An emerging application of wireless sensing is locating and tracking humans in their living environments, a primitive that can be leveraged in both daily life applications and emergency situations. However, most proposed methods have limited spatial resolution when multiple humans are in close vicinity. The problem becomes exacerbated when there is no line-of-sight path to the humans. In this paper, we consider multi-person localization of humans in close vicinity of each other. We propose the use of synthetic aperture radar that combines both translation and rotation to increase effective aperture size, leveraging small rhythmic changes in the radar range due to human breathing. We experimentally evaluate the proposed algorithm in both line-of-sight and through-wall cases with three to five humans in the scene. Our experimental results show that: (i) larger synthetic apertures due to radar translation improve multi-person localization, e.g., by 1.42× when the aperture size is increased by a factor of 2×, and (ii) rotation can largely compensate for gains provided by translation, e.g., rotating the radar over 360° without changing the aperture size results in 1.22× gains over no rotation. Overall, maximal gains of 2.19× are achieved by rotating and translating over a 2× larger aperture.

Accurate localization and robust navigation are crucial for autonomous robots or vehicles in GPS-denied environments, where traditional methods face signal obstructions and infrastructure limitations. This paper presents an integrated UltraWideband (UWB) based Simultaneous Localization and Mapping (SLAM) framework, implemented on a Robot Operating System (ROS) based Jetbot platform. The system utilizes UWB Time of Flight (ToF) and Angle of Arrival (AoA) for precise localization, along with LiDAR data for real-time mapping and obstacle avoidance. By fusing UWB and LiDAR data, the system tracks a moving target, along with accurate path planning. Experimental results show that the solution achieves a median tracking and navigation error of 18 cm, with 80 % of error remains below 20 cm, demonstrating its reliability. This study highlights the feasibility of UWB-based localization for autonomous platooning, offering a scalable and adaptable solution for real-time navigation in dynamic and infrastructure-limited environments.

Radiation shock waves from solar activities are known to be a menace to spaceborne electronic infrastructure. Recent deployments, like the SpaceX Starlink broadband mega-constellation, open up the possibility to measure such impact on Low Earth Orbit infrastructure at scale. Our tool, CosmicDance, enables a data-driven understanding of satellite orbital shifts due to solar radiations. CosmicDance could also signal corner cases, like premature orbital decay, that could lead to service holes in such globally spanning connectivity infrastructure. Our measurements with CosmicDance show that Starlink satellites experience both short and long-term orbital decay even after mild and moderate intensity solar events, often trespassing neighboring shells of satellites.

Real-time video analytics applications are one of the driving forces towards adoption of edge computing due to the latter’s ability to provide ‘near cloud-scale’ resources closer to the application site. However, striking a balance between system energy-efficiency and video quality satisfaction still remains a challenge. In this paper, we propose an edge-driven unified resource broker (URB) framework, viz., EdgeURB that seeks to find a trade-off between edge devices’ energy-efficiency and video configuration adaptation, with an aim to satisfying the real-time latency requirements without compromising analytics accuracy. Particularly, we design a two-stage algorithm: 1) a centralized algorithm for resource allocation, frame resolution selection, and user device to sever assignment and 2) a strategic game to further improve the energy-efficiency. We evaluate the performance of EdgeURB framework using an edge hardware testbed prototype that demonstrates EdgeURB’s success in simultaneously satisfying application latency, analytics accuracy, and devices’ energy consumption requirements. Also, through extensive simulations, we demonstrate EdgeURB’s schedulability and scalability improvement over baseline algorithms for a large number of devices and for varying edge resource availability.

In this article, we demonstrate that deep learning (DL) and spatiotemporal reasoning can effectively identify driving behavior based on the videos captured by roadside cameras. The use of roadside infrastructure for such determination is twofold: 1) a global view of the vehicles and their interactions and 2) no involvement or awareness of the vehicles or their drivers, so the determination is inexpensive, easy to deploy, and entirely nonintrusive. Furthermore, our method uses DL only for object detection and tracking and builds a flexible and explainable reasoning model to identify the driving behavior. The essential advantage of this approach is that we use DL only for tasks that can be accomplished efficiently and with high accuracy (i.e., object detection and tracking), which can be done in real time. Although there are DL models for detecting complex activities (e.g., aggressive driving), they are much harder to train, require higher accuracy, and inferencing time may not satisfy real-time constraints. By using a setup with program-controlled robocars, we demonstrate that we can achieve accuracies of 98% and 99% for driving behavior characterization, and the mechanism can provide detection of 650 ms on a very dated desktop. The characterization can provide feedback to the driver (or the automated car) for improved traffic safety and roadway throughput.

