Air components, including UAVs, planes, balloons, and satellites have been widely utilized since the fixed capacity of ground infrastructure cannot meet the dynamic load of the users. However, since those air components should be coordinated in order to achieve the desired quality of service, several next-generation paradigms have been defined including air computing. Nevertheless, even though many studies and open research issues exist for air computing, there are limited test environments that cannot satisfy the performance evaluation requirements of the dynamic environment. Therefore, in this study, we introduce our discrete event simulator, AirCompSim, which fulfills an air computing environment considering dynamically changing requirements, loads, and capacities through its modular structure. To show its capabilities, a dynamic capacity enhancement scenario is used to investigate the effect of the number of users, UAV s, and requirements of different application types on the average task success rate, service time, and server utilization. The results demonstrate that AirCompSim can be used for experiments in air computing.

Affective computing strives to recognize a person's affective state (e.g., emotion, mood) based on what can be observed. However, electroencephalogram (EEG) and video technologies have not been widely adopted for daily life affect monitoring due to obtrusiveness and privacy concerns. Although the connection between affective states and biophysical data collected with unobtrusive wrist-worn wearables in lab settings has been established successfully, the number of studies for affect recognition in the wild is still limited, and current methods have not yet provided the accuracy necessary for robust applications. In this study, we propose a smart mood-aware emotion detection method. The proposed emotion recognition method extracts the most distinctive features from the physiological data and adds the output of the automated mood detection system as an input to improve performance. The effect of the division of self-report scales into emotion classes is also investigated. The proposed system obtained higher emotion recognition accuracies than most in-the-wild studies when we tested it with the daily life data collected from 14 participants for one week.

Continuous high perceived workload has a negative impact on the individual's well-being. Prior works focused on detecting the workload with medical-grade wearable systems in restricted settings, and the effect of applying deep learning techniques for perceived workload detection in the wild settings is not investigated. We present an unobtrusive, comfortable, pervasive, and affordable Long Short-Term Memory Network based continuous workload monitoring system based on a smartwatch application that monitors the perceived workload of individuals in the wild. We have recorded physiological data from daily life with perceived workload questionnaires from subjects in their real-life environments over a month. The model was trained and evaluated with the daily-life physiological data coming from different days, which makes it robust to daily changes in the heart rate variability that we use with accelerometer features to assess low and high workload. Our system has the capability of detecting perceived workload by using traditional and deep classifiers. We discussed the problems related to ’in the wild’ applications with the consumer-grade smartwatches. We showed that Long Short-Term Memory Network with feature extraction outperforms traditional classifiers and Convolutional Neural Networks on discrimination of low and high perceived workload with smartwatches in the wild.

We construct a practical and real-time probabilistic framework for fine target tracking. The practicality comes from the application of the forward algorithm and the small parameter set used to build the hidden Markov model (HMM). A Bluetooth Low-Energy (BLE) beacon navigating in the environment publishes BLE packets which are captured by the stationary sensors. Fingerprints are formed by collecting received signal strength indicators (RSSI) of these packets, which are then processed into a high resolution emission matrix using a histogram combination technique. We convert the map of the area into a grid structure, the resolution of which is controlled by the grid cell size. The transition matrices are built by Gaussian blur masks parametrized by the size and diffusion extent. As the transition matrix is highly sparse, we make the exact inference tractable by adopting a sparse matrix representation and by intelligently controlling the mask size, diffusion factor and grid cell size. Filtering can then be directly performed by the forward algorithm given a series of real RSSI measurements along real trajectories. We measure the performance of the system by comparing the most likely positions at each step with the ground truth positions. We achieve promising results and evaluate the approach also by the runtime and memory usage.

The growing HCI interest in wellbeing has led to the emerging area of haptics for affect regulation. In such technologies, distinct haptic patterns are usually designed by researchers; however, current work provides a limited reflection on the rationale for the implemented patterns or the choice of haptic modality. We also know little about how people may benefit from engagement in designing such patterns and what design principles underpin them. We explored vibrotactile and thermal modalities to address these gaps and report on a study with 23 participants. These created haptic patterns for affect regulation during stress elicitation. Findings indicate that subjective and objective measures of anxiety and stress were lower in participants who received haptic patterns than those who did not, and highlighted key experiential qualities of vibrotactile and thermal patterns, and their potential for affect regulation. These open up new design opportunities for affect regulation technologies, including supporting implicit affect regulation through entrainment of slow bodily rhythms, decoupling it from predominant vibrotactile modality, designing thermal biofeedback patterns, and supporting personalized and adaptive patterns.

