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Monitoring the Internet of Things (IoT) Networks
Abstract
By connecting billions of things to the Internet, IoT created a plethora of applications that touch every aspect of human life. Time-sensitive, mission-critical services, require robust connectivity and strict reliability constraints. On the other hand, the IoT relies mainly on Low-power Lossy Networks, which are unreliable by nature due to their limited resources, hard duty cycles, dynamic topologies, and uncertain radio connectivity. Faults in LLNs are common rather than rare events, therefore, maintaining continuous availability of devices and reliability of communication, are critical factors to guarantee a constant, reliable flow of application data.After a comprehensive literature review, and up to our knowledge, it is clear that there is a call for a new approach to monitoring the unreliable nodes and links in an optimized, energy-efficient, proactive manner, and complete interoperability with IoT protocols. To target this research gap, our contributions address the correct assignment (placement) of the monitoring nodes. This problem is known as the minimum assignment problem, which is NP-hard. We target scalable monitoring by mapping the assignment problem into the well-studied MVC problem, also NP-hard. We proposed an algorithm to convert the DODAG into a nice-tree decomposition with its parameter (treewidth) restricted to the value one. As a result of these propositions, the monitor placement becomes only Fixed-Parameter Tractable, and can also be polynomial-time solvable.To prolong network longevity, the monitoring role should be distributed and balanced between the entire set of nodes. To that end, assuming periodical functioning, we propose in a second contribution to schedule between several subsets of nodes; each is covering the entire network. A three-phase centralized computation of the scheduling was proposed. The proposition decomposes the monitoring problem and maps it into three well-known sub-problems, for which approximation algorithms already exist in the literature. Thus, the computational complexity can be reduced.However, the one major limitation of the proposed three-phase decomposition is that it is not an exact solution. We provide the exact solution to the minimum monitor assignment problem with a duty-cycled monitoring approach, by formulating a Binary Integer Program (BIP). Experimentation is designed using network instances of different topologies and sizes. Results demonstrate the effectiveness of the proposed model in realizing full monitoring coverage with minimum energy consumption and communication overhead while balancing the monitoring role between nodes.The final contribution targeted the dynamic distributed monitoring placement and scheduling. The dynamic feature of the model ensures real-time adaptation of the monitoring schedule to the frequent instabilities of networks, and the distributed feature aims at reducing the communication overhead.
... Les travaux dans les réseaux de capteurs proposent majoritairement des solutions centralisées. Ceux proposant des systèmes distribués, comparé à la blockchain qui a un processus de travail bien élaboré sur trois piliers tel que : la génération des blocs, la vérification, le consensus et l'extension de la blockchain; restent très limités sur la fiabilité des données [6] [7]. La plupart des solutions abordant la blockchain pour l'IoT ne sont pas implémenter (cas d'utilisation) et soufre parfois du manque d'ajout des services de confidentialité [8] [9]. ...
This paper presents a hybrid wearable sensor network system towards the Internet of Things (IoT) connected safety and health monitoring applications. The system is aimed at improving safety in the outdoor workplace. The proposed system consists of a wearable body area network (WBAN) to collect user data and a low-power wide-area network (LPWAN) to connect the WBAN with the Internet. The wearable sensors in the WBAN are exerted to measure the environmental conditions around the subject using a Safe Node and monitor the vital signs of the subject using a Health Node. A standalone local server (gateway), which can process the raw sensor signals, display the environmental and physiological data, and trigger an alert if any emergency circumstance is detected, is designed within the proposed network. To connect the gateway with the Internet, an IoT cloud server is implemented to provide more functionalities, such as web monitoring and mobile applications.
Wireless Networks have many advantages for emergency situations especially disaster cases. A disaster might happen due to various reasons such as earthquakes, hurricanes, floods, tornadoes, etc. In these emergency situations, wireless communication technologies are very important for the rescue of human life. Wireless technologies especially body area networks might monitor, collect, and send data about human in trouble to rescue unit. In this paper, Internet of Things based Wireless Body Area Network is proposed for disaster cases. The proposed architecture guarantees lifesavings by using wireless technologies for location determination, vital signs transmission, and SOS calls. Also, a gateway selection algorithm based on fuzzy logic is developed for selecting more appropriate wireless technology.
