Spatial crowdsourcing (SC) enables the assignment of location-based tasks to mobile users who must travel to specific locations to perform sensing or service activities. However, SC systems often operate in strategic environments where both task requesters and task executors possess private valuation information, posing challenges for designing efficient and truthful incentive mechanisms. To address these issues, this paper proposes a truthful multi-task double Auction for quality-aware spatial crowdsourcing (TRUST-SC). The proposed framework adopts a three-tier architecture. First, task executors are grouped into spatial clusters to improve scalability and reduce allocation complexity. Second, reliable executors are identified through a majority-voting-based quality evaluation process. Third, tasks are allocated, and payments are determined through a multi-unit double-auction mechanism that guarantees incentive compatibility and individual rationality. Theoretical analysis and simulation results demonstrate that the TRUST-SC achieves truthfulness, individual rationality, quality task executors determination, and computational efficiency. Simulation also demonstrate the impact of clustering and quality task executors on the discussed set-up.

In crowdsourcing, a group of common people is asked to execute the tasks and in return will receive some incentives. In this article, one of the crowdsourcing scenarios with multiple heterogeneous tasks and multiple IoT devices (as task executors) is studied as a two-tiered process. In the first tier of the proposed model, it is assumed that a substantial number of IoT devices are not aware of the hiring process and are made aware by utilizing their social connections. Each of the IoT devices reports a cost (private value) that it will charge in return for its services. The participating IoT devices are rational and strategic. The goal of the first tier is to select the subset of IoT devices as initial notifiers so as to maximize the number of IoT devices notified with the constraint that the total payment made to the notifiers is within the budget. For this purpose, an incentive compatible mechanism is proposed. In the second tier, a set of quality IoT devices is determined by utilizing the idea of single-peaked preferences. The next objective of the second tier is to hire quality IoT devices for the floated tasks. For this purpose, each quality IoT device reports private valuation along with its favorite bundle of tasks. In the second tier, it is assumed that the valuation of the IoT devices satisfies gross substitute criteria and is private. For the second tier, the truthful mechanisms are designed independently for determining the quality IoT devices and for hiring them and deciding their payment respectively. Theoretical analysis shows that the proposed mechanisms are computationally efficient, truthful, correct, budget feasible, and individually rational. The simulation is done to measure the efficacy of the proposed mechanisms with the benchmark mechanisms based on truthfulness, budget feasibility, and running time.

Spatial crowdsourcing is a key paradigm for collecting location-based data using mobile crowd workers. Designing efficient and incentive-compatible task allocation mechanisms remains a fundamental challenge due to strategic behavior of the crowd workers and spatial constraints. In this paper, a rigorous comparative study of two classical auction mechanisms — First Price Auction (FPA) and the Vickrey-Clarke-Groves (VCG) mechanism in the spatial crowdsourcing framework is carried out. The problem is modeled as a two-sided market with private information and spatial feasibility constraints, and analyze key economic properties including truthfulness, individual rationality, budget balance, and social welfare maximization. After that, the running time analysis is carried out and simulations are conducted to evaluate performance under varying system parameters. Simulation results shows that VCG performs better than FPA on the ground of social welfare and utility, but it suffers from high computational overhead.

Mobile crowdsourcing refers to the use of mobile devices, such as smartphones and tablets, to gather information or perform tasks by leveraging the collective efforts of a large group of people. The paper considers one of the scenarios (setups) of the mobile crowdsourcing scenarios in a strategic setting. The setup consists of multiple task requesters and multiple task executors (or IoT devices). Each task requester is endowed with a single task. Each task requester reveals a preference list over the subset of IoT devices, and also each of the IoT devices gives a preference over the subset of task requesters. The preference lists of both parties (i.e., task requesters and IoT devices) are private in nature. Given such a scenario, we aim to allocate the best possible IoT device to each task requester from his/her revealed preference list. For this purpose, a truthful mechanism is proposed, and it is proved that it is computationally efficient. Simulation results show that the proposed mechanism outperforms the benchmark mechanism.