The tremendous potential of sensing and communication technologies has been explored and implemented for different remote event monitoring applications over the past two decades. However, the applicability of sensing and communication technologies is not necessarily limited to aboveground environments—it is also implementable and applicable for subterranean, underground scenarios. However, as opposed to air medium, underground communication medium is quite harsh due to the presence of heterogeneous underground materials along with underground aqueous components. In this article, we provide a technical overview of different underground wireless communication technologies, namely radio, acoustic, magnetic, and visible light, along with their potentials and challenges for several underground applications. We also lay out a detailed comparison among these technologies along with their pros and cons using detailed experimental results.

Multi-view 3D reconstruction driven augmented, virtual, and mixed reality applications are becoming increasingly edge-native, due to factors such as, rapid reconstruction needs, security/privacy concerns, and lack of connectivity to cloud platforms. Managing edge-native 3D reconstruction, due to edge resource constraints and inherent dynamism of 'in the wild' 3D environments, involves striking a balance between conflicting objectives of achieving rapid reconstruction and satisfying minimum quality requirements. In this paper, we take a deeper dive into multi-view 3D reconstruction latency-quality trade-off, with an emphasis on reconstruction of dynamic 3D scenes. We propose data-level and task-level parallelization of 3D reconstruction pipelines, holistic edge system optimizations to reduce reconstruction latency, and long-term minimum reconstruction quality satisfaction. The proposed solutions are validated through collection of real-world 3D scenes with varying degree of dynamism that are used to perform experiments on hardware edge testbed. The results show that our solutions can achieve between 50% to 75% latency reduction without violating long term minimum quality requirements.

Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.

In this paper, we present C-FAR, a framework for reasoning about anomalies in road-based intelligent transportation systems (ITS) based on video monitoring by the roadside camera infrastructure. The anomalies could span broad temporal and spatial ranges, including fine-grain (e.g., unsafe interactions among moving vehicles in real-time), medium-grain (e.g., aggressive/unsafe driving styles of individual vehicles over extended periods/distances), and coarse-grain (e.g., ensemble properties of the traffic over even longer time horizons). Unlike traditional approaches that utilize deep learning to recognize individual activities, C-FAR does so only for primitive movements and activities and then builds a comprehensive event logic framework. It also provides an optimal resolution of the detected/predicted anomalies by identifying the minimal changes in the controllable parameters of the system. We implemented a prototype system and tested it on three distinct real-world traffic data sets. We demonstrate that the proposed scheme can predict anomalies with over 84% recall level at 95% confidence level approximately 4.05 seconds before the incident.

Distributed machine learning (ML) was originally introduced to solve a complex ML problem in a parallel way for more efficient usage of computation resources. In recent years, such learning has been extended to satisfy other objectives, namely, performing learning in situ on the training data at multiple locations and keeping the training datasets private while still allowing sharing of the model. However, these objectives have led to considerable research on the vulnerabilities of distributed learning both in terms of privacy concerns of the training data and the robustness of the learned overall model due to bad or maliciously crafted training data. This article provides a comprehensive survey of various privacy, security, and robustness issues in distributed ML.

Sensing and communication technology has been used successfully in various event monitoring applications over the last two decades, especially in places where long-term manual monitoring is infeasible. However, the major applicability of this technology was mostly limited to terrestrial environments. On the other hand, underwater wireless sensor networks (UWSNs) opens a new space for the remote monitoring of underwater species and faunas, along with communicating with underwater vehicles, submarines, and so on. However, as opposed to terrestrial radio communication, underwater environment brings new challenges for reliable communication due to the high conductivity of the aqueous medium which leads to major signal absorption. In this paper, we provide a detailed technical overview of different underwater communication technologies, namely acoustic, magnetic, and visual light, along with their potentials and challenges in submarine environments. Detailed comparison among these technologies have also been laid out along with their pros and cons using real experimental results.

Large-scale loT systems are likely to involve mul-tiple subsystems deployed and operated by different “parties”, which must collaborate to ensure that their operational rules do not conflict. We codify the smooth functioning of the entire system through a set of “safety properties” that must be enforced collaboratively. However, this requires cross-party access to the sensors/actuators state and the ability to request remote actuations. In this paper, we define an access control architecture for such situations where we distinguish between the static authorization problem that selects parties tasked with safety property enforcement and the dynamic (run-time) control over accesses. This results in a unique enforcer selection problem for which we develop efficient algorithms and quantify their performance through a comprehensive emulation of an extensive smart home. We also show that the additional cost of granting access rights to the parties is quite small in a medium-size emulated multiparty loT environment.

