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Multi-sensor IoT devices can gather different types of data by executing different sensing activities or tasks. Therefore, IoT applications are also becoming more complex in order to process multiple data types and provide a targeted response to the monitored phenomena. However, IoT devices which are usually resource-constrained still face energy challenges since using each of these sensors has an energy cost. Therefore, energy-efficient solutions are needed to extend the device lifetime while balancing the sensing data requirements of the IoT application. Cooperative monitoring is one approach for managing energy and involves reducing the duplication of sensing tasks between neighboring IoT devices. Setting up cooperative monitoring is a scheduling problem and is challenging in a distributed environment with resource-constrained IoT devices. In this work, we present our Distributed Token and Tier-based task Scheduler (DTTS) for a multi-sensor IoT network. Our algorithm divides the monitoring period (5 min epochs) into a set of non-overlapping intervals called tiers and determines the start deadlines for the task at each IoT device. Then to minimize temporal sensing overlap, DTTS distributes task executions throughout the epoch and uses tokens to share minimal information between IoT devices. Tasks with earlier start deadlines are scheduled in earlier tiers while tasks with later start deadlines are scheduled in later tiers. Evaluating our algorithm against a simple round-robin scheduler shows that the DTTS algorithm always schedules tasks before their start deadline expires. 

Multi-sensor IoT devices enable the monitoring of different phenomena using a single device. Often deployed over large areas, these devices have to depend on batteries and renewable energy sources for power. Therefore, efficient energy management solutions that maximize device lifetime and information utility are important. We present SEMA, a smart energy management solution for IoT applications that uses a Model Predictive Control (MPC) approach to optimize IoT energy use and maximize information utility by dynamically determining task values to be used by the IoT device’s sensors. Our solution uses the current device battery state, predicted available solar energy over the short-term, and task energy and utility models to meet the device energy goals while providing sufficient monitoring data to the IoT applications. To avoid the need for executing the MPC optimization at a centralized sink (from which the task values are downloaded to the SEMA devices), we propose SEMA-Approximation (SEMA-A), which uses an efficient MPC Approximation that is simple enough to be run on the IoT device itself. SEMA-A decomposes the MPC optimization problem into two levels: an energy allocation problem across the time epochs, and task-dependent sensor scheduling problem, and finds efficient algorithms for solving both problems. Experimental results show that SEMA is able to adapt the task values based on the available energy, and that SEMA-A closely approximates SEMA in sensing performance. 

Timely delivery of the right information to the right first responders can help improve the outcomes of their efforts and save lives. With social media communications (Twitter, Facebook, etc.) being increasingly used to send and get information during disasters, forwarding them to the right first responders in a timely manner can be very helpful. We use Natural Language Processing and Machine Learning, to steer the social media posts to the most appropriate first responder.An important goal is to retrieve and deliver only the critical, actionable information to first responders in real-time. We examine the overall pipeline starting from retrieving tweets from the social media platforms, to their classification, and dissemination to first responders.We propose improvements in the area of data retrieval, relevance prediction and prioritizing information sent to the first responders by fusing NLP and ML classification techniques thus improving emergency response. We demonstrate the effectiveness of our proposed approach in retrieving and extracting 37,295 actionable tweets related to the IDA hurricane that occurred in the US in Aug.–Sep, 2021. 

In many scenarios, information must be disseminated over intermittently-connected environments when the network infrastructure becomes unavailable, e.g., during disasters where first responders need to send updates about critical tasks. If such updates pertain to a shared data set, dissemination consistency is important. This can be achieved through causal ordering and consensus. Popular consensus algorithms, e.g., Paxos, are most suited for connected environments. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is still need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends OTR to also support long-term network fragmentation and decision invalidation scenarios; we define local and global consensus pertaining to within and across fragments respectively. We integrate CoNICE's consistency preservation with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Using the Heard-Of model formalism, we prove CoNICE's consensus to be correct. Our technique extends previously established proof methods for consensus in asynchronous environments. Performing city-scale simulation, we demonstrate CoNICE's scalability in achieving consistency in convergence time, utilization of network resources, and reduced energy consumption. 

