Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems. Smart cities, smart homes, ambient intelligent systems, or network-centric defense systems are among these application domains. Data Distribution Service (DDS) is a communication mechanism based on Data-Centric Publish-Subscribe (DCPS) model. It is used for distributed systems with real-time operational constraints. Java Message Service (JMS) is a messaging standard for enterprise systems using Service Oriented Architecture (SOA) for non-real-time operations. JMS allows Java programs to exchange messages in a loosely coupled fashion. JMS also supports sending and receiving messages using a messaging queue and a publish-subscribe interface. In this article, we propose an architecture enabling the automated integration of distributed real-time and non-real-time systems. We test our proposed architecture using a distributed Command, Control, Communications, Computers, and Intelligence (C4I) system. The system has DDS-based real-time Combat Management System components deployed to naval warships, and SOA-based non-real-time Command and Control components used at headquarters. The proposed solution enables the exchange of data between these two systems efficiently. We compare the proposed solution with a similar study. Our solution is superior in terms of automation support, ease of implementation, scalability, and performance.

Ambient intelligence (AmI) is an emerging paradigm bringing intelligence into our lives with the help of intelligent interfaces and smart environments. AmI has the potential to affect our business environments significantly. With the help of AmI, we can find better ways to serve our customers and increase productivity. Internet of things (IoT) is a key enabling technology that provides the necessary infrastructure for ambient intelligence. In addition, ambient intelligence paradigm enhances the use and capabilities of IoT devices. As a result, businesses those want to benefit from this new paradigm and the relevant technologies need to build the necessary IoT infrastructure. In this study, our goal is to help the business and technical managers by developing an AmI enhanced business vision and managing an effective IoT transformation. In this chapter, we discuss an existing implementation of ambient intelligence in the business environment. Furthermore, we envision various future uses of AmI in business environments. We also present issues related to IoT technology transformations. In addition, we provide a set of guidelines, strategies, and best practices for business and IT managers for a successful IoT transformation leading to an ambient intelligence enhanced business environment. We divide the transformation issues into three categories: management issues, technical issues, and social issues. These issues are discussed in detail.

Plagiarism detection software packages have an important role in detection of plagiarism in exams, assignments, projects, and scientific researches. The main goal of this chapter is the selection of plagiarism detection software (PDS) and its integration into Moodle, an open source learning management system (LMS), for the use of a higher education institution. For this reason, first, the selection criteria are determined by nominal group technique (NGT) and then the most appropriate PDS is selected. At the end of the study, Crot, an open source PDS, is determined and integrated into Moodle. The suggested selection criteria would be useful for other higher education institutions in Turkey and other countries that rely on open software.

Recent advances in computer technologies result in the development of E-Learning and Learning Management Systems. They provide mechanisms to organize course contents and training systems including interactions of trainers and trainees. Moreover, virtual reality systems emerge in the training and education systems to increase the trainee participation at the process and visualize the training subject. Increasing data transfer rates of the computer networks also made distant learning activities possible. Moreover, the economical solutions with the cloud computing technologies for the abovementioned systems come up. All these improvements lead the technology towards smart classrooms. It seems to alter the educational habits towards the flipped and the blended learning. In this study, general information about emerging educational technologies is given and a short evaluation with suggestions is presented.

This paper aims at improving the performance of the track initiation process of a target tracking system in cluttered environments via weighted distance-based M out of N (M/N) track initiation methods. In the generic M/N method, the consecutive detections inside the validation gate are considered with identical weights (M is increased by one) regardless of the location of the detection. However, if the newly detected observations in the validation gate are away from the center of the predicted target position, the observations may not come from a real target, the origin of the measurement may be the clutter. Thus, the initiation counter weight of the observation should be adjusted according to the distance measure while utilizing the M/N method for the track initiation decision. We envisage different weighting schemes to determine the initiation counter weighting value. Consequently, the elliptical weighting scheme in M/N track initiation method shows promising results for cluttered environments in terms of decreasing the false track initiations while sustaining an admissible level of true track initiations.

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.