Biomedical systems of implanted miniaturized sensors and actuators interconnected into an intra-body area net-work could revolutionize treatment options for chronic diseases afflicting internal organs. Considering the well-understood limitations of radio frequency (RF) propagation in the human body, we have explored magnetic resonance (MR) coupling for both communications and energy transfer through the body. In this paper, we have discussed the design and implementation of a software-defined prototype using Universal Software Radio Peripheral (USRP) boards. We have reported experimental results on the achieved packet error rates at different positions through-the-body distances and packet sizes. We have observed experimentally that the MR signal propagates through the body substantially better than in the air, and can provide a practical means for energy transfer and communications in intra-body networks. It also works better than the better understood galvanic coupling.

Human body communication (HBC) has recently been explored extensively both for small wearable electronic gadgets and for implanted sensors to deliver relevant data to implanted therapeutic devices. In this paper, we conduct an experimental comparison of two of the promising technologies but for on-body use, namely ultrasound coupling (USC) and magnetic resonance coupling (MRC) based communications. We find that both of these propagate much better through the body than in the air, thereby making them attractive for communications between in-body nodes, in-body to on-body nodes, and on-body nodes where the direct path includes substantial body area. USC also involves a surface acoustic wave (SAW) between on-body nodes which may be broken to varying extent by clothing. We find that with SAW component, USC works better than MRC, but otherwise has similar performance. MRC is very robust and can travel up to the entire body length with 25dB or less loss.

The emerging systems are not only generating huge amounts of data but also expect this data to be analyzed expeditiously to drive online decision-making and control. Thus, identifying the most relevant data and making it available close to the computation becomes a central challenge in driving the big data revolution. Storage systems play a crucial role in enabling efficient access to the stored data and intelligent storage management techniques are thus central to addressing the problem. Generally, as the data volume increases, the marginal utility of an “average” data item tends to decline, which requires greater effort in identifying the most valuable data items and making them available with minimal overhead and latency. Data driven mechanisms have a big role to play in solving this needle-in-the-haystack problem. In this paper we propose a taxonomy to provide a structure for understanding the common issues surrounding these techniques. We discuss these techniques and articulate many research challenges and opportunities.

The emerging edge computing applications often use high definition cameras as edge devices to capture video streams that need to be analyzed in real-time for situational understanding and answering queries. However, such devices suffer from limited energy (and hence limited computing power) and limited bandwidth available to stream the data to the edge controllers that provide much higher computing capacities. In this paper, we address these issues in the context of vehicular traffic monitoring and develop a scheme that has two components: YLLO and BATS. YLLO is a lightweight object recognition algorithm that runs on the edge device itself and substantially reduces the frame rate sent to the edge controller without removing the important information. BATS adapts the transmissions to the available bandwidth by taking advantage of further redundancy in the video stream in both single and multi-camera scenarios. We show that these mechanisms together can maintain object identification accuracy of above 95 percent, while transmitting just$\sim$5–10 percent of all the frames recorded by the cameras.

In this paper we examine the problem of conflict detection and mitigation across multiple independently designed IoT subsystems deployed in a shared environment. The desired behavior of the system is codified in terms of predefined "safety properties". We allow both the operational rules and safety properties to include time and temporal logic operations and detect their potential violation proactively via a "look ahead" mechanism. The problematic operational rules are then perturbed within the allowable range for mitigation. We show that our mitigation approach, based on intelligent combinatorial optimization, can resolve the conflicts via perturbation in 100% of the cases where such a resolution is feasible.

Energy efficient task offloading within a fog computing environment comprising of end-devices and edge servers remains a challenging problem to solve, especially for real-time video processing applications due to such tasks' strict latency deadline demands. In this paper we propose an Energy-efficient Fog Computing framework (EFFECT) for real-time applications within mission-critical use cases. The proposed framework runs a Unified Resource Broker (URB) that implements: a) centralized sub-channel and transmission power allocation as well as end-device/edge server computation speed allocation algorithms, along with b) distributed multi-device, multi-server task offloading game based Directed Acyclic Graph (DAG) partition and edge server selection algorithms. The framework is designed, developed, implemented, and evaluated on an Amazon EC2 virtual testbed built using Apache Storm, which is a distributed computing platform. The results from the testbed experiments along with realistic simulations validate the utility of EFFECT task offloading strategy in minimizing energy consumption yet satisfying latency deadlines.

In this paper, we propose a quality of service (QoS) aware transport solution for storage access over data center network. Motivation for QoS differentiation comes from the emerging storage technologies which not only provide network comparable latency but also overwhelm the network bandwidth close to the storage servers. We consider both throughput and latency related QoS requirements and demonstrate how they can be enforced by Explicit Congestion Notification (ECN) enabled switches. The mechanism can be viewed as a way of adding QoS capability to the existing data center transport solutions. Our scheme can co-exist with existing congestion management schemes and ensures RTT fairness while providing service differentiation. We show that our scheme can provide differentiated treatment and besides achieving better throughput fairness during the congestion episode, our solution can reduce the target latency misses by up to 71% and 80% as compared to the existing TCP and RDMA transport.

