Named Data Networking (NDN) revolutionized IP-based communication by introducing a content-centric model, based on name-based communication. This paradigm shift offers benefits, including optimized network traffic through in-network caching, improved data security, and resilient communication for Internet of Things (IoT) applications. While these benefits are significant, the deterministic data delivery necessary for time-sensitive IoT applications cannot be guaranteed using the NDN’s best-effort routing mechanism. This paper addresses this challenge by proposing deterministic NDN of things (D-NDNoT), a protocol-level integration of a schedulability algorithm into NDN, making it deadline-aware and addressing the specific requirements of time-sensitive IoT applications. We present a time-sensitive NDN protocol incorporating a critical deadline-first scheduler to prioritize traffic. By integrating deadline awareness, quality of service metrics, and network characteristics, the algorithm ensures the delivery of time-sensitive data takes precedence over non-time-sensitive content. To validate the effectiveness of the proposed protocol, we evaluate using simulation experiments in OMNET++ and consider metrics such as end-to-end latency, delay, and deadline. The results demonstrate that the deadline-aware deterministic NDN protocol effectively meets the communication needs of time-sensitive IoT applications, ensuring the timely delivery of critical data.

This paper discusses the development of a hybrid Convolutional Neural Network (CNN)-Support Vector Machine (SVM) model for automated breast tumor detection using ultra-sound images. The study aims to compare the accuracy of the proposed model with the AlexNet CNN and develop a generalized image detection model that can detect tumors of all types across the human anatomy. The use of ultrasound imaging is justified based on its non-invasive nature and cost-effectiveness, and the possibility of collecting large datasets. The study reviews the prior work on deep learning and CNNs for tumor detection and segmentation in medical imaging, highlighting their potential for improving accuracy and efficiency. The findings of this study have the potential to improve the prognosis and treatment of cancer, a serious health condition affecting a significant number of people worldwide. CNN-SVM model had an accuracy of 91% and AlexNet at 88% with validation accuracy at 60% and 65% respectively. Showing the hybrid model is better in terms of accuracy and has more potential as a future base for medical image modeling prediciton.

Digital twin (DT)-assisted mobile edge network can achieve energy-efficient task offloading by optimizing the decision-making in real time. Although many DT-assisted task offloading solutions in mobile edge networks have been designed, stochastic asynchronizations between the DTs and physical entities are still ignored. In this paper, we investigate a task offloading problem in a DT-assisted URLLC-enabled mobile edge network which considered the uncertain deviation between DT estimated values and physical actual values. Specifically, we formulate a latency and energy consumption minimization problem by optimizing task offloading, resource allocation, and power management. To solve this problem, we propose a DT-assisted robust task offloading scheme (DTRTO) based on learning composed of decision and deviation networks. The deviation network predicts the worst-case deviations based on the pre-decision, and the decision network optimize the decision considered the worst-case deviation. The simulation results show that, compared to the baseline algorithms, the DTRTO scheme can realize low latency and energy consumption in task offloading while maintaining high robustness.

Due to the absence of distinct boundaries, wireless networks are vulnerable to a variety of intrusions. As the number of intruders has increased, the risks on critical infrastructures monitored by networked systems have also increased. Protecting shared information using effective and robust Intrusion Detection Systems (IDSs) remains a critical issue, especially with the growing implementation of vehicular networks. Building an IDS that detects threats efficiently with maximum accuracy and detection is a challenging undertaking. Machine Learning (ML) mechanisms have been successfully adopted in IDSs to detect a variety of network intruders. Split learning is considered one of the main developments in creating efficient ML approaches. In utilizing the Split Learning approach, an IDS is successful in performing at higher accuracy, and detection rate as well as a higher classification performance (Precision, Recall). In this work, a Split Learning-based IDS ($SplitLearn$) for Intelligent Transportation System (ITS) infrastructures has been proposed to address the potential security concerns. The proposed model has been evaluated and compared against other models (i.e., Federated Learning ($FedLearn$) and Transfer Learning ($TransLearn$)-based solutions). With the highest accuracy and detection rates, the proposed model ($SplitLearn$) outperforms$FedLearn$and$TransLearn$by 2 to 5 % respectively. We also see a decrease in power consumption when utilizing$SplitLearn$versus$FedLearn$.

