channels are derived in this paper. The average BEP performance under different conditions is evaluated numerically to confirm the accuracy of the analysis. Results are presented which show that the fading coefficient, the number of cascaded components, the relative geometrical gain, and the power allocation parameter have a significant influence on the average BEP performance.

ITU-R M.1842-1, as a well-known specification dedicated to maritime mobile applications, has standardized wireless transmission protocols according to the particular characteristics of a maritime communications scenario. A time division multiple access (TDMA) frame structure, along with modulation schemes to achieve a high data rate, has been described clearly in ITU-R M.1842-1. However, several specification items are still under "to be decided" status, which brings ambiguity to research works. In addition, the current version of ITU-R M.1842-1 is focused mainly on maritime transmissions in open-sea areas, where the cyclic prefix (CP) is set to zero and only 16-QAM is used in the multi-carrier (MC) system. System performance might be dramatically degraded in coastline areas due to the inter-symbol interference (ISI) caused by selective fading. This is because there is a higher probability that the signal will be reflected by obstacles in coastline areas. In this paper, we introduce the transmission resource block (TRB) dedicated to ITU-R M.1842-1 for a ship ad-hoc network (SANET), where the pilot pattern of TRB is based on the terrestrial trunked radio (TETRA). After that, we evaluated SANET performance under the maritime channel model in a coastline area. In order to avoid noise amplification and to overcome the ISI caused by selective fading, several strategies are suggested and compared in the channel estimation and equalization procedures, where the link-level simulation results finally validate our proposals.

The future mobile networks consist of hyper-dense heterogeneous and small cell networks of same or different radio access technologies (RAT). Integrating mobile networks of different RATs to provide seamless and smooth mobility service will be the target of future mobile converged network. Generally, handover from high-speed networks to low-speed networks faces many challenges from application perspective, such as abrupt bandwidth variation, packet loss, round trip time variation, connection disruption, and transmission blackout. Existing inter-RAT handover solutions cannot solve all the problems at the same time. Based on the high-layer convergence sublayer design, a new receiver-aided soft inter-RAT handover is proposed. This soft handover scheme takes advantage of multihoming ability of multi-mode mobile station (MS) to smooth handover procedure. In addition, handover procedure is seamless and applicable to frequent handover scenarios. The simulation results conducted in UMTS-WiMAX converged network scenario show that: in case of TCP traffics for handover from WiMAX to UMTS, not only handover latency and packet loss are eliminated completely, but also abrupt bandwidth/wireless RTT variation is smoothed. These delightful features make this soft handover scheme be a reasonable candidate of mobility management for future mobile converged networks.

In order to meet various requirements for transmission quality of both primary users (PUs) and secondary users (SUs) in cognitive radio networks, we introduce a channel bonding mechanism for PUs and a channel reservation mechanism for SUs, then we propose a novel spectrum allocation strategy. Taking into account the mistake detection and false alarm due to imperfect channel sensing, we establish a three-dimensional Markov chain to model the stochastic process of the proposed strategy. Using the method of matrix geometric solution, we derive the performance measures in terms of interference rate of PU packets, average delay and throughput of SU packets. Moreover, we investigate the influence of the number of the reserved (resp. licensed) channels on the system performance with numerical experiments. Finally, to optimize the proposed strategy socially, we provide a charging policy for SU packets.

Named-Data Networking (NDN) is one of the promising approaches for the Future Internet to cope with the explosion and current usage pattern of Internet traffic. Content provider mobility in the NDN allows users to receive real-time traffic when the content providers are on the move. However, the current solutions for managing these mobile content providers suffer several issues such as long handover latency, high cost, and non-optimal routing path. In this paper, we survey main approaches for provider mobility in NDN and propose an optimal scheme to support the mobile content providers in the large-scale NDN domain. Our scheme predicts the movement of the provider and uses state information in the NDN forwarding plane to set up an optimal new routing path for mobile providers. By numerical analysis, our approach provides NDN users with better service access delay and lower total handover cost compared with the current solutions.