Solid-State Drives (SSDs) involve a complex set of management activities in the background, resulting in unpredictable delays and occasional extended access latencies. However, there is an increasing demand for "deterministic" access latency in a growing number of scenarios. This demand has prompted a new feature in the NVMe storage access protocol called Predictable Latency Mode (PLM), which provides a way to tighten tail latency in SSDs. This paper presents the first study of the PLM feature in a single-host environment and its extension to multi-host settings. We propose a PLM Coordinator (PLMC) that regulates access to the PLM of a shared SSD device based on the hosts’ traffic characteristics. Our simulation experiments show that the proposed PLMC can achieve 82% improvement in 99.99% tail latency compared to a bare SSD without PLM feature. Moreover, the proposed coordinator with simple traffic prediction can perform 93.2% better than without coordinator on the 99%-tail latency values.

The interactive web applications increasingly demand an end-to-end latency that is not only low on the average but also is “deterministic” in that they avoid long tails. Storage systems today largely keep data in SSDs, but SSDs are known to have unpredictable latencies due to background activities such as garbage collection. The recent NVMe access protocol proposes a Predictable Latency Mode (PLM) which allows the SSD to cycle between deterministic window (DTWin) and nondeterministic window (NDWin) periods, with background activities largely pushed to the latter. However, this means that the number of read and write IOs during DTWin period is limited and need to be managed properly. Another challenge is that to date no real SSDs are available in the market with this feature. In this paper, we explore the possibility of emulating the PLM feature in regular SSDs using Intel Optane that does provide a rather deterministic access latency. In particular, we propose a write I/O friendly PLMlightcoordinator (PLMLC) that buffers writes in Optane and sends them to SSD during the NDWin-like period and also intelligently manages the limited number of IOs possible during DTWin-like period. The coordinator is designed to handle requests from multiple hosts that may access shared data on the SSD and may have different QoS requirements in terms of latencies. The results show that PLMLC improves the 99%-ile tail latency by 5.8x even without any sophisticated traffic estimation procedures.

Blockchain technology has gained a significant amount of interest in recent years due to its decentralized control, immutability, transparency and robustness. In this paper we propose an enhancement to BlockChain built using proof-of-elapsed-time (PoET) consensus protocol to further increase its efficiency and transaction throughput. The proposed scheme, called DC-PoET, exploits distributed coordination (DC) among the nodes to avoid unnecessary transmission of conflicting blocks inspired by a similar mechanism in WiFi networks. We show that DC-PoET can support around 465 transactions per seconds with 30 MB block size, and even higher for larger blocks. We have also developed detailed analytical modeling for the performance of DC-PoET scheme using a two-dimensional Markov Chain, along with the validation of such modeling using Matlab simulations. The security analysis of our proposed scheme is also discussed.

In this article, we propose a prekeying-based integrity protection mechanism for critical smart grid communications that are often left unprotected due to tight timing constraints. Our mechanism computes the key for the next message in advance followed by a simple exclusive-or operation with the message when it is generated. This provides both integrity and confidentiality at a very low latency cost. The rigorous security analysis shows that the proposed method is secure against cyclic redundancy check (CRC) and message replay attacks. The extensive evaluation shows that the method is up to 21 times faster than standard integrity protection algorithms, and can do the message encryption in under 1 ms even on a very low-end microcontroller.

Content-Centric Networking (CCN) is a promising framework for the next generation Internet architecture that exploits ubiquitous in-network caching to minimize content delivery latency and reduce network traffic. In this paper, we introduce a neighborhood aware mechanism for content caching, named Neighborhood Aware Caching and Interest Dissemination (NACID) that accounts for the popularity of contents and how close the content copies are in the neighborhood. We use a very low-overhead, Bloom Filter based dissemination of caching information in the neighborhood. Given the neighborhood cached contents, the proposed scheme decides when and how to handle the additional caching of content and its eviction. Simulation results show that NACID performs substantially better than the existing CCN caching policies. We also study different heterogeneous cache memory allocation strategies and show that the simpler homogeneous allocation strategies work almost as well.

This paper describes the development of an experimental wireless sensor network (WSN) testbed for studying the nature of irradiance measurements at the sensor nodes that are deployed at different points in the WSN, and oriented differently depending on the deployment geometry. The network was developed on the roof of an academic building at the University of North Carolina at Charlotte (UNC Charlotte), where the sensor nodes are equipped with pyranometer sensors to periodically collect the irradiance measurements at different points and send then to a centralized base station using multi-hop communication. The collected data is analyzed to demonstrate the spatial and temporal variation of energy availability at each individual node, resulting from the localized variations in the light levels.

