WiFi sensing technology has emerged as a promising technology for activity recognition, leveraging the Channel State Information (CSI) to capture fine-grained movement details. However, the difficulty and scarcity in collecting the required training CSI data with variations hinder the development and deployment of practical WiFi sensing systems in different settings. This paper presents MockiFi, a novel system that learns WiFi CSI data transformations across different activities of known individuals and generates CSI data for the activities of new individuals using their base activity CSI data and by mimicking transformations learned from known individuals. Our approach employs a Conditional Neural Process (CNP) to synthesize realistic activity patterns through the learning of pattern transitions using a cosine similarity-based loss function. The effectiveness of our approach is validated through extensive experiments, achieving a high cosine similarity score between the generated and real activity data, indicating the precision and reliability of the generated action fingerprints. We also show that a classifier trained on synthetic data of a new person can successfully recognize their actual activities, demonstrating zero-shot learning capabilities. These results show that MockiFi can help develop customized WiFi sensing systems without the need for collecting excessive new training data, and thus can facilitate their practical usage.

Sensing the temperature of liquids in containers is a critical process in various applications such as food safety, healthcare and environmental monitoring. Traditional approaches usually require a physical contact with the liquid, which may not be suitable for the liquids in sealed containers, and may also pose a risk for contamination in particular for the products used in healthcare. The contactless temperature sensing approaches that mainly rely on optical or laser based solutions, however, can only detect the temperature of a certain point or only the surface temperature of the liquid. Sensing through thermal cameras can provide scalability, however, they come with high costs. In this study, leveraging the ubiquitous availability of low-cost devices integrated with WiFi interfaces, we explore the feasibility of measuring the liquid temperature using fine-grained WiFi features. The proposed approach utilizes the amplitude variations across WiFi channel subcarriers extracted from Channel State Information (CSI) to detect the liquid temperature. In order to enhance the prediction accuracy, we also integrate principles from Newton’s Law of Cooling for regularization during the training of the neural network and propose a physics-guided machine learning framework (PMLF). Through our experiments with different liquids, temperature ranges and containers, we have demonstrated that the proposed cost-effective and scalable solution provides promising results in predicting the liquid temperatures.

Physical therapy exercises are critically important for the rehabilitation of patients with motor deficits. While these exercises can be most effective when performed properly under the supervision of a physical therapist, it may not be a viable option for all patients. Thus, there is a growing trend towards at-home physical rehabilitation tracking systems as they can be more accessible and flexible for patients. However, existing systems mostly depend on camera and wearable based solutions, which can be costly and limited. To this end, we propose a low-cost and non-intrusive end-to-end solution using IoT-based wireless sensing devices. Our solution, Wi-PT-Hand , leverages Channel State Information (CSI) captured from ambient WiFi signals and uses Bayesian optimizers and a hierarchical deep learning model trained to recognize the prescribed hand exercises. The proposed system includes (i) segmentation of the therapy time into activity and non-activity durations, (ii) recognition of the exercise performed in an activity segment, and (iii) counting of the number of repetitions of the exercise performed within that segment. Extensive experimental results show that the proposed system is robust and performs well in various real life scenarios, and thanks to the lightweight design it can work on low-resource edge devices properly.

Collection of environmental data from emergency sites where there is no or minimal cellular infrastructure exists is very critical for efficient response management. Unmanned aerial vehicles (UAV) can provide a tremendous support during that process thanks to their flexibility, agility and lower cost. A mesh network formed among the UAVs can facilitate the data collection process while also keeping the communication among them. However, as the age of the collected information (i.e., from the moment it is generated at the ground sensor node to the moment it is delivered to the emergency response center) defines the success of response tasks, the trajectory of the UAV mesh network should be determined carefully considering the timely delivery of the critical data. In this paper, we study the path planning problem for a UAV mesh network for AoI optimal data collection from the ground IoT devices in such emergency sites with minimal or no infrastructure. We explore different settings that could happen in such scenarios and develop an Integer Linear Programming (ILP) based model for each to optimize the UAV trajectories with the main goal of minimizing the maximum AoI from the collected data. In order to avoid the high complexity of ILP solutions, we also propose relaxed models. Through simulations, we compare the results in different scenarios in terms of the maximum AoI and UAV path lengths and discuss potential drawbacks in each.

Utilizing fine grained analysis of wireless signals for human activity recognition has gained a lot of traction recently. The unique changes to the ambient wireless signals caused by different activities made it possible to recognize these fingerprints through deep learning classification methods. Most of the existing work consider a set of physical activities or gestures and try to recognize each one of them as a separate class. However, this makes the classification task harder especially when the number of activities to recognize becomes larger and when these activities include movements from the same body parts. To address that, in this study, we consider the decomposition of each physical activity into the limbs and body parts involved in that activity and study a one-by-one recognition solution. We propose a Generative Adversarial Network (GAN)-based hierarchical method that not only recognizes the involved body limbs and facilitates the recognition of complex activities, but also mitigates the temporal effects in the collected signal data and thus provides a generalized solution. Our experimental evaluation shows that we can recognize unknown physical activities through the proposed hierarchical limb recognition based model with a small Hamming loss and by just using WiFi signal data from a single transmitter and receiver link.

Maintaining independence in daily activities and mobility is critical for healthy aging. Older adults who are losing the ability to care for themselves or ambulate are at a high risk of adverse health outcomes and decreased quality of life. It is essential to monitor daily activities and mobility routinely and capture early decline before a clinical symptom arises. Existing solutions use self-reports, or technology-based solutions that depend on cameras or wearables to track daily activities; however, these solutions have different issues (e.g., bias, privacy, burden to carry/recharge them) and do not fit well for seniors. In this study, we discuss a non-invasive, and low-cost wireless sensing-based solution to track the daily activities of low-income older adults. The proposed sensing solution relies on a deep learning-based fine-grained analysis of ambient WiFi signals and it is non-invasive compared to video or wearable-based existing solutions. We deployed this system in real senior housing settings for a week and evaluated its performance. Our initial results show that we can detect a variety of daily activities of the participants with this low-cost system with an accuracy of up to 76.90%.

Unmanned Aerial Vehicles (UAVs) can help data collection from ground sensors or Internet of Things (IoT) devices deployed even in hard to access areas and deliver them to their destinations as relays. However, the UAV trajectories should be planned carefully due to their limited battery lifetimes. Recently, Age of Information (AoI) has also been considered as a metric to quantify the freshness of the data collected during this process and the path of the UAVs are aimed to be optimized considering AoI. However, existing studies have defined the AoI of the collected data in the context of delivering the collected data to a specific destination only. Moreover, they assume the data is available at each IoT device before the UAV is dispatched. In this paper, we consider a set of base stations distributed in the area that a UAV travels through and define the AoI from the moment the data is generated till it is uploaded to any of the base stations by the cellular-connected UAV. We also consider data generation times at each IoT device requiring the UAV’s arrival to an IoT device after this time. Our goal is to minimize the maximum AoI of any collected data while also minimizing the mission time and the path of the UAV for energy saving. We model and solve the problem using Integer Linear Programming (ILP) and with a heuristic based solution. The results obtained in different scenarios show that heuristic approach can provide close to optimal ILP based results while running much faster.

