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Social media has enabled information-sharing across massively large networks of people without spending much financial resources and time that are otherwise required in the print and electronic media. Mobilebased social media applications have overwhelmingly changed the information-sharing perspective. However, with the advent of such applications at an unprecedented scale, the privacy of the information is compromised to a larger extent if breach mitigation is not adequate. Since healthcare applications are also being developed for mobile devices so that they also benefit from the power of social media, cybersecurity privacy concerns for such sensitive applications have become critical. This article discusses the architecture of a typical mobile healthcare application, in which customized privacy levels are defined for the individuals participating in the system. It then elaborates on how the communication across a social network in a multi-cloud environment can be made more secure and private, especially for healthcare applications. [ABSTRACT FROM AUTHOR]

The online smartphone-based human activity recognition (HAR) has a variety of applications such as fitness tracking, healthcare…etc. Currently, the signals generated from smartphone-embedded sensors are used for HAR systems. The smartphone-embedded sensors are utilized in order to provide an unobtrusive platform for HAR. In this paper, we propose a deep convolution neural network (CNN) model that provides an effective and efficient smartphone-based HAR system. For automatic local features extraction from the raw time-series data, we use the CNN while simple time-domain statistical features are used to extract more distinguishable features. Furthermore, we explore the impact of a novel data augmentation on the recognition accuracy of the proposed model. The performance of the proposed method is evaluated using two public data sets (UCI and WISDM) which are collected using smartphones. Experimentally, we show how the proposed model establishes the state-of-the-art performance using these datasets. Finally, to demonstrate the applicability of the proposed model for online smartphone-based HAR, the computational cost of the model is evaluated. [ABSTRACT FROM AUTHOR]

Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers. [ABSTRACT FROM AUTHOR]

Security in current/next generation wireless sensor network (WSN) is challenging, due to its special characteristics and the scarcity of energy, and processing power. Thus, many security methods are trying to solve the problem of security in wireless sensor networks. Many of these methods use symmetric cryptography, and others use asymmetric cryptography. Security in WSN demands authentication, confidentiality, integrity, balancing the energy cost, nonreputation, and scalability services. In this paper, we introduce two protocols to address these main security services. The proposed protocols integrate public key and symmetric key algorithms to ensure optimal usage of sensors' energy and processing power to provide adequate security in next generation WSN. The proposed protocols utilize Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), Certification Authority (CA), and Registration Authority (RA) to achieve a reasonable tradeoff. [ABSTRACT FROM AUTHOR]

Theft of electricity poses a significant risk to the public and is the most costly non-technical loss for an electrical supplier. In addition to affecting the quality of the energy supply and the strain on the power grid, fraudulent electricity use drives up prices for honest customers and creates a ripple effect on the economy. Using data-analysis tools, smart grids may drastically reduce this waste. Smart-grid technology produces much information, including consumers' unique electricity-use patterns. By analyzing this information, machine-learning and deep-learning methods may successfully pinpoint those who engage in energy theft. This study presents an ensemble-learning-based system for detecting energy theft using a hybrid approach. The proposed approach uses a machine-learning-based ensemble model based on a majority voting strategy. This work aims to develop a smart-grid information-security decision support system. This study employed a theft-detection dataset to facilitate automatic theft recognition in a smart-grid environment (TDD2022). The dataset consists of six separate electricity thefts. The experiments are performed in four different scenarios. The proposed machine-learning-based ensemble model obtained significant results in all scenarios. The proposed ensemble model obtained the highest accuracy of 88%, 87.24%, 94.75%, and 94.70% with seven classes including the consumer type, seven classes excluding the consumer type, six classes including the consumer type, and six classes excluding the consumer type. The suggested ensemble model outperforms the existing techniques in terms of accuracy when the proposed methodology is compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Pervasive environments are dynamically changing environments with enormous amounts of information available for access from anywhere. This paper presents a framework for context-aware access control using threshold cryptography (CAAC-TC) where the administration of access control is distributed between different context services. CAAC-TC encrypts information using threshold cryptography where the private key is split up between the different context conditions which must be captured or realized. The idea here is that not all specified context conditions must be captured, and k captured contexts are enough. The management of access decisions is distributed among the different contexts. In CAAC-TC, multiple encryption layers can be specified where each layer is an encryption scheme of either n-out-of-n or k-out-of-n. Multiple layers simulate the use of an "AND" operator. Some of the main characteristics of CAAC-TC are decentralization, context error tolerance (distributed trust), extensibility, flexibility, and scalability. [ABSTRACT FROM AUTHOR]

