Device-to-device (D2D) communication is a potential solution to the incessant increase in data traffic on cellular networks. The greatest problem is how to control the interference between D2D users and cellular mobile users, and between D2D users themselves. This paper proposes a solution for this issue by putting the full control privilege in cellular network using the software-defined networking (SDN) concept. A software virtual switch called Open vSwitch and several components are integrated into mobile devices for data forwarding and radio resource mapping, whereas the control functions are executed in the cellular network via a SDN controller. This allows the network to assign radio resources for D2D communication directly, thus reducing interference. This solution also brings out many benefits, including resource efficiency, energy saving, topology flexibility, etc. The advantages and disadvantages of this architecture are analyzed by both a mathematical method and a simple implementation. The result shows that implementation of this solution in the next generation of cellular networks is feasible.

Cloud computing is a paradigm in which information is permanently stored in servers on the Internet and cached temporarily on clients. Virtual private network (VPN) is the most widely used technology for secure cloud access. Unfortunately, VPN-based cloud services become unavailable when a VPN failure occurs. In this paper, we propose a new scheme to improve the availability of VPN connections against such failures, called high-availability virtual communication (HAVC). Unlike most of the multipath transmission schemes in the literature, the proposed scheme is implemented by using a virtualization technique, and its protocol functions are independent of existing networks - potential clients are not required to modify their applications or operating systems. Simulation results show that the HAVC can not only tolerate VPN failures but also achieve high transmission performance.

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

In heterogeneous networks (HetNets), energy saving is vital for a sustainable network development. Many techniques, such as spectrum allocation, network planning, etc., are used to improve the network energy efficiency (EE). In this paper, micro BSs utilizing cell range expansion (CRE) and spectrum allocation are considered in multi-channel heterogeneous networks to improve EE. Hotspot region is assumed to be covered by micro BSs which can ensure that the hotspot capacity is greater than the average demand of hotspot users. The expressions of network energy efficiency are derived under shared, orthogonal and hybrid subchannel allocation schemes, respectively. Particle swarm optimization (PSO) algorithm is used to solve the optimal ratio of subchannel allocation in orthogonal and hybrid schemes. Based on the results of the optimal analysis, we propose three service control strategies on the basis of large-scale user behaviors, i.e., adjust micro cell rang expansion (AmCRE), adjust micro BSs density (AmBD) and adjust micro BSs transmit power (AmBTP). Both theoretical and simulation results show that using shared subchannel allocation scheme in AmBD strategies can obtain maximal EE with a very small area ratio. Using orthogonal subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is larger. Using hybrid subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is large enough. No matter which service control strategy is used, orthogonal spectrum scheme can obtain the maximal hotspot user rates.

The Low Earth Orbit satellite network has the unique characteristics of the non-uniform and time-variant traffic load distribution, which often causes severe link congestion and thus results in poor performance for delay-sensitive flows, especially when the network is heavily loaded. To solve this problem, a novel adaptive routing algorithm, referred to as the delay-oriented adaptive routing algorithm (DOAR), is proposed. Different from current reactive schemes, DOAR employs Destination-Sequenced Distance-Vector (DSDV) routing algorithm, which is a proactive scheme. DSDV is extended to a multipath QoS version to generate alternative routes in active with real-time delay metric, which leads to two significant advantages. First, the flows can be timely and accurately detected for route adjustment. Second, it enables fast, flexible, and optimized QoS matching between the alternative routes and adjustment requiring flows and meanwhile avoids delay growth caused by increased hop number and diffused congestion range. In addition, a retrospective route adjustment requesting scheme is designed in DOAR to enlarge the alternative routes set in the severe congestion state in a large area. Simulation result suggests that DOAR performs better than typical adaptive routing algorithms in terms of the throughput and the delay in a variety of traffic intensity.

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.