Cellular-connected Unmanned Aerial Vehicles (UAVs) need consistent cellular network connectivity to effectively accomplish their designated missions. However, when navigating through regions with partial coverage, such as rural areas, the task of planning the flight paths for these UAV missions becomes notably intricate. Algorithms designed to solve this issue require significant computational resources, making them infeasible for active deployment where an algorithm must run in real time using small compute power. Furthermore, these algorithms exponentially scale in run-time with respect to the number of UAVs being considered. To tackle this problem, we model the parameter space as a discrete grid-world, enable collaboration between drones, and gather supervised data from nonlinear programming and unsupervised data from a simulated version of the environment with associated rewards. We then train a Deep Neural Network (DNN) on this data and approximate optimal results by combining imitation and reinforcement learning methods. This DNN can successfully be deployed at fast speeds using relatively small computational power and can generalize to unseen maps where drone collaboration can be used to reduce mission time. By using the results of a network trained on supervised data as a guiding hand during training, our reinforcement learning approach achieves results better than either method in isolation.

Since the rapid growth of e-commerce, the number of package deliveries to residential doorsteps has significantly increased. However, the convenience of online shopping has also led to a rise in package theft, resulting in frustration and financial loss for both consumers and companies. While various commercial solutions are available to address package theft, they often have considerable drawbacks, such as high ongoing costs and privacy concerns. In response to these issues, we introduce Wi-Alert, a budget-friendly WiFi sensing-based package detection system. Our solution analyzes Channel State Information (CSI) acquired from ambient WiFi signals and employs deep learning models trained to identify movements at the front door. The system accurately distinguishes between various actions, such as knocking, lingering visitors, package deliveries, and package theft. These actions trigger real-time alerts, enabling users to monitor their front door activity and swiftly respond to security threats. Through real-world experiments, we demonstrate the versatility and practical application of the system in diverse residential settings, including houses and apartment buildings. Our solution offers a convenient and economical approach to enhancing package security, providing peace of mind to individuals receiving deliveries.

Unmanned aerial vehicle (UAV) technology has become increasingly pivotal in various industries including agriculture, emergencies, and transportation. However, there is a growing need for more intuitive and unobtrusive control mechanisms. In response, our team has developed a groundbreaking technique that optimizes drone control and tracking through operator gaze. Through the use of eye-tracking interaction, we have created a more intuitive approach to human operation, which reduces operator workload and improves overall efficiency. After extensive testing on the Parrot ANAFI drone, we have concluded that this implementation has the potential to revolutionize drone control and elevate it to new heights.

WiFi sensing aims to utilize the changes in the Channel State Information (CSI) of WiFi signals due to the reflections from objects in the environment for sensing purposes. It uses machine learning classification models to predict physical actions being performed in a given environment (e.g., human activities such as walking, running). Thanks to the existing WiFi infrastructure in most indoor areas, this device-free technology can be used to provide low-cost motion detection and activity recognition opportunities for smart-homes. However, as the WiFi signals can be sniffed by adversaries, it can also be utilized by malicious actors to learn private information about the residents. To address this issue, motivated by the fact that the accuracy of WiFi sensing systems is highly reliant on the location of transmitter and receiver devices, we propose a simple yet effective solution based on the utilization of spatially distributed transmitter antennas (connected to a single source device) which communicate to a receiver device. The legitimate or allowed receiver is provided the schedule of transmitter antennas; thus, it can leverage this information to more accurately recognize activities performed within the environment. On the other hand, an eavesdropper who is unaware of the transmission schedule will encode the CSI frames from all transmitter antennas as if they were transmitted by a single source and thus will fail to recognize the activities properly. Through experiments, we show the effectiveness of this approach considering different number of transmitter antennas as well as against different levels of eavesdroppers.

Electric Vehicles (EVs) are being widely adopted as a green alternative to fossil-based vehicles. However, the current charging infrastructure for EVs is inadequate to meet the growing charge demand. Vehicle-to-Vehicle (V2V) charging offers a promising solution that enables a charge supplier EV to provide charging services to a charge demander EV in a distributed manner. Nevertheless, V2V matching and charge scheduling can disclose sensitive location information about the drivers, such as their whereabouts and driving patterns. In this paper, we propose a privacy-preserving scheme for centralized optimal matching of demander EVs with supplier EVs, while protecting their sensitive information. In our scheme, charge demanders report to a matching server their encrypted location information and the requested energy quantities, whereas charge suppliers report encrypted charge costs such that the matching server can learn only the cost to match each demander to each supplier without revealing any location information or the exchanged charge amount. Then, the Hungarian algorithm is used to match demanders to suppliers while minimizing the total cost. The security analysis and simulation results show that our scheme can achieve optimal V2V matching while preserving drivers’ privacy with negligible computation overhead. Overall, our proposed scheme provides an effective solution for V2V charging, while maintaining privacy and confidentiality of sensitive drivers’ information.

Physical therapy (PT) exercises are critically important for the rehabilitation of patients with motor deficits. While rehabilitation exercises can be most effective when performed properly under the supervision of a physical therapist, it can be costly in terms of several aspects and may not be a viable option for all patients. At-home systems offer more accessible and less costly solutions to patients while also providing flexibility in scheduling prescribed exercises. However, current systems mostly depend on camera based solutions that have limitations (i.e., deployment cost, requiring patients to be in the sight of camera, potential privacy violations) or wearable solutions that are cumbersome and intrusive. To this end, in this paper, our goal is to leverage the WiFi infrastructure available in most indoor locations (i.e., homes, apartments, nursing homes, etc.) for tracking the exercises prescribed to patients during their rehabilitation. Our solution, Wi-PT, is based on the analysis of Channel State Information (CSI) captured from ambient WiFi signals, and uses deep learning models trained to recognize the prescribed physical therapy exercises. Through our experiments, we show that the proposed solution can successfully recognize different types of physical therapy exercises such as hand and finger movements, limb movements and movements performed with exercise equipment. Moreover, we show that our system can recognize the person performing different activities and can identify when they are at rest or actively performing an exercise.