In this paper, we propose OnDroid , a prevention system to defend against outgoing malicious SMSs in Android devices. OnDroid is user-friendly as it considers the user's little understanding of the Android system. It also considers multiple threat scenarios and requires less interaction with the user. For each SMS-sending operation, OnDroid first checks if the mobile device state mismatches the user's behavior. If so, the operation is blocked. Otherwise, it is delayed for a while and the user is notified to confirm or reject the operation. If the SMS is considered normal, the user does not need to take any action and the SMS is sent when the delay expires. Efficiency analysis shows that malicious SMS operations might be missed when the user is unavailable. To deal with this issue, we propose a method by which the user behavior can be adjusted to achieve 100 % of malicious SMS prevention. Formal analysis as well as comparative study show that OnDroid offers a good tradeoff between security efficiency and usability. [ABSTRACT FROM AUTHOR]

A critical issue in environmental decision making is how to trust the monitoring information gathered by a multitude of devices at remote locations. The accuracy of information helps in enhancing the effectiveness of decisions, whether it is made by human beings or by an intelligent system. The researchers and practitioners from disciplines such as climatology, agriculture, and meteorology, which depend on the environmental data, are heavily affected in the case of inaccurate information. In order to deal with the above problem, in this paper a data encryption algorithm inspired by the blood brain barrier (BBB) system. A real scenario to irrigate a remote location based on a wireless sensor network (WSN) is simulated in network simulator 2 (ns-2) to study the performance of the encryption algorithm. Our result and analysis show that the proposed encryption mechanism can efficiently protect the data communication from brute-force search or exhaustive key search, eavesdropping, spoofed, altered or replayed routing information, selective forwarding, acknowledgement spoofing, sybil and hello flood attacks. Hence, the proposed data encryption algorithm ensures the confidentiality of critical information from source to a given destination, which ultimately helps in enhancing the human decision accuracy and learning environmental conditions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Machine to Machine (M2M) Low Cost and Secure (LCS) communication system for e-Healthcare society. The system is designed to take into consideration the psychological issues related to all actors in the e-Healthcare society such as: stress due to high workload, anxiety, and loneliness. The system is capable of performing most of the tasks in an autonomous and intelligent manner, which minimizes the workload of medical staffs, and consequently minimizes the associated psychological stress and improves the quality of patient care as well as the system performance. We show how the different actors in the e-Healthcare society can interact with each other in a secure manner. To ensure data privacy, the mechanism involves intelligent authentication based on random distributive key management, electronic certificate distribution, and modified realm Kerberos. The system handles dynamic assignments of doctors to specific patients. It also addresses the need for patients to share their health information with strangers while dealing with the privacy preservation issue. Finally, the simulation type implementation is performed on Visual Basic .net 2013 that shows the success of the proposed Low Cost and Secure (LCS) algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the ant colony optimization (ACO) based routing protocol against holes (or voids) to address user’s connectivity via Pervasive Wireless Sensor Networks (PWSNs). A hole is an area that has no active sensors, which makes a connection between one side of the network and the other side impossible. To avoid such holes, prior works detected them only when packets reached nodes near the hole, called dead-ends. In this case, the packets need to be rerouted, which results in additional communication cost. The ant colony optimization (ACO) approach is known to be suitable for dynamic environments, which makes it a good choice to deal with the hole problem. We study the capability of an ACO-based routing protocol, called the biologically inspired secure autonomous routing protocol (BIOSARP), for resolving this issue. Because of its routing criteria, BIOSARP does not try to detect the holes after their appearance, but rather avoids them. Network simulator 2 (ns-2) is utilized to perform an analysis by adopting a flag-based feedback mechanism in BIOSARP and is further compared with on-demand routing with the void avoidance (ODVA) protocol in terms of the delivery ratio and energy consumption. Findings clearly demonstrate that BIOSARP can efficiently maintain the network prior to any possible hole problems, by switching data forwarding to the most optimal neighboring node. Thus, it can self-adapt to faults appearing in PWSN and efficiently maintains the network communication. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic. [ABSTRACT FROM AUTHOR]

Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications. [ABSTRACT FROM AUTHOR]

• Use of the bootstrap-aggregated ensemble algorithm for pattern recognition. • Recognition of high-risk areas using open real data concerning Zika virus incidence. • Use of an ensemble learning approach for precision improvement during classification. • Performance evaluation using 10-fold cross-validation and confusion matrix indicators. The Zika (ZIKV) virus has been a potential cause for the birth of children with microcephaly. This situation is unprecedented all over the world, presenting a small number of records in the medical literature. The latest ZIKV epidemic confirms the potential risk of international propagation of mosquito-borne diseases, especially the Aedes albopictus and aegypti species. Therefore, a strategy that aims to define priority areas for intervention is critical to contributing to the prevention and control of these diseases. Hence, this paper proposes the use of an ensemble learning technique, named bootstrap-aggregated ensemble of fine decision trees, to identify epidemic risk areas, reducing its impact on the population, principally in pregnant women. The study analyzed reported cases of ZIKV (n = 112) in Recife, Brazil, during 2015 and 2016. The identification of mosquito activity areas can guarantee a healthy formation for the fetus during and after the gestation through implementation strategies of public policies and further study to control the mosquito vector responsible for these diseases. [ABSTRACT FROM AUTHOR]

