Flagging hate speech on social media messages has important societal benefits. While large language models have become increasingly able to identify hate speech with high accuracy, they come with a significant computational cost. Thus, there is a need for simpler models that detect hateful or abusive content by classifying an embedding of the text. Such models perform very well for explicitly abusive content, but struggle with the classification of implicit hate. Furthermore, it has been found that the performance decreases significantly in cross-dataset experiments. In this paper, we propose Closest Positive Cluster (CPC) an auxiliary loss that increases the generalizability of embedding-based explicit and implicit hate classifiers in crossdataset scenarios. Through experiments spanning ten different hate speech datasets, we found that the CPC loss increased the model performance by 0.17 - 7.4% when added to the binary crossentropy loss during training. The experiments also investigated whether models trained on specific hate speech datasets generalize better to other datasets.

Smart grids integrate intelligence, automation, and communication into the electrical grid infrastructure, primarily through the use of smart meters. These meters play a crucial role in collecting and transmitting data, either to the cloud, which may cause delays, or to the edge, where meters are closer to the data source. In this paper, we propose Q-Balance, a neural network-based solution for optimizing computational resources at the edge, thus minimizing service processing time. Q-Balance utilizes the Multi-Layer Perceptron (MLP) technique to estimate response times for requests processed by computational resources. Evaluation results demonstrate that Q-Balance can significantly reduce the average response time, achieving up to a 65% reduction compared to the Min-Load approach at the edge and up to 79% in the cloud.

This paper introduces Confidence Guided Path-planning (CGP), an algorithm for planning the path of mobile sensor nodes with the goal to increase confidence in the accuracy of the estimated model at any time point in the data collection process. The approach employs a local estimator based on a Gaussian process regressor and takes advantage of the uncertainty estimation to guide the sensor to areas of lower confidence. In an experimental study comparing CGP with systematic lawnmower-type exploration and random waypoint movement, we found that CGP achieves better scores than both during most of the exploration process, being outperformed only by a fully completed systematic exploration. We also found that, as an emergent property of pursuing higher confidence, CGP achieves good coverage of the area of interest. The proposed algorithm has wide applications in precision agriculture, wildlife tracking, and road monitoring, where exhaustive coverage is not feasible.

The movement of a mobile sensor has a critical impact on the information gathered from the area of interest, as well as the quality of the estimate that a model can build from the collected information at any moment in time. Both systematic exploration models, which make the sensor move in regular patterns, and random movement models have specific advantages. There is less research concerning models that are positioned between these two extremes. In this paper, we propose Grid Limited Randomness (GLR), a family of path planning algorithms based on sampling waypoints from a grid of a specific resolution. We propose three variations differentiated by the order in which the mobile sensor visits these waypoints: new samples added to the end of the path (GLR-EOP), smallest detour (GLR-SD), and the shortest path as approximated by Christofides' algorithm. An extensive simulation study in the Waterberry Farms benchmark shows that the GLR variations offer benefits that, in specific circumstances, make them preferable to both fully random and fully systematic exploration paths.

Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to privacy concerns, data size, data quality, and so on. To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase. We compared the Kullback–Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model. Furthermore, we used five key statistical parameters to describe the smart grid data distribution and compared them between synthetic data generated by both models and real data. Experiments indicate that the proposed synthetic data generative model outperforms the vanilla GAN network. The distribution of VAE-GAN synthetic data is the most comparable to that of real data.

Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSM0) and the overall efficiency of traffic networks. The real-time data, collected from Internet of Things (loT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learning-based approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.

Commercially available unmanned aerial vehicles (UAVs) are usually more affordable and feasible for easy deployment compared to military-level UAVs in civilian applications. However, having a bounded range limits the use of commercially available UAVs in package dropping scenarios. In this paper, we have generated a synthetic dataset for the scenario in which drones or UAVs are used to drop packages to two neighborhoods. The charging and package pick-up station is located between two neighborhoods. By leveraging the synthetic dataset, the location of the charging station is predicted by machine learning techniques given the package request frequency, package dropping times of the UAV, and targeted package delay for the neighborhoods. The results showed that deep neural networks and support vector regressor are more successful in deciding the charging station location.

A smart home energy management system (HEMS) can contribute towards reducing the energy costs of customers; however, HEMS suffers from uncertainty in both energy generation and consumption patterns. In this paper, we propose a sequence to sequence (Seq2Seq) learning-based supply and load prediction along with reinforcement learning-based HEMS control. We investigate how the prediction method affects the HEMS operation. First, we use Seq2Seq learning to predict photovoltaic (PV) power and home devices' load. We then apply Q-learning for offline optimization of HEMS based on the prediction results. Finally, we test the online performance of the trained Q-learning scheme with actual PV and load data. The Seq2Seq learning is compared with VARMA, SVR, and LSTM in both prediction and operation levels. The simulation results show that Seq2Seq performs better with a lower prediction error and online operation performance.

Unmanned aerial vehicles (UAVs) are widely used in many application areas within opportunistic networks. In this paper, we investigate the charging station placement problem in the application scenario with ten UAVs deployed in an opportunistic network environment. We have used a real-world dataset that contains human mobility traces from North Carolina State University. The UAVs cruise on the network with spiral shapes and distribute messages to the nodes on the ground. The charging station locations are generated with random, Density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering approaches. The evaluation results indicate that the k-means algorithm with three clusters outperformed the other two methods in terms of the success rates and the message delay.

Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities. In this paper, we propose to use an LSTM-based sequence-to-sequence (seq2seq) learning model that can capture the load profiles of appliances. We use a real dataset collected from four residential buildings and compare our proposed scheme with three other techniques, namely VARMA, Dilated One Dimensional Convolutional Neural Network, and an LSTM model. The results show that the proposed LSTM-based seq2seq model outperforms other techniques in terms of prediction error in most cases.

The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior. To speed up the learning, several researchers designed collaborative learning systems that use data from multiple users. However, disclosing the daily activities of an elderly or disabled user raises privacy concerns.In this paper, we use state-of-the-art deep neural network-based techniques to learn predictive human activity models in the local, centralized, and federated learning settings. A novel aspect of our work is that we carefully track the temporal evolution of the data available to the learner and the data shared by the user. In contrast to previous work where users shared all their data with the centralized learner, we consider users that aim to preserve their privacy. Thus, they choose between approaches in order to achieve their goals of predictive accuracy while minimizing the shared data. To help users make decisions before disclosing any data, we use machine learning to predict the degree to which a user would benefit from collaborative learning. We validate our approaches on real-world data.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

Short-term forecasting of the electric load in a household received significant research interest, with applications that include smart grid systems and the possibility to reduce the energy cost to the homeowner. Most previous research focused on forecasting the load at the level of the whole household. In this paper, we propose a novel approach for forecasting the load of individual electronic devices. Our approach uses a recurrent deep neural network with Long Short Term Memory (LSTM) cells. We train and validate the system using real-world datasets, and show that the approach outperforms the baseline forecasting approaches.