Cloud computing as a novel and entirely internet-based computing platform is emerging and its tenacious challenges become more vivid. A parallel genetic algorithm-based method for scheduling tasks with priorities is provided in this paper. The goal is to efficiently utilize resources and reduce resource wastage in cloud environments. This is achieved by improving the load balancing rate while better resources are selected to fulfill arrival tasks in a shorter time with lower task failure rate. To evaluate the proposed method, it is simulated using Matlab and compared with two existing methods, a hybrid Ant colony-honey method and a Round-Robin (RR) based load balancing method. The results show that the proposed method has 9% - 31% lower energy usage, 14% - 37% lower migration rate and 13%- 17% better Service Level Agreement (SLA) in comparison with the Hybrid and RR method.
The new era of technology is a rollback in which massive information comes from interconnected devices. These types of things that have Internet connectivity are called the Internet of Things (IoT). IoT and its applications have enabled novel service provisioning, whereas at the same time it has raised complex challenges of managing such network. Resource limitation such as computational power and energy limitation defines the characteristics of IoT that require a novel design of management framework. Designing a management framework that meets the IoT requirements is a daunting task. Recently, Software-Defined Networking (SDN) paradigm has eased the management of traditional Internet by provisioning centralized view of the network. This makes SDN a potential candidate for managing IoT. However, traditional design of the SDN paradigm needs to be redefined to adapt it for the IoT. Hence, in this paper, we have discussed trends and research of management architecture for IoT based on Software-Defined Networking principles. In addition, we discuss non-SDN-based approaches. In this paper, we compare all of the efforts in the last five years in addressing issues of IoT management that involves security service provisioning, fault tolerance, energy management, and load balancing. We also discuss future research directions that evolved from our detailed survey and taxonomy of the Software-Defined IoT (SDIoT) management frameworks.
Distributed network monitoring solutions face various challenges with the increase of line speed, the extending variety of protocols, and new services with complex KPIs. This
paper addresses one part of the first challenge: faster line speed necessitates time-stamping with higher granularity and higher precision than ever. Proper, system-wide time-stamping is inevitable for network monitoring and traffic analysis point of view. It is hard to find feasible time synchronization solutions for those systems that have nation-wide, physically distributed probes.
Current networking equipment reside in server rooms, and have many legacy nodes. Access to GPS signal is complicated in these places, and Precision Time Protocol (PTP) does not seem to be supported by all network nodes in the near future – so high
precision time-stamping is indeed a current problem. This paper suggests a novel, practical solution to overcome the obstacles.
The core idea is that in real-life, distributed network monitoring systems operate with a few, finite number of probe-clusters, and their site should have a precise clock provided
by PTP or GPS somewhere in the building. The distribution of time information within a site is still troublesome, even within a server rack. This paper presents a closed control loop solution implemented in an FPGA-based device in order to minimize the
jitter, and compensate the calculated delay.
To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via Lossy links. This work addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal Vertex Cover Problem with Constraint Generation. Assuming periodical functioning, Vertex Covers are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of Vertex Covers to periods is modeled as a Multi-objective Generalized Assignment Problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a Traveling Salesman Path Problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks.
The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized.
Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.
Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.
Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. textquoteleftConnected Cartextquoteright is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. textquoterightConnected carstextquoteright can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of textquoteleftconnected carstextquoteright can be used to perform textquoteleftpredictive car maintenancetextquoteright. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.
In our recent book Health-e Everything: Wearables and the Internet of Things for Health, we capture in an interactive e-book format some global thought-leader perspectives as well as early examples of case studies and novel innovations that are driving this emerging technology domain. Here, we provide a brief snapshot of key findings related to these novel technologies and use cases, which are driving both health care practitioners and health consumers (patients). As technologists, having a firm understanding of customer-driven innovation and the actual user benefits of interconnective devices for health will help us engineer better solutions that are more targeted to the triple aim of better, faster, and cheaper health solutions.
The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software Defined Networking (SDN) and Network Virtualization (NV) are two technologies that promise to costeffectively provide the scale and versatility necessary for IoT services. In this article, we survey the state-of-the-art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterwards, we move to the study of Wireless Sensor Networks (WSNs), arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on the topic.