Federated fog computing is an answer for horizontally upscaling fog resources to improve the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic nature of some IoT’s crucial components, such as the ones of Internet of Vehicles (IoV), may hinder the QoS improvement and result in its deterioration instead. Specifically, delays can occur due to the unoptimized distribution of services and unbalanced network traffic loads on the fog nodes. The current federated fog architectures ignore the mobility of users during the formation of fog federations. In this work, we present an adaptive and efficient fog federation formation scheme using game theory according to the service requirements. The problem formulation in terms of forming the federations and offloading requests among fog members is formulated as an integer program, then modeled as a Hedonic game. We adopt the Merge & Split as a formation technique, where the federations that are not satisfied in terms of QoS merge with other federations that would enhance the service performance. Our adaptive fog federation formation mechanism is designed to cope with the environmental changes in the IoV paradigm. Experimental evaluation shows that our framework can acquire better QoS and lower time to form the federations compared to the literature.

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.

With the evolution of wireless technologies, 5G and Beyond (5GB) communication is paving a way for efficient, ultrareliable, low-latent, and high converging services for the Internet of Things (IoT). Along with efficient communication, the security of messages is one of the concerns that must be maintained throughout the operations. Backhaul forms an essential part of 5GB with an ability to enhance the coverage and quality of service for IoT. However, conventional wired backhaul connection would cost operators thousands of dollars in the construction of 5GB infrastructure considering the ultradense nature of IoT. As a result, wireless backhaul is quickly becoming a feasible alternative to address 5GB’s direction toward network densification without affecting its other provisions. Wireless backhaul is expected to increase the landscape, covering from islands to mountains, which were difficult to access in the existing network generation. Moreover, it can effectively respond to the situation where the data traffic tremendously increased. Despite such provisioning, the wireless backhaul poses relatively various security threats and vulnerabilities due to the characteristics of wireless technologies. Several studies have been conducted to address the security problems; however, existing protocols do not support dynamic security policy and key management in a decentralized structure as well as secure handover in a specific scenario where Terminals (TMs) are moving. Motivated by this, we proposed the Mobile Terminal Handover (MoTH) security protocol to provide secure handover of mobile terminals between hubs. To solve the problem of existing protocols, a new entity called BMF is introduced to support distributed and dynamic security policy and key management in each serving network of the 5GB backhaul environment. The proposed protocol satisfies security requirements, including authentication and key management, confidentiality, integrity, and perfect forward secrecy. Additionally, it supports policy and key update services, and optimized handover. The security and correctness of the proposed protocol are thoroughly verified using the two formal security analysis tools: 1) BAN logic and 2) Scyther. Additionally, the performance evaluation shows that the proposed protocol is efficient.

The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.

Wireless Networks lack clear boundaries which leads to security concerns and vulnerabilities to numerous kinds of intrusions. With the growth of cyber intruders, the risks on crucial applications monitored by networked systems have also grown. Effective and vigorous Intrusion Detection Systems (IDSs) for protecting shared information continues to be an essential task to keep private data safe especially in the healthcare sphere. Constructing an IDS that detects and returns information efficiently and with the highest accuracy is a challenging task. Machine Learning (ML) techniques have been effectively adopted in IDSs to detect network intruders. Reinforcement learning is considered as one of the main developments in ML. IDS mainly performs a higher accuracy rate, detection rate as well as a higher performance of a classification (ROC curve). According to these and to tackle the security issues, a Federated Reinforcement Learning-based Intrusion Detection System (FRL-IDS) in the Internet of Things (IoT) networks for healthcare infrastructures has been proposed. The proposed model has been evaluated and compared to a similar model (i.e. SVM system). The proposed model shows superiority over the SVM-steered IDS with accuracy and detection rates of ≈ 0.985 and ≈ 96.5%, respectively. This proposed infrastructure will not only aid in intrusion detection of large health care systems but also other wireless decentralized networks found across multiple real-world applications.

