Energy forecasting is important because it enables infrastructure planning and power dispatching while reducing power outages and equipment failures. It is well-known that federated learning (FL) can be used to build a global energy predictor for smart grids without revealing the customers’ raw data to preserve privacy. However, it still reveals local models’ parameters during the training process, which may still leak customers’ data privacy. In addition, for the global model to converge, it requires multiple training rounds, which must be done in a communication-efficient way. Moreover, most existing works only focus on load forecasting while neglecting energy forecasting in net-metering systems. To address these limitations, in this article, we propose a privacy-preserving and communication-efficient FL-based energy predictor for net-metering systems. Based on a data set for real power consumption/generation readings, we first propose a multidata-source hybrid deep learning (DL)-based predictor to accurately predict future readings. Then, we repurpose an efficient inner-product functional encryption (IPFE) scheme for implementing secure data aggregation to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. To address communication efficiency, we use a change and transmit (CAT) approach to update local model’s parameters, where only the parameters with sufficient changes are updated. Our extensive studies demonstrate that our approach accurately predicts future readings while providing privacy protection and high communication efficiency.

Energy theft causes economic losses and power out-ages and disrupts energy generation and distribution of smart grids. A significant challenge is how to effectively use customers' power consumption data for energy theft detection while pre-serving security and privacy. One solution is to use federated learning (FL) to compute a global model to detect energy theft cyberattacks where detection stations train local models on their customers' power consumption data and send only the parameters of the models to an aggregator server. Nevertheless, revealing the model's parameters may still leak customers' private data by launching attacks such as membership and inference. Therefore, a secure aggregation scheme is needed to protect the models' param-eters. Furthermore, the existing privacy-preserving aggregation schemes suffer from high overhead and low model accuracy. This paper addresses these limitations by proposing a novel privacy- preserving, efficient, decentralized, aggregation scheme based on a functional encryption cryptosystem for energy theft detection in smart grids without requiring a key distribution center. Our scheme enables the detection stations to send encrypted training parameters to an aggregator, which calculates the aggregated parameters and returns the updated model parameters to the detection stations without being able to learn the parameters of the local models or the training data of the customers to preserve their privacy. Moreover, the results of our extensive experiments show that our FL-based detector can detect energy thefts accurately with low overhead because of our lightweight privacy-preserving aggregation scheme.

During the recent years, online users, particularly in social networks, have witnessed an upsurge in racism, sexism, and other types of aggressive and cyberbully content, which are often manifested through offensive, abusive, or hateful speech and harassment. This can lead to severe physical and psychological stress in young children and adolescents, leading to even suicides and negatively affecting social policies. Therefore, there is a significant need to identify and regulate harassing content posted on the Internet in a smart, automated, and accurate manner. With this aim, in this paper, we design and develop a hierarchical framework comprising machine learning algorithms in order of higher computational complexity to adaptatively switch among them for efficiently detecting hateful and abusive content. We combine simple machine learning models such as Naive Bayes/Logistic Regression classifiers with customized calibration and Expectation-Maximization (EM) algorithms, and compare them with the much stronger deep learning techniques. Our proposed hierarchical framework demonstrates a significant improvement of the automated detection of abusive contents in social networks with a relatively small twitter dataset in contrast with the deep learning-based counterpart, namely the Bidirectional Encoder Representations from Transformers (BERT) model, training of which typically requires a much higher volume of labeled documents to detect abusive comments.

Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this article, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads.

In the smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. This causes hefty financial losses to the utility and may degrade the grid performance because the reported readings are used for energy management. This article is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign data set for the net-metering system by processing a real power consumption and generation data set. Then, we propose a new set of attacks tailored for the net-metering system to create a malicious data set. After that, we analyzed the data and found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources, such as solar irradiance and temperature. Based on the data analysis, we propose a general multidata-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with a high detection rate of 98.59% and a low false alarm of 2.92%.

The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.

Vehicular Ad Hoc Networks (VANETs) have the potential to enable the next-generation Intelligent Transportation Systems (ITS). In ITS, data contributed by vehicles can build a spatio-temporal view of traffic statistics, which can improve road safety and reduce slow traffic and jams. To preserve drivers’ privacy, vehicles should use multiple pseudonyms instead of only one identity. However, vehicles may exploit this abundance of pseudonyms and launch Sybil attacks by pretending to be multiple vehicles. Then, these Sybil (or fake) vehicles report false data, e.g., to create fake congestion or pollute traffic management data. In this article, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle’s anonymous location. Proofs sent from multiple consecutive RSUs are used to create a trajectory which is used as vehicle anonymous identity. Also, contributions from one RSU are not enough to create trajectories, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. Then, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. To report an event, the vehicle has to send the latest trajectory to an event manager. Then, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle, and therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate of Sybil attacks with low false negative and acceptable communication and computation overhead.