The predominant use of today's networks is content access and distribution. Network coding (NC) is an innovative technique that has potential to improve the efficiency of multicast content distribution over multihop wireless mesh networks (WMNs) by allowing intermediate forwarding nodes (FNs) to encode and then forward data packets. Practical protocols are needed to realize the benefits of the NC technique. However, the existing NC-based multicast protocols cannot accurately determine the minimum number of coded packets that a FN should send in order to ensure successful data delivery to the destinations, so that many redundant packets are injected into the network, leading to performance degradation. In this paper, we propose HopCaster, a novel reliable multicast protocol that incorporates network coding with hop-by-hop transport. HopCaster completely eliminates the need for estimating the number of coded packets to be transmitted by a FN, and avoids redundant packet transmissions. It also effectively addresses the challenges of heterogeneous multicast receivers. Moreover, a cross-layer multicast rate adaptation mechanism is proposed, which enables HopCaster to optimize multicast throughput by dynamically adjusting wireless transmission rate based on the changes in the receiver population and channel conditions during the course of multicasting a coded data chunk. Our evaluations show that HopCaster significantly outperforms the existing NC-based multicast protocols.
The emergence of the Internet of Things (IoT) is introducing more and more services and applications such as smart cities. For some services, the availability of elements and the connectivity between them are necessary. The robust functioning of a fragile and often dynamic system in IoT needs strong monitoring which is investigated in this paper. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is the standardized routing protocol over IP-connected IoT networks. In tandem with RPL, we propose a new solution which aims to achieve distributed monitoring with minimal computational complexity. The main objective is to increase robustness in IoT via monitoring the links in the Destination Oriented Directed Acyclic Graphs (DODAG) constructed by RPL. Although RPL has a reactive repairing mechanism to tackle robustness in connectivity in case of node failures, we found that for real-time critical applications it is important to use proactive approaches for preventing faults and making recovery for connectivity faster. The problem can be modeled as a Vertex Cover Problem (VCP) on the DODAG. Such problem is a well known NP-hard optimization problem. The monitoring should be simple and energy aware. We demonstrate that the monitor placement in our case is only Fixed-Parameter Tractable, and also polynomial-time solvable, which is the best case.
One of the main research challenges faced in Wireless Sensor Networks (WSNs) is to preserve continuously and effectively the coverage of an area (or region) of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes. In this paper, we propose a protocol, called distributed lifetime coverage optimization protocol (DiLCO), which maintains the coverage and improves the lifetime of a wireless sensor network. First, we partition the area of interest into subregions using a classical divide-and-conquer method. Our DiLCO protocol is then distributed on the sensor nodes in each subregion in a second step. To fulfill our objective, the proposed protocol combines two effective techniques: a leader election in each subregion, followed by an optimization-based node activity scheduling performed by each elected leader. This two-step process takes place periodically, to choose a small set of nodes remaining active for sensing during a time slot. Each set is built to ensure coverage at a low energy cost, allowing to optimize the network lifetime. Simulations are conducted using the discrete event simulator OMNET++. We refer to the characteristics of a Medusa II sensor for the energy consumption and the computation time. In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides improved coverage performances.
In wired networks, monitor-based network tomography has been proved to be an effective technology for network internal state measurements. Existing wired network tomography approaches assume that the network topology is relatively static. However, the network topology of sensor networks is usually changing over time due to wireless dynamics. In this paper, we study the problem to assign a number of sensor nodes as monitors in large scale sensor networks, so that the end-to-end measurements among monitors can be used to identify hop-by-hop link metrics. We propose RoMA, a Robust Monitor Assignment, algorithm to assign monitors in large scale sensor networks with dynamically changing topology. RoMA includes two components, confidence-based robust topology generation and cost-minimized monitor assignment. We implement RoMA and evaluate its performance based on a deployed large scale sensor network. Results show that RoMA achieves high identifiability with dynamically changing topology and is able to assign monitors with minimum cost.
The Internet of Things (IoT) has various fields of application including health care, resource management, asset tracking, etc. Depending on the use case, various technologies like RFID, Wireless Sensor Network (WSN) or Smart Objects can be used. With each of these comes a specific vision of what the IoT and connected objects are and – to our knowledge – there is no global picture of the IoT. The issue with this approach is that specific problems have been addressed before global ones: what if something has been missed? We propose a definition and taxonomy for connected objects and the IoT.