Many elderly individuals have physical restrictions that require the use of a walker to maintain stability while walking. In addition, many of these individuals also have age-related visual impairments that make it difficult to avoid obstacles in unfamiliar environments. To help such users navigate their environment faster, safer and more easily, we propose a smart walker augmented with a collection of ultrasonic sensors as well as a camera. The data collected by the sensors is processed using echo-location based obstacle detection algorithms and deep neural networks based object detection algorithms, respectively. The system alerts the user to obstacles and guides her on a safe path through audio and haptic signals.

The smart home concept can significantly benefit from predictive models that take proactive management operations on home actuators, based on users' behavior evaluation. In this paper, we use a small-scale physical model, the ScaledHome-2 testbed, to experiment with the evolution of measurements in a suburban home under different environmental scenarios. We start from the observation that, for a home to become smart, in addition to IoT sensors and actuators, we also need a predictive model of how actions taken by inhabitants and home actuators affect the internal environment of the home, reflected in the sensor readings. In this paper, we propose a technique to create such a predictive model through machine learning in various simulated weather scenarios. This paper also contributes to the literature in the field by quantitatively comparing several machine learning algorithms (K-nearest neighbor, regression trees, Support Vector Machine regression, and Long Short Term Memory deep neural networks) in their ability to create accurate and generalizable predictive models for smart homes.

The unmanned aerial vehicles (UAVs) are widely used in many application areas including the opportunistic networks. In this paper, we investigate the efficient usage of UAVs in Unmanned Aerial Vehicle aided Opportunistic Networks (UAON). The UAVs act as message distributors whereas the nodes on the ground generate the messages. The simulation study is conducted on the real-world dataset of nodes moving around North Carolina State University. We have tested different cruising techniques and the cases without the usage of UAV. The simulations showed improved results in terms of message delay and success rate when the UAVs were used in an opportunistic network settings.

Recent advances in IoT sensors and actuators and smart home controllers allow us to collect real-time information about the state of the home and take intelligent actions that maximize the user's goals with respect to comfort, convenience, environmental awareness and cost. While thermal comfort is one of the primary concerns of many users, many homes use a very simple, energy inefficient approach that blankets the home with constant temperature air conditioning. Such systems do not take advantage of more energy efficient and environment friendly natural ways to manage the temperature, such as opening and closing windows, window shades and interior doors. In this paper we develop a deep neural network based model that predicts the temperature in various rooms of the home function of the state of the actuators. We also describe a scaled model of a four room home which allows us to control the doors and windows and collect data using IoT devices. We train and validate our temperature models on both data collected from the scaled model as well as from publicly available datasets from two real-world smart homes.

Visually impaired individuals often employ canes or guide dogs to help navigate complex environments. Individuals who are both visually and mobility impaired encounter greater difficulty, since conventional aids do not integrate well with walkers or rollators. In this paper we propose a smart walker architecture that is augmented with depth-sensing cameras that detect and recognize obstacles that may endanger the user, as well as obtain their distance from the user. This information is conveyed to the user through a haptic interface on the walker. Our design helps protect the user from colliding with obstacles in his/her path. In comparison with traditional mobility methods, the smart walker allows the user to navigate the environment faster and with less physical and cognitive load. Compared to previous designs, our approach completes this task at a significantly lower cost per unit.

Four legged walkers are used by many elderly persons to retain mobility. They are also used by patients recovering from leg injuries to facilitate rehabilitation. Unfortunately, these walkers are also associated with many injuries, some of which are caused by incorrect use. In this paper, we describe a walker augmented with IoT sensors which continuously monitors the weight distribution on the legs of the walker. We describe an approach where this data stream is processed by a deep neural network based classifier, which learns to recognize dangerous use patterns that can lead to falls and injury. The classifier is trained by providing examples of unsafe use, thus eliminating the costly engineering necessary to customize the algorithm to the specific user and walker. By alerting the user in real time about unsafe use patterns, the user can learn the correct and safe use of the walker.

The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset.

This paper focuses on the application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV) for animal tracking problem. The goal of this application is to monitor the target animals in large wild areas without any attachment devices. The WSN includes clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a model predictive control (MPC) method that is used to guide the UAV in planning its path. We first build a prediction model to learn the animal appearance patterns from the sensed historical data. Then, based on the real-time predicted animal distributions, we introduce a path planning approach for the UAV that reduces message delay by maximizing the collected rewards. The experimental results show that our approach outperforms the greedy and traveling salesmen problem-based path planning heuristics in terms of collected value of information. We also discuss the results of other performance metrics involving message delay and percentage of events collected.

We consider wireless sensor networks that nodes offload data to a central collector node (sink) via wireless communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of sensed data so that the Value of Information (VoI) of the data delivered to a sink is maximized while keeping energy usage as low as possible. Sleep scheduling is a widely used technique in MAC-layer to reduce unnecessary idle energy consumption in WSN; however, when it is carried out without paying attention to network-layer routing, it may adversely affect sensed data value of information. In this paper, we employ linear programming (LP) to establish a paradigm of cross-layer formulation to capture the interplay between scheduling and routing. We propose a biobjective model of data value of information maximization and energy cost minimization in a WSN. Compared to existing work, our formulation is not only bi-objective which considers both data value of information and energy consumption jointly, but also is more realistic given that it explicitly accounts for different types of signal interference that may affect a wireless transmission.

In this paper, we focus on a taxi dispatch system with the help of auxiliary models that predict future demand and destination. We build two different neural networks for learning taxi demand and destination distribution patterns based on historical data. The trained models can predict taxi demand and destination for any area in a city at a future time. Our proposed dispatch system relies on the predictions of the previous models and is designed not only to minimize the waiting time of passengers, but also to assign the taxis to passengers in a way to minimize the idle driving distances of taxis. In order to achieve this, we balance future taxi supply-demand over the city by solving a mixed-integer program (MIP). We validate our dispatch system as well as the prediction models using a dataset of taxi trips in the New York City.

Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, we propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here, since taxi requests in the future are correlated with information about actions that happened in the past. For example, someone who requests a taxi to a shopping center, may also request a taxi to return home after few hours. We use one of the best sequence learning methods, long short term memory that has a gating mechanism to store the relevant information for future use. We evaluate our method on a data set of taxi requests in New York City by dividing the city into small areas and predicting the demand in each area. We show that this approach outperforms other prediction methods, such as feed-forward neural networks. In addition, we show how adding other relevant information, such as weather, time, and drop-offs affects the results.

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

Many companies within the Internet of Things (IoT) sector rely on the personal data of users to deliver and monetize their services, creating a high demand for personal information. A user can be seen as making a series of transactions, each involving the exchange of personal data for a service. In this paper, we argue that privacy can be described quantitatively, using the game- theoretic concept of value of information (VoI), enabling us to assess whether each exchange is an advantageous one for the user. We introduce PrivacyGate, an extension to the Android operating system built for the purpose of studying privacy of IoT transactions. An example study, and its initial results, are provided to illustrate its capabilities.