Trustworthy transmission is a beneficial step toward the success of the new era of telecommunication technologies and online social networks (OSNs). Many sensitive applications can benefit from OSNs, e.g., eHealth and medical services. However, OSNs have always been prey to Sybil attacks where numerous fake nodes are being generated and propagated in social networks to mimic like real nodes for the purpose of achieving malicious goals. Thus, for security reasons and for the sensitivity of data used in eHealth applications, such fake nodes have to be detected and deactivated immediately. The emerging field of the Internet of Medical Things (IoMT) promotes trust management (TM) among various IoMT devices to provide accurate and reliable communications, which is quite essential in critical diseases such as COVID-19. TM provides a secure platform to IoMT devices using different security protocols in the IoMT network. Generically, if a device is not comfortable to connect with additional devices in a network, the motive of the communication process is not succeeded and leads to disappointment for one device toward others. To handle these types of situations, a TM mechanism, named fuzzy-based TM mechanism for preventing Sybil attacks in the Internet of Medical Things (FTM-IoMT), is proposed. The FTM-IoMT provides TM for the users of eHealth systems using IoMT infrastructures. It is an intelligent mechanism to recognize Sybil or untrustworthy nodes in the system. The proposed mechanism helps IoMT nodes to collect authentic and credible information from their neighboring nodes as well as to neglect Sybil nodes. The trust value of a node is evaluated using fuzzy logic processing followed by the trust attributes, such as integrity, receptivity, and compatibility of a node. The FTM-IoMT provides a double evaluation check based on fuzzy logic processing and fuzzy filter. The proposed scheme shows superior results when compared to the state-of-the-art approaches.

The rapid improvement in the global connectivity standards has escalated the level of trade taking place among different parties. Advanced communication standards are allowing the trade of all types of commodities and services. Furthermore, the goods and services developed in a particular region are transcending boundaries to enter into foreign markets. Supply chains play an essential role in the trade of these goods. To be able to realize a connected world with no boundary restrictions in terms of goods and services, it is imperative to keep the associated supply chains transparent, secure, and trustworthy. Therefore, some fundamental changes in the current supply chain architecture are essential to achieve a secure trade environment. This article discusses the supply chain's security-critical application areas and presents a detailed survey of the security issues in the existing supply chain architecture. Various emerging technologies, such as blockchain, machine learning (ML), and physically unclonable functions (PUFs) as solutions to the vulnerabilities in the existing infrastructure of the supply chain have also been discussed. Recent studies reviewed in this work reveal a growing sentiment in the industry toward new and emerging technologies, such as Internet of Things (IoT), blockchain, and ML. While many organizations have already adopted IoT applications and artificial intelligence systems in their businesses, widespread adoption of blockchain remains distant. It has also been found that over the past decade, PUF-based authentication systems have gained much ground. However, a proper reference model for their implementation in complex supply chains is still missing.

Smart city, as a new mode, is introduced to improve the level of city management for modern cities. In smart cities, a kernel field is health management for urban residents. Hospital management, as one of the most important components in health management, is concerned. To provide high-quality medical service for sick residents, accurate patient health data analysis is needed. Thus, data collection in patient health monitoring is necessary. To achieve this, massive Internet-of-Things devices are distributed; in general, they are resource-constrained devices. From this, lightweight index generation is needed. Furthermore, with the development of professional technologies in medical science, the hospital manager has to employ many different types of professional doctors. They need the shared patient health data to do a precise diagnosis and present an efficient therapeutic schedule for each patient. However, many secret details are recorded in the patient health data. Thus, data privacy of the shared patient health data should be maintained. In this article, we propose a new traceable patient health data search system for hospital management in smart cities. In this system, the system manager shares the encrypted patient health data to different doctors at the grain of hospital bed. Each doctor accurately finds a patient with a special feature from the patient health monitoring data. To prevent patient health data leakage, the functions of illegal search query blocking and inside malicious user tracing are designed. The performance analysis shows that our system is practical for lightweight data collecting devices.