The error rate performance of circularly distributed antenna system is studied over Nakagami-m fading channels, where a dual-channel receiver is employed for the quadrature phase shift keying signals detection. To mitigate the Co-Channel Interference (CCI) caused by the adjacent cells and to save the transmit power, this work presents remote antenna unit selection transmission based on the best channel quality and the maximized path-loss, respectively. The commonly used Gaussian and Q-function approximation method in which the CCI and the noise are assumed to be Gaussian distributed fails to depict the precise system performance according to the central limit theory. To this end, this work treats the CCI as a random variable with random variance. Since the in-phase and the quadrature components of the CCI are correlated over Nakagami-m fading channels, the dependency between the in-phase and the quadrature components is also considered for the error rate analysis. For the special case of Rayleigh fading in which the dependency between the in-phase and the quadrature components can be ignored, the closed-form error rate expressions are derived. Numerical results validate the accuracy of the theoretical analysis, and a comparison among different transmission schemes is also performed.

Dynamic topology is one of the main influence factors on network performability. However, it was always ignored by the traditional network performability assessment methods when analyzing large-scale mobile ad hoc networks (MANETs) because of the state explosion problem. In this paper, we address this problem from the perspective of complex network. A two-layer hierarchical modeling approach is proposed for MANETs performability assessment, which can take both the dynamic topology and multi-state nodes into consideration. The lower level is described by Markov reward chains (MRC) to capture the multiple states of the nodes. The upper level is modeled as a small-world network to capture the characteristic path length based on different mobility and propagation models. The hierarchical model can promote the MRC of nodes into a state matrix of the whole network, which can avoid the state explosion in large-scale networks assessment from the perspective of complex network. Through the contrast experiments with OPNET simulation based on specific cases, the method proposed in this paper shows satisfactory performance on accuracy and efficiency.

In the process of k-coverage of the target node, there will be a lot of data redundancy forcing the phenomenon of congestion which reduces network communication capability and coverage, and accelerates network energy consumption. Therefore, this paper proposes a novel energy balanced k-coverage control algorithm based on probability model (EBKCCA). The algorithm constructs the coverage network model by using the positional relationship between the nodes. By analyzing the network model, the coverage expected value of nodes and the minimum number of nodes in the monitoring area are given. In terms of energy consumption, this paper gives the proportion of energy conversion functions between working nodes and neighboring nodes. By using the function proportional to schedule low energy nodes, we achieve the energy balance of the whole network and optimizing network resources. The last simulation experiments indicate that this algorithm can not only improve the quality of network coverage, but also completely inhibit the rapid energy consumption of node, and extend the network lifetime.

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

Most of the research in the area of wireless communications exclusively relies on simulations. Further, it is essential that the mobility management strategies and routing protocols should be validated under realistic conditions. Most appropriate mobility models play a pivotal role to determine, whether there is any subtle error or flaw in a proposed model. Simulators are the standard tool to evaluate the performance of mobility models however sometimes they suffer from numerous documented problems. To accomplish the widely acknowledged lack of formalization in this domain, a Coloured Petri nets (CPNs) based random direction mobility model for specification, analysis and validation is presented in this paper for wireless communications. The proposed model does not suffer from any border effect or speed decay issues. It is important to mention that capturing the mobility patterns through CPN is challenging task in this type of the research. Further, an appropriate formalism of CPNs supported to analyze the future system dynamic status. Finally the formal model is evaluated with the state space analysis to show how predefined behavioral properties can be applied. In addition, proposed model is evaluated based on generated simulations to track origins of errors during debugging.

The nonorthogonality between the real and imaginary FBMC/OQAM modulated signals complicates the channel estimation (CE) process, and conventional OFDM CE methods cannot be directly applied to FBMC/OQAM. The conventional preamble-based CE schemes in FBMC/OQAM systems are mainly based on the interference approximation method (IAM) to improve the estimation performance. In this paper, we develop a novel preamble structure to improve the CE performance. We exploit the symmetry pattern to cancel interference and take into account the interference weights in this symmetric structure. The conventional preamble and the proposed preamble are compared via simulations in the IEEE 802.22, 3GPP Vehicular A and Pedestrian A channels. Numerical simulation results demonstrate that the proposed preamble can achieve better bit error ratio (BER) and mean squared error (MSE) performance under the three channel models considered.