In this paper, we focus on an application of recurrent neural networks for learning a model that predicts taxi demand based on the requests in the past. A model that can learn time series data is necessary here since taxi requests in the future relate to the requests in the past. For instance, someone who requests a taxi to a movie theater, may also request a taxi to return home after few hours. We use Long Short Term Memory (LSTM), one of the best models for learning time series data. For training the network, we encode the historical taxi requests from the official New York City taxi trip dataset and add date, day of the week and time as impacting factors. Experimental results show that our approach outperforms the prediction heuristics based on feed-forward neural networks and naive statistic average.

Smartphone carrier companies rely on mobile networks for keeping an accurate record of customer data usage for billing purposes. In this paper, we present a vulnerability that allows an attacker to force the victim's smartphone to consume data through the cellular network by starting the data download on the victim's cell phone without the victim's knowledge. The attack is based on switching the victim's smartphones from the Wi-Fi network to the cellular network while downloading a large data file. This attack has been implemented in real-life scenarios where the test's outcomes demonstrate that the attack is feasible and that mobile networks do not record customer data usage accurately.

BACKGROUND: Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset. RESULTS: This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections. CONCLUSIONS: We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data.

In Underwater Sensor Networks (UWSNs) with high volume of data recording activity, a mobile sink such as a Autonomous Underwater Vehicle (AUV) can be used to offload data from the sensor nodes. When the AUV approaches the underwater node, it can use high data rate optical communication. However, the data is not considered delivered when it was transferred from the sensor node to the AUV, but when the AUV had resurfaced and transferred the data to the sink. If the data is not time sensitive, it is sufficient for the AUV to resurface only once at the end of its data collection path. However, for time-sensitive data, it is more advantageous for the AUV to resurface multiple times during its path, and upload the data collected since the previous resurfacing. Thus, a resurfacing schedule needs to complement the path planning process. In this paper we are using the metric of Value of Information (VoI) as the optimization criteria to capture the time- sensitive nature of collected information. We propose a genetic algorithm based approach to determine the resurfacing schedule for an AUV which is already provided with the sequence of nodes to be visited.

In this paper, we propose an infrastructure-independent opportunistic mobile social networking strategy for efficient message broadcasting in campus environments. Specifically, we focus on the application scenario of university campuses. In our model, the students' smart-phones forward messages to each other. The messages are created spontaneously as independent events in various places of the campus. The events can be either urgent security alerts or private announcements to the students currently on the campus. Our proposed state- based campus routing (SCR) protocol is based on the idle and active states of the students in indoor and outdoor places. The proposed model is analyzed through extensive network simulations using mobility datasets collected from students on University of Milano and University of Cambridge campuses. The opportunistic network model and the SCR protocol are compared with epidemic, epidemic with TTS (Times To Send), PROPHET, and random routing protocols. The message delivery performance of SCR is close to Epidemic and PROPHET while SCR reduces the amount of message transmissions.

In a wireless sensor network, virtual coordinates provide most of the advantages of geographic routing strategies without actually relying on the location information of the nodes. Using a mobile sink provides advantages such as distributing energy consumption throughout the network. However, nodes need to be updated about the new virtual coordinate of the mobile sink as it moves. In this paper, we propose Circular Update-Directional Virtual Coordinate Routing (CU-DVCR), an algorithm specialized in routing towards a mobile sink in virtual coordinates. Through a set of experimental studies we show that CU-DVCR consumes less energy compared to alternative algorithms while providing comparable performance.

In times of natural or man-made disasters, missions such as safe evacuation of people from the disaster areas have critical importance. Considering large areas with limited vehicle use such as theme parks and state fairs, search and rescue of the pedestrians is a major challenge. Moreover, as an effect of disaster, damages to infrastructure may disrupt the use of Internet services. Therefore, alternative communication systems with disaster resilience are necessary for evacuation planning and guidance. In this paper, we develop a method for tracking pedestrians using smart-phones during disasters. In the network model, sensors store and carry messages to a limited number of mobile sinks. We propose physical force based (PF), grid allocation based (GA) and road allocation based (RA) approaches for sink placement and mobility. The proposed approaches are analyzed through extensive network simulations using real theme park maps and a theme park pedestrian mobility model for disaster scenarios.

Underwater Sensor Networks (UWSNs) provide valuable data for research studies and underwater monitoring and protection. UWSNs need to overcome the handicap that high data rate wireless transmissions are not available underwater. Acoustic communications are used as a medium but they are only good for transmitting e.g. signalling information. Autonomous Underwater Vehicles (AUVs) can serve as mobile sinks that gather and deliver larger amounts of data from the underwater sensor network nodes. Value of Information (VoI) is a data tag that encodes the importance and time-based-relevance of a data chunk residing at a sensor node. VoI, therefore, can serve as a heuristic for path planning and prioritizing data retrieval from nodes. The novelty of this paper lies in providing algorithms which schedule multiple mobile sinks (AUVs) for data retrieval from nodes while maximizing the retrieved VoI. The class of algorithms discussed are based on greedy heuristics.

In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring problem in terms of the value of information (VoI) and rewards. We design a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. We used real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.

Environmental monitoring applications for wireless sensor and actor networks rely on position estimation in order to process or evaluate the observed data. The absence of efficient positioning techniques for sensor nodes operating in harsh environments calls for novel approaches. While monitoring the Amazon river, unprecedented characteristics of the river and its surroundings challenge the node communications and drifting of the nodes makes it difficult to use the existing positioning methods. To address these challenges, we propose a multi-hop localization technique that takes advantage of sensor mobility with local information exchange. The collected information is used to enrich the environmental data with location information. The maximum hop distance for actor affiliation is also adapted according to network characteristics to improve energy consumption behavior. The motion of the sensor nodes follows the advection of the fluid parcels in the river, which is modeled as a combination of a central streamline with a meandering motion around the rough surface. This translates into a stretching topology with correlated motion for sensor nodes. Through extensive simulations, we show that the nodes can be efficiently positioned using the proposed approach, as our technique is compliant with the movement patterns of the sensor nodes in the realistic mobility model of the river.

Realistic human mobility modeling is critical for accurate performance evaluation of mobile wireless networks. Movements of visitors in theme parks affect the performance of systems which are designed for various purposes including urban sensing and crowd management. Previously proposed human mobility models are mostly generic while some of them focus on daily movements of people in urban areas. Theme parks, however, have unique characteristics in terms of very limited use of vehicles, crowd's social behavior, and attractions. Human mobility is strongly tied to the locations of attractions and is synchronized with major entertainment events. Hence, realistic human mobility models must be developed with the specific scenario in mind. In this paper, we present a novel model for human mobility in theme parks. In our model, the nondeterminism of movement decisions of visitors is combined with deterministic behavior of attractions in a theme park. The attractions are categorized as rides, restaurants, and live shows. The time spent at these attractions are computed using queueing-theoretic models. The realism of the model is evaluated through extensive simulations and compared with the mobility models SLAW, RWP and the GPS traces of theme park visitors. The results show that our proposed model provides a better match to the real-world data compared to the existing models.