With the growing momentum around Open RAN (O-RAN) initiatives, performing dynamic Function Splitting (FS) in disaggregated and virtualized Radio Access Networks (vRANs), in an efficient way, is becoming highly important. An equally important efficiency demand is emerging from the energy consumption dimension of the RAN hardware and software. Supplying the RAN with Renewable Energy Sources (RESs) promises to boost the energy-efficiency. Yet, FS in such a dynamic setting, calls for intelligent mechanisms that can adapt to the varying conditions of the RES supply and the traffic load on the mobile network. In this paper, we propose a reinforcement learning (RL)based dynamic function splitting (RLDFS) technique that decides on the function splits in an O-RAN to make the best use of RES supply and minimize operator costs. We also formulate an operational expenditure minimization problem. We evaluate the performance of the proposed approach on a real data set of solar irradiation and traffic rate variations. Our results show that the proposed RLDFS method makes effective use of RES and reduces the cost of an MNO. We also investigate the impact of the size of solar panels and batteries which may guide MNOs to decide on proper RES and battery sizing for their networks.

The Internet of Vehicles (IoV) vision encompasses a wide range of novel intelligent highway scenarios that rely on vehicles with an ever-increasing degree of autonomy and the prospect of sophisticated services like e-Horizon and cognitive driving assistance. The self-driving vehicle, on the other hand, entails a new passenger profile where sophisticated infotainment applications are expected to enhance the quality of travel. From the technical stand point, for this vision to become a reality a streamlined edge computing infrastructure, namely Vehicular Edge Computing (VEC), is required where computationally intensive workloads are offloaded to a nearby VEC infrastructure. However, the highly dynamic environment renders it difficult to efficiently operate a VEC system to yield the crisp performance required on an autonomous vehicle. In this setting, where to offload each task stands out as a crucial decision problem, and the conventional methods prove insufficient for its solution. In our work, we proposed a two-stage machine learning-based vehicular edge orchestrator which takes into account not only the task completion success but also the service time. To demonstrate how our approach performs in a realistic setting, we employed EdgeCloudSim to design extensive experiments where the characteristics of the vehicular applications, upload/download sizes, computational footprints of the tasks, the LAN, MAN and WAN network models, and the mobility are considered. Detailed performance evaluation of the proposed system via simulation is carried out where both overall and service type-specific performance scores in comparison with opponent schemes are reported.

IoT devices generate massive amounts of biomedical data with increased digitalization and development of the state-of-the-art automated clinical data collection systems. When combined with advanced machine learning algorithms, the big data could be useful to improve the health systems for decision-making, diagnosis, and treatment. Mental healthcare is also attracting attention, since most medical problems can be associated with mental states. Affective computing is among the emerging biomedical informatics fields for automatically monitoring a person’s mental state in ambulatory environments by using physiological and physical signals. However, although affective computing applications are promising to improve our daily lives, before analyzing physiological signals, privacy issues and concerns need to be dealt with. Federated learning is a promising candidate for developing high-performance models while preserving the privacy of individuals. It is a privacy protection solution that stores model parameters instead of the data itself and abides by the data protection laws such as EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). We applied federated learning to heart activity data collected with smart bands for stress-level monitoring in different events. We achieved encouraging results for using federated learning in IoT-based wearable biomedical monitoring systems by preserving the privacy of the data.

Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.

Recent improvements of Virtual Reality (VR) technology have enabled researchers to investigate the benefits VR may provide for various domains such as health, entertainment, training, and education. A significant proportion of VR system evaluations rely on perception-based measures such as user pre-and post-questionnaires and interviews. While these self-reports provide valuable insights into users' perceptions of VR environments, recent developments in digital sensors and data collection techniques afford researchers access to measures of physiological response. This work explores the merits of physiological measures in the evaluation of emotional responses in virtual environments (ERVE). We include and place at the center of our ERVE methodology emotional response data by way of electrodermal activity and heart-rate detection which are analyzed in conjunction with event-driven data to derive further measures. In this paper, we present our ERVE methodology together with a case study within the context of VR-based learning in which we derive measures of cognitive load and moments of insight. We discuss our methodology, and its potential for use in many other application and research domains to provide more in-depth and objective analyses of experiences within VR.