Among the panoply of applications enabled by the Internet of Things (IoT), smart and connected health care is a particularly important one. Networked sensors, either worn on the body or embedded in our living environments, make possible the gathering of rich information indicative of our physical and mental health. Captured on a continual basis, aggregated, and effectively mined, such information can bring about a positive transformative change in the health care landscape. In particular , the availability of data at hitherto unimagined scales and temporal longitudes coupled with a new generation of intelligent processing algorithms can: (a) facilitate an evolution in the practice of medicine, from the current post facto diagnose-and-treat reactive paradigm, to a proactive framework for prognosis of diseases at an incipient stage, coupled with prevention, cure, and overall management of health instead of disease, (b) enable personalization of treatment and management options targeted particularly to the specific circumstances and needs of the individual, and (c) help reduce the cost of health care while simultaneously improving outcomes. In this paper, we highlight the opportunities and challenges for IoT in realizing this vision of the future of health care.
We study extension variants of the classical problems Vertex Cover and Independent Set. Given a graph G=(V,E) and a vertex set , it is asked if there exists a minimal vertex cover (resp. maximal independent set) S with (resp. ). Possibly contradicting intuition, these problems tend to be -complete, even in graph classes where the classical problem can be solved efficiently. Yet, we exhibit some graph classes where the extension variant remains polynomial-time solvable. We also study the parameterized complexity of theses problems, with parameter |U|, as well as the optimality of simple exact algorithms under ETH. All these complexity considerations are also carried out in very restricted scenarios, be it degree or topological restrictions (bipartite, planar or chordal graphs). This also motivates presenting some explicit branching algorithms for degree-bounded instances. e further discuss the price of extension, measuring the distance of U to the closest set that can be extended, which results in natural optimization problems related to extension problems for which we discuss polynomial-time approximability.
This paper presents a wearable Internet of Things (IoT) sensor node aimed at monitoring harmful environmental conditions for safety applications via LoRa wireless technology. The proposed sensor node is low-power and supports multiple environmental sensors. A LoRa based gateway is used to connect sensors to the Internet. We mainly focus on monitoring carbon monoxide, carbon dioxide, ultraviolet, and some general environmental parameters. Poor environment quality could cause severe health problems to individuals. Therefore, surrounding environmental data is gathered by the wearable node in a real-time manner and then transmitted to a remote cloud server. The data can then be displayed to authorized users through a web-based application located in the cloud server and the device will give alert to the user via mobile application when an emergency condition occurs. The experimental results indicate that our safety monitoring network can work reliably with low power consumption.
Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.
Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.
NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph an-alytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, NR incorporates many social and col-laborative aspects that facilitate scientific research, e.g., users can discuss each graph, post observations, and visualizations.
Present approaches to achieve k-coverage for Wireless Sensor Networks still rely on centralized techniques. In this paper, we devise a distributed method for this problem, namely Distributed VOronoi based Cooperation scheme (DVOC), where nodes cooperate in hole detection and recovery. In previous Voronoi based schemes, each node only monitors its own critical points. Such methods are inefficient for k-coverage because the critical points are far away from their generating nodes in k-order Voronoi diagram, causing high cost for transmission and computing. As a solution, DVOC enables nodes to monitor others' critical points around themselves by building local Voronoi diagrams (LVDs). Further, DVOC constrains the movement of every node to avoid generating new holes. If a node cannot reach its destination due to the constraint, its hole healing responsibility will fall to other cooperating nodes. The experimental results from the real world testbed demonstrate that DVOC outperforms the previous schemes.