In semi-opportunistic mobile crowdsensing (SO-MCS), workers are asked to provide the matching platform with multiple paths they find acceptable between their starting locations and destinations in order to alleviate the problem of poor coverage in opportunistic MCS without forcing them to take potentially much costly and hence undesirable paths as in participatory MCS. While these alternative paths open up new assignment possibilities between workers and tasks, they also make it more challenging to find a stable or preference-aware task assignment (TA), as they bring a new dimension to the TA problem (i.e., workers/paths/tasks instead of workers/tasks as in previous work), and introduce complex requirements to achieve stability by satisfying user preferences. In this paper, we formally define the stability conditions for three-dimensional task assignments in SO-MCS, and propose two polynomial-time TA algorithms: an exact algorithm for SO-MCS systems with uniform worker qualities, and a c-approximate algorithm for general SO-MCS systems, where c is the number of the acceptable paths of the worker with the largest set of acceptable paths. Through extensive simulations, we demonstrate that the proposed algorithms significantly outperform the state-of-the-art TA algorithms in terms of stability (or user happiness) in most scenarios.

International mobile subscriber identity (IMSI) sharing-based aggregated communication aims to connect multiple Internet of Things (IoT) devices to the mobile operator’s core network over the same subscriber line. IoT devices with low data rates and long data sending intervals are first grouped together and assigned the same subscriber identity. They then take turns to perform their data exchanges using the same cellular connection, yielding huge savings in resource (e.g., number of active bearers) usage. Current solutions however do not consider different device traffic characteristics, the flexibility in traffic patterns, and dynamic network environments where new IoT devices join and existing ones leave the network. In this article, we study the problem of the grouping of IoT devices that will share the same subscriber identity based on their traffic patterns which can also be slightly shifted. We also study the efficient regrouping of these devices as the set of devices in the network changes. We first solve the optimal grouping and traffic aggregation problem for the initial and updated network states using integer linear programming (ILP). Then, to avoid the high complexity of ILP solutions, we develop heuristic-based solutions. Through extensive simulations, we show that heuristic-based algorithms can provide close to optimal ILP-based results while running much faster. The results also show that shifting-based grouping provides more resource saving compared to no-shifting-based aggregation and the proposed solution for dynamic environments can maintain the resource saving with a much lower complexity.

WiFi sensing using channel state information (CSI) offers a device-free and nonintrusive method for human activity monitoring. However, the data-hungry and location-specific training process hinders its scalable deployment at large sizes. In this work, we proposeWiFederated, a federated learning (FL) approach to train machine learning models for WiFi sensing tasks. Using WiFederated, client devices can not only perform training in parallel at the edge instead of sequentially at a central server but can also collaboratively learn and share generalizable location-independent traits about physical actions being monitored. We demonstrate that an FL model trained on as few as 2–3 locations can provide high prediction accuracy in new locations even without any data available from them. We also demonstrate how new locations can achieve higher prediction accuracy even with a small number of available samples when using the pretrained FL model rather than training from scratch. The results show that the FL model can save local training epochs and reduce the need for large data collection at each new location. Thus, the proposed WiFederated system scales as more locations are added. We show that WiFederated provides a more accurate and time-efficient solution compared to existing transfer learning and adversarial learning solutions thanks to the parallel training ability at multiple clients. By introducing new client selection methods during the FL process, we also show that accuracy can further increase. Finally, we evaluate the feasibility of training models at the edge and introduce continuous annotation to allow for continuous learning over time.

In opportunistic mobile crowdsensing, participants (workers) accept to carry out the requested sensing tasks only if they are already close to or within the regions of interest. Thus, the existence of an assignment opportunity between a worker-task pair strictly depends on whether or not the worker will visit the task region. However, when worker trajectories are uncertain and hence not known in advance, existing solutions fail to produce an effective task assignment. Besides, a satisfactory task assignment should respect the preferences and capacity constraints of workers and task requesters, which are generally neglected in the literature. In this study, we address all of these issues together and propose novel task assignment algorithms for different settings, which we prove to be optimal in terms of preference awareness (or stability). Extensive simulations performed on both synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms significantly outperform the existing solutions in terms of preference awareness and average quality of sensing attained in the final task assignment in almost all scenarios.

Through-wall sensing systems can aid in performing building security, and collecting analytics or more ominously be leveraged for surveillance. With the pervasive nature of WiFi routers and devices in our office buildings and homes, we essentially place an unencrypted (at the frame level) transmitting source directly in our buildings which can then be leveraged for surveillance by adversaries. In this work, we study such a device-free WiFi sensing system for occupancy monitoring and crowdcounting and evaluate it in a number of through-wall conditions. We demonstrate that with a proper analysis of Channel State Information (CSI) collected from the WiFi signals, we can recognize both the presence of targets as well as their moving direction in a hallway environment which can be leveraged to track and count the flow of traffic throughout a building. We specifically demonstrate through real world experiments how an adversary with very limited physical access to a building can still successfully collect surveillance data of a target area through the wall.

Precision agriculture uses precise sensor data collected throughout farmland to give farmers better insight into their land, allowing for greater crop yields and reduced resource usage. However, existing solutions require high hardware costs thus limiting large scale deployments. To address that, we propose a low-cost and scalable solution for sensing physical attributes of soil using IoT based WiFi sensing devices. By understanding variations in WiFi radio signals with channel state information (CSI) and machine learning models, we evaluate the proposed soil sensing system through experiments on physical soil traits such as soil moisture content, soil texture and position. Moreover, we also demonstrate how a mesh network of WiFi sensing devices allows us to predict the physical traits of the soil in the area between each pair of sensors, allowing for an increase in sensing area coverage as nodes are added.

Cellular-enabled unmanned aerial vehicles (UAVs) require almost continuous cellular network connectivity to fulfill their missions successfully. However, the area (e.g., rural) they fly over may have partial coverage, making the path planning of such UAV missions a challenging task. Recently a tolerable outage duration is taken into account for such UAVs, and the trajectory optimization under this outage duration is studied. However, these existing studies consider only a single UAV and focus on optimization of each UAV's own path separately even in multi-UAV scenarios. In this paper, we study the trajectory optimization problem for cellular-enabled UAVs by taking into account the collaboration among UAVs. That is, for a given set of UAVs, each with a mission to fly from a starting point to an ending point, we aim to optimize the total mission completion time for all UAVs such that none of them has a connection outage more than a threshold. We let UAVs collaborate and provide connectivity as relays to each other to solve their outage problem and shorten their trajectories. We first model and solve this problem using nonlinear programming after discretization of the problem. Since it takes longer to solve the problem with such an approach, we then provide a graph-based approximate solution that runs fast. Numerical results show that the proposed approximate solution provides close to optimal results and performs better than state-of-the-art solutions that consider each UAV separately without collaboration among UAVs.

