As the number of EVs on the road increases, new and innovative charging solutions are needed to support their energy demand, especially in areas lacking infrastructure, in order to alleviate range anxiety in EV owners. Mobile Charging Stations (MCSs) have emerged as a viable solution, offering on-demand charging by traveling to areas of need. Researchers have explored several allocation mechanisms for MCS dispatch, including energy auctions, which perform well under the assumption that all participants adhere to protocol. However, customer no-shows—a common challenge in retail services—can significantly disrupt MCS operations. A high rate of EV no-shows leads to stagnation and resource wastage, ultimately reducing the efficiency of charging service delivery. To address this, we propose a reputation management system that enables MCS fleets to prioritize well-behaved customers, ensuring better resource utilization. Additionally, EVs are incentivized to form coalitions with their neighbors, submitting aggregate requests backed by a deposit to promote responsible behavior. The proposed coalition- and reputation-based energy auction system is evaluated across various quality-of-service metrics to assess its effectiveness.

Mobile crowdsensing has become a key paradigm in IoT technology. It allows utilization of built-in sensors of participants’ mobile devices to provide sensing as a service. However, ensuring data reliability remains a critical challenge due to the presence of erroneous, or malicious data, which can compromise the integrity and accuracy of crowdsensed information. This paper addresses this challenge by introducing anchor nodes to enhance the quality of data fusion in mobile crowdsensing. Anchor nodes are identified through a systematic process incorporating a dynamic reputation adjustment mechanism and weighted data fusion. Validation using a real sensor dataset demonstrates the effectiveness of the proposed approach. It shows an improvement in data fusion quality. The results highlight the importance of anchor-based trust management (TM) in enhancing data fusion quality in crowdsensing.

This paper addresses the challenges of Last Mile Delivery (LMD) in crowdsourced platforms under time and budget constraints. LMD service providers face a continuous increase in demand with limited resources, such as workers and budgets. With tasks that vary in urgency, limited resources often lead to task failures. Furthermore, the increasing number of requesters and workers complicates the governance of the LMD platform, making it difficult to maintain credibility, integrity, and transparency. Current LMD solutions generally focus on route optimization and service acceleration, overlooking the challenge of combined time-critical and budget-constrained tasks. This creates the need for an agile, accelerated, and decentralized LMD framework that can 1) handle tasks instantly upon submission, 2) motivate successful completion based on urgency, and 3) compensate for tasks with deficient budgets to fortify long-term success and avoid failure due to constrained resources. For this purpose, this paper introduces the first Blockchain Hybrid Crowdsourced Auction-based LMD framework (BHCA-LMD), which is designed to effectively manage time and budget constraints. Built on-chain for transparency and decentralization, the framework uses drones and ground vehicles as a hybrid delivery mode for their capabilities in speeding up deliveries. BHCA-LMD incorporates an auctioning system to alleviate failures due to limited budgets, with a feedback mechanism to prevent entities from abusing the platform’s profit for their benefit, ensuring the credibility and reliability of the framework. The evaluation shows an on-time task completion rate of up to 74% with the presence of malicious behavior and a 15% lower gas consumption compared to the closest benchmark.

Artificial intelligence (AI) has the potential to revolutionize healthcare by automating the detection and classification of events and anomalies. In the scope of this work, events and anomalies are abnormalities in the patient’s data, where the former are due to a medical condition, such as a seizure or a fall, and the latter are erroneous data due to faults or malicious attacks. AI-based event and anomaly detection (EAD) and their classification can improve patient outcomes by identifying problems earlier, enabling more timely interventions while minimizing false alarms caused by anomalies. Moreover, the advancement of Medical Internet of Things (MIoT), or wearable devices, and their high processing capabilities facilitated the gathering, AI-based processing, and transmission of data, which enabled remote patient monitoring, and personalized and predictive healthcare. However, it is fundamental in healthcare to ensure the explainability of AI systems, meaning that they can provide understandable and transparent reasoning for their decisions. This article proposes an online EAD approach using a lightweight autoencoder (AE) on the MIoT. The detected abnormality is explained using KernelSHAP, an explainable AI (XAI) technique, where the explanation of the abnormality is used, by an artificial neural network (ANN), to classify it into an event or anomaly. Intensive simulations are conducted using the Medical Information Mart for Intensive Care (MIMIC) data set for various physiological data. Results showed the robustness of the proposed approach in the detection and classification of events, regardless of the percentage of the present anomalies.