research-article Share on Analytical Models for the Scalability of Dynamic Group-key Agreement Protocols and Secure File Sharing Systems Authors: Gokcan Cantali Dept. of Computer Engineering, Bogazici University, Istanbul, Turkey Dept. of Computer Engineering, Bogazici University, Istanbul, TurkeyView Profile , Orhan Ermis Dept. of Computer Engineering, Bogazici University and EURECOM, Sophia-Antipolis, France, Dept. of Computer Engineering, Bogazici University and EURECOM, Sophia-Antipolis, France,View Profile , Mehmet Ufuk Çağlayan Dept. of Computer Engineering, Yasar University, Izmir, Turkey Dept. of Computer Engineering, Yasar University, Izmir, TurkeyView Profile , Cem Ersoy Dept. of Computer Engineering, Bogazici University, Istanbul, Turkey Dept. of Computer Engineering, Bogazici University, Istanbul, TurkeyView Profile Authors Info & Claims ACM Transactions on Privacy and SecurityVolume 22Issue 4November 2019 Article No.: 20pp 1–36https://doi.org/10.1145/3342998Published:25 September 2019Publication History 0citation349DownloadsMetricsTotal Citations0Total Downloads349Last 12 Months22Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Edge computing is based on the philosophy that the data should be processed within the locality of its source. Edge computing is entering a new phase where it gains wide acceptance from both academia and the industry as the commercial deployments are starting. Edge of the network presents a very dynamic environment with many devices, intermittent traffic, high mobility of the end user, heterogeneous applications and their requirements. In this scene, scalable and efficient management and orchestration remains to be a problem. We focus on the workload orchestration problem in which execution locations for incoming tasks from mobile devices are decided within an edge computing infrastructure, including the global cloud as well. Workload orchestration is an intrinsically hard, online problem. We employ a fuzzy logic-based approach to solve this problem by capturing the intuition of a real-world administrator to get an automated management system. Our approach takes into consideration the properties of the offloaded task as well as the current state of the computational and networking resources. Detailed set of experiments are designed with EdgeCloudSim to demonstrate the competitive performance of our approach for different service classes.

Although Dynamic Adaptive Streaming over HTTP (DASH) is the pillar of multimedia content delivery mechanisms, its purely client-based adaptive video bitrate mechanisms have quality-of-experience fairness and stability problems in the existence of multiple DASH clients and highly fluctuating background traffic on the same shared bottleneck link. Varying chunk duration among different titles of multiple video providers exacerbates this problem. With the help of the global network view provided by the software-defined networking paradigm, we propose a centralized joint optimization module-assisted adaptive video bitrate mechanism that takes diversity of chunk sizes among different content into account. Our system collects possible video bitrate levels and chunk duration from DASH clients and simply calculates the optimal video bitrates per client based on the available capacity and chunk duration of each client’s selected content while not invading users’ privacy. By continuously following the background traffic flows, it asynchronously updates the target video bitrate levels to avoid both buffer stall events and network underutilization issues rather than bandwidth slicing, which brings about scalability problems in practice. It also guarantees fair startup delays for video sessions with various chunk duration. Our experiments clearly show that our proposed approach considering diversity of chunk duration and that background traffic fluctuations can significantly provide a better and fair quality of experience in terms of structural similarity--based video quality and startup delay compared to both purely client-based and state-of-the-art software-defined networking--based adaptive bitrate mechanisms.

In addition to traditional network services, it is now possible to realize innovative services through the integration of small-form smart devices into our daily lives. Although the set of modern end-user devices have a certain processing power, the execution of the service routines may be handled by remote servers due to battery limitation and other capacity restrictions. The traditional network infrastructure and location- centric communication mechanisms provided by the TCP/IP protocol stack have become inadequate for orchestrating these highly-dynamic operations in a smooth manner. Therefore, it is envisioned that the network structure should be service-oriented to decouple the service instances from their locations. In order to leverage the service-centric model within the network, this study proposes a programmable network solution based on P4. The design of the solution is discussed with the P4 terminology and the implementation details are presented with a demonstrative example to validate the applicability of the solution. The fully-programmable P4 environment enables to facilitate a long-term solution that is compatible with the current network infrastructure and TCP/IP protocol stack.