Wireless sensor networks (WSNs), as a collection of sensing nodes with limited processing, limited energy reserve and radio communication capabilities, are widely implemented in many areas of applications such as industry, environment, healthcare, etc. Regarding this large range of applications, many research issues are introduced including the applications, performance, reliability, lifetime, etc. The WSNs lifetime considered in this work is the period of time through which theWSN is perfectly completing its function. This lifetime is affected by many factors including the amount of energy available, failure probability and components degradation. The amount of energy available become the most important factor in case of non renewable components applications. Different algorithms, strategies and optimization techniques were developed and implemented for this purpose based on the possibility of activating a subset of sensors that satisfied the monitoring constraint, while keeping the others in sleep mode to be implemented later. This is an NP complete maximization problem that can be solved using disjoint set covers (DSCs). But the solution obtained using DSCs does not extend always significantly the WSNs lifetime. So, the present work aims to search for a better solution using non-disjoint set covers (NDSCs). This approach gives the opportunity for a sensor to be implemented in one or more subset covers. For that purpose, we studied a binary representation based model to maximize the number of NDSCs. Also, we developed a genetic algorithm based heuristic based on this model to find out the maximum number of NDSCs in a reasonable time. Thus, for a set of m sensors used to monitor a set of n targets or a field, this heuristic allows to construct a maximum number q of NDSCs. Additional effort is required to find the best scheduling for implementing the NDSCs so as to maximize the lifetime of the sensors involved in the WSNs, considering their limited available energy. This problem is formulated using integer linear programming (ILP) mathematical model. The objective function of this problem is the sum of all monitoring seasons on which all q NDSCs scheduled, and the constraint is the energy consumption in all sensors included in all NDSCs. Solving this problem using ILP in a period of time depends on the complexity of the model and the instances used. To find the solution in reasonable time, we have developed a NDSCs based genetic algorithm (NDSC-GA). The candidate solutions are represented in chromosomes composed of a number of genes equal to the number q of NDSCs, and each gene is the number of monitoring seasons on which a NDSC is scheduled. We have then developed a GA that combines the four crossover operators and four mutation operators. The GA based methods are coded in C programming language to obtain a satisfying solution and the Cplex software was used to obtain the corresponding exact solution. Comparing the optimal solution obtained using the ILP on small instances, to the solutions obtained using our GA based method explained that our methods can find a solution near the optimal in reasonable time. Then, comparing the solution obtained using our NDSCs GA based methods, to the DSCs GA based method in the literature, we showed that the NDSCs GA can prolong the WSNs lifetime better than DSCs GA for the same instances. Our approach combines together the scheduling principles and the optimization techniques to maximizing the WSNs lifetime
The Internet of things (IoT) has recently become an important research topic because it integrates various sensors and objects to communicate directly with one another without human intervention. The requirements for the large-scale deployment of the IoT are rapidly increasing with a major security concern. This study focuses on the state-of-the-art IoT security threats and vulnerabilities by conducting an extensive survey of existing works in the area of IoT security. The taxonomy of the current security threats in the contexts of application, architecture, and communication is presented. This study also compares possible security threats in the IoT. We discuss the IoT security scenario and provide an analysis of the possible attacks. Open research issues and security implementation challenges in IoT security are described as well. This study aims to serve as a useful manual of existing security threats and vulnerabilities of the IoT heterogeneous environment and proposes possible solutions for improving the IoT security architecture.
Because of the exponential growth of Internet of Things (IoT), several services are being developed. These services can be accessed through smart gadgets by the user at any place, every time and anywhere. This makes security and privacy central to IoT environments. In this paper, we propose a lightweight, robust, and multi-factor remote user authentication and key agreement scheme for IoT environments. Using this protocol, any authorized user can access and gather real-time sensor data from the IoT nodes. Before gaining access to any IoT node, the user must first get authenticated by the gateway node as well as the IoT node. The proposed protocol is based on XOR and hash operations, and includes: (i) a 3-factor authentication (ie, password, biometrics, and smart device); (ii) mutual authentication; (iii) shared session key; and (iv) key freshness. It satisfies desirable security attributes and maintains acceptable efficiency in terms of the computational overheads for resource constrained IoT environment. Further, the informal and formal security analysis using AVISPA proves security strength of the protocol and its robustness against all possible security threats. Simulation results also prove that the scheme is secure against attacks.
Fog/edge computing has been proposed to be integrated with Internet-of-Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between Cyber-Physical Systems (CPS) and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications
Wireless Sensor Networks (WSNs) are commonly used information technologies of modern networking and computing platforms. Today's network computing applications are faced with a high demand of powerful network functionalities. Functional network reach is central to customer satisfaction such as in mobile networks and cloud computing environments. However, efficient management of WSNs remains a challenge, due to problems supplemental to them. Recent technology shift proposes Software Defined Networking (SDN) for improving computing networks. This review paper highlights application challenges faced by WSNs for monitored environments and those faced by the proposed approaches, as well as opportunities that can be realized on applications of WSNs using SDN. We also highlight Implementation considerations by focusing on critical aspects that should not be disregarded when attempting to improve network functionalities. We then propose a strategy for Software Defined Wireless Sensor Network (SDWSN) as an effort for application improvement in monitored environments.