Multiagent deep reinforcement learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent works proposing federated reinforcement learning (FRL) to tackle these issues suffer from problems related to model restrictions and maliciousness. Other proposals using reward shaping (RS) require considerable engineering and could lead to local optima. In this article, we propose a novel Blockchain-assisted multiexpert demonstration cloning (MEDC) framework for MDRL. The proposed method utilizes expert demonstrations in guiding the learning of new MDRL agents, by suggesting exploration actions in the environment. A model sharing framework on Blockchain is designed to allow users to share their trained models, which can be allocated as expert models to requesting users to aid in training MDRL systems. A Consortium Blockchain is adopted to enable traceable and autonomous execution without the need for a single trusted entity. Smart Contracts are designed to manage users and models allocation, which are shared using IPFS. The proposed framework is tested on several applications and is benchmarked against existing methods in FRL, RS, and imitation learning-assisted RL. The results show the outperformance of the proposed framework in terms of learning speed and resiliency to faulty and malicious models.

This article addresses the problem of multiple source localization (MSL) using the Internet of Things (IoT) sensors. MSL entails determining the locations of multiple unknown sources by fusing sensory data within a designated Area of Interest (AoI). Existing solutions suffer from limitations, such as increased algorithmic complexity, as the number of sources increases and degraded performance in sparse sensor placement scenarios. This article proposes a novel source-independent approach resilient to sparse sensor placements based on conditional generative adversarial networks (cGANs) and image processing-based peak finding with subpixel peak refinement to address the MSL problem. The proposed approach formulates the MSL problem into two subproblems: 1) image-to-image translation and 2) 2-D peak finding. The cGAN translates the raw measurement data to an intensity field through image-to-image translation. Then, a peak-finding algorithm based on persistent homology with subpixel peak refinement is applied to localize the unknown sources accurately. The proposed approach is tested through radioactive source localization experiments, benchmark comparisons, and adaptability evaluation in unseen environments.

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.

This paper proposes implementation-friendly cooperative spectrum-sensing (CSS) algorithm for the data fusion based cooperative cognitive-radio network. It has been designed with the notion of alleviating the computational complexity that results in efficient hardware design, without degrading the detection performance. In addition, this work presents hardware-efficient architecture of cooperative spectrum sensor (CSR), based on the suggested CSS algorithm and with the aid of resource-sharing architectural optimization. Extensive performance analysis of our CSS algorithm showed that the detection probability of 0.9 has been achieved at the signal-to-noise ratio (SNR) of −2.6 dB which is adequate for many real-world applications. Furthermore, hardware implementation of the proposed CSR architecture is carried out on the FPGA platform (Nexys-4 DDR Artix-VII board) and its functional validation is performed in real-world scenario. Subsequently, the suggested CSR is ASIC synthesized and post-layout simulated in UMC 90 nm-CMOS technology node. As a result, it occupies a silicon area of 0.0369 mm2 and it is capable of operating at a maximum operating frequency of 251 MHz. This design has a latency of 79 clock cycles and it delivers a sensing time of$0.31 ~\mu \text{s}$while operating at the aforementioned maximum clock-frequency. In comparison to the state-of-the-art implementation, the proposed CSR occupies 34.1% lesser area, delivers$10\times $shorter sensing time, and has achieved$15.45\times $better hardware efficiency.

Electric vehicle (EV) charging infrastructure development is one of the key aspects of the electrification of transportation systems. However, incorporating EV public charging facilities has practical and financial concerns, especially in developing economies. Recently, mobile public charging stations (MPCS) have been considered an alternate, practical, commercially scalable solution. Present research on MPCS operation algorithms assumes centralized algorithm execution and does not offer any features related to consumer data security. Moreover, they do not exploit the multi-port charging facility of MPCS, which is more economical for scheduling services. In this paper, a comprehensive framework for the decentralized execution of the MPCS operation algorithm for EV charging service using a hybrid cloud-edge server architecture is proposed. The decentralized execution enhances EV data security and reduces load on the cloud server, data transmission, and storage charges. Four vehicle routing algorithms, including exact integer programming (IP), Greedy, Greedy+2Opt heuristic, and meta-heuristic Tabu search algorithms, are applied to test the efficacy of the proposed framework. From the studies, the proposed framework with Greedy+2Opt performed well concerning scalability. Compared to one-to-one MPCS-EV charging, the proposed one-to-many charging framework offered a significant reduction in the transportation cost and the required number of MPCSs for a given energy delivered. Further, for a given MPCS battery capacity and service time window, the framework delivered higher energy compared to one-to-one service model.

Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with$3.3~kW$to$19~kW$need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port$(N (+),O(-))$is formed using the neutral of the EV motor winding$(N)$and negative rail of the drivetrain inverter$(O)$. Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.