It is not practically feasible to apply hardware-based fault-tolerant schemes, such as hardware replication, in mobile devices. Therefore, software-based fault-tolerance techniques, such as checkpoint and rollback schemes, are required. In checkpoint and rollback schemes, the optimal checkpoint interval should be applied to obtain the best performance. Most previous studies focused on minimizing the expected execution time or response time for completing a given task. Currently, most mobile applications run in real-time environments. Therefore, it is extremely essential for mobile devices to employ optimal checkpoint intervals as determined by the real-time constraints of tasks. In this study, we tackle the problem of determining the optimal inter-checkpoint interval of checkpoint and rollback schemes to maximize the deadline meet ratio in real-time systems and to build a probabilistic cost model. From this cost model, we can numerically find the optimal checkpoint interval using mathematical tools. The performance of the proposed solution is evaluated using analytical estimates.

Recently Service Function Chaining (SFC) is promising to innovate the network service mode in modern networks. However, a feasible implementation of SFC is still difficult due to the need to achieve functional equivalence with traditional modes without sacrificing performance or increasing network complexity. In this paper, we present a configurable network service chaining (CNSC) mechanism to provide services for network traffics in a flexible and optimal way. Firstly, we formulate the problem of network service chaining and design an effective service chain construction framework based on integrating software-defined networking (SDN) with network functions virtualization (NFV). Then, we model the service path computation problem as an integer liner optimization problem and propose an algorithm named SPCM to cooperatively combine service function instances with a network utility maximum policy. In the procedure of SPCM, we achieve the service node mapping by defining a service capacity matrix for substrate nodes, and work out the optimal link mapping policies with segment routing. Finally, the simulation results indicate that the average request acceptance ratio and resources utilization ratio can reach above 85% and 75% by our SPCM algorithm, respectively. Upon the prototype system, it is demonstrated that CNSC outperforms other approaches and can provide flexible and scalable network services.

Wireless mesh networks (WMNs) have been increasingly applied in private and public networks during the last decade. In a different context, voice over IP (VoIP) has emerged as a new technology for making voice calls around the world over IP networks and is replacing traditional telecommunication systems. The popularity of the two technologies motivated the deployment of VoIP over WMNs. However, VoIP over WMNs suffers from inefficient bandwidth utilization because of two reasons: i) attaching 40-byte RTP/UDP/IP header to a small VoIP payload (e.g., 10 bytes) and ii) 841 μs delay overhead of each packet in WMNs. Among several solutions, VoIP packet multiplexing is the most prominent one. This technique combines several VoIP packets in one header. In this study, we will survey all the VoIP multiplexing methods over WMNs. This study provides a clear understanding of the VoIP bandwidth utilization problem over WMNs, discusses the general approaches in which packet multiplexing methods could be performed, provides a detailed study of present multiplexing techniques, shows the aspects that hinder the VoIP multiplexing methods, discusses the factors affected by VoIP multiplexing schemes, shows the merits and demerits of different multiplexing approaches, provides guidelines for designing a new improved multiplexing technique, and provides directions for future research. This study contributes by providing guidance for designing a suitable and robust method to multiplex VoIP packets over WMNs.

Increasingly, numerous and various Internet-capable devices are connected in end user networks, such as a home network. Most devices use the combination of TCP and 802.11 DCF as a system platform, but whereas some devices such as a streaming video persistently generate traffic, others such as a motion sensor do so only intermittently with lots of pauses. This study addresses the issue of performance in this heterogeneous traffic wireless LAN environment from the perspective of fairness. First, instantaneous fairness is introduced as a notion to indicate how immediately and how closely a user obtains its fair share, and a new time-based metric is defined as an index. Second, extensive simulation experiments have been made with TCP Reno, Vegas, and Westwood to determine how each TCP congestion control corresponds to the instantaneous fairness. Overall, TCP Vegas yields the best instantaneous fairness because it keeps the queue length shorter than the other TCPs. In the simulations, about 60% of a fair share of the effective user bandwidth is immediately usable in any circumstance. Finally, we introduce two simple strategies for adjusting TCP congestion controls to enhance instantaneous fairness and validate them through simulation experiments.

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.

The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.