Abstract Intelligent Energy Networks are comprised of devices capable of fulfilling their functions in an energy-efficient fashion and with communication and remote control capabilities. Therefore, some of these devices, such as smart energy meters, become attractive for use in the power generation and distribution industry, achieving the vision of Smart Grids. However, many are the challenges that need to be overcome in order to reach a fully-functional and security-aware smart grid. Providing measurement, control, communication, power, display, and synchronization capabilities shall be no easy task for smart meters. In this context, this paper elaborates on a detailed description of the main functionalities that smart meters must provide, along with the analysis of existing solutions that make use of smart meters for smart grids. Moreover, open challenges in the topic are identified and discussed. By the end of this research piece, the reader should be able to have a detailed view of the capabilities already offered by smart meters and the ones they will have available in order to tackle the challenges smart grids present. [ABSTRACT FROM AUTHOR]

The Internet-of-Things (IoT) represents the next groundbreaking change in information and communication technology (ICT) after the Internet. IoT is concerned with making everything connected and accessible through the Internet. However, IoT objects (things) are characterized by constrained computing and storage resources. Therefore, the Cloud of Things (CoT) paradigm that integrates the Cloud with IoT is proposed to meet the IoT requirements. In CoT, the IoT capabilities (e.g., sensing) are provisioned as services. Unfortunately, the two-tier CoT model is not efficient in the use cases sensitive to delays and energy consumption (e.g., in healthcare). Consequently, Fog Computing is proposed to support such IoT services and applications. This paper reviews the most relevant Fog-enabled CoT system models and proposes an energy-aware allocation strategy for placing application modules (tasks) on Fog devices. Finally, the performance of the proposed strategy is evaluated in comparison with the default allocation and Cloud-only policies, using the iFogSim simulator. The proposed solution was observed to be more energy-efficient, saving approximately 2.72% of the energy compared to Cloud-only and approximately 1.6% of the energy compared to the Fog-default. [ABSTRACT FROM AUTHOR]

The current century has brought an unimaginable growth in information and communications technology (ICT) and needs of enormous computing. The advancements in computer hardware and software particularly helped fuel the requirements of human beings, and revolutionized the smart products as an outcome. The advent of wearable devices from their development till successful materialisation has only taken less than a quarter of a century. The huge benefits of these smart wearable technologies cannot be fully enjoyed until and unless the reliability of a complete system is ensured. The reliability can be increased by the consistent advancements in hardware and software in parallel. User expectations actually are the challenges that keep the advancements alive while improving at an unmatchable pace. The future of wearable and other smart devices depends on whether they can provide a timely solution that is reliable, richer in resources, smaller in size, and cheaper in price. This paper addresses the threats and opportunities in the development and the acceptance of immersive and wearable technologies. The hardware and software challenges for the purpose of development are discussed to demonstrate the bottlenecks of the current technologies and the limitations that impose those bottlenecks. For the purpose of adoption, social and commercial challenges related to innovation and acceptability are discussed. The paper proposes guidelines that are expected to be applicable in several considerable applications of wearable technologies, for example, social networks, healthcare, and banking. [ABSTRACT FROM PUBLISHER]

The deployment of intelligent remote surveillance systems depends on wireless sensor networks (WSNs) composed of various miniature resource-constrained wireless sensor nodes. The development of routing protocols for WSNs is a major challenge because of their severe resource constraints, ad hoc topology and dynamic nature. Among those proposed routing protocols, the biology-inspired self-organized secure autonomous routing protocol (BIOSARP) involves an artificial immune system (AIS) that requires a certain amount of time to build up knowledge of neighboring nodes. The AIS algorithm uses this knowledge to distinguish between self and non-self neighboring nodes. The knowledge-building phase is a critical period in the WSN lifespan and requires active security measures. This paper proposes an enhanced BIOSARP (E-BIOSARP) that incorporates a random key encryption mechanism in a cost-effective manner to provide active security measures in WSNs. A detailed description of E-BIOSARP is presented, followed by an extensive security and performance analysis to demonstrate its efficiency. A scenario with E-BIOSARP is implemented in network simulator 2 (ns-2) and is populated with malicious nodes for analysis. Furthermore, E-BIOSARP is compared with state-of-the-art secure routing protocols in terms of processing time, delivery ratio, energy consumption, and packet overhead. The findings show that the proposed mechanism can efficiently protect WSNs from selective forwarding, brute-force or exhaustive key search, spoofing, eavesdropping, replaying or altering of routing information, cloning, acknowledgment spoofing, HELLO flood attacks, and Sybil attacks. [ABSTRACT FROM AUTHOR]

The article discusses the guidelines and open issues in systems support for ubiquitous computing (Ubisys) workshops in 2003 and 2004. The enjoyable features of the systems has been the freeform small group and plenary discussion sessions. In 2005, graceful degradation and self-configuration of systems were discussed. Existing supporting infrastructures often make unreasonable assumptions about the degree to which the location, context and users of devices involved in typical Ubicomp application scenarios are known in the system.