One of the key problems in mobile crowdsensing (MCS) systems is the assignment of tasks to users. Most of the existing work aim to maximize a predefined system utility (e.g., quality of service or sensing), however, users (i.e., task requesters and performers/workers) may value different parameters and hence find an assignment unsatisfying if it is produced disregarding these parameters that define their preferences. While several studies utilize incentive mechanisms to motivate user participation in different ways, they do not take individual user preferences into account either. To address this issue, we leverageStable Matching Theorywhich can help obtain a satisfying matching between two groups of entities based on their preferences. However, the existing approaches to find stable matchings do not work in MCS systems due to the many-to-one nature of task assignments and the budget constraints of task requesters. Thus, we first define two different stability conditions for user happiness in MCS systems. Then, we propose three efficient stable task assignment algorithms and discuss their stability guarantees in four different MCS scenarios. Finally, we evaluate the performance of the proposed algorithms through extensive simulations using a real dataset, and show that they outperform the state-of-the-art solutions.

Identifying positions of mobile devices within indoor environments allows for the development of advanced applications with context and environmental awareness. Classic localization methods require GPS; an expensive, high power consuming and inaccurate solution for indoor situations. Relative positioning allows nodes to recognize their location in relation to neighboring nodes to develop an internal mapping of their own position compared to those around them. This enables a quick deployment of a given system in new, unknown indoor environments without requiring prerequisite human mapping steps. In this paper, we develop a Monte Carlo Localization (MCL) based anchorless, relative positioning algorithm which simplifies the problem to considering three states of interaction between devices: approaching, retreating and invisible. Considering three states contributes to existing MCL methods which so far only consider binary states of visible or invisible. Through our anchorless approach, we show by simulations that TrinaryMC can provide more accurate positioning information than existing anchor based methods without relying on GPS, hence decreasing hardware costs and energy consumption from the use of GPS modules as well as reducing communication overhead compared to state-of-the-art MCL methods.

In mobile crowdsensing applications that rely on opportunistic sensing and communication, efficient task assignment strategies are needed to ensure that the tasks are completed before their expiration time. This requires to optimize the tradeoff between high task completion ratio and cost-efficiency by assigning tasks only to a small group of users who are expected to be of most assistance to task owners. To address this issue, in this paper, we propose two new task assignment protocols based on a new metric that accurately measures the utility of users to each other in performing tasks in specific regions. Through simulations we show that the proposed protocols not only provide a high task completion ratio, but also utilize the network resources efficiently by assigning tasks to as few users as possible, hence they perform better than the previous work.

Thanks to the recent advances in wireless power transfer technology and its adoption in mobile portable devices (e.g., smartphones), an alternative energy replenishment option for users has emerged through peer-to-peer energy sharing among devices. Such a notion of energy sharing among nodes in a mobile opportunistic network can help balance the energy levels of nodes and can keep them connected, prolonging the lifetime of the network. Existing works studying the energy balancing problem mainly focus on decreasing of the energy difference among nodes as fast as possible, thus consider sharing of the energy among meeting nodes equally. However, in an opportunistic network consisting of mobile devices carried by people (i.e., also called mobile social network), due to the underlying social relations between people, there will be multiple social groups affecting the contact relations between nodes. While nodes in the same group interact more often, the nodes in different groups interact less frequently. Moreover, there is usually a smaller number of nodes from different groups that interact (i.e., bridge nodes), providing limited opportunity for energy transfer between groups. In this study, we look at the energy balancing problem considering the underlying social network structure and present a two-stage social-aware energy balancing protocol for a fast balancing process. To this end, we integrate the roles of nodes (e.g., bridge/non-bridge node) as well as the average energy differences between different groups to determine the amount of energy transfer between meeting nodes. Through simulations, we demonstrate that the proposed social-aware energy balancing protocol performs better than the state-of-the-art.

Current research that use wireless charging for the energy replenishment of nodes in a network mostly considers charging of sensors from special mobile charging vehicles (MCV) and focuses on optimal path planning of these MCVs. However, it may not be practical to use such vehicles due to its operational cost and other restrictions. To this end, in this paper, we consider to utilize smartphones owned by people and let the low cost Internet of Things (IoT) devices harvest energy from the smartphones that pass by. We study the wireless crowd charging of such IoT devices from these smartphones in an opportunistic manner, without changing their actual trajectories. As each smartphone user will limitedly support such a crowd charging process, the selection of IoT devices that will be charged from each smartphone has to be determined based on the trajectories of smartphone users. To address that, we model the problem using Mixed Integer Linear Programming (MILP) and decide the optimal charging relation between smartphones and IoT devices. Through simulations on both synthetic and real user traces, we show that MILP based solution offers a more successful crowd charging outcome with a better charging ratio than the greedy approach where the IoT devices can harvest maximum possible energy from all users encountered.

Aggregated Internet of Things (IoT) communication aims to use core network resources efficiently by providing cellular access to a group of IoT devices over the same subscriber identity. Leveraging the low data rates and long data sending intervals of IoT devices, several of the IoT devices in the same serving area of the core network are grouped together and take turns to send their data to their servers without causing overlaps in their communication. In this paper, we take this approach further and benefiting from the flexibility in data sending schedules, we aim to increase savings in cellular resources by shifting (delaying or performing earlier) the regular traffic patterns of IoT devices slightly. To this end, we consider two different traffic shifting models, namely, consistent and inconsistent shifting. We first solve the optimal aggregation of IoT devices under each model by using Integer Linear Programming (ILP). In order to avoid the high complexity of ILP solution, we then develop a heuristic based solution that runs in polynomial time. Through simulations, we show that heuristic based solution provides close to optimal results in various scenarios and shifting based aggregated communication offers more resource optimization (i.e., smaller number of bearers needed to connect all IoT devices) than the aggregated communication with no shifting.

Finding efficient task assignments is key to the success of mobile crowdsensing campaigns. Many studies in the literature focus on this problem and propose solutions that optimize the goals of mobile crowdsensing platform, but disregard user preferences. On the other hand, in a few recent studies that consider user preferences, workers are assigned a single task at a time, and the effect of these assignments to their prospective utilities is ignored. In this paper, we address these issues and study the task assignment problem considering both the user preferences and impact of each task assignment on the long-term utility of workers given the spatio-temporal characteristics of tasks. We propose a dynamic programming based task assignment algorithm that guarantees the satisfaction of users with their assignments. Through simulations, we compare it with a state-of-the-art algorithm and show the superiority of our algorithm in various aspects.