Power electronics is an emerging and rapidly advancing technology within the realm of electrical engineering, finding wide-ranging applications in areas such as renewable energy, electric vehicles, and industrial automation. This paper aims to provide an in-depth exploration of the tools employed for comprehending power electronics technology. A notable aspect of this paper is its systematic approach, guiding students through a sequential progression of design, hardware implementation, and procedural steps for a series of power electronic circuits. Moreover, the paper elucidates the various tools, software, and their respective applications in the power electronics domain, contributing to a comprehensive understanding of the field.

This paper tackles the problem of frequent disconnections in the Quality-of-Service Optimized Link State Routing (QoS-OLSR) protocol due to mobility in mesh networks. Different routing protocols are proposed for mesh networks, such as Better Approach to Mobile Ad-hoc Networking (BATMAN), Optimized Link State Routing (OLSR), and QoS-OLSR 1.0. QoS-OLSR 1.0 improves the performance compared to the other protocols by incorporating cluster head formation and a Quality-of-Service metric used in cluster head selection. However, it still does not mitigate the impact of mobility in the formulated QoS metric. In this work, we propose and implement a cluster-based QoS-OLSR protocol for mesh networks that accounts for mobility. The work proposes a global QoS metric for nodes, calculated as the average quality of all neighbors’ links. Also, a relative QoS metric is proposed and calculated based on the direct link with the neighbor and its global QoS. Based on the relative QoS, nodes in the network select cluster heads. In case of disconnection from the selected cluster head, the nodes join an existing cluster head to recover from disconnection. Cluster heads in the proposed protocol are responsible for determining Multi-point relays (MPRs) to 2-hop and 3-hop away cluster heads to enhance connectivity. The proposed protocol is implemented as a Linux-compatible protocol for emulations using Mininet-WiFi and compared to BATMAN and QoS-OLSR 1.0. The proposed protocol outperforms the benchmark protocols in terms of throughput, Packet Delivery Ratio, and Round Trip Time with low and high mobility nodes part of the network.

To cope with the increasing growth of last-mile delivery, crowdsourcing last-mile delivery has been adopted as a flexible and cost-efficient way to deliver parcels quickly and efficiently. However, some potential downsides to crowdsourcing last-mile delivery include concerns about safety, reliability, and transparency. Therefore, blockchain has been adopted to promote transparency in the last-mile delivery process. Despite the impact of a worker’s reputation on task completion, existing works do not offer a traceable and transparent reputation metric for lastmile delivery workers. This paper proposes a blockchain-based framework for reputation management in crowdsourced last-mile delivery. The proposed framework is designed as smart contracts that maintain and update crowdsourced workers’ information, mainly reputation, in a transparent and traceable manner. In addition, the framework allows requesters to create their delivery tasks and workers to get allocated available tasks. The proposed framework uses Solidity to interact with smart contracts for requesters and workers. The cost analysis demonstrates the proposed framework’s feasibility and cost efficiency.

Mobile Crowdsensing (MCS) is a sensing paradigm where individuals collectively perform a sensing task using their smart devices . Sensing tasks can be classified as one-time or continuous. In the former, only one-time readings from the devices of the recruited workers are needed. However, in continuous sensing tasks, collecting information continuously during a specific period is required. Due to workers’ mobility, ensuring a satisfactory level of the quality of information (QoI) of the sensing data is challenging since workers may leave the Area of Interest (AoI) before the task is over, causing low area coverage. Current existing recruitment systems for continuous sensing rely on historical mobility traces to recruit the group of workers. However, since workers’ mobility patterns are dynamic in nature, thus, a real-time prediction of their locations in the AoI needs to be considered to ensure that the required value of QoI is achieved. Hence, in this work (1) machine learning is employed to predict users’ location during the sensing period and (2) a novel recruitment system is proposed for continuous sensing tasks. The simulation results, using a real-life trajectories dataset, show the efficacy of the proposed solution when compared to benchmark.