Internet of Things (IoT) is a new paradigm that integrates the Internet and physical objects belonging to different domains such as home automation, industrial process, human health and environmental monitoring. It deepens the presence of Internet-connected devices in our daily activities, bringing, in addition to many benefits, challenges related to security issues. For more than two decades, Intrusion Detection Systems (IDS) have been an important tool for the protection of networks and information systems. However, applying traditional IDS techniques to IoT is difficult due to its particular characteristics such as constrained-resource devices, specific protocol stacks, and standards. In this paper, we present a survey of IDS research efforts for IoT. Our objective is to identify leading trends, open issues, and future research possibilities. We classified the IDSs proposed in the literature according to the following attributes: detection method, IDS placement strategy, security threat and validation strategy. We also discussed the different possibilities for each attribute, detailing aspects of works that either propose specific IDS schemes for IoT or develop attack detection strategies for IoT threats that might be embedded in IDSs.
This paper presents a fault diagnosis protocol for wireless sensor networks (WSNs) based on neural network approach. A particle swarm optimization based fuzzy multilayer perceptron is used in the fault detection and classif cation phase of the protocol. The proposed protocol considers the composite fault model such as hard permanent, soft permanent, intermittent, and transient fault. The performance of the proposed algorithm is evaluated by using generic parameters such as detection accuracy, false alarm rate, and false positive rate. The simulation is carried out by standard network simulator NS-2.35 and the performance is compared with the existing fault diagnosis protocols. The result shows that the proposed protocol performs superior than the existing protocols.
Every year, mining industry sees huge losses in terms of human lives and valuable infrastructure due to accidents and disasters. Besides other measures, effective monitoring and control can greatly reduce the risks of such incidents. Wireless sensor networks (WSNs) are increasingly being used for such applications. This paper proposes a WSN-based system, which is capable of detecting and identifying events of interest (with 90% success rate) and localization of miners (2–4 m) and roof falls (10–12 m). A comprehensive integrated system covering a range of aspects from radio frequency propagation, communication protocol with latency, and energy–efficiency tradeoff and autonomous event detection is presented. The results show a lower path loss for 433 MHz operating frequency compared to 868 MHz. Moreover, a novel energy-efficient hybrid communication protocol using both periodic and aperiodic modes of communication while adhering to low latency requirement for emergency situations is proposed and implemented. Finally, for intelligent processing of gathered data, a spatio-temporal and attribute-correlated event detection mechanism suitable for the highly unreliable mine environment is described.
Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things (IoT), LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine (M2M) applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LORa Alliance,WEIGHTLESS-SIG, and DASH7 Alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.
Highlights Elitism technique with unusual selections is adopted to evade premature convergence. Statistical analyzes on the different randomly generated graphs are done. The proposed technique is translated into a verifiable behavioral model. Abstract Cloud computing is a new platform to manage and provide services on the internet. Lately, researchers have paid attention a lot to this new subject. One of the reasons to have high performance in a cloud environment is the task scheduling. Since the task scheduling is an NP-Complete problem, in many cases, meta-heuristics scheduling algorithms are used. In this paper to optimize the task scheduling solutions, a powerful and improved genetic algorithm is proposed. The proposed algorithm uses the advantages of evolutionary genetic algorithm along with heuristic approaches. For analyzing the correctness of the proposed algorithm, we have presented a behavioral modeling approach based on model checking techniques. Then, the expected specifications of the proposed algorithm is extracted in the form of Linear Temporal Logic (LTL) formulas. To achieve the best performance in verification of the proposed algorithm, we use the Labeled Transition System (LTS) method. Also, the proposed behavioral models are verified using NuSMV and PAT model checkers. Then, the correctness of the proposed algorithm is analyzed according to the verification results in terms of some expected specifications, reachability, fairness, and deadlock-free. The simulation and statistical results revealed that the proposed algorithm outperformed the makespans of the three well-known heuristic algorithms and also the execution time of our recently meta-heuristics algorithm.
In this paper the concept of network monitoring implemented in SDN architectures are explored. This new programmable network architecture ensures a significant advantage over other because it is introduce new functionalities and easier to tune up to provide guaranteed quality of services from end-to-end for each priority flow. Monitoring the network is the first step towards a SDN forwarding protocol capable to ensure sufficient QoS for all types of applications and traffic. A new measuring method for packet delay has been proposed. The method is capable to provide statistics for every different type of service that passes through the network.