In recent decades, scheduling healthcare services, such as doctors, nurses, and operating theaters, within hospitals has been a key focus. More recently, a parallel research direction has emerged that explores hiring healthcare experts from outside hospitals in both strategic and non-strategic settings. This paper contributes to that line of work by addressing expert hiring in a strategic environment. We consider a scenario involving multiple patients and multiple hospitals, where each hospital has a pool of experts. Patients submit their preferred sets of experts along with private bids. The objective is to allocate expert sets to patients without conflicts, while maximizing overall social welfare. We propose mechanisms that are truthful, and asymptotically efficient. These mechanisms ensure incentive compatibility and practical feasibility in expert scheduling across institutions. The simulation results demonstrate the effectiveness and robustness of the proposed mechanisms in various scenarios, validating their utility in real-world e-healthcare applications.

Mobile crowdsourcing has emerged as a powerful paradigm where multiple task requesters submit diverse tasks to be executed by heterogeneous IoT devices acting as task executors in strategic setting. In such strategic environments, both task requesters and executors may misreport their private preferences to obtain favorable allocations, which threatens fairness, efficiency, and participation. To address these challenges, this paper introduces TMTMMMC, a truthful and Pareto-optimal two-sided matching mechanism tailored for preference-aware task allocation in IoT-based mobile crowdsourcing. The proposed framework models each requester’s heterogeneous tasks and each executor’s distinct capacity using the notion of virtual task requesters and virtual task executors. Both sides submit private preference lists over their feasible counterparts. A novel mechanism is designed that ensures each virtual requester is matched to the best possible virtual executor in a way that is truthful, computationally efficient, and Pareto optimal. Theoretical analysis confirms that the mechanism guarantees strategy-proofness, Pareto optimality, and efficiency, while extensive simulations demonstrate superior performance over benchmark random mechanisms in terms of truthfulness, the proportion of agents obtaining their first preferences, average preference positions, and runtime scalability.

In high-volume, high-velocity contexts, threat identification requires effective real-time data stream analysis. This study offers a novel architecture—real-time processing of high-speed data streams—that is critical for effective threat identification in dynamic contexts. By using a publish-subscribe approach with Apache Kafka, the system is able to manage differences in data volume between many nodes. Experiments on the CICIoV24 and the CICEVSE2024 datasets indicate that the XGBoost model performs better, with high accuracy and robustness against adversarial attacks. Its performance degrades during the HopSkipJump attack, however, defence training can help with it. Moreover, our analysis shows that RandomForest and ExtraTrees perform better in noisy data from the CICIoV24 and XGBoost perform better in noisy data from the CICEVSE24 dataset, emphasizing the importance of selecting algorithms based on performance indicators. The architecture utilizes PyFlink’s distributed computation framework to improve computational efficiency for real-time processing and solves idea drift to ensure flexibility in changing data attributes.

Crowdsensing, also known as participatory sensing, is a method of data collection that involves gathering information from a large number of common people (or individuals), often using mobile devices or other personal technologies. This paper considers the set-up with multiple task requesters and several task executors in a strategic setting. Each task requester has multiple heterogeneous tasks and an estimated budget for the tasks. In our proposed model, the Government has a publicly known fund (or budget) and is limited. Due to limited funds, it may not be possible for the platform to offer the funds to all the available task requesters. For that purpose, in the first tier, the voting by the city dwellers over the task requesters is carried out to decide on the subset of task requesters receiving the Government fund. In the second tier, each task of the task requesters has start and finish times. Based on that, firstly, the tasks are distributed to distinct slots. In each slot, we have multiple task executors for executing the floated tasks. Each task executor reports a cost (private) for completing the floated task(s). Given the above-discussed set-up, the objectives of the second tier are: (1) to schedule each task of the task requesters in the available slots in a non-conflicting manner and (2) to select a set of executors for the available tasks in such a way that the total incentive given to the task executors should be at most the budget for the tasks. For the discussed scenario, a truthful incentive based mechanism is designed that also takes care of budget criteria. Theoretical analysis is done, and it shows that the proposed mechanism is computationally efficient, truthful, budget-feasible, and individually rational. The simulation is carried out, and the efficacy of the designed mechanism is compared with the state-of-the-art mechanisms.

Network Security is a major challenge due to the rapid growth and expansion of modern networks. There is an imminent need to protect organizations from malicious attacks by using intrusion detection systems. Conventional IDS techniques may not be able to identify new intrusions, hence building an IDS with existing intrusion detection strategy becomes essential. In this paper a hybrid feature selection for a robust IDS is proposed. It finds optimal features from an effectively preprocessed data to detect attacks successfully. Our proposed model is validated using benchmark dataset UNSW_NB15 and its performance is compared to various existing approaches and found that our model outperformed them.