In target localization applications, readings from multiple sensing agents are processed to identify a target location. The localization systems using stationary sensors use data fusion methods to estimate the target location, whereas other systems use mobile sensing agents (UAVs, robots) to search the area for the target. However, such methods are designed for specific environments, and hence are deemed infeasible if the environment changes. For instance, the presence of walls increases the environment’s complexity and affects the collected readings and the mobility of the agents. Recent works explored deep reinforcement learning (DRL) as an efficient and adaptable approach to tackle the target search problem. However, such methods are either designed for single-agent systems or for noncomplex environments. This work proposes two novel multiagent DRL models for target localization through search in complex environments. The first model utilizes proximal policy optimization, convolutional neural networks, Convolutional AutoEncoders to create embeddings, and a shaped reward function using breadth first search to obtain cooperative agents that achieve fast localization at low cost. The second model improves the first model in terms of computational complexity by replacing the shaped reward with a simple sparse reward, subject to the availability of Expert Demonstrations. Expert demonstrations are used in Demonstration Cloning, a novel method that utilizes demonstrations to guide the learning of new agents. The proposed models are tested on a scenario of radioactive target localization, and benchmarked with existing methods, showing efficacy in terms of localization time and cost, in addition to learning speed and stability.

Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool of workers is available. However, this assumption is not always true, especially in cold-start situations, where a new MCS task has just been released. Additionally, studies show that up to 96% of the available candidates are usually not willing to perform the assigned tasks. To tackle these issues, recent works use Online Social Networks (OSNs) and Influence Maximization (IM) to advertise about the desired MCS tasks through influencers, aiming to build larger pools. However, these works suffer from several limitations, such as 1) the lack of group-based selection methods when choosing influencers, 2) the lack of a well-defined worker recruitment process following IM, 3) and the non-dynamicity of the recruitment process, where the workers who refuse to perform the task are not substituted. In this paper, an Influence- and Interest-based Worker Recruitment System (IIWRS), using OSNs, is proposed. The proposed system has two main components: 1) an MCS-, group-, and interest-based IM approach, using a Genetic Algorithm, to select a set of influencers from the network to advertise about the MCS tasks, and 2) a dynamic worker recruitment process which considers the social attributes of workers, and is able to substitute those who do not accept to perform the assigned tasks. Empirical studies are performed using real-life datasets, while comparingIIWRSwith existing benchmarks.

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federations of fog providers for task offloading purposes. Nonetheless, a few challenges remain a burden for the formation of the federations. The formation mechanisms used to structure the federations of providers are still not fully stable. This causes a problem because a structureless federation can lead to an underperforming infrastructure. Furthermore, most of the literature ignored the honesty metrics of the providers and how trustworthy they are in allocating the agreed-upon resources for processing the tasks. Moreover, adopting a central reputation mechanism is questionable in terms of reliability due to many complications including the lack of consensus. In this work, we develop a Blockchain-based reputation mechanism for assisting the formation of fog federations for IoV applications. Our mechanism comprises on-chain smart contracts for storing and manipulating the providers’ reputations, and an off-chain Hedonic-based formation process that considers the parameters extracted from the chain to build the federations. We develop smart contracts using Solidity and deploy them on the Ethereum Blockchain. We test our mechanism using the EUA dataset as a proof of concept and compare it to other works in the literature. The results obtained show that our approach is able to enhance the overall payoff and quality of service in the IoV paradigm.

Multilevel inverters are gaining importance in industrial applications, so new topologies are being proposed, particularly for increasing the number of levels while minimizing the number of power switches. This paper proposed a switched capacitors-based multilevel inverter topology with a level doubling network (LDN) to almost double the output levels. The LDN is simply a capacitor-fed half-bridge structure with self-regulating capacitor voltage. The proposed topology can generate nine-level at the output by using nine switches to achieve a system’s low cost. A switched capacitor in the proposed topology is self-balanced without any additional circuit. It uses only one input source to attain a voltage gain of two. A simple modulation scheme is used to keep switched capacitor balanced at all modulation values. The circuit’s operation is validated using MATLAB/Simulink simulation and experimental results.

This paper tackles the problem of cluster head and Multi-Point Relay (MPR) selection for the Quality-of-Service Optimized Link State Routing (QoS-OLSR) protocol in mesh networks. Mesh networks emerged to extend the connectivity of users and to enable the exchange of messages in an adhoc manner. Mesh networks apply routing protocols such as Better Approach to Mobile Adhoc Networking (BATMAN) and Optimized Link State Routing (OLSR) where the latter was deemed more suitable for high mobility networks. Research efforts have proposed extending the OLSR protocol to a QoS-OLSR protocol that takes into consideration metrics such as bandwidth, connectivity, remaining energy, and velocity in QoS computation. The proposed QoS is used for cluster head and MultiPoint Relays (MPRs) selection. In this work, we propose and implement a cluster-based QoS-OLSR protocol for mesh networks. A QoS metric that aggregates the average estimated transmission count and the reachability of a node is defined. The proposed protocol is implemented by modifying the Linux OLSR distribution to perform emulations using meshnet-lab under different mobility models. In a static network, the proposed protocol outperforms BATMAN and OLSR in terms of Packet Delivery Ratio (PDR) and Round Trip Time (RTT). In addition, the proposed protocol outperforms BATMAN in terms of RTT and provides comparable results to OLSR in networks with low and high mobility nodes.