With the increasing demand to deploy convolutional neural networks (CNNs) on 5G mobile platforms, architecture designs with efficient sparse kernels (SKs) were proposed, which can save more parameters than the standard convolution while maintaining the high accuracy. Despite the great potential, neural network designs with SKs still require a lot of expert knowledge and take ample time. In this paper, we first propose a search scheme that effectively reduces the SK design space based on three aspects: composition, performance, and efficiency. Meanwhile, we completely eliminate the model training from our search scheme. Instead, an easily measurable quantity, the information field, is identified and used to predict the model accuracy in the searching process. Additionally, we provide a detailed efficiency analysis on the final designs found by our scheme. Second, based on the analysis we propose a model transformation scheme to better utilize the SK designs on existing models to either reduce the number of parameters or increase the accuracy. Last, considering the extra programming overhead and the expert knowledge required by the model transformation scheme, we develop a compiler prototype to automate the entire process, given the source code of an existing model. Experimental results show that models composed of the sparse kernel designs searched by our search scheme can beat state-of-the-art networks such as ResNets in terms of the accuracy and the efficiency. Also by using our model transformation scheme we can easily improve the accuracy (the same number of parameters) or the efficiency (the same accuracy) upon existing state-of-the-art models.

Stemming from the recent evolutionary progress of the 5G wireless communication and Internet of Things (IoT) relevant technologies, the vision of the Internet of Connected Vehicles (IoCV) has become more apparent. On the basis of the state-of-the-art IoCV conceptual implementations, the location of the vehicles is one of the essential driven data of IoCV, and the location privacy issue needs to be taken into account. However, when scrutinizing into IoCV, noticeable challenges of location-aware scenario has raised. For IoCV, location is more than just query criteria like in Location-based Services (LBSs) of mobile Internet. It is also the underpinning data of various types of IoCV underlying mechanisms and functions. This difference makes preserving location privacy in IoCV quite different from the traditional privacy scenarios. In this paper, an overall analysis of end-user location privacy in IoCV was performed. To solve the location privacy dilemma, we proposed an intent prediction-based approach named LocJury, which benefits from the emerging concept of Intent-based Networking (IBN). LocJury provides location privacy by learning and estimate the intent of location access and will penalize those malicious location accesses. By simulating the conceptual IBN-based IoCV application scenario, which relies on the location accesses, the performance of LocJury is evaluated under various circumstances. The simulation result verified the effectiveness of our proposed method.

IoT networks continue to grow as devices become more accessible and affordable. Due to the mass exodus of production and use, there is room for hackers to take advantage of vulnerabilities in the system. The cloud-based infrastructure although creating an advantage in terms of growing the resources for any given IoT network, the disadvantage in terms of security intrusion are many. In this paper, we analyze a proposed hierarchical distance bound security model. This model takes on a more efficient approach to patching large IoT systems to mitigate the spread of several different types of malware attacks. We utilize SEIR disease spread model to measure the effectiveness of the security model's mitigation of malware spread in the IoT network.

The exponential growth in the use of Internet of Things (IoT) devices has introduced numerous challenges, in particular dealing with new security threats. In addition, for connecting heterogeneous devices using different protocols, large networks need resilient software-based security systems that can defend against unprecedented attacks for which the traditional security countermeasures prove to be ineffective. Furthermore, to deal with the ever growing onslaught on data and networks, modern security systems need to utilize novel machine learning mechanisms. This paper proposes a software-based architecture that provides network function virtualization (NFV) capability to combat malware spread for heterogeneous IoT networks. To build a scalable and generalized Intrusion Detection System (IDS), we propose for these networks a RNN-LSTM learning model that can predict malware attacks in a timely manner for the NFV to deploy appropriate countermeasures. In addition, we investigate the scalability of the network and discuss how the generalized IDS can deal with a broad range of malwares that can be detected. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) epidemic model to moniter the spread of the malware attack and subsequently provides patching to the system. Our analysis focuses primarily on the feasibility and the performance evaluation of the proposed integrated RNN-LSTM and NFV architecture.

