The increasing frequency of natural disasters has heightened the demand for UAV (Unmanned Aerial Vehicle) technologies. UAVs, especially drones, can monitor remote disaster areas and provide situational awareness to emergency responders. Equipped with cameras and onboard computers, drones can detect survivors in real-time, enhancing the efficiency of Search and Rescue (SAR) operations. Due to limited battery capacity, the drones must be deployed along a path of the shortest possible length to avoid delays in detecting the survivors in a given disaster area. Traditional path-planning algorithms struggle to address the dynamic conditions in disaster areas. We propose an adaptive drone path planning framework for real-time survivor detection in disaster areas to address this. This framework aims to improve survivor detection by guiding UAVs along routes with higher probabilities of the presence of survivors. Adopting a ”Learn-As-You-Go” strategy, it trains a Potential Survivor Location (PSL) prediction model to identify way-points for drone sorties. Next, it leverages a novel computationally efficient path planning approach called Prediction-Based Priority-Aware Path Planning (PB-PAPP) to navigate towards the identified PSLs. Also, we present a Weight Synthesis module that enhances the prediction quality over time by aggregating the weights of the models trained by the drones, allowing continuous adaptation in changing environments. Finally, we present a prototype lightweight decentralized machine learning system that combines the above modules to facilitate real-time survivor detection. Compared to existing algorithms, our framework demonstrates an 84-97% reduction in overhead for adaptive path-planning.

QUIC (Quick UDP Internet Connections) is a relatively recent transport layer protocol that Google deployed and implemented for the first time in 2012. The key aspect of this protocol is that it is faster than TCP, more secure than UDP , and more efficient regarding resource usage. It has been adopted by some Internet-based applications, viz., YouTube, Gmail, etc. Recent advancements in 5G/6G communication technology have enabled the integration of QUIC with many real-time applications. One of the drawbacks in the design of the QUIC protocol is its vulnerability against attacks related to connection ID, and a recent attack of this type is the retire connection ID stuffing attack . This attack leads to a denial of service (DoS) condition, thus hindering network operations and services. Few preventive solutions have been proposed, but they focus on closing the connection after detecting an attack scenario, which results in service disruption . In this paper, we attempted to render flexibility to this rigid security defense mechanism situation by proposing iQUIC , an intelligent framework to configure a network condition monitoring QUIC server. The framework inputs the network data to a local Advantage Actor–Critic (A2C) Reinforcement Learning (RL) engine to support decision-making regarding accepting/rejecting a request from a client or issuing a warning signal to it. The framework also enables the server to stochastically suspend connections with the client(s) following in ϵ -greedy approach after a predefined observation window. To replicate a real-world QUIC-enabled network, we devised a small QUIC network consisting of two clients and a server and generated substantial QUIC traffic by implementing a U-Net-based GAN (Generative Adversarial Network) model from scratch. A simulation-based performance evaluation demonstrates that the QUIC server powered by the actor–critic RL learns to make optimal decisions with time.

The transition of the web from centralized to decentralized or distributed architectures offers numerous advantages but also introduces significant challenges. One of the key challenges is user profiling to provide personalization, particularly personalized content recommendations. Traditional centralized recommendation systems rely on aggregated user data and central servers, making them incompatible with the principles of decentralization in Web 3.0. To bridge this gap, we propose D-RecSys , a decentralized recommendation framework specifically designed for Web 3.0-based content-sharing dApps. D-RecSys combines federated learning and clustering algorithms to deliver personalized recommendations while preserving user privacy and anonymity. The framework leverages blockchain technology for trustless coordination, enabling the generation of a global model through a modified block structure and mining algorithm. This structure facilitates the aggregation of local models into intermediate block models and subsequently produces the global model. To validate the effectiveness of D-RecSys , we conducted a number of experiments in a simulated Web 3.0 environment. To ensure the generalization capability of the framework, we used three datasets from different domains, i.e., anime recommendation, e-commerce product recommendation, and cellphone recommendation. The results demonstrate that D-RecSys achieves performance levels comparable to centralized recommendation systems while adhering to the core principles of decentralization, user anonymity, and data privacy.

The increasing usage of the Internet of Things (IoT) across various domains has led to a significant surge in data generation and processing. This exponential growth has introduced numerous data security, privacy, integrity, and availability challenges. Blockchain has emerged as a promising technology to address these challenges. Nevertheless, the resource-constrained nature of IoT devices does not align well with the excessive resource requirements of traditional blockchain systems. While state-of-the-art research has mainly concentrated on making the blockchain lightweight in terms of computational overhead, the issue of managing the append-only, immutable, and ever-growing ledger remains largely unaddressed. In this paper, we propose a novel approach, termed FabMAN, to effectively manage the continuously expanding ledger in the context of Hyperledger Fabric, a permissioned blockchain platform widely adopted for blockchain-based IoT applications. We introduce an adaptive algorithm to dynamically adjust its “Batch size" and “Batch timeout to optimize its ledger growth." The adaptive algorithm ensures that the ledger growth is nearly optimal, considering the incoming transaction proposal rate. To validate the effectiveness and performance of the proposed method, we conduct comprehensive performance analyses through simulation and prototype implementation and compare the results with the same system model without the ledger management system. Our approach to managing the ever-growing ledger in Hyperledger Fabric-based IoT applications shows significant improvement.