The current developments in smart devices, wearable gadgets and IoT (Internet-of-Things) are triggering a variety of novel use cases and services. Once the technology matures, a wide range of services is expected to be provided at the edge of the network in coordination with the cloud computing infrastructure. These services will be highly dynamic meaning that they can be served from edge computing facilities and from central cloud servers being transferred back and forth. Also, considering the mobility of the users and the varying demand, those services might need to be live migrated between nearby edge servers on-the-fly. This setup creates an environment that needs to be transparent to the end users. The current legacy networking paradigm, however, allow services to be reached by IP and port addresses, not by their content. Alternatively, in the service-centric approach the services themselves are handled independent of their location and the focus shifts to “what ” instead of “where ”. Due to the complexities involved, it is not a straightforward task to establish service-centricity at the edge scenarios. As a remedy, this paper proposes a service-centric approach at the edge servers using orchestration capabilities offered by the Software-Defined Networking (SDN) technology. To demonstrate how SDN can help to alleviate the problem, an emulation environment is used in which northbound applications are implemented in order to setup the service-centric structure. The effect of service-centric approach is shown with the load balancing experiments where the performance of the proposed system is evaluated for various use cases.

The exponential growth in mobile data exchange rates, which is further augmented by the rapid proliferation of mobile devices, significantly increases the capital and operational expenses of the cellular network operators. Growing expenditures combined with environmental awareness have driven the network operators to provide more energy-efficient services and reduce their carbon dioxide footprint by using green networking methods. In this paper, we focus on green dynamic base station (BS) topology management according to the varying traffic conditions for the next-generation heterogenous cellular networks. We first create a detailed 3-D map of a selected pilot application area in order to obtain a realistic spatio-temporal user density estimation. Then, we formulate a novel optimization problem to find the best possible topology, which minimizes the overall network power consumption while satisfying quality-of-service requirements of the subscribers. As an additional problem, we derive a pico BS deployment methodology on top of the existing network infrastructure to accommodate the peak traffic conditions. Finally, we propose offline-centralized and online-distributed green dynamic BS operation algorithms for power saving during the operation phase. Extensive simulations based on a realistic and a random test case scenario show that our dynamic BS topology management methods achieve significant power savings compared with static operation.

In a typical wireless sensor network, the batteries of the nodes near the sink deplete quicker than other nodes due to the data traffic concentrating towards the sink, leaving it stranded and disrupting the sensor data reporting. To mitigate this problem, mobile sinks are proposed. They implicitly provide load-balanced data delivery and achieve uniform-energy consumption across the network. On the other hand, advertising the position of the mobile sink to the network introduces an overhead in terms of energy consumption and packet delays. In this paper, we propose Ring Routing, a novel, distributed, energy-efficient mobile sink routing protocol, suitable for time-sensitive applications, which aims to minimize this overhead while preserving the advantages of mobile sinks. Furthermore, we evaluate the performance of Ring Routing via extensive simulations.

Human behavior modeling enables many applications for smart cities, smart homes, mobile phones and other domains. We present a hierarchical hidden Markov model for human activity recognition that uses semi-supervised learning to automatically learn the model parameters using only labeled data of the top-layer of the hierarchy. This significantly reduces the annotation requirements for such a model and simplifies the design of such a model, since the inherent structure of the activity is automatically learned from data. The design consideration that remains is the number of states used for representing the actions that an activity consists of. Using multiple real world datasets we show that the same model works both for the recognition of activities of daily living in a smart home and for recognizing office activities from audio data. We show how a variable number of action states per activity can result in a significant increase in performance over using a fixed number per activity. Finally, we show how the use of Bayesian and Akaike information criterion results in models using a sub-optimal set of action states, since a model using intuitively chosen set states is able to outperform them.

Mobile sinks are proposed as a possible solution to the hotspot problem, which leads to the early death of the nodes close to the sink, due to the concentration of data traffic towards the sinks. However, the route updates employed by the source nodes to determine the position of the sinks introduce an overhead in terms of data reporting delays and energy consumptions. On the other hand, decreased frequency of route updates causes stale sink position information to be utilized for data packets which results in suboptimal data dissemination paths. In this paper, we investigate this inherent trade-off and provide an insight on how to fine tune the route update parameters of a mobile sink routing protocol. For this purpose, we define a system employing a simple routing protocol and determine the optimal operating points from the results obtained via simulations, under varying degrees of route updates and sink mobility.