The advances of artificial intelligence (AI) propels big data processing and transmission for Internet of Things (IoT), by capturing and structuring big data produced by heterogeneous devices. While applying blockchain to manage IoT devices and associated big data, the blockchain itself suffers from abuse of decentralization from anonymous users. Specifically, it has been utilized to facilitate black market trades and illegal activities. Ateniese et al. proposed using the chameleon hash (CH) to derive redactable blockchain (EuroS&P), which works by embedding a trapdoor in the basic hash function so that block content can be rewritten without causing major hard forks. In short, the redacted block hash remains unchanged. However, there is lacking intelligent design where any mistakes observed in the chain can be corrected universally and automatically. This creates disincentives to use redactable blockchain (RB) for managing big data or any data-driven business mainly due to ineffective chain redaction. To solve this problem, in this article, we propose the notion of the self-redactable blockchain (SRB) to support intelligent execution of chain redaction. Specifically, we propose the first revocable chameleon hash (RCH) to power RB. It enables an ephemeral trapdoor for finding collision without any co-operation. Periodical expiration is applied to committed hash and an ephemeral trapdoor to prevent any abuses of redaction power. We instantiate how to use our RCH to build SRB as an intelligent trust-layer for IoT. We also give a rigorous analysis as well as comprehensive experiments to validate our proposals. The evidence showed that our proposal is secure and acceptably efficient for IoT devices.

Energy management of mobile devices is a crucial issue in fifth generation (5G) network due to their limited battery capacity. Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging technique which allows mobile devices to harvest energy from radio frequency (RF) signals. Moreover, Non-Orthogonal Multiple Access (NOMA) serves multiple users simultaneously using the same subchannel inter-user interference mitigation. By considering the aforementioned issues, in this paper, we propose a subchannel assignment scheme for SWIPT-NOMA based pico base station/femto base station with macro-cellular networks. The energy-efficient subchannel assignment is a probabilistic mixed non-convex optimization problem by considering imperfect channel state information (CSI). To address this problem, many-to-many matching theory is used in the proposal. Numerical results show that the proposed algorithm performs better in terms of numbers of PUs/FUs, average energy efficiency (EE) of the Picocells/Femtocells, in comparison to the orthogonal frequency division access scheme and conventional NOMA.

Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.

Internet of Things (IoT) plays a key role in connecting the e-health system with the cyber world through new services and seamless interconnection between heterogeneous devices. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Therefore, keeping in view the needs above, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy, and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.

In Vehicular Delay Tolerant Networks (VDTNs), a number of Roadside Units (RSUs) are deployed along the road and connected to the infrastructure network to provide various services to the vehicles on the road. However, it is hard to cover the long highways completely, due to the deployment cost. In such uncovered areas between two neighboring RSUs, a connection between a vehicle and an RSU cannot be established. To cope with this, few schemes have been proposed recently, enabling one RSU to select one relay vehicle to provide continuous communications for the vehicle moving in the uncovered area. However, the selection of an appropriate relay vehicle for pre-storing maximum data is an open issue. In this paper, we, therefore, propose an adaptive multiple-relay selection scheme that allows RSU to select relay vehicles while taking most relevant multiple criteria into the account. The relay selection is initiated when a vehicle is unable to receive all the data that is requested in the coverage of the RSU. The simulation results show that our scheme enables vehicles to retrieve maximum amount of the requested data in uncovered areas.