Internet of Things (IoT) enables the collection of large volumes of data by billions of pervasive intelligent devices and sharing them with remote cloud servers for processing, resulting in increasing network congestion and server response times. The advent of edge computing has addressed these challenges by introducing an intermediate edge layer comprising networked fog nodes that provide on-demand computation, caching, and communication services to meet critical Quality-of-Service (QoS) requirements. However, in a challenging environment brought by disaster and aftershocks, the QoS is hampered as several fog nodes in the edge layer are damaged. Nevertheless, the existing network infrastructure should still support the uninterrupted flow of time-critical contextual data between the survivors and rescuers for quick recovery operations. In this work, we envision that the IoT devices and existing fog nodes will collaborate to form ad-hoc networks for emergency message delivery under disaster situations. We present a distributed routing mechanism, termed motif centrality-based routing ($MCR$), that leverages the concept of network motifs (subgraphs) seen in social and biological networks. Specifically, the proposed mechanism addresses three QoS requirements of an ad-hoc network: 1) robustness against component failures; 2) low latency; and 3) energy efficiency. We experimentally show that the$MCR$-based routing ensures high data delivery, low latency, and comparable efficiency in energy usage. Finally, an extensive simulation-based study shows that$MCR$outperforms the related benchmarks in terms of the three QoS requirements.

Wireless Sensor Networks (WSNs) enable promising solutions to large-scale industrial, medical and environmental tracking and monitoring applications. Software Defined Networking (SDN) is a new paradigm that decouples the network control and data forwarding functionalities and may potentially improve data sensing in a highly dynamic environment. The networking community is directing its efforts towards ensuring that the software defined WSNs (SD-WSN) carry out the data sensing even in a hostile environment characterized by node or link failures. In this work, we present an adaptive topology control strategy based on reinforcement learning for mobile SD-WSN. The approach employs the notion of statistically significant subgraphs, called motifs, that have been shown to render graph robustness to biological networks. Our simulation experiments on the map of New York City shows that this approach is capable of modulating network parameters to achieve varying goals such as high data delivery, low latency and energy efficiency.

Software Defined Wireless Sensor Networks (SD-WSN) is a promising paradigm in wireless communication that offers high flexibility in network management by enabling dynamic and programmable network control. SDN controller has a centralized global view of the network, making it an ideal choice for data sensing in a highly dynamic sensing environment. We proposed a reinforcement learning (RL) based adaptive topology control approach (Roy et al., IEEE CCNC 2020) that employs periodic node mobility to meet diverse network objectives, such as data delivery, latency, and energy efficiency. We also demonstrated that erratic mobility can considerably hamper the learning of the RL module resulting in poor overall quality of service. In this work, we present a customized network simulation environment that captures the variations in the performance of the proposed SD-WSN framework. Finally, we present a new approach based on supervised machine learning that can identify periodic mobility and mitigate the ill-effects of erratic mobility.

Vehicular cyber-physical systems (VCPS), among several other applications, may help address an ever-increasing challenge of traffic congestion in large cities. Nevertheless, VCPS can be hindered by information falsification problem, resulting due to the wrong perception of a traffic event or deliberate faking by the participating vehicles. Such information fabrication causes the re-routing of vehicles and artificial congestion, leading to economic, safety, environmental, and health hazards. Thus, it is imperative to infer truthful traffic information in real-time to restore the operational reliability of the VCPS. In this work, we propose a novel reputation scoring and decision support framework, called Spoofed and False Report Eradicator (SAFE) , which offers a cost-effective and efficient solution to handle information falsification problem in the VCPS domain. The framework includes humans in the sensing loop by exploiting the paradigm of participatory sensing , a concept of a mobile security agent (MSA) to nullify the effects of deliberate false contribution, and a variant of the distance bounding mechanism to thwart location-spoofing attacks. A regression-based model integrates these effects to generate the expected truthfulness of a participant’s contribution. To determine if any contribution is true or false, a generalized linear model is used to transform the expected truthfulness into a Quality of Contribution (QoC) score. The QoC of different reports is aggregated to compute user reputation. Such reputation enables classification of different participation behaviors. Finally, an Expected Utility Theory (EUT) -based decision model is proposed that utilizes the reputation score to determine if event-specific information should be published or dropped. To evaluate the SAFE framework through experimental study, we used both simulated and real data to compare its reputation-based user segregation performance with state-of-the-art frameworks. Experimental results exhibit that SAFE captures the fine differences in participants’ behavior through the quality and quantity of participation, and the accuracy of their informed location. It also significantly improves operational reliability through publishing the information of only legitimate events.