Geographical routing can provide significant advantages in wireless sensor networks. However in many sensor networks, it is difficult or costly to find the exact location of the nodes. The virtual coordinate techniques allow a network to acquire a coordinate system without relying on geographical location. In this paper, we describe MS-DVCR, an extension of a state-of-the-art virtual coordinate routing protocol (DVCR) with the ability to route towards a mobile sink. We describe the design principles and implementation of the proposed protocol and through an experimental study, we show that it matches the performance of a simple extension of DVCR for mobile sinks while providing a significantly lower energy consumption.

Aerial wireless sensor and actor networks are composed of multiple unmanned aerial vehicles. An actor node in the network has the capabilities of both acting on the environment and also performing networking functionalities for sensor nodes. Thus, positioning of actors is critical for the efficient data collection. In this paper, we propose an actor positioning strategy, which utilizes a hybrid antenna model that combines the complimentary features of an isotropic omni radio and directional antennas. We present a distributed algorithm for fast neighbor discovery with the hybrid antenna. The omni module of the hybrid antenna is used to form a self organizing network and the directional module is used for reliable data transmission. Extensive simulations show that our protocol improves the packet reception ratio by up to 50% compared to omnidirectional antenna. Moreover, the network reorganization delay is also reduced. The tradeoff between coverage and reorganization delay is also illustrated.

We consider an underwater wireless sensor network where baseline communication happens over acoustic, multi-hop routes from the underwater nodes to an on-shore station. The data collected by the nodes greatly exceeds the baseline communication capability. At best, the nodes can transmit digests of their full observations. In order for the sink to receive all sensed data, an autonomous underwater vehicle (AUV) is sent to each node for collecting data over short-distance, high data rate optical connections. The AUV then offloads all collected information to the terrestrial station via wireless communication when it surfaces. The observations made by the nodes vary in size and urgency. The information they provide has an associated value. Given a path of the AUV, we design scheduling strategies for the nodes to decide when and how much information (i.e., which digest) to transmit via acoustic routes so that the value of information reaching the terrestrial station is maximized. These strategies are compared via simulations on realistic scenarios. Our results show that scheduling algorithms that are able to locally estimate the value of information of a data digest provide the delivery of data with a significantly higher value of information. In contrast, uninformed algorithms, i.e., strategies that do not consider the value of information at the node level, provide only a marginal increase over the benchmark case of using only the AUV for data collection.

We have developed a multi-agent negotiation system to distribute decision making in cognitive radio networks through argumentation. The challenge in wireless network negotiation is to efficiently exchange information to facilitate a deal without incurring excessive communication overhead or indeterminate negotiation time. Our goal is to improve both total network throughput and the number of total supported connections. We detail a set of rules, a protocol, and a compact set of messages to conduct these negotiations and complete in finite time and with little overhead. We describe our simulation environment and present results of an illustrative scenario with various conditions. This scenario includes the ability of an agent to assert high priority, possibly triggering a downgrade of an existing, non-priority connection to a slower rate in order to accommodate more connections. We compare our system's total network throughput, number of connections, and request satisfaction score to several baselines with various levels of reconsideration and conclude that our system outperforms these other approaches in all metrics.

In this paper, we propose a scenario-specific human mobility model (TP-D) in theme parks. We focus on the disaster scenarios which have significant differences compared to the ordinary mobility behavior of the theme park visitors. The main goal of the theme park operation in disaster scenarios is the evacuation of the visitors from the disaster areas. We first model theme park as a combination of roads, obstacles, lands, and disaster events. We use real theme park maps for generating the theme park models. We incorporate the macro and micro mobility behaviors of the visitors in theme park models. We use the social force concept to model the impact of social interactions on the micro mobility of the visitors. Macro mobility decisions are based on the local knowledge of the visitors, the waypoints, and the disaster events. We analyze and compare the results of the simulation of our model with simulations of currently used models and real-world GPS traces of visitor movement.

Social networking websites have been increasingly popular in the recent years. The users create and maintain their social networks by themselves in these websites by establishing or removing the connections to friends and sites of interests. The smart phones not only create a high availability for social network applications, but also serve for all forms of digital communication such as voice or video calls, e-mails and texts, which are also the ways to form or maintain our social network. In this paper, we deal with the problem of automatically generating and organizing social networks by analyzing and assessing mobile phone usage and interaction data. We assign weights to the different types of interactions. The interactions among users are then evaluated based on these weight values for certain periods of time. We use these values to rank the friends of users by a sports ranking algorithm, which recognizes the changes in the collected data over time.

Theme parks are large crowded areas with unique characteristics in terms of movement behavior of visitors, attractions in different locations and walking paths connecting the attractions. Wireless sensor networks (WSNs) with mobile sinks can be used for various purposes including security and emergency issues as major challenges in such environments. Modeling of human mobility in theme parks allows us to consider scenario-specific applications of WSNs in these entertainment areas for event coverage purposes. In this paper, we propose a WSN model with mobile sinks and provide a novel approach to cover the events occurring in the environment. Furthermore, we propose new strategies for mobile sink positioning and event handling decision problems. We evaluate the benefits of our approach through extensive simulations using two sophisticated human mobility models for visitor movement.

This paper proposes a reporting decision protocol called IVE (for Information Value - Energy tradeoff), where individual nodes of an intruder tracking sensor network make decisions about the transmission of information chunks. Instead of trying to achieve raw data metrics (such as total transmitted data) the protocol aims to optimize the value of information (VoI) maintained by the customer. To achieve this, the nodes will need to perform inferences about the behavior of other nodes and the customer, such that the nodes do not need to send information which the customer already received from other sources or information which it can guess based on previous data. A simulation study compares the performance of the IVE protocol with the current state of the art of on-demand periodic reporting.

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SUMMARY Location estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks. The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring relies on an accurate position estimation to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth, and geographical features. The actors are not equipped with positioning adaptors, and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actors, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the nodes can be efficiently positioned using a multi‐hop approach with local information exchange only. The introduced approach is also applied to a scenario, where monkey swarm monitoring is simulated, to test the generalizability of the algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT In this paper, we propose two certificate mechanisms for preventing the Sybil attack in a vehicular ad hoc network (VANET): the timestamp series approach and the temporary certificate approach. We focus on an early‐stage VANET when the number of smart vehicles is only a small fraction of the vehicles on the road and the only infrastructure components available are the roadside units (RSUs). Our approach does not require a dedicated vehicular public key infrastructure to certify individual vehicles but RSUs are the only components issuing certificates. The vehicles can obtain certificates by simply driving by RSUs, without the need to pre‐register at a certificate authority. The timestamp series approach exploits the fact that because of the variance of the movement patterns of the vehicles, it is extremely rare that the two vehicles pass by a series of RSUs at exactly the same time points. The vehicles obtain a series of certificates signed by the RSUs, which certify their passing by at the RSU at a certain time point. By exploiting the spatial and temporal correlation between vehicles and RSUs, we can detect the Sybil attack by checking the similarity of timestamp series. In the temporary certificate‐based approach, an RSU issues temporary certificates valid only in a particular area for a limited time. To guarantee that each vehicle is assigned only a single certificate, at the issuance of the first certificate, it is required that the RSU physically authenticate the vehicle. When driving by the subsequent RSUs, however, the certificate can be updated in a chained manner. By guaranteeing that each vehicle is issued a single certificate in a single area, the Sybil attack is prevented. We provide mathematical analysis and simulation for the timestamp series approach. The simulation shows that it works with a small false‐positive rate in simple roadway architecture. Copyright © 2013 John Wiley & Sons, Ltd.