Charging coordination is necessary for the successful integration of the Energy Storage Units (ESUs), including electric vehicles and home batteries, into the smart grid. To coordinate charging, the ESUs should send charging requests including time-to-complete-charging (TCC) and battery state-of-charge (SoC) to the charging controller (CC) for scheduling charging, but these data can reveal sensitive information on the ESUs’ owners such as their locations, when they return home and whether they are on travel. In this article, we propose centralized and decentralized privacy-preserving and collusion-resistant charging coordination schemes for ESUs. In the centralized scheme, ESUs authenticate their requests using anonymous tokens. To thwart linkability attacks where the CC uses TCC and SoC to link requests sent from the same ESU at consecutive time slots, an ESU needs to send multiple charging requests with different TCC and SoC values instead of only one request. In the decentralized scheme, charging is coordinated in a distributed way using a privacy-preserving data aggregation technique. The idea is that each ESU selects some ESUs to act as proxies, and shares a secret mask with each proxy. Then, each ESU adds a mask to its charging request and encrypts it so that by aggregating all requests, all masks are nullified and the total charging demand is known, and then it is used to compute the charging schedules. Due to using masking technique, the scheme is secure against collusion attacks. The results of extensive experiments and simulations confirm that our schemes are efficient and secure, and can preserve ESU owners’ privacy and thwart linkability attacks.

An energy-trading system is essential for the successful integration of Electric vehicles (EVs) into the smart grid. Existing systems merely focus on making optimal decisions while others depend on anonymization to achieve EVs drivers' privacy which is not enough because they can be identified from visited locations. In this paper, leveraging blockchain technology, we propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. To preserve privacy, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.

To outsource medical data to the cloud, several schemes have been recently proposed to enable search over encrypted data to preserve data owners’ privacy. However, most of the existing schemes are either inefficient as they suffer from high computation/communication overheads or they are designed only to support single-data-owner and they unfortunately do not take in consideration the multi-data-owner setting of the e-health applications. In medical applications, a user, e.g., a doctor or a Primary Care Provider (PCP), should be able to search over data of many patients. Also, the existing searchable encryption schemes do not allow the user to customize his search scope to a subset of documents related to his focus based on some search conditions. So, the user receives unrelated documents to his search focus and spends too much computation overhead to decrypt them which makes the search process inefficient. In this paper, we propose a customized searchable encryption scheme with efficient key management over medical cloud data. Each data owner, e.g, a patient, sends to the cloud server an encrypted index with each document and it contains the keywords of this document. Each doctor sends to the cloud server an encrypted trapdoor contains the keyword of the search. In a nutshell, the cloud server can compute the similarity score of the encrypted indices and the encrypted trapdoor without learning the keywords of the document and the query, and then it returns to the doctors the documents of his interest. By using customized search, doctor can limit his search scope to a subset of documents related to his interest based on some search conditions. Our formal analysis of the privacy preservation and performance evaluation indicate that our scheme is secure, expressive, and efficient compared to the literature’s existing approaches.

Ride-sharing is a service that enables drivers to share trips with riders, which leads to several benefits such as sharing the travel cost and reducing traffic congestion. However, most of the existing ride-sharing systems rely on a central trusted unit to organize the service, which makes them subject to a single point of failure and attack, and lack of transparency. A few works have investigated decentralized ride-sharing systems, but they either do not consider privacy preservation or suffer from a tradeoff between privacy protection and accuracy due to using location cloaking technique. This paper proposes a Blockchain-based ride sharing organization system with accurate matching and privacy preservation. To achieve the accurate matching, instead of representing the ride-sharing area by a single grid, it is represented by several overlapping grids so that only near drivers/riders share rides. To preserve privacy, drivers/riders encrypt their offers/requests using a lightweight cryptosystem, and the Blockchain matches the encrypted offers and requests without being able to decrypt them. Our security and privacy analysis demonstrate that our system can organize the ride-sharing service in a secure and transparent way, and also preserve the privacy of drivers and riders. To evaluate the performance of our system, we have implemented it, and our measurements indicate that our system requires low communication and computation overheads.