Channel state information (CSI) provides rich insight into the physical characteristics of an environment through radio subcarrier frequencies in orthogonal frequency-division multiplexing (OFDM) systems. Many recent studies explore this rich source of data to produce quite accurate results in device-free localization, human-body pose recognition, and device-free person identification under the umbrella of WiFi Sensing. Most works thus far rely on the use of the Intel 5300 Network Interface Card (NIC), a device requiring connection to a host computer to function. Because of this requirement, the weight and form factor of CSI recording capable devices (receiver or RX) has limited the abilities of researchers to explore certain aspects of WiFi sensing such as active repositioning and mobility of RX devices. To address this, in this paper, we use the ESP32 microcontroller to develop a simple and lightweight solution for CSI collection leveraging recent additions to the Espressif IoT Development Framework which allows user developed programs to access CSI directly. The system can work standalone or attached to a smartphone for advanced online computations. Thus, it can be easily deployed, repositioned, and carried on mobile objects, which can then help improve the performance of sensing tasks. We evaluate the performance of our proposed system through several deep-learning based human activity recognition experiments and show that the repositioning and mobility of RX devices can provide increases in accuracy upwards of 29.4% and 28.2%, respectively, compared to the commonly considered static RX scenario. Finally, we produce an easy to use open source codebase for researchers to immediately begin exploring the new possibilities (e.g., massive deployment) available by the usage of the proposed system.

Energy management is a challenging issue to be addressed in networks consisting of battery-powered devices. With the recently emerging wireless power transfer technology, many studies have utilized wireless charging to address this problem and provide energy ubiquitously to these devices for making them functional continuously. Besides the well- studied problems such as optimal scheduling of mobile chargers, recently an interesting problem of energy balancing among a population of mobile nodes has been considered to prolong the lifetime of the network through the opportunistic energy exchanges between the nodes. The state-of-the-art solutions target an energy balance among the devices as fast as possible but they waste energy due to the loss during peer-to-peer energy transfer. In this paper, we study the energy balancing problem that aims to minimize both the energy difference between nodes and the energy loss during this process. To this end, we propose three different energy sharing protocols between nodes based on different heuristics. Through simulations, we show that all the proposed algorithms show better performance than the state-of-the-art. The third proposed algorithm achieves the best performance by reaching an energy balance between nodes while keeping the maximum possible energy in the network (i.e., minimum loss).

One of the fundamental challenges in mobile crowdsensing (MCS) systems is efficient task assignment. Existing solutions consider the problem from system's point of view and try to maximize the system utility or minimize the cost of sensing. However, such a task assignment process does not consider the user (i.e., workers and task requesters) preferences and can yield unhappy users with their assignments, impairing the participation of users in the future. To incentivize the user participation, stable matching based solutions can be utilized to result in satisfactory assignments that will make the users happy based on their preferences. However, this may adversely affect the system utility especially when the set of eligible number of workers for each task is limited. To address this issue, we study the task assignment problem in MCS systems that maximizes the main system utility (i.e., number of workers and tasks assigned) as a system oriented goal while generating as happy users as possible with their assignment. As the problem is NP-complete, we first solve the problem optimally using Integer Linear Programming (ILP) and then we propose a heuristic based polynomial solution that runs very fast. Through simulations, we show that the proposed approach achieves the maximum possible system utility while generating small and close to optimal user unhappiness as in ILP results.

Crowdsourcing applications are proven to be a promising tool to gather valuable information, which can be used for a wide range of tasks, such as ensuring public safety. Traffic data collected using these applications have been used for efficient evacuation planning in large cities. In this paper, we propose to use regression-based machine learning methods to predict hourly taxi rides for a given location in a target day of week and month. The presented method can be used for the following purposes: 1) Predicting the number of taxi rides for a given location at a given time, 2) Identifying hot spots in a city, 3) Getting a rough count of the population density at a given location at a targeted hour, and 4) Planing evacuation routes for possible disasters. The presented approach has potential use for resource planning and evacuation in large cities. The Taxi and Limousine Commission (TLC) trip record data collected from 2017 to 2018 was used for this experiment. It was found that random forest regression can successfully predict hourly taxi drop-offs for a given taxi zone as well as for the entire city of New York.

SDN is based on the idea of a centralized controller with a global view of the network topology. However, in large networks, multiple controllers work together to perform global network functions. This presents challenges in load balancing, consistent network view, and controller placement for scalability and reliability. When multiple controllers reside in different domains, their communication challenges are increased as each may have its own security and access policies. We present a reactive distributed SDN application built as a custom module in Floodlight that allows multiple controllers to make joint decisions where the controllers reside in different domains by communicating with an external server for information about participating organizations. Such a framework would be useful, for example, for providing access to patient information distributed among different hospital networks, big data sets between research institutions, or public safety data sets during disaster or emergency. The resulting approach will allow the SDN application layer to handle inter-domain traffic and enable data access between different organizations/agencies while respecting their different respective policies.

Energy is a scarce resource in mobile networks consisting of devices running on batteries. Thus, many studies have looked at the energy management issue in these networks from different aspects. Thanks to the recent advances in wireless power transfer (WPT) technology, the wireless charging of the mobile devices has been considered for their continuous operation. While most of the research efforts have focused on the scheduling of mobile chargers for charging of the devices (e.g., sensor) in the field, interesting research problems such as energy balancing among a population of nodes have also emerged with the consideration of bidirectional wireless charging among nodes. Energy balancing aims to balance the energy among nodes towards prolonging the network lifetime especially when external energy sources are not available. Previous studies target an energy balance among the devices as fast as possible but they waste energy in the network during this process due to the excessive interactions between nodes. Moreover, they do not take into account the heterogeneous contact relations between the nodes in the network. In this paper, we address these issues and present efficient and loss-aware energy balancing protocols considering the contact graph heterogeneity between nodes and a time threshold for completing the energy balancing. Simulation results show that the proposed algorithms outperform the previous work by reaching a better energy balance with a lower energy loss within the restricted relations among nodes in the network.

Device-to-device (D2D) communication enables machine-type devices (MTD) in Internet-of-Things (IoT) network communicate directly with each other and offload the cellular network. However, it may introduce interference as they share the same spectrum with the other devices that are directly connected to the base station. In this study, we look at the problem of assigning D2D communicating IoT pairs to the IoT devices that are directly connected to the base station such that the overall system throughput is not only maximized but also a stable matching is obtained. Different than previous work, we study many-to-one matching and propose a distributed privacy preserving stable matching process for efficient resource allocation without releasing location information.