Advances in unmanned aerial vehicle (UAV) technology and wireless sensor and actor networks (WSAN) made it possible to equip small UAVs with sensors and deploy aerial sensor and actor networks. Aerial sensor networks enable high quality observation of events while reducing the number of requirements. Positioning of UAVs with actor nodes is critical in these systems for effective data collection. In this paper we propose an actor positioning strategy for aerial WSANs considering the scenario of toxic plume observation after a volcanic eruption. The positioning algorithm utilizes the Valence Shell Electron Pair Repulsion (VSEPR) theory of chemistry, which is based on the correlation between molecular geometry and the number of atoms in a molecule. The limitations of the basic VSEPR theory are eliminated by extending the approach for multiple central data collectors. The simulations show that the proposed system provides high connectivity and coverage for the aerial sensor network.

Distributed systems such as the Internet and wireless sensor networks must provide a high degree of resilience against errors and attacks. Besides steps that increase reliability of data and resources of the network, the topology structure itself plays a crucial role in the efficacy of the fault-tolerance behavior. The network topology is a supportive factor to reduce or avoid malfunction behavior of the system after a strike on a strategic node or a random failure of a node. For a self-organizing topology with numerous nodes, it is necessary to have a local fault tolerance measure instead of collecting information of the entire network to adjust the topology locally when needed. The local clustering coefficient determines the degree of the connectedness of the node's neighbors. The correlation between the clustering coefficient and fault tolerance is an open research problem. In this paper, we propose the clustering coefficient as a local metric for fault tolerance, in particular for wireless sensor networks. We describe how to increase the clustering coefficient by (a) exclusively adding and (b) exclusively removing links to a wireless sensor network topology. Simulation results indicate that the clustering coefficient is correlated to the fault tolerance of the system.

Sensor networks are distributed systems where nodes embedded in the environment collect readings through their sensors and transmit data to customers. The overall goal of these systems can be stated as maximizing a metric of the sensing quality while limiting the consumption of a set of scarce resources. In this paper we consider an intruder detection and tracking system where the sensing quality is a metric of the pragmatic value of the information provided by the network. This metric depends not only on the quantity and accuracy of information, but also on when and how the customers will use this information. We design a system which adapts its information transmission to the disruptive decisions made by the user, including a consideration for the cost of incorrect decisions.

Realistic modeling of the movement of people in an environment is critical for evaluating the performance of mobile wireless systems such as urban sensing or mobile sensor networks. Existing human movement models are either fully synthetic or rely on traces of actual human movement. There are many situations where we cannot perform an accurate simulation without taking into account what the people are actually doing. For instance, in theme parks, the movement of people is strongly tied to the locations of the attractions and is synchronized with major external events. For these situations, we need to develop scenario specific models. In this paper, we present a model of the movement of visitors in a theme park. The nondeterministic behavior of the human walking pattern is combined with the deterministic behavior of attractions in the theme park. The attractions are divided into groups of rides, restaurants and live shows. The time spent by visitors at different attractions is calculated using specialized queuing-theoretic models. We compare the realism of the model by comparing its simulations to the statistics of the theme parks and to real-world GPS traces of visitor movement. We found that our model provides a better match to the real-world data compared to current state-of-the-art movement models.

Wild life monitoring requires a sophisticated process of planning, installation, execution, data collection, and data interpretation. The effort and time spent increases tremendously with the area and the number of observed objects as well as with the time frame of the observations. However, the application of wireless sensor nodes enables a scalable sampling method and fine granularity of data difficult to obtain otherwise. By including resource rich actor nodes, the data collection and evaluation are further optimized. In this paper, a wireless sensor and actor network (WSAN) protocol is designed for capturing and monitoring the social interactions of the complex social network of gorillas. The nodes are intended to be attached on the apes forming a mobile network. The local interaction patterns among the nodes are analyzed throughout the network life time. The protocol then determines the social roles of the gorillas based on previous research findings about their social structure, and builds a profile for each ape, which demonstrates the status, kinship and role played within the society. As a result, the proposed protocol generates a social directory of the ape troop under observation, namely fAPEbook. The contribution of this paper is to evaluate the efficiency of the proposed algorithm by its ability to capture the different characteristics in the society with the established mobility models. The results suggest the applicability of the algorithm in field tests with wild life.

The worst case scenario for the life cycle of a sensor network is the fragmentation of a network which still has many functional and well-powered nodes. The loss of connectivity renders even the functional nodes useless as the nodes are not able to transmit their observations to the sink. A well-engineered sensor network will not fragment due to the energy consumption occurring during normal functioning. However, catastrophic events, which unpredictably destroy a large subset of the nodes, can transform a well engineered network into a heavily unbalanced one. Very often, even if the network is not yet fragmented, the connectivity is relying on one or more bridge nodes, which survived the catastrophic event accidentally. If the network operates as before, the bridge nodes will soon exhaust their power resources by having to route an unexpectedly large number of packets. This paper describes the Bridge Protection Algorithm (BPA), a combination of techniques which, in response to a catastrophic event, change the behavior of a set of topologically important nodes in the network. These techniques protect the bridge node by letting some nodes take over some of the responsibilities of the sink. At the same time, they relieve some other overwhelmed nodes and prevent the apparition of additional bridge nodes. To achieve this, BPA sacrifices the length of some routes in order to distribute routes away from critical areas. Through a simulation study we show that the application of these techniques can significantly decrease the load of the nodes in the critical areas, while only minimally affecting the performance of the network.

The effort and time spent in wild life monitoring increases with the area and the number of objects to observe. Deployment of wireless sensor nodes enables a scalable sampling method and fine granularity of data collection. When we include resource rich actor nodes the data collection and evaluation are further optimized. However realistic mobility data is missing for various animal species to develop prototypes of wireless network based monitoring systems. In this paper, the problem of the absence of realistic data is considered from a social network perspective. Node deployment and mobility algorithms are provided to model a complete system of an animal swarm to be used for animal social life monitoring. A novel spatial cut-off preferential attachment model and center of mass concept are used and extended for the models according to the characteristics of the animal swarms. In the application scenario of a gorilla swarm, each gorilla is equipped with a sensor node for the monitoring of the social system. The local interaction patterns among the mobile nodes are also monitored throughout the network life time to observe the social interactions among animals and to determine the role of each animal in the society. The performance of the monitoring protocol and the applicability of the deployment and mobility models are presented through extensive simulations.