Emerging multimedia applications for sensor networks require the co-existence of different types of traffic with different QoS provisions in terms of latency and throughput. Prioritization-based service differentiation mechanisms are applied in all layers of communication to satisfy the QoS requirements of each traffic class. The prioritization in the contention is one of these differentiation methods applied in the medium access layer. In this article, we propose an analytical model for the contention latencies and energy expenditures of different classes in a prioritized contention structure with uniform backoff scheme. The contention window is divided into three partitions which are allocated for the use of only high-priority, both priorities, and only low-priority classes. We further generalize the model for binary exponential backoff schemes and for more than two priority classes. In the analysis, we explore the optimum sizes of these partitions in terms of contention latency and the total energy expenditure for each priority class. Our model is also useful for the evaluation of various recent contention prioritization schemes in WSNs.

Forest fires lead to high amount of environmental and economic loss all over the world. Prevention and early detection efforts aim to eliminate or minimize the damage that will be caused by a fire incident. Current surveillance systems for forest fires do not provide dense real-time monitoring and hence they lack prevention or early detection of a fire threat. Wireless sensor networks (WSNs), on the other hand, can collect real-time information such as temperature and humidity from almost all points of a forest and can provide fresh and accurate data for the fire-fighting management center quickly. In this work, we aim to evaluate the reporting performance of a WSN under realistic workload. Since fires are destructive and burning a deployed WSN is not feasible, simulation is the appropriate way to assess the reporting capability of a WSN during a forest fire. We integrate WSN simulator with a realistic fire propagation simulator which is modified to provide time based temperature field information while the fire propagates through the deployment area. Temperature information is used for the generation of realistic workloads and the determination of sensor destruction times that affects the routing decisions in WSN simulations. We present the effects of WSN related factors; such as reporting rate, number of the sinks, and the sink locations together with the effects of environmental factors such as the wind speed and the number of ignition points in terms of temperature reporting performance and freshness of temperature map.

Visual capability introduced to Wireless Sensor Networks (WSNs) render many novel applications that would otherwise be infeasible. However, unlike legacy WSNs which are commercially deployed in applications, visual sensor networks create additional research problems that delays the real-world implementations. Conveying real-time video streams over resource constrained sensor hardware remains to be a challenging task. As a remedy, we propose a fairness-based approach to enhance the event reporting and detection performance of the Video Surveillance Sensor Networks. Instead of achieving fairness only for flows or for nodes as investigated in the literature, we concentrate on the whole application requirement. Accordingly, our Event-Based Fairness (EBF) scheme aims at fair resource allocation for the application level messaging units called events. We identify the crucial network-wide resources as the in-queue processing turn of the frames and the channel access opportunities of the nodes. We show that fair treatment of events, as opposed to regular flow of frames, results in enhanced performance in terms of the number of frames reported per event and the reporting latency. EBF is a robust mechanism that can be used as a stand-alone or as a complementary method to other possible performance enhancement methods for video sensor networks implemented at other communication layers.

It is a known phenomenon that in a wireless sensor network, sensors communicating directly with a sink deplete their energy faster than the others. As a result, the so-called neighboring sensors can die, disconnecting some of the sinks from the rest of the network, even though most of the sensors are still fully functional. One possible remedy is to balance the relaying load of the sensors using mobile sinks and controlling their mobility, which has attracted the interest of researchers. In this work, we extend the relevant literature by introducing two new mathematical programming models. They intend to maximize the network lifetime through the controlled mobility of a sink with nonzero travel times with and without limiting the number of hops by which the data originating from the sensors reach the sink. Both models allow more than one tour of the sink during the network lifetime and determine the optimal sink route and sojourn times. Since the models are computationally difficult to solve, we propose efficient heuristics methods to compute near-optimal solutions. On the basis of the computational results performed on randomly generated problem instances, we can say that their performance is remarkable.

Falls are identified as a major health risk for the elderly and a major obstacle to independent living. Considering the remarkable increase in the elderly population of developed countries, methods for fall detection have been a recent active area of research. However, existing methods often use only wearable sensors, such as acceloremeters, or cameras to detect falls. In this demonstration, in contrast to the state of the art solutions, we focus on the use of multi-modal wireless sensor networks within the WeCare framework. WeCare system is developed as a solution for independent living applications by remotely monitoring the health and well-being of its users. We describe the general structure of WeCare and demonstrate its fall detection method. Our set-up not only includes scalar sensors to detect falls and motion but also consists of embedded cameras and RFID tags and uses sensor fusion techniques to improve the success of fall detection and minimize the false positives.