The emergence of the Internet of Things (IoT) and its applications has taken the attention of several researchers. In an effort to provide interoperability and IPv6 support for the IoT devices, the Internet Engineering Task Force (IETF) proposed the 6LoWPAN stack. However, the particularities and hardware limitations of networks associated with IoT devices lead to several challenges, mainly for routing protocols. On its stack proposal, IETF standardizes the RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) as the routing protocol for Low-power and Lossy Networks (LLNs). RPL is a tree-based proactive routing protocol that creates acyclic graphs among the nodes to allow data exchange. Although widely considered and used by current applications, different recent studies have shown its limitations and drawbacks. Among these, it is possible to highlight the weak support of mobility and P2P traffic, restrictions for multicast transmissions, and lousy adaption for dynamic throughput. Motivated by the presented issues, several new solutions have emerged during recent years. The approaches range from the consideration of different routing metrics to an entirely new solution inspired by other routing protocols. In this context, this work aims to present an extensive survey study about routing solutions for IoT/LLN, not limited to RPL enhancements. In the course of the paper, the routing requirements of LLNs, the initial protocols, and the most recent approaches are presented. The IoT routing enhancements are divided according to its main objectives and then studied individually to point out its most important strengths and weaknesses. Furthermore, as the main contribution, this study presents a comprehensive discussion about the considered approaches, identifying the still remaining open issues and suggesting future directions to be recognized by new proposals. [ABSTRACT FROM AUTHOR]

Summary: Orthogonal frequency division multiplexing (OFDM) is an alternative technology that is constrained to keep up with the increasingly high data transmission rate due to its robustness against selective frequency fading and its resistance to intersymbol interference (ISI). This paper aims to study and optimize its performance, namely, its probability of communication failure and signal‐to‐interference‐plus‐noise ratio (SINR) metrics. The mathematical tool used to perform OFDM evaluation and analysis is the Fourier transform and its properties. The obtained results of those equations led to the demonstration of the probability of communication outage and to notice that the cutoff time occurs after the 10th iteration for values of λ less than or equal to 1. It could also be checked that this period is small if λ exceeds 1 by considering the sensitivity factor. Moreover, for the obtained results, in terms of bit error rate (BER) and SINR, the influence of the modulation on the error vector magnitude (EVM) shows the sensitivity factor Ω presents a key role in the transmission chain. A schematic illustration of the proposed optimal IEEE 802.11a (Wi‐Fi) and IEEE 802.16e (WiMAX) performances over the OFDM physical layer. The obtained results provide insight on the cutoff time according to values of λ. If λ ≤ 1, cutoff is achieved after iteration 10. If this parameter exceeds 1, the sensitivity factor must be analyzed. [ABSTRACT FROM AUTHOR]

Nature presents an infinite source of inspiration for computational models and paradigms, in particular for researchers associated with the area known as natural computing. The simultaneous optimization of the architectures and weights of artificial neural networks (ANNs) through biologically inspired algorithms is an interesting approach for obtaining efficient networks with relatively good generalization capabilities. This methodology constitutes a concordance between a low structural complexity model and low training error rates. Currently, complexity and high error rates are the leading issues faced in the development of clinical decision support systems (CDSSs) for pregnancy care. Hence, in this paper the use of a biologically inspired technique, known as particle swarm optimization (PSO), is proposed for reducing the computational cost of the ANN-based method referred to as the multilayer perceptron (MLP), without reducing its precision rate. The results show that the PSO algorithm is able to improve computational model performance, showing lower validation error rates than the conventional approach. This technique can select the best parameters and provide an efficient solution for training the MLP algorithm. The proposed nature-inspired algorithm and its parameter adjustment method improve the performance and precision of CDSSs. This technique can be applied in electronic health (e-health) systems as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy. The proposed method outperformed, on average, other approaches by 26.4% in terms of precision and 14.9% in terms of the true positive ratio (TPR), and showed a reduction of 35.4% in the false positive ratio (FPR). Furthermore, this method was superior to the MLP algorithm in terms of precision and area under the receiver operating characteristic curve by 2.3 and 10.2%, respectively, when applied to the delivery outcome for pregnant women. [ABSTRACT FROM AUTHOR]