This paper demonstrates a technique to write in air using Micaz motes. The application, named MicaPen is low-cost and can be used by the disabled who does not have fingers or limbs. Patients can wear the mote as a wrist watch or bracelet and move their hands based on what character they want to write. The mote detects the movement pattern using the accelerometer of MTS310 sensorboard and displays the characters on the screen. The application can also be extended in several applications including virtual touchscreen or remotely interact with different controlled devices.

In this article, we consider a software-defined wireless sensor network (WSN) architecture which conserves energy by applying asynchronous duty cycling. In asynchronous sensor networks, the overhearing adversely impacts the energy consumption of the nodes. Using a mathematical model, we compute the maximum lifetime of the network and accordingly propose a multichannel operation and transmit power control to reduce the effect of overhearing in asynchronous networks. Our comprehensive test results confirm that for the network with load-aware nonuniform sensor node deployment, the network lifetime is much higher compared to a uniform deployment. We also show that the use of data aggregation software-defined WSNs (SDWSNs) can improve the network lifetime as compared to the data gathering networks.

Large networks of IoT devices, each consisting of one or more sensors, are being increasingly deployed for comprehensive real-time monitoring of cyber-physical systems. Such networks form an essential component of the emerging edge computing paradigm and are expected to increase in complexity and size. The physical phenomenon sensed by different sensors (within the same or different IoT devices in close proximity) often have relationships that makes them correlated. This is a form of proxy sensing that can be exploited for achieving better energy efficiency and higher robustness in monitoring. In this article, we explore how a set of sensors can optimize its data collection rates efficiently in a semi-distributed manner and yet provide the advantages of autonomy, relative isolation, and distributed control that is essential in a large-scale network.

Electronic communication is crucial for monitoring the rescue-relief operations and providing assistance to the affected people during and after disasters. Given the ubiquity of smartphones, we envision that smartphones with lost connection (due to damage) to the communications infrastructure are nevertheless integrated seamlessly into the network as far as possible. To achieve this, we propose to build ad hoc subnetworks of disconnected smartphones using the WiFi tethering technology and ultimately connect them to either the emergency communication equipment deployed in the disaster area or to other smartphones that have still the network connectivity. The proposed architecture for such integration and a defined software-based control through the emergency control center (ECC) enables battery aware collection of critical data through smartphone sensors. The developed solution supports mobility of all smartphones, including those that have lost direct cellular connectivity as well as those that have not and are willing to act as gateways. We demonstrate how the proposed scheme can be tied to the standardized wireless emergency alert service and how it can effectively handle mobility tolerant device discovery and data transfer.

Transportation and distribution (T8D) of fresh food products is a substantial and increasing part of the economic activities throughout the world. Unfortunately, fresh food T8D not only suffers from significant spoilage and waste, but also from dismal efficiency due to tight transit timing constraints between the availability of harvested food until its delivery to the retailer. Fresh food is also easily contaminated, and together with deteriorated fresh food is responsible for much of food-borne illnesses. The logistics operations are undergoing rapid transformation on multiple fronts, including infusion of information technology in the logistics operations, automation in the physical product handling, standardization of labeling, addressing and packaging, and shared logistics operations under 3rd party logistics (3PL) and related models. In this article, we discuss how these developments can be exploited to turn fresh food logistics into an intelligent cyberphysical system driven by online monitoring and associated operational control to enhance food freshness and safety, reduce food waste, and increase T8D efficiency. Some of the issues discussed in this context are fresh food quality deterioration processes, food quality/contamination sensing technologies, communication technologies for transmitting sensed data through the challenging fresh food media, intelligent management of the T8D pipeline, and various other operational issues. The purpose of this article is to stimulate further research in this important emerging area that lies at the intersection of computing and logistics.

Spatial clustering of the events scattered over a geographical region has many important applications, including the assessment of needs of the people affected by a disaster. In this paper we consider spatial clustering of social media data (e.g., tweets) generated by smart phones in the disaster region. Our goal in this context is to find high density areas within the affected area with abundance of messages concerning specific needs that we call simply as “situations”. Unfortunately, a direct spatial clustering is not only unstable or unreliable in the presence of mobility or changing conditions but also fails to recognize the fact that the “situation” expressed by a tweet remains valid for some time beyond the time of its emission. We address this by associating a decay function with each information content and define an incremental spatial clustering algorithm (ISCA) based on the decay model. We study the performance of incremental clustering as a function of decay rate to provide insights into how it can be chosen appropriately for different situations.