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

Web-based collaboration provides a platform which allows users from different domains to share and access information. In such an environment, mitigating threats from insider attacks is challenging, particularly if state-of-the-art token-based access control is used to authorize (permit or deny) requests. This entails the need for an additional layer of authorization based on soft-security factors such as the reputation of the requesters, risks involved in requests, and so on to make the final decision. In this paper, we propose a novel risk quantification framework, called $R2Q$, which exploits a weighted regression approach to compute the expected threat related to a collaboration request. Our model combines the shared object's sensitivity, access mode of the request, requester's security level and reputation, and maps the expected threat to a risk score using the prospect theory (PT) inspired value functions to actualize decision making under uncertainty of economic outcomes (loss or gain). Simulation-based performance evaluation validates the efficacy of our framework and demonstrates that it can classify requesters based on their past behaviours, and also enables the collaboration platform to achieve higher rates of successful authorization.

Recent years have seen a proliferation of intelligent (automated) decision support systems for various smart city applications such as energy management, transportation, healthcare, environment monitoring, and so on. A key enabler in the smart city paradigm is the Internet-of-Things (IoT) network of smart sensing and actuation devices assisting in real-time detection and monitoring of physical phenomena. The underlying IoT network must be energy-efficient for application sustainability and also quality of information (QoI)-aware for near-perfect device actuation. To this end, this paper proposes bioSmartSense, a novel bio-inspired distributed event sensing and data collection framework, based on the gene regulatory networks (GRNs) in living organisms. The idea is to make the sensing and reporting tasks energy-efficient through self-modulation of IoT device energy levels, analogous to the activation or repression of genes by the regulating proteins, called Transcription Factors (TFs). To support energy-efficient and QoI-aware information dissemination, we first customize a heuristic designed for the Maximum Weighted Independent Set problem encompassing both `quality' and `quantity' of sensed data, where the former depends on the device energy levels while the latter on the number of events sensed. We utilize the heuristic to propose a sub-optimal device selection mechanism constrained on the IoT network's overall residual energy. Simulation experiments demonstrate that the bioSmartSense framework achieves better energy-efficiency while maximizing event reporting compared to a state-of-the-art data collection approach for smart city applications.

The smart home is one of the most prominent applications in the paradigm of the Internet of Things (IoT). While, it has added a level of comfort and convenience to our everyday life, at the same time, it brings a unique security challenge of mitigating insider threats, posed by legitimate users. Such threats primarily arise due to sharing of IoT devices and the presence of complex social and trust relationships among the users. The state-of-the-art home IoT platforms manage access control by deploying various multifactor authentication mechanisms. Nevertheless, such hard-security measures are inadequate to thwart insider threats, and there is a growing need to integrate user behavior and environmental contexts to make intelligent authorization decisions. In this article, we propose a novel context-sensitive and behavior-based security framework, calledSoftAuthZ, that incorporates soft-security mechanisms, such as belief, confidence, etc., to support authorization decisions. Our framework integrates multiple IoT environment-specific attributes, such as environmental context, nature of the device, requested capabilities (actions), users’ trust levels concerning the home environment, and variability in device access requests into a linear regression model, and computes confidence related to access requests. Such confidence scores can be used by the home IoT platform to make authorization decisions. Extensive analysis and simulation-based performance evaluation validate the efficacy of our framework, demonstrating that it can classify users based on their device usages, and also achieve higher rates of successful authorization.

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.

Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.

Participatory sensing (PS) is an emerging socio-technological paradigm in which citizens voluntarily participate and contribute to a distributed information system using applications installed in their hand-held devices. It can be found in a number of real-life applications, viz. traffic monitoring, air/sound pollution, garbage monitoring, social networking, commodity pricing, and so on. In these systems, information sensed by the user helps the peers in decision making. Present work considers vehicular participatory sensing systems, where registered user senses (perceives) the traffic incident and submits its report(s) to a PS application server. PS application server in turn, broadcasts those reports as alerts to its subscribers. To promote the participation, the PS systems used to have incentive schemes for the participants. However, a common problem in participatory sensing is the generation of false reports either due to wrong perception of an event or to maliciously increase the degree of participation to gain undue incentives. Such false reports make the usage of the PS system unreliable and vulnerable to the illusion attack. This work proposes a novel approach to make PS applications more reliable by identifying and filtering out the falsely reported event through automated confidence assignment based on a probabilistic model. Waze traffic alerts have been used as the dataset to validate the proposed filtering mechanism. Finally, simulation-based experiments and performance evaluation have been done to demonstrate that the proposed approach is relatively accurate.

Online collaboration service has become a popular offering of present day Software-as-a-Service (SaaS) clouds. It facilitates sharing of information among multiple participating domains and accessing them from remote locations. Owing to loosely-coupled nature of such collaborations, access request from a remote user is made in the form of a set of permissions. The cloud vendor maps the requested permissions into appropriate local roles in order to allow resource access. However, coexistence of such multiple simultaneous role activation requests may introduce conflicts which violate the principle of security. In this paper, we propose a distributed secure collaboration framework which enables collaborating domains to detect and remove these conflicts. Two features of our framework are: (i) it requires only local information, and (ii) it detects and removes conflicts on-the-fly. Formal proofs have been provided to establish the correctness of our approach. Experimental results and qualitative comparison with related work demonstrate the efficacy of our approach in terms of response time, thus addressing the scalability requirement of cloud services.

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients' data. If loss of data occurs due to an outage, the customer's business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider's SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.