In mobile social networks, the mobility and connectivity of nodes are often non-deterministic. Source and destination nodes may not have a meeting opportunity and the content dissemination and delivery most of the time require the cooperation of nodes. However, this causes nodes spend energy, thus, they may be reluctant to participate in the dissemination process to conserve energy. One approach to motivate user participation is to transfer energy for their service so that their potential loss is compensated. However, this makes routing problem much challenging as the source node needs to decide not only the best relay nodes but also the amount of energy transfer to them. In this paper, we study this energy sharing based content delivery problem in mobile social networks. To this end, we assume that a node is willing to carry the content as long as the energy received for this delivery lasts, after which it drops the content (i.e., time-to-live). We utilize optimal stopping theory and dynamic programming to model the content delivery problem under this energy sharing paradigm between the nodes. The simulation results show that energy sharing based content delivery can potentially increase the routing performance under certain settings.

Increasing software capabilities and complicated applications running on smartphones have increased the quality of life for users. However, the battery lives of smartphones have stayed limited due to the respectively slower improvements in battery technology. Users are often required to find a charging port and connect the phone to the port through a cable. A lot of times, this process can be irritating or even infeasible. Through adoption of emerging wireless power transfer technology in these devices, charging process has transformed into a new dimension. Moreover, this has brought the opportunity for wireless energy exchange between mobile devices ubiquitously. In this paper, we investigate the potential of peer-to-peer energy sharing to reduce the burden of traditional cord- based charging process. The devices of users can make use of energy available from other users' devices based on their meeting patterns so that the battery level of their devices could be maintained within acceptable level without the need of charging it through a cable frequently. Our specific goal in this study is to find the maximum number of traditional way of charging times that could be skipped through utilization of available energy in other users in the vicinity with wireless energy sharing. To this end, we use dynamic programming approach to find the optimal skipping patterns for selfish and cooperative energy exchange cases and verify the results with brute force.

Limited battery capacity has been the main bottleneck for smartphones. Users are required to charge their smartphones frequently to keep them alive. Access to a charging facility, however, may not be possible especially when users are outside. This has caused users to charge their devices at every opportunity with as much power as possible. While this results in overcharging of devices unnecessarily, it might have brought an opportunity for the realization of power sharing among mobile devices. In this paper, we introduce the concept of crowdcharging which aims to provide mobile users with ubiquitous power access through crowdsourcing. We first discuss the feasibility of crowdcharging from users' perspective and present some analysis and survey results showing the interest and need. We then look at the software and hardware challenges to build such a system. To this end, we have developed a mobile app that builds a mobile social network environment among the users and manages the entire process of power sharing between the mobile devices. We present the software implementation details using P2P wireless energy sharing and provide initial lab results with actual wireless charging hardware.

In this paper, we address the problem of path planning for multiple unmanned aerial vehicles (UAVs), to gather data from a number of roadside units (RSUs). The problem involves finding time-optimal paths for multiple UAVs so that they collectively visit all the RSUs, while also exchanging information at their own point when they fly from a starting point to the final location. We solve the problem by applying modified evolutionary methods based on genetic algorithm (GA) and harmony search (HS). The modified search methods seek to determine the overall shortest path utilizing various evolutionary operators regarding each UAV which has identical properties at the start location. Numerical results are introduced under different scenarios and the performances of the proposed algorithms are evaluated.

The potential of electric vehicles (EV) to reduce foreign-oil dependence and improve urban air quality has triggered lots of investment by automotive companies recently and mass penetration and market dominance of EVs is imminent. However, EVs need to be charged more frequently than fossil-based vehicles and the charging durations are much longer. This necessitates in advance scheduling and matching depending on the route of the EVs. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where there can be a lot of privacy exposure if matching of suppliers and EVs is achieved in a centralized manner. To address this issue, in this paper, we propose a privacy-preserving distributed stable matching of EVs with suppliers (i.e., public/private stations, V2V chargers) using preference lists formed by partially homomorphic encryption-based distance calculations while hiding the locations. The simulation results indicate that such a local matching of supplier and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users.

Electric vehicles are becoming parts of our daily lives with the increasing investment from auto industry. However, their charging is an issue as this requires frequent charging and longer waiting times compared to traditional gasoline-based vehicles. The charging is typically done at residential or public charging stations. With the increased dominance of electric vehicles, one potential solution is to exploit vehicle-to-vehicle charging (V2V) where an electric vehicle can charge another one through a converter-cable assembly. In such cases, however, there needs to be a protocol between the charge supplier and receiver to authenticate each other and authorize the vehicle to open its charging ports. In this paper, we study this problem of authentication and propose a protocol that will utilize key exchange among the users without relying on certificates. We implemented the proposed protocols under WiFi-direct and Bluetooth and demonstrated that the approach can provide the necessary framework of communication before charging starts without any additional overhead.

Vehicle-to-vehicle charging/discharging system can provide more flexibility to electric vehicles (EV) with increased range operation. In this paper, we propose the usage of EVs with excess energy than they need as alternative charging points for other EVs that are in need of urgent charge energy. To this end, we develop a mobility model for EVs during their trips and provide communication with other EVs in proximity through a location based social networking system. Simulation results show that such a system can decrease the number of drivers with range anxiety yielding larger number of EVs operating in the area while providing mutual benefits to sellers and buyers without having installation costs of new dedicated charging stations.

Fueled by users' demand, mobile devices are becoming more complex, increasing their power requirements. However, the battery technology is advancing slower than demand which results in shrinking battery lives (i.e., currently around a day). Hence, users need to charge their devices frequently, mostly by tethering them to a cord. Anxiety of losing power in the middle of a critical task during which users may not have access to charging facilities have increased interest in opportunistic charging with the aim of keeping the devices with as much power as possible. Recently power sharing technologies and gadgets have emerged enabling harvesting power from other mobile devices in the user's vicinity. In this paper, we discuss the energy sharing in mobile social networks whose nodes are human-carried mobile devices operating on batteries. We investigate the limits of power sharing among mobile devices by analyzing their current charging patterns and the social (i.e., close-proximity) interactions between the people carrying these devices. Moreover, we propose an energy sharing model by pairing the nodes in a mobile network into power buddies. Simulation results show that a typical application scenario of energy sharing among power buddies provides a remarkable amount of saving in the utilization of power available in the entire network.

WiFi offloading has been exploited as a quick and viable solution to decrease the burden on cellular networks. In this paper, we study the problem of deploying new WiFi access points (AP) in a city-wide area for offloading purposes. Different than previous work which look at the problem only from operator's perspective and targets the maximization of offloaded traffic volume, we approach the problem by integrating the user perspective as well. We propose a new AP deployment scheme that aims to increase average individual user satisfaction while still achieving high offloaded total data traffic volume from all users. As the simulation results demonstrate, the proposed approach can achieve more user level satisfaction compared to other algorithms that only target offloaded traffic maximization while keeping operator's benefit from offloading close to others.