Magnetic Induction (MI) based communication is a near-field communications technology that can work reliably in a variety of difficult propagation media and thus can be useful in many short-range IoT applications. In this paper, we explore low-power protocols for MI communications with low data rate requirements but a premium on energy consumption. In particular, we exploit communication through silence (CTS), and show that it can reduce the energy expenditure of the communication by up to 67% as opposed to typical binary packet based transmission. We also discuss a multi-channel tree based routing protocol to reduce energy consumption from overhearing in asynchronous MI communication networks and show that the proposed scheme can reduce the overhearing counts by ~60% with two channels and by ~80% with four channels.

Radio frequency (RF) communications, although most popular, are unsuitable for environments involving aqueous and animal/plant tissue media, dense environments (e.g., small regions with many radios), applications requiring extremely low power consumption, etc. For such environments, magnetic induction (MI) communications are an emerging technology that appears to be very attractive. Although MI communication has been studied for some RF-challenged environments such as underwater, underground and body area networks, most of the studies so far are simulation based with minimal experimentation. In this paper, we show the feasibility of the proposed MI communications, by developing a small testbed using Freelinc boards. We also compare and contrast the existing theoretical claims regarding MI communications with some detailed experimental outcomes, and show how much they differ.

In this article, we introduce the concept of Water Flow Driven Sensor Networks for leakage and contamination monitoring in urban water distribution systems. The unique aspect of our work is that the sensor network can be deployed in the underground water network with only access to connection points (through manholes) and driven only by water harvested energy without the need for AC power or frequent battery changes. Although water systems may be affected by a large variety of contaminants, only a few sensors can be practically deployed. Thus, many types of contaminants are sensed via “proxy sensing,” which may not be 100% reliable. The main problems addressed are (a) adaptation of the network to the available energy to maximize leak/contamination detection and (b) minimal artificial water circulation or leakage to improve detectability during periods of almost zero natural water flow. The article shows, through extensive simulations, that the proposed approach can drastically reduce the leakage/contamination reporting time (from 3.5h up to ∼6min), and the adaptation can reduce this circulation by ∼33% and yet enhance the collected/transmitted data by 30%.

Emergency communication networks are crucial for monitoring and providing assistance to affected people during long-persisting disasters. Given substantial and increasing penetration of smart-phones throughout the world, we envision future emergency networks to consist of smart-phones in the disaster area, un-failed portions of the cellular network, and the communication capabilities provided by the specially deployed emergency equipment (e.g., fixed and mobile wireless access points deployed on the ground or in air via helicopters, satellite radio interfaces, etc.). We envision the emergency network for bulk data transmission - such as transmitting multiple pictures/sounds captured by the phone to help with rescue/safety assessment while keeping the delay/energy expenditure minimum. With extensive simulations, we show that the proposed scheme forwards the sensed data to the control centers with small latency (<; 3 seconds) while keeping the WiFi radios on for less than 1% of time.

Applications and network services in enterprise cloud environments have direct and indirect dependencies. The configuration of these services varies based on business needs. However, accurate and complete documentation of the configuration may not exist at all times. Thus, failure diagnosis becomes further complex with such unknown/uncertain dependencies. To cope with this, some probing stations need to be installed in suitable locations in the network to provide full monitoring and diagnosing capability. In this paper we develop a novel CloudMiner architecture for failure diagnosis in enterprise clouds that consist of developing intelligent probing station selection, failure detection and diagnosis across the network components using the minimum set of network probes, considering the inter-dependencies across the network services/components. Extensive simulation results show that CloudMiner can always identify the faulty components among the list of a small set of suspected components, the size of which is as low as ~3 for a network with 460 components.

The paper proposes a situational awareness service, named StayTuned that collects information from social media, extracts relevant messages, and broadcasts them to the subscribers through wireless emergency alert system. StayTuned uses automated filtering and summarization of messages and updates subscribers with real-time situational summaries. Extensive experiments were conducted using twitter data collected during the Sandy hurricane to evaluate performance of the automated message extraction.

Most large systems involve multiple zones with distinct ownership or control and one or more such zones must be traversed by the transactions. Therefore, in case of misconfigurations, it is necessary to conduct tests that go across parties. Such tests are complex as they must consider composition of test functionalities provided by each party and the feasible tests must abide by the access restrictions. In this paper we propose a framework for defining test functionalities their composition, and their access control. We then discuss an efficient algorithm to determine the realization of the given test via valid compositions of individual functionalities in a way to minimize the number of parties involved.