There are different metering systems adopted in the advanced metering infrastructure (AMI) of the smart grid. Among these systems, the net-metering is a promising system that motivates customers to install renewable resources at their premises to generate electricity and sell it to the utility. In this system, the customer’s home is equipped with one smart meter to report the net readings representing the difference between the power consumed from the power grid and the power injected into the grid. However, malicious customers may compromise their meters to report false readings to the utility to illegally achieve financial gains. This not only causes huge losses to the utility, but also deteriorates the grid performance. To the best of our knowledge, this problem has not been investigated. Therefore, in this paper, we investigate the detection of false-reading attacks in the net-metering system for the first time. Specifically, we propose four sophisticated attacks customized for the net-metering system and use them to create a dataset containing both benign and malicious samples. We have analyzed the dataset and detected time correlations between the readings within the benign samples. Based on the data analysis, we propose a general deep-learning-based detector with hybrid architecture involving convolutional neural network (CNN) and gated recurrent unit neural network (GRU). We have evaluated our detector, and the results demonstrate that the detector can detect the false-reading attacks with high precision and recall, and low false alarm.

There are two power consumption readings collection approaches adopted in the advanced metering infrastructure (AMI) of the smart grid; periodic transmission (PT) and change and transmit (CAT) AMI systems. Among these approaches, CAT is a promising approach that collects these readings efficiently by sending the readings only when there is enough change in consumption to reduce the number of transmitted readings. However, CAT AMI system suffers from electricity theft cyber-attacks that can be launched by malicious customers who may compromise their meters and manipulate their power consumption readings to illegally reduce their bills. These attacks do not only cause hefty financial losses but may also degrade the grid performance because the readings are used for grid management. Therefore, this paper is the first work that investigates this problem for CAT AMI system, in which the power consumption readings are not sent periodically to the system operator. We first prepare a benign dataset for the CAT AMI by processing a real power consumption readings dataset. Next, we propose a new set of attacks tailored for the CAT AMI to create a malicious dataset. Then, we propose a general and hybrid deep-learning electricity theft detector to identify malicious customers. The proposed detector is trained on both benign and malicious data from all customers using the reported CAT readings. Extensive test studies are carried out to investigate the detector’s performance using publicly available real data of power consumption from 114 customers. Simulation results demonstrate our models can detect malicious customers with high detection rate and low false alarm.

Reporting fine-grained power consumption readings periodically in advanced metering infrastructure (AMI) results in transmitting a massive amount of data by each smart meter (SM). To collect these readings efficiently, change and transmit (CAT) approach can be used. In CAT, the SM sends a consumption reading only when there is enough change in the consumption, which reduces the number of transmitted readings. However, using the CAT approach may trigger attackers to launch a presence-privacy attack (PPA) to infer sensitive information such as the absence of the house occupants by analyzing their SM’s transmission pattern. Therefore, in this paper, we propose a scheme, called “STID”, for collecting the power consumption readings efficiently in AMI networks while preserving the consumers’ privacy by transmitting spoofing transmissions based on an interactive deep-learning defense model. First, we create a dataset that contains the CAT transmission patterns using real power consumption readings and a clustering technique. Next, we train a deep-learning-based attacker model to launch PPA, and the results indicate that the success rate of the attacker is about 90%. Finally, to mitigate the PPA, we train a defense model using deep-learning to transmit spoofing transmissions. The evaluations of our envisioned STID scheme demonstrate a significant reduction in the attacker’s success rate while achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that our proposed STID can reduce the attacker’s success rate to 6.12% and increase efficiency by about 38% compared to transmitting readings periodically.

Spatial-temporal charging coordination mechanisms are developed to avoid electrical overload at the charging stations and extravagant waiting time for electric vehicle drivers. Though, attackers could attack these mechanisms by launching distributed attacks against charging stations to prevent legitimate drivers from charging their vehicles. To attack a charging station, an attacker can compromise a set of vehicles, e.g., by disseminating a malware, and instruct them to send fake charging requests simultaneously to reserve the available energy capacity that is provided to a charging station without having the intention for charging, and thus benign vehicles do not find charging slots. This paper introduces an anomaly-based detection technique to identify the charging stations under this denial of charge (DoC) attacks using convolutional neural networks. The main idea is that each charging station has a normal energy demand pattern and launching DoC attacks changes this pattern. To capture such anomalous pattern, we use convolutional neural model to capture the temporal features within the demand of the charging station. To train our anomaly detector, we first create a benign dataset that could be utilized in other research areas such as load forecast and energy management. Then, we introduce a group of attacks that are used to create the malicious dataset. Finally, we used the benign and malicious datasets to train and test the deep neural model to detect DoC attacks. Our experiments show that our detector has high detection and low false alarm rates.