The soundness of the evaluation model used in wireless sensor networks (WSN) affects the soundness of the results. Most proposed models in the literature assume a distance based sensing and 2D freespace communication for a randomly deployed WSN scenario. However, random sensor deployment commonly takes place in 3D inaccessible terrains. In this study, we investigate the incorporation of a realistic 3D terrain model into the performance evaluation of a target tracking WSN. Our observation is that the unrealistic and contradicting 2D terrain assumptions, in which communication and sensing is not blocked due to neglected topographic formations, result in optimistic and unrealistic WSN performance. The performance evaluations compare the mean error of target localization for a given target route in various artificially generated yet realistic terrains. The effect of terrain parameters on mean error is also investigated. Our simulations show that the performance predictions could be misleading on the paper design due to unrealistic assumptions with regards to the WSN deployment region.

A fully-distributed collaborative multi-target tracking framework that eliminates the need for a central data associaton or a central coordinating node for wireless sensor networks is defined. Details of the distributed data association architecture, which is more feasible than the ones relying on a coordinating entity, is described. It is shown that for target tracking applications, the collaboration improves the target localization performance of the distributed data collecting devices. In order to reduce the communication energy exhausted for collaboration, the performance of the collaboration logic manager is examined. Simulation results show that collaborating about a single target information is a rational decision. The problem of deciding which target information to collaborate among the detected targets arises. A mutual information based metric is shown to be a good candidate for deciding on the target which the sensor will collaborate about with the network.

An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). The network lifetime is prolonged by selecting a subset of sensors that are informative and provide non-redundant data so that a desired accuracy level is maintained. A distributed data fusion architecture provides the collaborative tracking framework. A mutual information-based sensor selection algorithm (MISS) is adopted for participation in the fusion process. MISS allows the most informative subset of the sensors to transmit data so that in the energy consumption is reduced while the desired accuracies of the target position estimation are preserved. Simulation results that demonstrate the performance improvement offered by the MISS algorithm are also furnished.

For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the energy constraints of these small, sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. In addition to a selective sensor activation strategy based on the maximum mutual information metric, in this paper, we devise the Information-Controlled Transmission Power (ICTP) adjustment in order to improve the energy savings. The essence of the proposed ICTP scheme for collaborative target tracking lies behind the idea that the sensors with more information use higher transmission powers than the sensors with less information in order to share their target state information with the neighboring sensors.

In this chapter, the sensing coverage area of surveillance wireless sensor networks is considered. The sensing coverage is determined by applying Neyman-Pearson detection and de.ning the breach probability on a grid-modeled field. Using a graph model for the perimeter, Dijkstra’s shortest path algorithm is used to find the weakest breach path. The breach probability is linked to parameters such as the false alarm rate, size of the data record and the signal-to-noise ratio. Consequently, the required number of sensor nodes and the surveillance performance of the network are determined. For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the present energy constraints of these small sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. We adopt a mutual information based metric to select the most informative subset of the sensors to achieve reduction in the energy consumption, while preserving the desired accuracies of the target position estimation.

In this paper a novel handoff decision algorithm for the mobile subsystem of tactical communications systems is introduced. In this algorithm, handoff decision metrics are: received signal strength measurements from the access points, the ratio of the used capacity to the total capacity for the access points, and relative directions and speeds of the mobiles to the access points. We use a fuzzy inference system to process these metrics. We also introduce a "membership value" to represent the degree of membership of a mobile to an access point and "membership value threshold" to represent the minimum membership value before a mobile considers handing off to another access point. We compare our algorithm with the received-signal strength-based handoff decision algorithms. Our tests have shown that the proposed scheme, with the lowest membership value threshold, achieves the minimum number of handoffs, and the proposed scheme with the highest membership value threshold achieves the minimum call blocking rate.

A novel handoff decision algorithm, namely multicriteria handoff decision algorithm (MDA), for the virtual cell layout based mobile subsystem of the next generation tactical communications systems is introduced. In this algorithm, handoff decision metrics are received signal strength measurements from the access points, soft capacities of the access points, and relative directions and speeds of the access points and the base transceivers. Soft capacity of an access point is affected by the interference in the environment. We use a fuzzy inference system to process these metrics. We compare our algorithm with the received signal strength based handoff decision algorithms. Our tests have shown that MDA with lowest membership value threshold achieves minimum number of handoffs, and MDA with highest membership value threshold achieves minimum call blocking rate (GoS), where the call blocking rate is still in an acceptable level in the former case and number of handoffs is in an acceptable level in the latter case.