The node mobility is a natural element of many wireless sensor and actor network (WSAN) applications. Recent advances in the development of small unmanned aerial vehicles (UAVs) with built in sensors made it possible to deploy aerial sensor and actor networks. An aerial network composed of small UAVs enables high quality observation for events while reducing the number of personnel and the risk for the operators. In order to have an effective data collection, the positioning of actors plays a critical role in aerial WSANs. In this paper we propose an actor positioning strategy for aerial WSANs considering the scenario of toxic plume observation after a volcanic eruption, which is one of the emerging applications of aerial UAV networks. Measuring the composition of volcanic plumes allows the computation of volcanogenic fluxes and provides insights into volatile degassing mechanisms. The actors in the proposed approach use a lightweight and distributed algorithm to form a self organizing network around the central UAV, which has the role of the sink in the WSAN. Our algorithm makes use of the Valence Shell Electron Pair (VSEPR) theory of chemistry, which is based on the correlation between molecular geometry and the number of atoms in a molecule. The performance of the proposed practical positioning algorithm is presented through extensive simulations.

A large part of the energy budget of traditional sensor networks is consumed by the hop-by-hop routing of the collected information to the static sink. In many applications it is possible to replace the static sink with one or more mobile sinks that move in a sensor field and collect the data through one-hop transmissions. This greatly reduces the power consumption of the nodes, which can be further reduced by choosing the appropriate moment of transmission. In general, the transmission energy increases quickly with the distance, and thus it makes sense for the nodes to transmit when one of the mobile sinks is in close proximity. Seeing the node as an autonomous agent, it needs to choose its actions of transmitting or buffering the collected data based on what it knows about the environment and its predictions about the future. The sensor agent needs to appropriately balance the following two objectives: the maximization of the utility of the collected and transmitted data and the minimization of the energy expenditure. We introduce the cummulative policy penalty as an expression of this interdependent pair of requirements. As a baseline, we describe a graph-theory-based approach for calculating the optimal policy in a complete knowledge setting. Then, we describe and compare three heuristics based on different principles (imitation of human decision making, stochastic transmission and constant risk). We compare the proposed approaches in an experimental study under a variety of scenarios.

Abstract While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks (WLAN), its study for multi‐hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by manipulating intelligently MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi‐hop fashion over wireless links. In this case, an attempting greedy node, if it behaves similarly as in a WLAN, trying to starve all its neighbors, then its next hop forwarder will be also prevented from forwarding its own traffic, which leads obviously to an end to end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc network, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. Then, we present a new strategy to launch such a greedy attack in a proactive routing based wireless ad hoc network. A detailed description of the proposed strategy is provided along with its validation through extensive simulations. The obtained results show that a greedy node, applying the defined strategy, can gain more bandwidth than its neighbors and keep the end‐to‐end throughput of its own flows highly reasonable. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless sensor and actor networks (WSANs) can be used for monitoring physical environments and acting according to the observations. In order to differentiate the actions based on the sensed information, WSANs comprise of various applications with different quality of service (QoS) requirements. QoS solutions for WSANs are challenging compared to traditional networks because of the limited resource capabilities of sensor nodes. In terms of QoS requirements, WSANs also differ from WSNs since actors and sensors have distinct resource constraints. In this paper we present LRP-QS, lightweight routing protocol with QoS support for WSANs. Our protocol provides QoS by differentiating the rates among different types of applications with dynamic packet tagging at the sensor nodes and per flow management at the actor nodes. Through extensive simulations we observe a greater packet delivery ratio and a better memory consumption rate in comparison with the related mechanisms.

Clustering allows efficient data routing and multi-hop communication among the nodes. In this paper, we propose Overlapping Clusters Algorithm (OCA) for mobile ad hoc networks. The goal of OCA is to achieve network reliability and load balancing. The algorithm consists of two discrete phases. The start-up phase takes battery and bandwidth capacity, transmission range, density, mobility, and buffer occupancy as input parameters to performs initial clustering for the entire network. The maintenance phase monitors the status of the network and keeps the network topology updated through local and global re-clustering. We compare the performance of OCA with Lowest ID, Highest Degree, WCA, and LCC algorithms in YAES simulator. The simulation results show that OCA outperforms all the compared algorithms in terms of network reliability and load distribution. The average numbers of global re-clusterings and reaffiliations were much lower in OCA than the other algorithms. However, OCA generates a larger number of clusters, which is expected considering that the nodes are allowed to be members of multiple clusters at the same time.

Wireless sensor and actor networks (WSAN) have become increasingly popular in recent years. The cooperative operation between sensor and actor nodes results in a major advantage against pure sensor networks and extends the range of possible application scenarios. One emerging application is the Amazon scenario in which stationary actors are deployed at accessible points in a thick forest structure and sensor nodes are thrown in a river flowing through the forest to gather observations from unreachable areas of the forest. This unprecedented and unique setting exposes two important challenges: (a) the dynamics of the river forms a continuously varying topology of sensor nodes requiring a highly adaptive network organization and (b) the inherent features of sensor and actor nodes, combined with rapid changes in the link structure of the network require efficient bandwidth utilization and data transmission. In this paper, we address these challenges by introducing SOFROP, a novel self-organizing and fair routing protocol for WSANs. The extensive simulations that are carried out for evaluation point out two highlights of SOFROP. These are the lightweight and efficient routing that is optimized for fairness and the locally acting adaptive overlay network formation.

Location estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks (WSAN). The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring in particular, relies on an accurate position estimation in order to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth and geographical features. The actors are not equipped with positioning adaptors and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actor nodes, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the positioning of the nodes is feasible using a multi-hop approach with local information exchange only.

The prevalence of the small-world phenomenon in numerous efficient networks, such as social networks, Internet, nervous systems, implies that small-worlds are an evolutionary solution for locally growing networks. These networks require short communication distances between their nodes in spite of their potentially large network diameter but at the same time are robust against randomly occurring failures. Integrating these properties into human-made communication networks drastically increases their efficiency and performance but represents an enormous challenge in the case of mobile ad hoc networks since their communication graphs rely on local construction principles. In this paper, the focus is on backbone-assisted ad hoc networks. In such networks devices connect arbitrarily to other devices within their transmission range, and dedicated devices are able to connect to a backbone network. This construction principle leads to a geometric graph that has a small average path length, but that is sensitive to random attacks or failures. The challenge in evoking small-world properties in backbone-assisted mobile ad hoc networks is to build a topology control algorithm that works with localized data (i.e. using neighboring information) despite knowledge of average path length and clustering coefficient requiring global information. Such a topology control algorithm is described here. Empirical results show that depending on network density, small-world properties can be efficiently evoked.