Overlay network topology together with peer/data organization and search algorithm are the crucial components of unstructured peer-to-peer (P2P) networks as they directly affect the efficiency of search on such networks. Scale-free (power-law) overlay network topologies are among structures that offer high performance for these networks. A key problem for these topologies is the existence of hubs, nodes with high connectivity. Yet, the peers in a typical unstructured P2P network may not be willing or able to cope with such high connectivity and its associated load. Therefore, some hard cutoffs are often imposed on the number of edges that each peer can have, restricting feasible overlays to limited or truncated scale-free networks. In this paper, we analyze the growth of such limited scale-free networks and propose two different algorithms for constructing perfect scale-free overlay network topologies at each instance of such growth. Our algorithms allow the user to define the desired scale-free exponent ( γ). They also induce low communication overhead when network grows from one size to another. Using extensive simulations, we demonstrate that these algorithms indeed generate perfect scale free networks (at each step of network growth) that provide better search efficiency in various search algorithms than the networks generated by the existing solutions.

Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context-free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context-free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: 1) recognizing mobility patterns of monitored agents in different event data sets collected by sensor networks, and 2) modeling and generating node movements in mobile networks. We also discuss the model's performance in simulations utilizing both synthetic and real-world mobility traces.

Routing in delay tolerant networks is a challenging problem due to the intermittent connectivity between nodes resulting in the frequent absence of end-to-end path for any source-destination pair at any given time. Recently, this problem has attracted a great deal of interest and several approaches have been proposed. Since Mobile Social Networks (MSNs) are increasingly popular type of Delay Tolerant Networks (DTNs), making accurate analysis of social network properties of these networks is essential for designing efficient routing protocols. In this paper, we introduce a new metric that detects the quality of friendships between nodes accurately. Utilizing this metric, we define the community of each node as the set of nodes having close friendship relations with this node either directly or indirectly. We also present Friendship-Based Routing in which periodically differentiated friendship relations are used in forwarding of messages. Extensive simulations on both real and synthetic traces show that the introduced algorithm is more efficient than the existing algorithms.

Routing in delay tolerant networks (DTNs) is challenging due to their unique characteristics of intermittent node connectivity. Different protocols (single-copy, multi-copy, erasure-coding-based etc.) utilizing store-carry-and-forward paradigm are proposed to achieve routing of messages in such environments by opportunistic message exchanges between nodes that are in the communication range of each other. The sparsity and distributed nature of these networks together with the lack of stable connectivity between source destination pairs make these networks vulnerable to malicious nodes which might attempt to learn the content of the messages being routed between the nodes. In this paper, we consider DTNs in which malicious nodes are present, to which we refer to as compromised DTNs. We discuss and analyze the effects of presence of malicious nodes in the compromised DTN on routing of messages. We propose a two period routing approach which aims to achieve desired delivery ratio by a given delivery deadline in presence of malicious nodes. Our results show that, with proper parameter setting, the desired delivery ratio by a given delivery deadline can be achieved most of the time by the proposed method.

Routing in delay tolerant networks (DTN) have attracted a great interest recently. Increasingly popular type of DTNs are mobile social networks (MSN) also called pocket switched networks. Hence, analyzing accurately social network properties has become an important issue in designing efficient routing protocols for MSNs. In this paper, we first introduce a new metric for detecting the quality of friendships accurately. Using the introduced metric, each node defines its friendship community as the set of nodes having close friendship with itself either directly or indirectly. Then, we present Friendship Based Routing in which temporally differentiated friendships are used to make the forwarding decisions of messages. Real trace-driven simulation results show that the introduced algorithm achieves better delivery rate while forwarding fewer messages than the existing algorithms.

This paper introduces a novel method of generating mobility traces based on Probabilistic Context Free Grammars (PCFGs). A PCFG is a generalization of a context free grammar in which each production rule is augmented with a probability with which this production is applied during sentence generation. A concise PCFG can be inferred from the given real world trace collected from the actual mobile node behaviors. The resulting grammar can be used to generate sequences of arbitrary length mimicking the mobile node behavior. This is important when new protocol designs for mobile networks are tested by simulation. In the paper, we describe the methods developed to construct such grammars from training data (mobility history). We also discuss how to generate the synthetic data with an already constructed grammar. We present the experimental results on two real data sets, measuring similarity of the actual traces with the synthetic ones. We compare our grammar based method to a 2-level Markov Model based trace generation method. The results demonstrate that the grammar based approach works as an excellent compression method for the actual data. On many metrics, the synthetic data generated from the PCFG match the training data much better than the one generated by the Markov Model.

Routing in delay tolerant networks (DTNs) in which most of the nodes are mobile and intermittently connected is a challenging problem because of unpredictable node movements and lack of knowledge of future node connections. To ensure reliability against failures and increase the success rate of delivery, erasure coding technique is used to route messages in DTNs. In this paper, we study how the cost of erasure coding based routing protocols can be reduced. Specifically, we analyze the effects of different spraying algorithms, right parameter selection and splitting spraying phase on the cost of message delivery. We also perform simulations to evaluate the proposed approaches and demonstrate that the cost of erasure coding based routing can be reduced considerably with the proposed strategies while maintaining the delivery rate and delay objectives.

Coverage redundancy problem is one of the significant problems in wireless sensor networks. To reduce the energy consumption that arises when the high number of sensors is active, various coverage control protocols (sleep scheduling algorithms) have been proposed. In these protocols, a subset of nodes necessary to maintain sufficient sensing coverage are kept active while the others are put into a sleep mode to reduce the energy consumption. In this paper, we study the coverage redundancy problem in a sensor network where the locations of nodes and the distances between nodes are neither known nor could be easily calculated. We define a neighbor graph as the graph formed by the neighbors of a node and analyze the effect of different levels of connectivity in neighbor graphs on the coverage redundancy of sensor nodes. Moreover, we apply our results to a lightweight deployment-aware scheduling algorithm and demonstrate the improvement in the performance of the algorithm.

In a delay tolerant network (DTN), nodes are connected intermittently and the future node connections are mostly unknown. Since in these networks, a fully connected path from source to destination is unlikely to exist, message delivery relies on opportunistic routing. However, effective forwarding based on a limited knowledge of contact behavior of nodes is challenging. Most of the previous studies looked at only the pairwise node relations to decide routing. In contrast, in this paper, we analyze the correlation between the meetings of each node with other nodes and focus on the utilization of this correlation for efficient routing of messages. We introduce a new metric called conditional intermeeting time, which computes the average intermeeting time between two nodes relative to a meeting with a third node using only the local knowledge of the past contacts. Then, we show how we can utilize the proposed metric on the existing DTN routing protocols to improve their performance. For shortest-path based routing protocols in DTNs, we propose to route messages over conditional shortest paths in which the link cost between nodes are defined by conditional intermeeting times. Moreover, for metric-based forwarding protocols, we propose to use conditional intermeeting time as an additional delivery metric while making forwarding decisions of messages. Our trace-driven simulations on three different datasets show that the modified algorithms perform better than the original ones.