In this paper, a blockchain-based management platform, PackChain, for last-mile delivery is proposed. The growing popularity of online shopping has put immense pressure on the supply-chain industry, especially on last-mile delivery. The available solutions suffer from high cost, and lack of transparency. Therefore, assuring traceability of users' actions has become a critical requirement to establish trust between parties. The proposed PackChain framework uses the Ethereum blockchain to offer a crowdsourcing platform for last-mile delivery with autonomous and transparent processes. PackChain provides all the core functions needed for the delivery framework to operate through smart contracts such as managing user information, accepting offers, verifying transactions, and handling payments. In addition, the framework relies on proofs of delivery as an arbitration mechanism between users to release or hold funds. The proposed framework is implemented using Solidity and Web3.js to interact between clients and carriers. The emulation results demonstrate the feasibility and cost-efficiency of the proposed solution11The full code of the smart contract and the related logic is also made publicly available on Github..

The increasing number of Internet of Things (IoT) devices and low-cost sensors have facilitated developments in large-scale monitoring applications. However, the accuracy of low-cost sensors remains questionable. Monitoring applications, such as environmental monitoring, try to detect “interesting” data points or patterns, known as anomalies, that do not conform to the norm. These include erroneous data caused by hardware failures or malicious attacks, and nonerroneous data due to unexpected phenomenon, caused by events, such as unexpected high traffic volume. Traditionally, IoT devices collect raw data and periodically upload them to the cloud for processing, which includes anomaly detection. However, the increasing processing capabilities of IoT devices have made the on-device anomaly detection possible in an online and real-time manner. In this article, multivariate long short-term memory (LSTM) autoencoder is proposed for anomaly and event detection in IoT devices. In addition, the proposed approach integrates smart inference, based on a game-theoretical approach, which dynamically changes the period of detection based on the stability of the data, aiming to optimize power consumption and elongate the lifetime of the device. The proposed anomaly and event detection model was simulated and implemented on an STM32H743 Nucleo board, and results show the robustness of the model regardless of the number of anomalies and events present.

The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this article, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

Electric Vehicles (EVs) are being widely adopted to completely replace vehicles with internal combustion engines. However, the development of charging infrastructure to support the growing number of EVs has been lacking primarily due to the high cost of installation. Additionally, the immature and non-uniform deployment of charging stations has led to the absence of charging infrastructure in areas such as highways and rural areas. As a result, emerging concepts such as, EV-to-Home (V2H), EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence. In this paper, the V2V energy sharing concept is exploited, where an intelligent and comprehensive framework to manage and allocate energy between EVs is proposed. This work presents a realistic modeling of the V2V energy sharing problem and proposes a two-layer matching approach that can efficiently match the EVs. The proposed approach not only optimizes the cost, but also the time, system energy efficiency, user satisfaction, and social welfare; hence making the approach more realistic and inclusive. Gale-Shapley is utilized to produce stable matchings, in the first layer, whereas a user-satisfaction model is devised to ensure realistic matchings, in the second layer. Additionally, a real-life dataset is developed from commercially available EVs and the performance of the system is evaluated using this dataset, in addition to realistic parameters derived from real-life data. The proposed approach is compared with a benchmark, where the results show that efficient, realistic, and effective V2V matches are achievable, paving the way for the adoption of such framework.

With the advent of mobile crowd sourcing (MCS) systems and its applications, theselectionof the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.

With the tremendous advances in ubiquitous computing, and more specifically with mobile phones, mobile crowd sensing (MCS) has become an appealing part of IoT. However, MCS is vulnerable to multiple types of attacks which could be caused by external or internal adversaries. While external attacks, such as spoofing and jamming are addressed using network's security measures, internal attacks such as maliciously degrading the Quality of Service (QoS) of the tasks by intentionally submitting false reports, should be handled by the MCS systems. The current solutions aim to maximize the completion of tasks in selection based on the reputation or the credibility of the workers, but without consideration of internal attacks threat. This paper focuses on internal misbehaving act, similar to Sybil attacks, where users impersonate multiple identities to change the majority-voting result of the task or to selfishly maximize their profit with minimal costs, using multiple devices. These workers take advantage of the anonymity of MCS for privacy protection to pose the attack. This paper studies the need for a resilient approach which detects and eliminates misbehaving devices during workers' selection in MCS.