Social media applications offer multimedia content to enrich the user experience in pocket-sized mobile phones. The highly diverse features of social media applications put pressure on the battery life of smartphones. Moreover, accessing social content from cloud-based infrastructure involves greater access latency because of the high geographical distance between the cloud and mobile users. To meet the increasing demands of lower energy and latency applications, caching in mobile edge computing plays a vital role by pushing processing and storage resources at the edge of the network. Mobile edge computing offers minimum delays as it provides data content near to the end-user in a ubiquitous environment. However, not all the content can be cached at the edge node. We propose a community-based clustering framework that is used to identify the users having similar interests. For community detection, we propose a hybrid framework with a combination of minibatch K-means and DBSCAN. The framework scales well in terms of the size of the data set. We determine a set of popular social content from each community and cache them at the edge of the network. In comparison to traditional cloud and popularity based content delivery schemes, our proposed edge-based framework provides lower access latency and higher smartphone battery lifetime.

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, and billing. However, fraudulent consumers launch electricity theft cyber attacks by reporting false readings to reduce their bills illegally. These attacks do not only cause financial losses but may also degrade the grid performance because the readings are used for grid management. To identify these attackers, the existing schemes employ machine-learning models using the consumers' fine-grained readings, which violates the consumers' privacy by revealing their lifestyle. In this article, we propose an efficient scheme that enables the SO to detect electricity theft, compute bills, and monitor load while preserving the consumers' privacy. The idea is that SMs encrypt their readings using functional encryption (FE), and the SO uses the ciphertexts to: 1) compute the bills following the dynamic pricing approach; 2) monitor the grid load; and 3) evaluate a machine-learning model to detect fraudulent consumers, without being able to learn the individual readings to preserve consumers' privacy. We adapted an FE scheme so that the encrypted readings are aggregated for billing and load monitoring and only the aggregated value is revealed to the SO. Also, we exploited the inner-product operations on encrypted readings to evaluate a machine-learning model to detect fraudulent consumers. The real data set is used to evaluate our scheme, and our evaluations indicate that our scheme is secure and can detect fraudulent consumers accurately with low communication and computation overhead.

Over the last few years, finding vacant parking spaces has become a hassle for drivers especially in crowded cities. This problem leads to wasting drivers' time, traffic congestion, and air pollution. Recently, smart parking systems aim to address this problem by enabling drivers to have real-time parking information about vacant parking spaces. However, the existing parking systems rely on a central third party to organize the service, which makes them subject to a single point of failure and privacy breach concerns by both internal and external attackers. In this paper, we propose a secure smart parking system using blockchain technology. Specifically, a consortium blockchain is made of parking lots to ensure security, transparency, and availability of the parking system. Then, to protect the drivers' location privacy, we use cloaking technique to hide the drivers' locations. The blockchain validators return available parking offers with in the cloaked area. Finally, the driver selects the best offer and makes reservation directly with the parking lot. Evaluations are conducted to evaluate the proposed scheme, and results indicate practicality of our scheme.

Preserving the customers' privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.

Ride-sharing is a service that enables drivers to share their trips with other riders, contributing to improving traffic congestion as well as assist in reducing Carbon Dioxide (CO2) emission and fuel consumption. It has come to the forefront in recent years as a Green service in large cities. However, the majority of existing ride-sharing services rely on a central third party, which makes them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users. There is also high service fees paid to the ride-sharing service provider. In this paper, we propose to decentralize ride-sharing services based on a public Blockchain. Our scheme enables drivers to propose ride-sharing services without relying on a trusted third party. To preserve location privacy, riders send cloaked ride requests to hide their exact pick-up/drop-off locations, and departure/arrival dates. Then, by using an off-line matching technique, drivers sends their offers encrypted to ensure data confidentiality. Upon receiving the ride-offers, the rider can find a ride match using some heuristics as well as the bid price included in the offer. To preserve anonymity, riders/drivers use pseudonyms that change per trip to ensure unlinkabilty. We envision the application of this technology in Green Internet of Things connected smart cities, where ride sharing services are common. Finally, we implement our scheme and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol.