The Vehicular Ad-hoc Networks (VANET) raises as an emerging technology for smart transport as observed in the recent decade. However, there are some hurdles affect the applications of VANET, the advancement of VANET needed to cope up with the requirement of smart transport in smart cities. The routing is the important factor for having effective communication between smart vehicles, which need to be addressed smartly. Many factors affect the communication between the vehicles such as topology fragmentation which results in the phenomenon of Local-Maximum-Problem that lead to packet delivery failure. The traditional routing schemes failed to address the problem of the trade-off between Packet-Delivery-Ratio and the high cost, in term of End-to-End-Delay, of packet recovery from the failures that occurs frequently because of Local-Maximum-Problem. Therefore, this article works out to analyses the performance of existing position-based routing protocols for Inter-vehicle ad-hoc network and introduced Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol. IDTAR aims to provide optimal performance in environments of smart transport in smart cities. The experiments evaluate IDTAR against famous position-based routing protocols such as GyTAR, A-STAR-SR and GSR considering several densities of vehicles. Simulation result shows that IDTAR protocol has a fewer End-to-End-Delay and high packet delivery ratio in smart cities circumstances. This concludes that IDTAR can be adaptive for smart transport in smart cities communication with some consideration in terms of its security, compatibility and reliability.

This paper concerns the design of classifiers for medical decision making, and proposes a novel probabilistic neural network classifier with the assistance of a genetic algorithm. Unlike the conventional probabilistic neural networks that use all patterns in data sets as hidden nodes, the proposed neural network adopts some centers through clustering algorithms and the output of data set as the hidden nodes. Comparing with the conventional probability neural network classifiers, the proposed approach significantly decreases the time consuming. Furthermore, a genetic algorithm is utilized to optimize feature selection from the data set. Experimental results are presented on several benchmarks illustrating the relationship between selected features and disease.

Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing. The location of landmark can calibrate the position of users, and accurate localization can optimize the landmark's location in turn. In this work, we define landmarks as certain characteristic structures with iBeacons. To tackle the challenge of low efficiency of a landmark, we combine Bluetooth signals and sensor readings to reflect the distinctive signature of a landmark and then design a method for a reliable detection of a landmark. Moreover, we investigate the calibration bias of a landmark to ensure the calibration accuracy. For improving the accuracy of inertial navigation, we personalize the step length model and correct the user's heading with the aid of crowd sensing. We have built an indoor localization system integrating the above modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and easy to deploy. Extensive experiments on users with mobile devices, with over 37 subjects walking over an aggregate distance of over 30 km, were carried out. Evaluation results show that our system can achieve a mean accuracy of 2 m initially and 1 m with the calibration of landmarks in a 39 m × 21 m testing area. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregation process. In this paper, we have highlighted the gains of the existing schemes for node clustering‐based data aggregation along with a detailed discussion on their advantages and issues that may degrade the performance. Also, the boundary issues in each type of clustering technique have been analyzed. Simulation results reveal that the efficacy and validity of these clustering‐based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in various other environmental conditions. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car-following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections. Copyright © 2016 John Wiley & Sons, Ltd.

With the frequent appearance and spread of infectious diseases and their casualties in major populated areas, many researchers and organizations became interested to find different ways to forecast the spread behaviors of these diseases before they occur. This can allow them to better prepare and confine the disease in small regions and therefore reduce the loss of human lives. In this paper we study the behavior and the spread of infectious diseases and model their spread in a city. This study helps generate various prevention and control techniques to build a better and a much safer living environment. This is accomplished by using a combination of geographic information system (GIS) tools and environmental controls to create a scalable, two layered; spatio-temporal Agent-Based Model (ABM) that visualizes disease spread throughout any region by showing the interaction between agents/individuals. This specific model will be tested using data from a one thousand seven hundred kilometer square (1,700 Km2) size city with a population of over three hundred thousand individuals (300,000 people). The first layer was to formulate probabilistic models to create a population, synthesizing a population of around two hundred thousand agents that would interact in a geospatial context. The second layer uses another set of probability distributions to predict a disease spread model based on health information that has been provided by the city's health officials. This model has been created with scalability in mind whether it is for a different geographical location or a larger data set. The simulation results show that this system is very efficient and confirms that it could be used in a larger setting.