Name-based pub/sub allows for efficient and timely delivery of information to interested subscribers. A challenge is assigning the right name to each piece of content, so that it reaches the most relevant recipients. An example scenario is the dissemination of social media posts to first responders during disasters. We present FLARE, a framework using federated active learning assisted by naming. FLARE integrates machine learning and name-based pub/sub for accurate timely delivery of textual information. In this demo, we show FLARE’s operation. 

During disasters, it is critical to deliver emergency information to appropriate first responders. Name-based information delivery provides efficient, timely dissemination of relevant content to first responder teams assigned to different incident response roles. People increasingly depend on social media for communicating vital information, using free-form text. Thus, a method that delivers these social media posts to the right first responders can significantly improve outcomes. In this paper, we propose FLARE, a framework using 'Social Media Engines' (SMEs) to map social media posts (SMPs), such as tweets, to the right names. SMEs perform natural language processing-based classification and exploit several machine learning capabilities, in an online real-time manner. To reduce the manual labeling effort required for learning during the disaster, we leverage active learning, complemented by dispatchers with specific domain-knowledge performing limited labeling. We also leverage federated learning across various public-safety departments with specialized knowledge to handle notifications related to their roles in a cooperative manner. We implement three different classifiers: for incident relevance, organization, and fine-grained role prediction. Each class is associated with a specific subset of the namespace graph. The novelty of our system is the integration of the namespace with federated active learning and inference procedures to identify and deliver vital SMPs to the right first responders in a distributed multi-organization environment, in real-time. Our experiments using real-world data, including tweets generated by citizens during the wildfires in California in 2018, show our approach outperforming both a simple keyword-based classification and several existing NLP-based classification techniques. 

Graph-based namespaces are being increasingly used to represent the organization of complex and ever-growing information eco-systems and individual user roles. Timely and accurate information dissemination requires an architecture with appropriate naming frameworks, adaptable to changing roles, focused on content rather than network addresses. Today's complex information organization structures make such dissemination very challenging. To address this, we propose POISE, a name-based publish/subscribe architecture for efficient topic-based and recipient-based content dissemination. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking solutions in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. POISE supports in-network graph-based naming, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE supports adaptive load-sharing via multiple RPs, each managing a dynamically chosen subset of the namespace graph. Excessive workload may cause one RP to turn into a ``hot spot'', impeding performance and reliability. To eliminate such traffic concentration, we propose an automated load-splitting mechanism, consisting of an enhanced, namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid, iterative bi-partitioning solution, consisting of an initial and a refinement phase. We also implemented POISE on a DPDK-based platform. Using the important application of emergency response, our experimental results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing. 

Delivering the right information to the right people in a timely manner can greatly improve outcomes and save lives in emergency response. A communication framework that flexibly and efficiently brings victims, volunteers, and first responders together for timely assistance can be very helpful. With the burden of more frequent and intense disaster situations and first responder resources stretched thin, people increasingly depend on social media for communicating vital information. This paper proposes ONSIDE, a framework for coordination of disaster response leveraging social media, integrating it with Information-Centric dissemination for timely and relevant dissemination. We use a graph-based pub/sub namespace that captures the complex hierarchy of the incident management roles. Regular citizens and volunteers using social media may not know of or have access to the full namespace. Thus, we utilize a social media engine (SME) to identify disaster-related social media posts and then automatically map them to the right name(s) in near-real-time. Using NLP and classification techniques, we direct the posts to appropriate first responder(s) that can help with the posted issue. A major challenge for classifying social media in real-time is the labeling effort for model training. Furthermore, as disasters hits, there may be not enough data points available for labeling, and there may be concept drift in the content of the posts over time. To address these issues, our SME employs stream-based active learning methods, adapting as social media posts come in. Preliminary evaluation results show the proposed solution can be effective. 

In many scenarios, information must be disseminated over intermittently-connected environments when network infrastructure becomes unavailable. Example scenarios include disasters in which first responders need to send updates about their critical tasks. If such updates pertain to a shared data set (e.g., pins on a map), their consistent dissemination is important. We can achieve this through causal ordering and consensus. Popular consensus algorithms, such as Paxos and Raft, are most suited for connected environments with reliable links. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users' views, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends basic OTR to support long-term fragmentation and critical decision invalidation scenarios. We integrate the multilevel consistency schema of CoNICE, with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Performing city-scale simulation experiments, we demonstrate that CoNICE is effective in achieving its consistency goals, and is efficient and scalable in the time for convergence and utilized network resources.