With wider adoption of Software-Defined Networking (SDN), network obfuscation and resource adaptation within a cloud environment have emerged as cost-effective solutions against cyber attacks. In spite of their implementation simplicity, shortcomings of such one-dimensional strategies are considerable against sophisticated attacks where the attacker/s have enhanced visibility to the cloud network. In this paper, we propose Whack-a-Mole, a SDN-driven cloud resource management scheme through network obfuscation that can help Cloud Service Providers (CSPs) to: a) proactively protect critical services from impending DDoS attacks and b) contribute very little service interruption footprint while doing so. Whack-a-Mole works at two levels: it employs a novel virtual machine (VM) spawning model that not only creates multiple VM-replicas of critical services to new cloud resource instances, but also assigns VM-replicas' IP addresses through address space randomization. Using numerical results, we show how such VM spawning can be optimized based on realistic cloud Service Level Agreements (SLA) without compromising its effectiveness. Finally, Whack-a-Mole is implemented through SDN/OpenFlow controllers over Open vSwitches on a GENI testbed where the efficacy and effectiveness of the scheme is evaluated. The results show Whack-a-Mole to be as effective as random obfuscation in evading attack events and more than 2x better on average in attack avoidance over other static resource adaptation based defense strategies.

Content-Centric Networking (CCN) is a promising framework for the next generation Internet architecture, by exploiting ubiquitous in-network caching to minimize content delivery latency and reducing the network traffic. In this paper, we introduce a neighborhood aware mechanism for content caching, named Neighborhood Aware Caching and Interest Dissemination (NACID) that accounts for the popularity of contents and how close the content copies are there in the neighborhood. We have adopted a Bloom Filter based dissemination of caching information in the neighborhood so that its overhead remains small. Given the neighborhood cached contents the proposed scheme decides when and how to handle the additional caching of content and its eviction. Simulation results show that NACID provides an increase in up to ~3 times of cache hits, and decrease in up to ~30% the number of hops required to get the contents than existing CCN caching policies.

Sensing of food spoilage and contamination is an active area of research, with many types of contact and noncontact sensors are being developed that can track fresh food quality throughout the distribution process. In this paper, we consider the communication of the sensed product quality along with the box position (in the stack of boxes in the truck or in a warehouse room) to the next level, in order to make the logistics more efficient and less wasteful. Given the water-rich, inhomogeneous biological media, RF or ultrasonic based communications are inappropriate in such environments, and we instead explore Magnetic Induction (MI) based communication framework in the HF band (3-30 MHz). We propose a novel magnetic induction based localization scheme to localize the boxes and study its accuracy via extensive simulations. We show that with a small number of anchor nodes, the localization can be done without any errors for boxes as small as 0.5 meter on the side, and with small errors even for boxes half as big. Our preliminary analysis suggests that such sensors can last for several years without any battery replacement.

Private IP addressing is commonly used in enterprise networks. Different enterprises or even different locations/business units of the same enterprise may use the same IP address ranges as long while those networks are separate. Consequently, during mergers and acquisitions, or network consolidations within an enterprise, overlapped and conflicted IP segments (subnets) arise frequently. These must be identified and resolved to allow communication between any pair of source and destination hosts in the merged networks. Furthermore, the combined network may unnecessarily use many disparate IP address ranges which increases the size of the routing tables and makes routing integrity verification difficult. In this paper, we identify different conflict scenarios and consider ways of resolving those conflicts to minimize manual changes and to minimize the routing table sizes. The problem turns out to be NP-hard and rather complex, and we devise effective heuristics to solve the problem. By taking some real-world examples, we show that by changing 6-8% of the subnet addresses the outlined method can effectively resolve the subnet conflicts. The scheme also reduces the number of subnet entries by 80-90% by consolidating the subnet entries, which significantly reduces the routing table sizes.

In this paper we develop a general framework for multi-sensor, heterogeneous sensing in collaborative wireless sensor networks (WSNs) that can be used in a variety of large scale monitoring applications. In order to achieve better tolerance against unstable wireless links and nodes with inadequate battery, it is important to consider distributed approaches for sampling rate adaptation. We show that the fully distributed mechanisms suffer from high convergence time, which make them difficult to implement in large-scale WSNs. To overcome this limitation, we next propose two alternate approaches. We perform extensive simulations to compare these schemes and argue their scalability and applicability in real world monitoring scenarios.

The current bulk transit systems (e.g., buses) and local perishable food distribution logistics, both suffer from significant fuel inefficiency along with food wastage due to quality degradation in the distribution pipeline. In this paper we present a mechanism that exploits automated electric vehicles (AEVs) in future smart cities and regions to provide both people transport and fresh food distribution that minimizes empty miles of the vehicles (and thus enhances transport efficiency) while meeting the constraints on passenger transit time and food freshness. We devise an optimization framework and show how it can be solved using genetic algorithms in order to handle dynamic demands for passenger transport/products, uncertain supply delays, and variations in product availability. Performance evaluations with extensive simulations show that flexibly deciding the AEV routes improves the transportation efficiency by ~24-78% whereas improves the delivery quality by ~2 times compared to the typical fixed routes/schedules used both by regular passenger bus services and by local distribution operations.