Crowdsourcing is a process of engaging a ‘ crowd ’ or a group of common people for accomplishing the tasks. In this work, the time-bound tasks allocation problem in IoT-based crowdsourcing is investigated in strategic setting. The proposed model consists of multiple task providers (or task requesters) and several IoT devices (or task executors), and each of the task providers carries a task that have start time and completion time. Each of the participating IoT devices provide a preference ordering (order of their interest for the tasks) over a subset of tasks. Given the time bound tasks and ranking (or preference ordering) of the task executors, the objectives are: (1) to assign the tasks to different slots so that they are non-conflicting in nature, and (2) to allocate at most one task to each of the task executors from their respective preference ordering. To achieve the above objectives, a truthful mechanism is proposed namely T ruthful M echanism for T ime-bound T asks in IoT-based Crowdsourcing (TMTTC). Through theoretical analysis, it is proved that TMTTC satisfies the properties such as computational efficiency , truthfulness , Pareto optimality , and The Core . Through simulation, it is shown that TMTTC performs better than benchmark mechanism on the ground of truthfulness .

With the advent of new technologies and the internet around the globe, many cities in different countries are involving the local residents (or city dwellers) for making decisions in various government policies and projects. In this paper, the problem of detecting tourist spots in a city with the help of city dwellers, in strategic setting, is addressed. The city dwellers vote against the different locations that may act as a potential candidate for the tourist spot. For the purpose of voting, the concept of single peaked preferences is utilised, where each city dweller reports a privately held single peaked value that signifies the location in a city. Given the above discussed scenario, the goal is to determine the location in the city as a tourist spot. For this purpose, we have designed the mechanisms (one of which is truthful). For measuring the efficacy of the proposed mechanisms the simulations are done.

Food donation is an absolute necessity for today's society in order to counter the wastage of edible food. The key to this food movement is volunteer availability which aids in delivering the surplus food to a geographically distant receiver audience. Incentives attract a greater participation of volunteers who contribute to a greater food mobility. In this paper, we present a recursive incentive generation mechanism that helps to increase volunteer participation. This stabilizes the availability of volunteers which helps the main matching module to perform better in terms of matches produced and geographical area covered. We propose two mechanisms for this. The first one provides a solid foundation for the incentive structure while the second one improves it by providing significant financial benefits to the platform so that it could be better sustainable. We evaluate them with simulations carried out for both the mechanisms. The simulation results presented later show that while the former mechanism does ensure a hard upper limit on costs, the latter provides significant savings over it. Analytical results are also presented to ensure that the incentive distribution is within the available budget.

Object identification, is one of most important roles in computer vision, has been a hotspot for research and application over the past 20 years. Its purpose is to recognise and locate a large number of items in a given environment that fall into specific categories rapidly and consistently. It has gained a lot of study attention because of its tight association with image and video analysis. Its purpose is to discover and locate a large number of things in a given image that belong to specified categories rapidly and consistently. More sophisticated tools that can learn semantic, high-level, richer aspects are being offered to address the existing issues as deep learning advances. There are several types of algorithms. Based on the model training approach, the algorithms can be divided into two categories: single-stage detection algorithms and two-stage detection algorithms. Our investigation begins with an overview of deep learning and its most prominent tool, the Convolutional Neural Network (CNN). We'll also look at a common traditional object detection framework, along with some variations. Other tasks such as face detection and object tracking, as well as other important characteristics, would be implemented. Thus, future work in both object detection and relevant neural network-based learning systems should adhere to these guidelines and be useful. Important terms: SSD, Convolution Neural Network, YOLO