The Internet of Things (IoT) is a paradigm based on the Internet that comprises many interconnected technologies like RFID (Radio Frequency IDentification) and WSAN (Wireless Sensor and Actor Networks) in order to exchange information. The current needs for better control, monitoring and management in many areas, and the ongoing research in this field, have originated the appearance and creation of multiple systems like smart-home, smart-city and smart-grid. However, the limitations of associated devices in the IoT in terms of storage, network and computing, and the requirements of complex analysis, scalability, and data access, require a technology like Cloud Computing to supplement this field. Moreover, the IoT can generate large amounts of varied data and quickly when there are millions of things feeding data to Cloud Computing. The latter is a clear example of Big Data, that Cloud Computing needs to take into account. This paper presents a survey of integration components: Cloud platforms, Cloud infrastructures and IoT Middleware. In addition, some integration proposals and data analytics techniques are surveyed as well as different challenges and open research issues are pointed out.
Let denote a path in a graph with vertices. A vertex cover set in is a vertex subset such that every in has at least a vertex in . The Vertex Cover problem is to find a vertex cover set of minimum cardinality in a given graph. This problem is NP-hard for any integer . The parameterized version of Vertex Cover problem called -Vertex Cover asks whether there exists a vertex cover set of size at most in the input graph. In this paper, we give two fixed parameter algorithms to solve the -Vertex Cover problem. The first algorithm runs in time in polynomial space and the second algorithm runs in time in exponential space. Both algorithms are faster than previous known fixed-parameter algorithms.
Reliability of Nanoscale Circuits and Systems: Methodologies and Circuit Architectures Milos Stanisavljevic Alexandre Schmid Yusuf Leblebici Future integrated circuits are expected to be made of emerging nanodevices and their associated interconnects, but the reliability of such components is a major threat to the design of future integrated computing systems. Reliability of Nanoscale Circuits and Systems: Methodologies and Circuit Architectures confronts that challenge. The first part discusses the state-of-the-art of the circuits and systems as well as the architectures and methodologies focusing the enhancement of the reliability of digital integrated circuits. It proposes circuit and system level solutions to overcome high defect density and presents reliability, fault models and fault tolerance. It includes an overview of nano-technologies that are considered in the fabrication of future integrated circuits and covers solutions provided in the early ages of CMOs as well as recent techniques. The second part of the text analyzes original circuit and system level solutions. It details an architecture suitable for circuit-level and gate-level redundant modules implementation and exhibiting significant immunity to permanent and random failures as well as unwanted fluctuation and the fabrication parameters. It also proposes a novel general method enabling the introduction of fault-tolerance and evaluation of the circuit and architecture reliability. And the third part proposes a new methodology that introduces reliability in existing design flows. That methodology consists of partitioning the full system to design into reliability optimal partitions and applying reliability evaluation and optimization at local and system level. © Springer Science+Business Media, LLC 2011. All rights reserved.
Critical infrastructure systems perform functions and missions that are essential for our national economy, health, and security. These functions are vital to commerce, government, and society and are closely interrelated with people's lives. To provide highly secured critical infrastructure systems, a scalable, reliable and robust threat monitoring and detection system should be developed to efficiently mitigate cyber threats. In addition, big data from threat monitoring systems pose serious challenges for cyber operations because an ever growing number of devices in the system and the amount of complex monitoring data collected from critical infrastructure systems require scalable methods to capture, store, manage, and process the big data. To address these challenges, in this paper, we propose a cloud computing based network monitoring and threat detection system to make critical infrastructure systems secure. Our proposed system consists of three main components: monitoring agents, cloud infrastructure, and an operation center. To build our proposed system, we use both Hadoop MapReduce and Spark to speed up data processing by separating and processing data streams concurrently. With a real-world data set, we conducted real-world experiments to evaluate the effectiveness of our developed network monitoring and threat detection system in terms of network monitoring, threat detection, and system performance. Our empirical data indicates that the proposed system can efficiently monitor network activities, find abnormal behaviors, and detect network threats to protect critical infrastructure systems.