Delay tolerant networks are characterized by the sporadic connectivity between their nodes and therefore the lack of stable end-to-end paths from source to destination. Since the future node connections are mostly unknown in these networks, opportunistic forwarding is used to deliver messages. However, making effective forwarding decisions using only the network characteristics (i.e. average intermeeting time between nodes) extracted from contact history is a challenging problem. Based on the observations about human mobility traces and the findings of previous work, we introduce a new metric called conditional intermeeting time, which computes the average intermeeting time between two nodes relative to a meeting with a third node using only the local knowledge of the past contacts. We then look at the effects of the proposed metric on the shortest path based routing designed for delay tolerant networks. We propose Conditional Shortest Path Routing (CSPR) protocol that routes the messages over conditional shortest paths in which the cost of links between nodes is defined by conditional intermeeting times rather than the conventional intermeeting times. Through trace-driven simulations, we demonstrate that CSPR achieves higher delivery rate and lower end-to-end delay compared to the shortest path based routing protocols that use the conventional intermeeting time as the link metric.

In this paper, we present a novel multiperiod spraying algorithm for routing in delay-tolerant networks (DTNs). The goal is to minimize the average copy count used per message until the delivery while maintaining the predefined message delivery rate by the given deadline. In each period, some number of additional copies are sprayed into the network, followed by the wait for message delivery. At any time instance, the total number of message copies distributed to the network depends on the urgency of achieving the delivery rate by the given deadline for that message. Waiting for early delivery in the initial periods with a small number of copies in existence decreases the average number of copies sprayed in the network till delivery. We first discuss two- and three-period variants of our algorithm, and then we also give an idea of how the presented approach can be extended to more periods. We present an in-depth analysis of the algorithm and validate the analytical results with simulations. The results demonstrate that our multiperiod spraying algorithm outperforms the algorithms with a single spraying period.

Target tracking is a typical and important cooperative sensing application of wireless sensor networks. We study it in its most basic form, assuming a binary sensing model in which each sensor returns only 1-bit information regarding target's presence or absence within its sensing range. A novel, real-time and distributed target tracking algorithm is introduced. The algorithm is energy efficient and fault tolerant. It estimates the target location, velocity, and trajectory in a distributed and asynchronous manner. The accuracy of the algorithm is analytically derived under an ideal binary sensing model and extensive simulations of ideal, imperfect, and faulty sensing models show that the algorithm achieves good performance. It outperforms other published algorithms by yielding highly accurate estimates of the target's location, velocity, and trajectory.

Delay Tolerant Networks (DTNs) are wireless networks in which at any given time instance, the probability of having a complete path from a source to destination is low due to the intermittent connectivity between nodes. Several routing schemes have been proposed for such networks to make the delivery of messages possible despite the intermittent connections. In this paper, in addition to intermittent connectivity which impacts routing most strongly, we also analyze the effects of underlying social structure over the communication network. In a social network, nodes interact in diverse ways so that some nodes meet each other more frequently than others. In the paper, we first propose a new network model to reflect the underlying social structure over the network nodes, then we study the effects of this model on the performance of multi-copy based routing algorithms. We also analyze the performance of routing and validate our analysis with simulations.

One of the most common and important applications of wireless sensor networks is target tracking. We study it in its most basic form, assuming the binary sensing model in which each sensor can return only information regarding target's presence or absence within its sensing range. However, unlike most of traditional approaches to binary sensing, we allow sensors to recognize not only target's range but also a sector within the circular range around it. Examples of such sensors include cameras, infrared sensors, ultrasonic sensors, etc. For simplicity, we assume that either a group of sensors are collocated in a single spot providing 360 degree coverage or a sensor has multiple antennas or camera providing such coverage. A novel, real-time and distributed target tracking algorithm with directional binary sensor networks is proposed. It is an extension of our previous work on omni-directional binary sensor networks. Using simulations, we demonstrate that this new algorithm achieves high performance and outperforms other algorithms by yielding accurate estimates of the target's location. In addition, we discuss the fundamental performance limits and improvement of the tracking performance resulting from providing direction range in addition to a distance range for the algorithm.

Multipath routing can reduce the need for route updates, balance the traffic load and increase the data transfer rate in a wireless sensor network, improving the utilization of the limited energy of sensor nodes. However, previous multiple path routing methods use flooding for route discovery and transmit data with maximum power regardless of need, which results in waste of energy. Moreover, often a serious problem of collisions among multiple paths arises. In this paper, we propose an energy efficient and collision aware (EECA) node-disjoint multipath routing algorithm for wireless sensor networks. With the aid of node position information, the EECA algorithm attempts to find two collision-free routes using constrained and power adjusted flooding and then transmits the data with minimum power needed through power control component of the protocol. Our preliminary simulation results show that ECCA algorithm results in good overall performance, saving energy and transferring data efficiently.

Intermittently connected mobile networks, also called delay tolerant networks (DTNs), are wireless networks in which at any given time instance, the probability of having a complete path from a source to destination is low. Several routing algorithms have been proposed for such networks based on control flooding in which there is a fixed number of copies for each message. Although a DTN is delay tolerant by definition, often there is an upper bound imposed on message delivery delay. In this paper, we propose a novel spraying algorithm in which the number of message copies in the network depends on the urgency of meeting the expected delivery delay for that message. The main objective of this algorithm is to give a chance to early delivery with small number of copies in existence, consequently decreasing the average number of copies sprayed in the network. We derive the formula for the optimum borders of periods for spraying for two-period and three-period variants of our algorithm. We also present simulations of the method and compare their results with the analytical ones and observe the good match between them. Furthermore, we demonstrate that time dependent spraying algorithm provides a significant decrease in average copy count per message while preserving the percentage of the messages delivered before the upper bound of the acceptable delay expires.

We study target tracking with wireless sensor networks in its most basic form, assuming a binary sensing model in which each sensor can return only 1-bit information regarding target's presence or absence in its sensing range. A novel, real-time and distributed target tracking algorithm for imperfect binary sensing models is proposed, which is an extension of our previous work on the ideal binary sensing model. The algorithm estimates target's location, velocity and trajectory in a distributed and asynchronous manner. Extensive simulations show that our algorithm achieves high performance and outperforms other algorithms in terms of accuracy of the estimation of target's location, velocity and trajectory.