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

This paper presents a scalable coding method for depth images by considering the quality of synthesized images in virtual views. First, we design a new edge detection algorithm that is based on calculating the depth difference between two neighboring pixels within the depth map. By choosing different thresholds, this algorithm generates a scalable bit stream that puts larger depth differences in front, followed by smaller depth differences. A scalable scheme is also designed for coding depth pixels through a layered sampling structure. At the receiver side, the full-resolution depth image is reconstructed from the received bits by solving a partial-differential-equation (PDE). Experimental results show that the proposed method improves the rate-distortion performance of synthesized images at virtual views and achieves better visual quality.

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

We-the-media (aka wedia) is a concept where the users of social networking sites, such as Facebook, turn into the broadcasters. This study used the popular application Bitstrips as the experiment tool. Facebook was used as the Wedia platform for publishing designed comics, then used the three elements of Goffman's dramaturgy model-role, scene and dialog-to analyze 265 comics created by 3 researchers and observe the audience's responses within 9 months. The results showed that people want to see a good story with positive dialogue, and prefer scene is school more than work. As all these elements are controllable, Wedia communication has the potential for more applications. We also found that including the elements of news, gambling and gift-giving tended to trigger greater response. Furthermore, We suggesting that such embedding of product information in web episodes (webisodes) with caricature could be a successful marketing strategy.

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.

Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.

WebView is a vital component in smartphone platforms like Android, Windows and iOS that enables smartphone applications (apps) to embed a simple yet powerful web browser inside them. WebView not only provides the same functionalities as web browser, it, more importantly, enables a rich interaction between apps and webpages loaded inside the WebView. However, the design and the features of WebView lays path to tamper the sandbox protection mechanism implemented by browsers. As a consequence, malicious attacks can be launched either against the apps or by the apps through the exploitation of WebView APIs. This paper presents a critical attack called Supplementary Event-Listener Injection (SEI) attack which adds auxiliary event listeners, for executing malicious activities, on the HTML elements in the webpage loaded by the WebView via JavaScript Injection. This paper also proposes an automated static analysis system for analyzing WebView embedded apps to classify the kind of vulnerability possessed by them and a solution for the mitigation of the attack.

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

A novel jamming detection technique to detect the presence of jamming in the downstream direction for cluster based wireless sensor networks is proposed in this paper. The proposed technique is deployed in base station and in cluster heads. The proposed technique is novel in two aspects: Firstly, whenever a cluster head receives a packet it verifies whether the source node is legitimate node or new node. Secondly if a source node is declared as new node in the first step, then this technique observes the behavior of the new node to find whether the new node is legitimate node or jammed node. In order to monitor the behavior of the existing node and new node, the second step uses two metrics namely packet delivery ratio (PDR) and received signal strength indicator (RSSI). The rationality of using PDR and RSSI is presented by performing statistical test. PDR and RSSI of every member in the cluster is measured and assessed by the cluster head. And finally the cluster head determines whether the members of the cluster are jammed or not. The CH can detect the presence of jamming in the cluster at member level. The base station can detect the presence of jamming in the wireless sensor network at CH level. The simulation result shows that the proposed technique performs extremely well and achieves jamming detection rate as high as 99.85%.

Although many revocable group signature schemes has been proposed in vehicular ad hoc networks (VANETs), the existing schemes suffer from long computation delay on revocation that they cannot adapt to the dynamic VANETs. Based on Chinese remainder theorem and Schnorr signature algorithm, this paper proposes an efficient revocable group signature scheme in VANETs. In the proposed scheme, it only need to update the corresponding group public key when a member quits the group, and in the meanwhile the key pairs of unchanged group members are not influenced. Furthermore, this scheme can achieve privacy protection by making use of blind certificates. Before joining to the VANETs, users register at local trusted agencies (LTAs) with their ID cards to obtain blind certificates. The blind certificate will be submitted to road-side units (RSUs) to verify the legality of users. Thus, the real identities of users can be protected. In addition, if there is a dispute, users can combine to submit open applications to RSUs against a disputed member. And LTAs can determine the real identity of the disputed member. Moreover, since the key pairs employed by a user are different in different groups, attackers are not able to track the movement of users with the obtained public keys in a group. Furthermore, performance analysis shows that proposed scheme has less computation cost than existing schemes.