Internet of things (IoT) technologies have been widely used in industrial systems to control the manufacturing environment and monitor production lines. An industrial IoT system can perform data collection and processing and provide services to production decisions. However, the challenge remains for the IoT system to ensure the quality and quantity of data collected from sensor networks. To address the issue, an independent regional connectivity model is presented in the context of sensor networks to guarantee global connectivity with satisfied quality of data service. We also investigate the optimization of sensing coverage and regional connectivity in an industrial IoT system in both deterministic and random deployment. First, a novel optimal network that achieves full sensing coverage and guarantees regional connectivity is presented for deterministic deployment. The optimal pattern is derived, and the advantage of the proposed model is analyzed. Second, based on the assumption that the given sensors are deployed as a Poisson point process, theoretical analysis is presented to determine the minimum number of sensors used for random deployment to achieve certain coverage and connectivity degrees. Numerical results show that our proposed models are efficient for the application of sensor networks in industrial IoT systems.
We investigate the problem of placing monitors to localize node failures in a communication network from binary states (normal/failed) of end-to-end paths, under the assumption that a path is in normal state if and only if it contains no failed nodes. To uniquely localize failed nodes, the measurement paths must show different symptoms (path states) under different failure events. Our goal is to deploy the minimum set of monitors to satisfy this condition for a given probing mechanism. We consider three families of probing mechanisms, according to whether measurement paths are (i) arbitrarily controllable, (ii) controllable but cycle-free, or (iii) uncontrollable (i.e., determined by the default routing protocol). We first establish theoretical conditions that characterize network-wide failure identifiability through a per-node identifiability measure that can be efficiently evaluated for the above three probing mechanisms. Leveraging these results, we develop a generic monitor placement algorithm, applicable under any probing mechanism, that incrementally selects monitors to optimize the per-node measure. The proposed algorithm is shown to be optimal for probing mechanism (i), and provides upper and lower bounds on the minimum number of monitors required by the other probing mechanisms. In the special case of single-node failures, we develop an improved monitor placement algorithm that is optimal for probing mechanism (ii) and has linear time complexity. Using these algorithms, we study the impact of the probing mechanism on the number of monitors required for uniquely localizing node failures. Our results based on real network topologies show that although more complicated to implement, probing mechanisms that allow monitors to control measurement paths substantially reduce the required number of monitors.
The public IPv4 address space managed by IANA (http://www.iana.org) has been completely depleted by Feb 1st, 2011. This creates by itself an interesting challenge when adding new things and enabling new services on the Internet. Without public IP addresses, the Internet of Things capabilities would be greatly reduced. Most discussions about IoT have been based on the illusionary assumption that the IP address space is an unlimited resource or it is even taken for granted that IP is like oxygen produced for free by nature. Hopefully, the next generation of Internet Protocol, also known as IPv6 brings a solution. In early 90s, IPv6 was designed by the IETF IPng (Next Generation) Working Group and promoted by the same experts within the IPv6 Forum since 1999. Expanding the IPv4 protocol suite with larger address space and defining new capabilities restoring end to end connectivity, and end to end services, several IETF working groups have worked on many deployment scenarios with transition models to interact with IPv4 infrastructure and services. They have also enhanced a combination of features that were not tightly designed or scalable in IPv4 like IP mobility, ad hoc services; etc catering for the extreme scenario where IP becomes a commodity service enabling lowest cost networking deployment of large scale sensor networks, RFID, IP in the car, to any imaginable scenario where networking adds value to commodity. For that reason, IPv6 makes feasible the new conception of extending Internet to Everything. IPv6 spreads the addressing space in order to support all the emerging Internet-enabled devices. In addition, IPv6 has been designed to provide secure communications to users and mobility for all devices attached to the user; thereby users can always be connected. This work provides an overview of our experiences addressing the challenges in terms of connectivity, reliability, security and mobility of the Internet of Things through IPv6 in order to reach the Internet of Everything. This describes the key challenges, how they have been solved with IPv6, and finally presents the future works and vision that describe the roadmap of the Internet of Everything in order to reach an interoperable, trustable, mobile, distributed, valuable, and powerful enabler for emerging applications such as Smarter Cities, Human Dynamics, Cyber-Physical Systems, Smart Grid, Green Networks, Intelligent Transport Systems, and ubiquitous healthcare.
JuMP is an open-source modeling language that allows users to express a wide
range of optimization problems (linear, mixed-integer, quadratic,
conic-quadratic, and nonlinear) in a high-level, algebraic syntax. JuMP takes
advantage of advanced features of the Julia programming language to achieve
performance on par with commercial modeling tools. In this work we will provide
benchmarks, present the novel aspects of the implementation, and discuss how
JuMP can be extended to new problem classes and composed with state-of-the-art
tools for visualization and interactivity.