This paper brings out a neoteric frame of reference for visual semantic based 3d video search and retrieval applications. Newfangled 3D retrieval application spotlight on shape analysis like object matching, classification and retrieval not only sticking up entirely with video retrieval. In this ambit, we delve into 3D-CBVR (Content Based Video Retrieval) concept for the first time. For this purpose we intent to hitch on BOVW and Mapreduce in 3D framework. Here, we tried to coalesce shape, color and texture for feature extraction. For this purpose, we have used combination of geometric & topological features for shape and 3D co-occurrence matrix for color and texture. After thriving extraction of local descriptors, TB-PCT (Threshold Based- Predictive Clustering Tree) algorithm is used to generate visual codebook. Further, matching is performed using soft weighting scheme with L₂ distance function. As a final step, retrieved results are ranked according to the Index value and produce results .In order to handle prodigious amount of data and Efficacious retrieval, we have incorporated HDFS in our Intellection. Using 3D video dataset, we fiture the performance of our proposed system which can pan out that the proposed work gives meticulous result and also reduce the time intricacy.

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

Patients' health data is very sensitive and the access to individual's health data should be strictly restricted. However, many data consumers may need to use the aggregated health data. For example, the insurance companies needs to use this data to setup the premium level for health insurances. Therefore, privacy-preserving data aggregation solutions for health data have both theoretical importance and application potentials. In this paper, we propose a privacy-preserving health data aggregation scheme using differential privacy. In our scheme, patients' health data are aggregated by the local healthcare center before it is used by data comsumers, and this prevents individual's data from being leaked. Moreover, compared with the existing schemes in the literature, our work enjoys two additional benefits: 1) it not only resists many well known attacks in the open wireless networks, but also achieves the resilience against the human-factor-aware differential aggregation attack; 2) no trusted third party is employed in our proposed scheme, hence it achieves the robustness property and it does not suffer the single point failure problem.

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

Compared to the classical cryptography, lattice-based cryptography is more secure, flexible and simple, and it is believed to be secure against quantum computers. In this paper, an efficient signature scheme is proposed from the ring learning with errors (R-LWE), which avoids sampling from discrete Gaussians and has the characteristics of the much simpler description etc. Then, the scheme is implemented in C/C++ and makes a comparison with the RSA signature scheme in detail. Additionally, a linearly homomorphic signature scheme without trapdoor is proposed from the R-LWE assumption. The security of the above two schemes are reducible to the worst-case hardness of shortest vectors on ideal lattices. The security analyses indicate the proposed schemes are unforgeable under chosen message attack model, and the efficiency analyses also show that the above schemes are much more efficient than other correlative signature schemes.

Like natural climate change on the planet earth, the climate in software development environments is also changing (fast). Like the natural weather, the software environment is also disruptive. As the climate experts alert and suggest taking necessary measures to overcome certain challenges to make this earth a safer and comfortable living place, likewise this article also alerts the relevant stakeholders of software craftsmanship about the dynamic challenges that traditional Software Engineering (SE) with purely "Engineering mind-set" is not capable to respond. Hence, some new thoughts to overcome such challenges are shared. Fundamentally, based on the historical evidences, this article presents the authors' observation about continuous shift from traditional "Engineering-based" software development approaches to disruptive approaches - "Vibrant Softology". The authors see the cause of this shift as disruptive transformational force, which is so powerful that it is uncontrollably diminishing the "Engineering-based" approach from software development environments. The authors align it with climate change analogy. Based on this analogy, the authors feel the need to theoretically re-coin the notion of SE to some new term; perhaps Vibrant/Dynamic Softology (VS or DS). Hence, the authors suggest "a new (disruptive and dynamic) way of thinking is required to develop software". It is worth mentioning that the purpose of article and this new theory is not to disparage the notion of software engineering altogether, rather the aim is to highlight the importance of transformation from SE to its next level (perhaps VS/DS) due to the emerging needs in the software craftsmanship environment.