While the problem of greedy behavior at the MAC layer has been widely explored in the context of wireless local area networks, its study for multi-hop wireless networks still almost an unexplored and unexplained problem. Indeed, in a wireless local area network, an access point mostly forwards packets sent by wireless nodes over the wired link. In this case, a greedy node can easily get more bandwidth share and starve all other associated contending nodes by intelligently manipulating the MAC layer parameters. However, in wireless ad hoc environment, all packets are transmitted in a multi-hop fashion over wireless links. Therefore, if a greedy node behaves similarly as in WLAN case, trying to starve its neighbors, then its next hop forwarding node will also be prevented to forward its own traffic, which leads to an end-to-end throughput collapse. In this paper, we show that in order to have a more beneficial greedy behavior in wireless ad hoc networks, a node must adopt a different approach than in WLAN to achieve a better performance of its own flows. We then present a strategy to launch such greedy attack in a proactive routing based wireless ad hoc network. Through the extensive simulations, the obtained results show that by applying the proposed algorithm, a greedy node can gain more bandwidth than its neighbors and keep the end-to-end throughput of its own flows highly reasonable.

Many sensor networks are deployed to detect and track intruders. If the existence and location of sensor nodes is disclosed to the opponent, the nodes can be easily disabled or compromised. Wireless transmissions in the presence of the opponent are an important source of disclosure. In this paper, we first describe a way to quantify the stealthiness of the sensor node, with a numerical stealthiness metric. Then, we introduce a local model based dissemination protocol, try and bounce (TAB) which takes into account stealth considerations while reporting and forwarding observation reports.

Designing a sensor network congestion avoidance algorithm is a challenging task due to the application specific nature of these networks. The frequency of event sensing is a deciding factor in the occurence of congestion. Numerous sensors, simultaneously transmitting data, increase the probability of packet drops due to congestion close to the base station(s). In this paper, we propose a novel distributed congestion avoidance algorithm which uses the ratio of the number of downstream and upstream nodes along with available queue sizes of the downstream nodes to detect incipient congestion. Monitoring queue sizes of candidate downstream nodes helps ensure effective load balancing and fairness in our avoidance algorithm. Through simulation studies we observe a greater packet delivery ratio and higher network lifetime in comparison with other prevalent mechanisms.

Autonomous rechargeable sensor networks are becoming a feasible solution to many real world applications. In this paper, we propose a Uniform Sensing Protocol for autonomous rechargeable sensor networks. Our protocol aims to provide uniformly distributed sensing throughout the entire life-time of the network, thus increasing the overall network reliability. It considers the amount of available energy in the environment as well as the probability of encountering a specific number of threats. Using these parameters, each node estimates its own active period, such that uniform sensing is established. We compare the performance of our protocol with static and dynamic active time slot approaches. The simulation results show that the Uniform Sensing Protocol generates fewer failures and has a significantly longer mean time to failure than the other two schemes.

The urban environment of the early 21st century contains a large number of consumer devices under private and organizational ownership. Many of these devices contain sensors as well as communication devices. However, most of these sensors are only used for purposes internal to the device. By interconnecting these sensors we can obtain a network which can serve important societal goals. The technological challenges of interconnecting these sensors are relatively minor. The main problem is the human aspect: why would the owners of the sensors offer their readings for public use? In fact, privacy considerations might advise the exact opposite. Such networks will not be accepted unless every owner is motivated to allow the participation of its devices. In this paper we describe an architecture which enables such a system by the formal model of motivational gradients. The original source of motivational gradients are always humans or organizations; however, nodes acting as autonomous agents can negotiate motivational microgradients based on the original macrogradient introduced by humans. We evaluate the networking and computer-human interaction aspects of the proposed architecture.

Abstract Mobile sinks can significantly extend the lifetime of a sensor network by eliminating the need for expensive hop‐by‐hop routing. However, a sensor node might not always have a mobile sink in transmission range, or the mobile sink might be so far that the data transmission would be very expensive. In the latter case, the sensor node needs to make a decision whether it should send the data now, or take the risk to wait for a more favorable occasion. Making the right decisions in thistransmission scheduling problemhas significant impact on the performance and lifetime of the node. In this paper, we investigate the fundamentals of the transmission scheduling problem for sensor networks with mobile sinks. We first develop a dynamic programming‐based optimal algorithm for the case when the mobility of the sinks is known in advance. Then, we describe two decision theoretic algorithms which use only probabilistic models learned from the history of interaction with the mobile sinks, and do not require knowledge about their future mobility patterns. The first algorithm uses Markov Decision Processes with states without history information, while the second algorithm encodes some elements of the history into the state. Through a series of experiments, we show that the decision theoretic approaches significantly outperform naive heuristics, and can have a performance close to that of the optimal approach, without requiring an advance knowledge of the mobility. Copyright © 2007 John Wiley & Sons, Ltd.

The current methodology for distributing the frequency spectrum among users is in need of revamping. This is necessary in order to increase the number of users able to access the communication channels; to better manage usage in metropolitan areas, where the usage of wireless communication is still rapidly increasing; and to ensure adequate frequency allocation for users during peak usage times. Several methods and paradigms have been suggested as replacements for the current scheme of distributing spectrum among users. This paper proposes a simulation framework to simulate a broker-based system to implement a dynamic spectrum allocation environment. The broker operates based upon pre-defined spatial and temporal requirements in a manner where operational parameters are set up or collected dynamically. The broker system responds to the radio devices, allocating the suitable amount of frequency spectrum required for a particular application. The results of the simulations show a promising and efficient utilization of frequency spectrum resources.

In this paper, we argue that the roaming functionalities implemented at the data link layer (Layer 2) have certain drawbacks causing delay in the handoff process for mobile hosts in a wireless local area network. Through real experiments, we show how the triggering of the roam process is delayed which affects data transmission. Even though the network interface cards and drivers were from different manufactures, the decision process related to the hand-offs had significant similarities - the use of a crude timer and an un-sophisticated approach for detecting error conditions. Identifying the mechanisms that cause a mobile host to roam, we suggest a pro-active algorithm that eases the effects of waiting too long to roam. By monitoring the current received signal strength, we were able to initiate the probing process early enough which in turn helped the roaming process.

In this paper, we report on a series of experiments involving the speedups obtainable with time-parallel simulation of wireless ad hoc networks. A mobile ad hoc network scenario involving the AODV and DSDV routing protocols was simulated. The results and the performance of the serial NS-2 simulator were compared to the time-parallel simulation method for wireless ad hoc networks, previously introduced by the authors. The results of the time-parallel simulation are approximations, and we find that there is a trade off between the precision of the simulation and the achievable speedup. However, it is possible to find compromises where a precision of the range of 95-98%, sufficient for most applications, can be obtained up to 10 times faster than the time needed by a serial simulation

Asymmetric links are common in wireless networks for a variety of physical, logical, operational, and legal considerations. An asymmetric link supports uni-directional communication between a pair of mobile stations and requires a set of relay stations for the transmission of packets in the other direction. We introduce a MAC layer protocol for wireless networks with Asymmetric links (AMAC). The MAC layer protocol requires fewer nodes to maintain silence during a transmission exchange than the protocols proposed in [1, 2]. We present a set of concepts and metrics characterizing the ability of a medium access control protocol to silence nodes which could cause collisions.