The border region monitoring task can be achieved using surveillance wireless sensor networks. The network detects trespassers and inform the sink node. There are many mobility models proposed for users within an ad hoc network. On the other hand, the behavior of trespassers are generally different than mobile users and does not follow a random pattern. Observations from previous security related studies are used to form a realistic model representing a border region. The performance of a surveillance network depends on its risk prediction capabilities and detection ratio. A risk assessment framework is proposed to be used for the performance evaluation. The framework quantizes the security risk value of each point within the monitored zone. The performance metric takes environmental parameters such as sensor coverage, miss probability and breach frequency into consideration and gives a normalized risk value for a given point. The metric can be customized for different types of networks and scenarios easily. The framework is used for forecasting probable threat points in order to take the necessary counter measures. In this paper, additional sensor deployment is proposed as a such measure and the improvement in detection performance using different deployment schemes is measured. It is shown that the additional deployment done using risk assessment framework gives better detection improvement.

Environmental factors such as unavoidable physical constraints, intentional destruction of the sensors or asymmetric load distribution will lead to formation of holes in the wireless sensor networks. Holes hinder the operational quality of the network, where earlier formations have higher impact. In this paper, we study the sensing hole problem and propose a redeployment method to mitigate it. Image processing algorithms are used for identifying the sensing holes. A portion of the sensors are kept as spare; after identifying the holes, they are redeployed over the holes. The results indicate that the method leads to a considerable increase on the sustainable sensing quality of the network.

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.

The network lifetime measures for wireless sensor networks are mostly based on the percentage of live sensors. However, the lifetime definition must be in accordance with the primary functionality of the network. In surveillance applications, the network must be regarded as dead if the required security level cannot be provisioned. In this paper, we analyze the temporal resilience of deployment quality in surveillance wireless sensor networks and propose several lifetime definitions. It is concluded that having just the necessary number of sensors to satisfy the initial deployment quality requirement is the best strategy to favor the network lifetime.

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.

The distribution of the sensor locations may be nonuniform under some terrain imposed physical constraints or different sensing quality requirements in different parts of a wireless sensor network. Given such a Nonuniformly Deployed Sensor Network, some bottleneck areas will form soon after the network becomes operational. Those areas will severely decrease the lifetime of the network. We propose an algorithm to locate those areas prior to operation. The algorithm progressively tries to form compact clusters whose borders do not intersect. We show, using different scenarios, that by means of small redeployments over the areas that separate these clusters, we increase the lifetime considerably.

Wireless Sensor Networks (WSNs) give rise to a new networking paradigm in which energy efficiency is a high priority goal. A direct measure of the energy efficiency is the network lifetime which WSN proposals strive to extend. To correctly quantify the lifetime, the metric must be defined in an application dependent manner. In this paper, we propose a generic lifetime measurement framework called Weighted Cumulative Operational Time (WCOT) for the performance evaluation of the WSNs. Novelty brought by WCOT is twofold: First, it defines a utility based interface for the diverse WSN applications to incorporate their scenario specific requirements into the metric itself. Second, WCOT assigns different weights to the operational durations that have different utilities and perform a weighted summation to calculate the cumulative lifetime thereafter. With this mechanism, a more representative lifetime metric which maps the complete network behavior into a numeric value is obtained. This is in contrast with metrics which focus solely on certain milestones of the network functionality to quantify the lifetime which include the first node death, the last node death.

Coverage is a fundamental task in sensor networks. We present two binary integer programming models for the effective sensor placement on a grid-structured sensor field when there are more than one type of available sensors with varying sensing quality and price. The first model assumes perfect sensor detection. The second one is more realistic and gives an optimal placement strategy for probabilistic sensing. Both models suffer from the intractability of the binary integer programming formulations. We therefore suggest greedy and Lagrangean heuristics, which are both accurate and efficient. Computational results are also provided.

In this paper, we propose a method to determine the quality of deployment in a surveillance wireless sensor network in an environment that contains obstacles. The maximum detection probability on the potential breach paths is used as the deployment quality measure. The watershed segmentation algorithm is applied on the graph formed by the iso-sensing curves to identify the possible breach paths. An algorithm is proposed to convert the watershed segmentation to an auxiliary graph which is then employed to determine the deployment quality measure.