As digital evidence has a highly influential role in proving the innocence of suspects, methods for integrity verification of such digital evidence have become essential in the digital forensic field. Most surveillance camera systems are not equipped with proper built-in integrity protection functions. Because digital forgery techniques are becoming increasingly sophisticated, manually determining whether digital content has been falsified is becoming extremely difficult for investigators. Hence, systematic approaches to forensic integrity verification are essential for ascertaining truth or falsehood. We propose an integrity determination method that utilizes the structure of the video content in a Video Event Data Recorder (VEDR). The proposed method identifies the difference in frame index fields between a forged file and an original file. Experiments conducted using real VEDRs in the market and video files forged by a video editing tool demonstrate that the proposed integrity verification scheme can detect broken integrity in video content.

With Internet of Things (IoT) technology, home environment becomes smarter than ever. Not only smart devices such as smart phone or smart TV, but also various IoT devices including sensor, smart thermostat, and smart scale has now become very common on the market. These devices have connectivity to the Internet, so that user can read data from the device or control the device using Internet technology. However, due to diversity of smart home requirements, device collaboration in smart home remains a challenging task still. Usually smart home is built with various technologies to fulfill its own purpose, and these purposes cover very wide area from controlling low-power sensor devices to controlling high-performance devices like smart TV and smart phone. This variety of smart home requirements makes smart home very complicated due to mixed network architecture, protocol and technology. In this paper, a framework to enable managing and collaborating heterogeneous IoT devices in smart home environment is proposed. Several programming models are defined in the proposed framework to make application development for heterogeneous devices more intuitive. The proposed framework has been implemented as a web service, and a case study with real-world smart home IoT devices is presented.

Organizations have been implementing mobile applications that actually connect to their backend enterprise applications (e.g. ERP, SCM, etc.) in order to increase the enterprise mobility. However, most of the organizations are still struggling to fully satisfy their mobile application users with the enterprise mobility. Even though it has been regarded as the right direction that the traditional enterprise system should move on, the studies on the success model for mobile enterprise applications in user's acceptance perspective can hardly be found. Thus, this study focused not only to redefine the success of the mobile enterprise application in user's acceptance persepective, but also to find the impacts of the factors on user's usage behavior of the mobile enterprise applications. In order to achieve this, we adopted the Technology Acceptance Model 2 (TAM2) as a model to figure out the user's behavior on mobile applications. Among various mobile enterprise applications, this study chose mobile ERP since it is the most representing enterprise applications that many organizations have implemented in their backend. This study found that not all the constructs defined by Davis in TAM2 have a significant influence on user's behavior of the mobile-ERP applications. However, it is also found that most social influence processes of TAM2 influence user's perception of the degree of interaction by mobile-ERP applications.

Business analysis is a key factor of project success or failure in information systems. However, there are few studies on business analysis competencies. The objective of this paper is to identify which competencies a business analyst (BA) needs, and analyze the importance weights and priorities of business analysis competencies. Literature review yielded 6 competency dimensions and 30 competencies. Based on interviews with 12 experts and analytic hierarchy process analysis, the relative importance weight and priority of each business analysis competency were analyzed. Moreover, an importance-perception gap between stakeholders in different positions was identified. This result can be used as selection and development criteria for superior BAs that are responsible for solving business problems using information systems solutions.

This paper tries to extend the conventional conceptual framework of the Internet of Things (IoT) so as to reify an advanced pervasive IoT-community collaboration concept, which is called the process-aware Internet of Things. The extended conceptual framework is embodied as a referential architecture that can be a standardized reference model supporting the conceptual integration of the Internet of Things and the process awareness. The extended referential architecture covers the full range of the architectural details from abstracting the process-aware behavioral semantics to reifying the IoT-process enactments. These extended framework and architecture ought to be the theoretical basis for implementing a process-aware IoT-community computing system supporting process-aware collaborations of Things in pervasive computing environments. In particular, we do point up that the proposed framework of the process-aware Internet of Things is revised from the Internet of Things framework announced in ITU-T SG13³ Y.2060 [26] by integrating the novel concept of process awareness. We strongly believe that the extended conceptual framework and its referential architecture are able to deliver the novel and meaningful insight as a standardized platform for describing and achieving the goals of IoT-communities and societies.