Crowdsourcing with the intelligent agents carrying smart devices is becoming increasingly popular in recent years. It has opened up meeting an extensive list of real-life applications such as measuring air pollution levels, road traffic information, etc. In literature, this is known as mobile crowdsourcing or mobile crowdsensing. In this paper, the discussed set-up consists of multiple task requesters (or task providers) and multiple IoT devices (as task executors), where each of the task providers has multiple homogeneous sensing tasks. Each task requester reports a bid and the number of homogeneous sensing tasks to the platform. On the other side, multiple IoT devices report the ask (the charge for imparting its services) and the number of sensing tasks they can execute. The valuations of task requesters and IoT devices are private information, and both might act strategically. One assumption that is made in this paper is that the bids and asks of the agents (task providers and IoT devices) follow decreasing marginal returns criteria. Given the above-discussed scenario, the objectives are: (1) to determine a set of quality IoT devices for each of the tasks held by the task requesters, and (2) to select a subset of quality IoT devices from among the available quality IoT devices for each of the sensing tasks. In this paper, a truthful mechanism is proposed for allocating the IoT devices to the sensing tasks carried by task requesters that also keep into account the quality of IoT devices. Through theoretical analysis, it is shown that the mechanism is truthful, budget balanced, individually rational, computationally efficient, correct, and prior-free. Further, probabilistic analysis is carried out to estimate the average number of tasks that get executed for any task requester. The simulations are carried out to measure the performance of the proposed mechanism against the benchmark mechanisms on the ground of truthfulness, budget balance, satisfaction level, average incentive, and computational efficiency.

Cloud computing, as an infrastructure less service, has gained a lot of attention over a decade now. The surge for the resource allocation and pricing have been at the centre stage of the research for a while in cloud computing. In this paper, we have proposed an efficient resource allocation and dynamic pricing algorithm for completion time failure in cloud computing (RADPACTF). Theoretical analysis is also provided in support of the proposed algorithm.

Crowdsourcing with intelligent agent felicitated with portable smart devices is popularly known as mobile crowdsourcing (MCS) or participatory sensing (PS). To motivate the task executors for performing the available tasks has been a challenge in an MCS environment. In this paper we have addressed this issue in a double auction environment when the tasks are time restricted (each task has start time and finish time) and may be overlapped. Here, we have taken an egalitarian approach so that a balanced allocation of tasks can be established to the task executors. In this, first the tasks are partitioned into several slots in a non-overlapping manner and then allocated to the task executors through double auction. Our proposed mechanism satisfies several economic properties such as truthfulness, individual rationality, and budget balanced. It is also exhibited via simulation that our proposed mechanism performs better when the agents (task executors and task providers) misreport their valuations.

In this chapter, we study some research issues from IoT-based spectrum trading in Wireless Communication in a strategic setting. We consider the scenario in which there are multiple secondary users (such as non-governmental organizations (NGOs), institutional organizations, foundations, etc.) having available un-utilized spectrum and multiple tertiary users (such as small farms, agricultural enterprises or people residing in different localities). Tertiary users provide preferences over the subset of all the available secondary users (NGOs, hereafter). Based on their preference ordering, the tertiary users are allocated the best possible NGOs among the available ones and under the restrictions that each user is assigned to at most one NGO. However, it is to be noted that, in this model, the allocated spectrum might not be available through out a long period of time but rather for a short duration of time within a time period. Therefore, tertiary users have to be able to work off-line and access cached data at the Edges of Internet even if the Internet access is not available. For the purpose of storing and retrieving the cached data several algorithms are designed and their computational complexity is analyzed. In order to empirically measure the efficacy of the proposed mechanisms the simulations are carried out and are compared with the benchmark mechanism. The proposed allocation mechanisms are envisaged as especially useful tools for emerging scenarios of smart farming and precision agriculture, where in situ infrastructures are not available.

We consider one of the scenarios in IoT-based mobile crowdsourcing in strategic setting, where we have single task requester (or task provider) and multiple task executors (or IoT devices). In this, a task requester have multiple heterogeneous tasks along with the publicly known budget. One constraint that is taken into consideration in terms of budget is that, a task provider is not having an entire budget available a priori, but only a part of the overall budget is available at the time of floating of the tasks. It means that, the overall budget comes incrementally in multiple phases. On the other hand, each IoT device reports valuation—the costs it charges for executing the tasks. The valuations of each of the IoT devices are private (only known to it and not known to others). Given this scenario, the goal is to determine a set of quality task executors for each of the tasks in such a way that the overall payment made to the task executors is less than or equal to the available budget. In this paper, a mechanism is designed that is both truthful and budget feasible. Along with truthfulness and budget feasibility, the mechanism helps in selecting the quality task executors for each task. For measuring the performance of the proposed mechanism on the basis of truthfulness and budget feasibility, the simulations are done.