In this paper we consider sensor networks for detecting contamination in urban water distribution systems. We assume that the sensor nodes are installed at connection points only (through the manholes) and are driven by super-capacitors charged by water flow. Although water systems may be affected by a large variety of contaminants, only a few sensors can be practically deployed. Thus many types of contaminants are sensed via “proxy sensing”, which may not be 100% reliable. In this paper we consider such a situation and examine the problem of collaborative adaptation of heterogeneous set of sensors in order to maximize contamination detection, especially during periods of almost zero natural water flow. The paper shows, through extensive simulations, that the proposed approach can drastically reduce the contamination reporting time from 31/2 hours to ~6 minutes, compared to the case without adaptation.

In this paper, we introduce a novel all-optical Data center networking (DCN) fabric, by leveraging the reconfigurability of the optical transceivers and switches, while dynamically changing the end-to-end optical routes to match the varying traffic demands. The dynamic flow scheduling along with their wavelength assignment turns out to be a NP-hard problem. We propose centralized heuristics for the inter-rack flow scheduling, by exploiting the optical wavelength division multiplexing, while minimizing the number of intermediate optical hops. Through extensive simulations, we show that the proposed architecture and flow scheduling reduces the network congestion by a factor of 15-18, compared to state-of-the-art part-time optical DCNs. For most of the traffic patterns, the proposed scheme can deliver >90% of the inter-rack traffic through direct optical communication.

In this paper, we introduce the concept of Water flow Driven Sensor Networks for leakage and contamination monitoring in urban water distribution systems. The unique aspect of our work is that the sensor network can be deployed in the underground water network with only access to connection points (through manholes) and driven only by water harvested energy so as to avoid access to AC power or need for frequent battery changes. The main problems addressed are (a) adaptation of the network to the available energy in order to maximize leak/contamination detection, and (b) minimal artificial water circulation or leakage to improve detectability during periods of almost zero natural water flow. The paper shows, through extensive simulations, that the proposed approach can drastically reduce the leakage/contamination reporting time (more than 3 hours to ~30 minutes), and the adaptation can reduce this circulation by ~33% and yet enhance the collected/transmitted data by 30%.

We propose PCOR, a power control and routing scheme for rechargeable wireless sensor networks (WSNs) that are characterized by spatial and temporal variations of energy resources. The proposed scheme is applied to asynchronous WSNs where overhearing plays a dominant role in the energy consumption. PCOR performs quality aware route selection while reducing the energy consumption in sensor nodes that have low remaining battery life through cooperative and network-wide adaptations of transmit power levels and parent selection. Performance evaluations are presented from extensive simulation studies as well as from an experimental testbed.

We consider wireless sensor networks with data collection traffic that apply asynchronous duty cycling to conserve energy, which is common in many environmental monitoring applications. Under these assumptions, the effect of overhearing dominates the energy consumption of the nodes. We propose multichannel operation with dynamic channel selection and power control for reducing the effect of overhearing in such asynchronous networks. A mathematical model is presented to calculate the maximum network lifetime under these considerations.

We consider the channel assignment problem in multi-channel wireless sensor networks for maximizing the network lifetime. We assume a data collection traffic pattern where all nodes forward data periodically to a sink and propose a distributed channel selection scheme that tries to maximize the lifetime of the nodes by controlling the energy consumption from overhearing. Some initial experimental results are included to show the effectiveness of the proposed scheme.

In this paper we consider the joint channel assignment and routing problem in multi-radio multi-gateway wireless mesh networks for improving the quality of communications in the network. This channel assignment problem is proven to be an NP-complete problem. We present a novel backtracking and genetic algorithm based channel assignment and quality aware route selection scheme to maximize the overall performance of communications while reducing the computational complexity. We perform extensive simulation studies that show that our proposed channel assignment and route selection scheme performs significantly better than single channel and random channel selection based schemes.

We present an analytical model to evaluate the performance of the IEEE 802.11 Distributed Coordination Function (DCF) with and without the RTS/CTS handshake in radio-over-fiber (RoF) wireless LANs. The model captures the effects of contending nodes as well as hidden terminals under non-saturated traffic conditions assuming large buffer sizes. The effect of fiber propagation delay is considered. The proposed models are validated using computer simulations. Comprehensive performance evaluations of RoF networks obtained from the proposed model as well as simulations are presented.

We consider anycast routing to improve the quality of communications in multi-gateway wireless mesh networks. A centralized gateway and route selection scheme is proposed that tries to maximize the end-to-end probability of success and minimize the end-to-end delay of all active traffic flows in the network. The proposed scheme employs a novel route quality metric that is based on the effects of interference on data packet transmissions. We present performance evaluations of the proposed scheme using ns-2 simulations.