We introduce a scalable algorithm for time-parallel simulations of wireless ad hoc networks and report on our results. Our approach decomposes the simulation into overlapping temporal components; the individual components are computed using an unmodified sequential network simulator such as NS-2. Our algorithm is iterative and the accuracy of the results increases with the number of iterations. We find that the approach allows the simulation of ad hoc networks with a number of nodes larger than those feasible with sequential network simulators on single CPUs. The algorithm is scalable, we can simulate larger time intervals by increasing the number of virtual processors carrying out the simulation. We identify the parameters that can be investigated with the algorithm and report on the accuracy of our results and on the achieved simulation speedup

The global Internet is rich in commodity resources but scarce in specialized resources. We argue that a grid framework can achieve better performance if it separates management of commodity tasks from the management of the tasks requiring specialized resources. Assuming a relative homogeneity of the commodity resource providers, the determining factor of grid performance becomes the latency of entering into execution. This effectively transforms the resource allocation problem into a routing problem. We present an approach in which commodity tasks are distributed to the commodity service providers by request forwarding on the n-cycle overlay network. We provide algorithms for task allocation and for the maintenance of the overlay network. By ensuring that the algorithms use only narrow local information, the approach is easily scalable to millions of nodes. For task allocation algorithms in a commercial setting, fairness is of paramount importance. We investigate the properties of the proposed algorithms from the fairness point of view and show how adding several hops of random pre-walk to the algorithm can improve its fairness. Extensive simulations prove that the approach provides efficient task allocation on networks loaded up to 95% of their capacity.

Developing network protocols for mobile wireless systems is a complex task, and most of the existing simulator frameworks are not well suited for experimental development. The YAES simulation framework was specifically developed such that it allows the fast prototyping of networking protocols, and support real-time experimentation and refactoring. By providing a large set of abstractions and generic implementations, a number of frequently used techniques such as genetic algorithms or neural networks can be created in matter of minutes. Our experience shows that by requiring only Java programming skills which computer science and engineering students commonly possess, YAES can be a useful tool for classroom use, as well.This paper presents the considerations behind the YAES architecture and provides a description of the system. As a case study, we present the steps necessary for running experiments on the energy efficiency behavior of the Weighted Clustering Algorithm (WCA).

We have developed adaptive coordinated medium access control (AC-MAC), a contention-based medium access control protocol for wireless sensor networks. To handle the load variations in some real-time sensor applications, ACMAC introduces the adaptive duty cycle scheme within the framework of sensor-MAC (S-MAC). The novelty of our protocol is that it improves latency and throughput under a wide range of traffic loads while remaining as energy-efficient as S-MAC. We illustrate such optimized trade-offs of AC-MAC via extensive simulations performed over wireless sensor networks. Our simulation results show that AC-MAC is as energy-efficient as S-MAC while its latency and throughput are always trying to follow the classic IEEE 802.11 MAC (no duty cycle), which outperform the S-MAC (fixed duty cycle), specially under the heavy load.

In this paper, we demonstrate how simulated annealing algorithm can be applied to clustering algorithms used in ad hoc networks; specifically our recently proposed weighted clustering algorithm (WCA) is optimized by simulated annealing. As the simulated annealing stands to be a powerful stochastic search method, its usage for combinatorial optimization problems was found to be applicable in our problem domain. The problem formulation along with the parameters is mapped to be an individual solution as an input to the simulated annealing algorithm. Input consists of a random set of clusterhead set along with its members and the set of all possible dominant sets chosen from a given network of N nodes as obtained from the original WCA. Simulated annealing uses this information to find the best solution defined by computing the objective function and obtaining the best fitness value. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA.

We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weighted clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the clusterheads and the members thereof, as obtained from the original WCA. The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control (MAC) protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA. Moreover, the loads among clusters are more evenly balanced by a factor of ten.

Virtual Reality Modeling Language (VRML) is widely used to represent, create, and display virtual reality objects and their environment. Some VRML applications require concurrent interaction by multiple users in a real-time distributed fashion. Such applications need a method for users to share and update the VRML objects in real-time. To allow concurrent shared real-time access, our approach is to store the VRML objects in an object-oriented database system (ObjectStore) in order to utilize the concurrency control mechanisms of the system. The authors present an architecture that allows multiple users to interact in a non-trivial way in such a shared VRML environment. We outline how the VRML world can be saved in ObjectStore and implement a series of test cases demonstrating concurrency issues arising from simultaneous updates. Our architecture uses ordinary Java enabled Web browsers with a VRML plug-in. A commercial Web server routes client requests to a custom application server which interacts with the object-oriented database. As users change the VRML world, our application server orders the requests and updates the master copy in the database.

We propose a new protocol for efficiently managing large ad hoc networks, i.e., networks in which all nodes can be mobile. We observe that, since nodes in such networks are not necessarily equal in that they may have different resources, not all of them should be involved in basic network operations such as packet forwarding, flooding, etc. In the proposed protocol, a small subset of the network nodes is selected based on their status and they are organized to form a backbone (whence the name "backbone protocol" or simply B-protocol to our proposed solution). The B-protocol operates in two phases: first the "most suitable" nodes are selected to serve as backbone nodes, then the selected nodes are linked to form a backbone which is guaranteed to be connected if the original network is. The effectiveness of the B-protocol in constructing and maintaining in face of node mobility and node/link failure a connected backbone that uses only a small fraction of the nodes and of the links of the original networks is demonstrated via simulation. The obtained results show that both the selected backbone nodes and the links between them in the backbone are considerably smaller than the nodes and the links in the flat network.

We consider a multi-cluster, multi-hop packet radio network architecture for wireless systems which can dynamically adapt itself with the changing network configurations. Due to the dynamic nature of the mobile nodes, their association and dissociation to and from clusters perturb the stability of the system, and hence a reconfiguration of the system is unavoidable. At the same time it is vital to keep the topology stable as long as possible. The clusterheads, which form a dominant set in the network, decide the topology and are responsible for its stability. In this paper, we propose a weighted clustering algorithm (WCA) which takes into consideration the ideal degree, transmission power, mobility and battery power of a mobile node. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol, Our clusterhead election procedure is not periodic as in earlier research, but adapts based on the dynamism of the nodes. This on-demand execution of WCA aims to maintain the stability of the network, thus lowering the computation and communication costs associated with it. Simulation experiments are conducted to evaluate the performance of WCA in terms of the number of clusterheads, reaffiliation frequency and dominant set updates, Results show that the WCA performs better than the existing algorithms and is also tunable to different types of ad hoc networks.