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.

The battery resource of the sensor nodes should be managed efficiently, in order to prolong network lifetime in wireless sensor networks. Moreover, in large-scale networks with a large number of sensor nodes, multiple sink nodes should be deployed, not only to increase the manageability of the network, but also to reduce the energy-dissipation at each node. In this paper, we focus on the multiple sink location problems in large-scale wireless sensor networks. Different problems depending on the design criteria are presented. We consider locating sink nodes to the sensor environment, where we are given a time constraint that states the minimum required operational time for the sensor network. We use simulation techniques to evaluate the quality of our solution.

The conceptual shift in expectations of wireless users from voice towards multimedia, from availability towards acceptable quality, and from stand-alone towards group-oriented computing has a big impact on the needs of today's networks in terms of mobility, quality of service (QoS) support, and multicasting. Ad hoc networks can provide users with these features. However, it is necessary to develop QoS multicasting strategies for them. This paper defines the building blocks of an ad hoc QoS multicasting (AQM) protocol, which achieves multicasting efficiency by tracking resource availability in a node's neighbourhood based on previous reservations, and announces the QoS conditions at session initiation. When nodes join a session with certain QoS requirements, this information is used to select the most appropriate routes. AQM is compared to a non-QoS scheme with emphasis on service satisfaction for sessions. By applying QoS restrictions, AQM improves the multicasting session efficiency. The results show that QoS is essential for and applicable to ad hoc networks.

The sensing coverage area of surveillance wireless sensor networks, which is determined by applying the Neyman-Pearson detection rule, is considered. Using a graph model for the perimeter, the weakest breach path is found through Dijkstra's shortest path algorithm. The breach probability is a critical parameter for assessing the required number of sensor nodes and the surveillance performance of the network. Analysis indicates that for the Neyman-Pearson detection model, the most significant parameter is the false alarm rate, which is inversely proportional to the breach probability. Furthermore, the width of the field has a significant impact on the breach probability.

Location area (LA) planning plays an important role in cellular networks because of the tradeoff caused by paging and registration signalling. The upper boundary for the size of an LA is the service area of a mobile services switching center (MSC). In that extreme case, the cost of paging is at its maximum but no registration is needed. On the other hand, if each cell is an LA, the paging cost is minimal but the cost of registration is the largest. Between these extremes lie one or more partitions of the MSC service area that minimize the total cost of paging and registration. In this paper, we seek to determine the location areas in an optimum fashion. Cell to switch assignments are also determined to achieve the minimization of the network cost. For that purpose, we use the available network information to formulate a realistic optimization problem, and propose an algorithm based on simulated annealing (SA) for its solution. Then, we investigate the quality of the SA-based technique by comparing it to greedy search, random generation methods, and a heuristic algorithm.

Abstract The validity of the mobility model used to evaluate a cellular network determines the validity of the evaluation. In the literature, unrealistic assumptions on mobility are exercised for the sake of simplicity. In this paper, we present a novel mobility model which is realistic in the sense that it captures themoving‐in‐groups,conscious travelingandinertialbehaviours of the subscribers while respecting thenon‐pass‐throughfeature of structures like households and preserving the autonomy of the subscribers. The mobility and call patterns of the subscribers are determined according to the locus of the subscriber over a real map. Thus, our model allows the subscribers to leave home or arrive home, walk or drive in the streets, get on the highways at specific entry points together with numerous hot and blind spots in the terrain, like city centers and lakes. The call pattern of a subscriber is affected by the type of structure he is in. The model can work on real maps to simulate the mobility patterns in real life. We have evaluated the proposed model against the well‐known way point mobility model. We also analyzed the effect of the mobility model on systems with and without guard channels. Copyright © 2004 John Wiley & Sons, Ltd.

Satellite systems are designed as a backup system to the existing land based wireless systems. Because of the global coverage of the system it becomes more economic for the rural areas where there is no wired infrastructures of communication. Besides being more economic to the rural areas the satellite networks are not affected by the natural disasters and continue to give the service in such situations. In this research a routing method in a routing set concept is investigated. The system tries to minimize the maximum flow over a given set of shortest paths from the source to destination and aims to leave additional capacity over the inter-satellite links to be able to utilize better the limited capacity and, also, to avoid the congestion.