This paper studies the problem of scheduling sensors for target coverage in a surveillance environment. In this problem, coverage of target areas must be guaranteed during the system timeframe. First, we formulate an independent scheduler of sensors, where scheduling at each time epoch is independent from the sensing patterns among all sensors during the timeframe. Then, we formulate a cooperative scheduler, where each sensor has a potential sensing pattern that is shared with other sensors. We use the number of sensing times (i.e., actual activation times for sensors) as a performance metric. We show by simulations that the cooperative sensor scheduler significantly reduces the number of sensing times compared to the independent scheduler. Furthermore, we combine the concept of cooperative scheduling of surveillance systems with mobile sensors, and we study the performance metric of the independent and cooperative schedulers. We use a realistic coverage model to describe the physical availability of mobile sensors for covering target areas. We find that using mobile sensors significantly reduces the number of sensing times compared to using only fixed sensors.

In this paper, we study the performance of sensing in mobile sensor networks with imperfect knowledge of neighborhood mobility. We examine the impact of exchanging incorrect mobility information on the cost of sensing and the required target coverage. The study is performed for two target coverage models: an independent coverage model and a Markovian one. We demonstrate via extensive simulations that a small amount of the mobility information is required to be successfully exchanged between the mobile sensors to provide the required coverage of targets. Finally, we conduct an experiment at the University of Toronto campus to show the impact of the mobility knowledge in sensing. The experiment results match our expectation in terms of the target coverage, sensing cost, and trade-off between the two metrics.

In this paper, we show that knowledge of mobility helps in sensing and coverage in vehicular sensor networks. First, we propose a mathematical formulation for the stationary sensing-coverage problem in terms of the maximization of a utility function. Then, we propose a method to solve the sensing problem for mobile sensors. We solve the two problems via the branch and bound approximation algorithm. Simulation results show that mobility improves vehicular sensing by selecting an optimal number of sensors to provide the same quality of coverage over an interval of time. In addition, we study the model by using probabilistic node availability.

Vehicular ad hoc networks are expected to provide a reliable networking platform for cooperative safety communication systems. Those systems are of a broadcast nature and require to deliver both safety messages and vehicle tracking information while being interfered by other types of lower priority messages on the same channel. Vehicle tracking information are necessary to enable safety communication systems and intelligent transportation systems. Due to the large number of communicating vehicles and the amount of traffic exchanged in the broadcast mode, network congestion often occurs in vehicular communication systems. In this paper, a new methodology for avoiding such a network congestion is proposed. We identify the sparsity of the vehicle tracking information and propose a novel information recovery scheme. The proposed scheme reduces the amount of data exchanged due to vehicle tracking packets while providing a robust information reception at the receivers. Essentially, it utilizes compressive sensing to transmit a few measurements of the vehicles velocity vector that allows perfect recovery of the original vector at the receiver with a minimal error. Extensive simulation results demonstrate the effectiveness of applying compressive sensing in recovering vehicles tracking information, and relieving the network from unnecessary congestion due to the large amount of data exchanged.

An efficient medium access control (MAC) protocol should yield maximum throughput and fairness. However, because these two performance metrics have conflicting interests, an effective solution must address the trade-off between them. In this paper, we study the performance of the IEEE 802.11p MAC protocol in vehicle-to-infrastructure communications. The main focus is the trade-off between the system throughput and the level of fairness among the communicating nodes, which leads to a customized optimization problem. Accordingly, we formulate a multiobjective optimization problem to optimize both throughput and fairness, and propose a dynamic mechanism to maintain fairness among the mobile nodes. The optimization problem is solved by means of an approximate numerical solution, the results of which demonstrate the effectiveness of the proposed MAC scheme in terms of throughput and short-/long-term fairness.

Vehicular ad hoc networks (VANETs) are an extreme case of mobile ad hoc networks (MANETs). High speed and frequent network topology changes are the main characteristics of vehicular networks. These characteristics lead to special issues and challenges in the network design, such as in medium access control (MAC). Due to the high speed and frequent network partitions, it is difficult to design a MAC scheme in VANETs that satisfies quality-of-service (QoS) requirements in all network scenarios. In this paper, we provide an evaluation of the mobility impact on the IEEE 802.11p MAC performance. In this evaluation, we identify a new unfairness problem in the vehicle-to-vehicle (V2V) communications. To achieve better performance, we propose two dynamic contention window mechanisms to alleviate network performance degradation due to high mobility. Simulation results demonstrate the effectiveness of the proposed MAC schemes.