During the optimization of the Global System for Mobile Communications (GSM) network, the operations and maintenance center (OMC) measurements are of significant importance. Because of false predictions, the capacity planning of a cell may be done inaccurately. If the capacity of the cell is not adequate to handle all of the busy-hour requests, the requests are not granted a channel and users are blocked. Thus, when the blocking ratio is high, the cellular capacity should be replanned. However, the capacity planning should not rely on the raw OMC measurements since these are inflated by the redials and retrials of fresh calls, as well as the retrials of the hand-off call attempts. In this paper, we provide a simple-to-implement analytical model that computes the average number of retrials and redials per original call attempt using only the OMC statistics. The effective load is then determined through appropriate scaling. That way, unnecessary waste of channels to sustain the retrials and redials is avoided during the cellular capacity replanning phase.

Location area (LA) planning plays an important role in cellular networks because of the trade-off caused by paging and registration signaling. The upper bound on the size of an LA is the service area of a mobile switching center (MSC). In that extreme case, the cost of paging is at its maximum, but no registration is needed. On the other hand, if each cell is an LA, the paging cost is minimal, but the registration cost is the largest. In general, the most important component of these costs is the load on the signaling resources. Between the extremes lie one or more partitions of the MSC service area that minimize the total cost of paging and registration. In this paper, we try to find an optimal method for determining the location areas. For that purpose, we use the available network information to formulate a realistic optimization problem. We propose an algorithm based on simulated annealing (SA) for the solution of the resulting problem. Then, we investigate the quality of the SA technique by comparing its results to greedy search and random generation methods.

GSM operators work with call data that include retrials and redials as well. Based on these inflated, virtual call attempt statistics, it appears that enormous demands are put on the available spectrum. In this paper, we develop simple models to estimate the expected number of retrials and redials per original call attempt, which is used to determine the effective load and the number of traffic channels required to meet the grade of service. It is shown that significantly fewer channels are capable of handling high traffic loads.

One way of improving the performance of cellular networks is to build a second layer of macrocells on top of the microcell level. The system performance can further be increased by using guard channels and allowing calls to overflow to the upper layer when needed. In this study, we used simulated annealing (SA) to determine the design parameters of two-tier cellular networks for which the cost is minimized. We experimented with the SA-based technique on different example problems and obtained promising results.

Personal mobility and terminal mobility which are viable through wireless access to communication networks are the most fundamental goals of personal communication services (PCS). The technology needed for the wireless access in PCS networks is generally provided by the cellular networks and intelligent networks (IN). In cellular networks, the wireless mobile terminals (WMT) communicate with base stations (BS) which convey the messages among the WMTs and switching centers (SC). SCs and BSs are usually wired to the upper layers of the communication network. In this paper, we briefly review PCS and then propose an algorithm which determines the number of SCs and the BSs attached to the determined SCs in a network for which BS information together with the traffic requirements are given. While the algorithm produces these solutions, it also satisfies some constraints determined by the network designers such as the maximum capacities and the minimum utilization for the SCs.

The authors describe a methodology for designing interconnected LAN/MAN networks with the objective of minimizing the average network delay. They consider IEEE 802 standard LANs interconnected by transparent bridges. These bridges are required to form a spanning tree topology. The authors propose a simulated annealing-based algorithm for designing minimum delay spanning tree topologies. In order to measure the quality of the solutions, a lower bound for the average network delay is found. The algorithm is extended to design the overall LAN/MAN topology consisting of a MAN or high-speed data service interconnecting several clusters of bridged LANs. Comparison with the lower bound and several other measures show that the solutions are not very far from the global minimum.>

The authors describe a methodology for designing interconnected local area network/metropolitan area network (LAN-MAN) networks with the objective of minimizing the average network delay. They consider IEEE 802.3-5 LANs interconnected by transparent bridges. These bridges are required to form a spanning tree topology. The optimization algorithm for finding a minimum delay spanning tree topology is based on simulated annealing. In order to measure the quality of the solutions, a lower bound for the average network delay is found. The comparison of results with this lower bound and several other goodness measures shows that the solutions are not very far from the global minimum. The authors extend the present algorithm for finding minimum delay LAN-MAN topologies consisting of fiber distributed data interface (FDDI) MANs or switched multi-megabit data service (SMDS) interconnecting several clusters of bridged LANs.>