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Internet of Things (IoT) is an emerging technology that has captured the attention of academia and industry. IoT technology creates a smart platform and reduces the need for human interaction, which may result in more precise reactions. The battery is the power supply when IoT devices are distributed in a vast area with a lack of direct power connection, for example in agricultural fields or wildlife animal monitoring. In such scenarios, battery replacement can be a hard or sometimes impossible task. In this research, we propose a data transmission model to improve data routing in a clustered IoT platform possible to develop in vast areas such as farms. The proposed approach will balance power consumption in inter-cluster data routing. Hence the lifetime of relay cluster heads have been improved and nodes can operate for a longer period of time. Our results show that considering power level and workload as factors to select the next cluster head relay node in inter-cluster data routing will improve network stability and reduce packet loss in the network.

We present a calibration procedure for a humidity sensor made of a fiber Bragg grating covered by a polyimide layer. FBGs being intrinsically sensitive to temperature and strain, the calibration should tackle three variables, and, therefore, consists of a three-variable, two-level factorial design tailored to assess the three main sensitivities, as well as the five cross-sensitivities. FBG sensing information is encoded in the reflection spectrum from which the Bragg wavelength should be extracted. We tested six classical peak tracking methods on the results of the factorial design of the experiment applied to a homemade FBG humidity sensor. We used Python programming to compute, from the raw spectral data with six typical peak search algorithms, the temperature, strain and humidity sensitivities, as well as the cross-sensitivities, and showed that results are consistent for all algorithms, provided that the points selected to make the computation are correctly chosen. The best results for this particular sensor are obtained with a 3 dB threshold, whatever the peak search method used, and allow to compute the effective humidity sensitivity taking into account the combined effect of temperature and strain. The calibration procedure presented here is nevertheless generic and can thus be adapted to other sensors.

The dynamic behaviour of natural disasters and their probabilistic failure pattern indicates a need for a dynamic, risk-based protection approach to reduce the number of disrupted connections in the network. Appropriate traffic protection against a time-varying destructive phenomenon serves to prevent damage before it occurs. In this case, the level of risk for traffic routes should be evaluated and the flow should be rerouted to more reliable paths prior to failure. The high-risk paths can be identified based on appropriate decision parameters in a preventive protection scheme as an effective dynamic probabilistic solution to address large-scale failure scenarios. In this paper, we study the effect of dynamic tuning of decision parameters on network performance and discuss their impact on traffic protection. Furthermore, we develop a self-adapting preventive approach to enhance traffic protection with respect to disaster behaviour and undamaged, operational network resources. The proposed approach dynamically adjusts rerouting decision parameters to provide an appropriate level of protection while the impact domain of the natural disaster expands through the region and increases the risk of failure for network components. Our simulations, conducted in real-world topologies, confirm the feasibility of the proposed approach for traffic protection in large-scale failure scenarios.

The Internet of Things (IoT) is an emerging technology that incentivizes industry, agriculture and academia to develop smart platforms. One of the areas where IoT is being developed is in smart farms. IoT in agricultural systems helps farmers collect and transfer important environmental parameters in an automated way, which may lead to increased productivity and reduced labor costs. Power for distributed IoT nodes in a field is usually supplied by batteries, which drain during data transmission, and battery consumption is increased if there is a need for data retransmission in the case of packets loss. Signal strength is the main parameter for pairing nodes in data transmission, however, considerations involving CPU usage and battery life will keep IoT platform operating longer and may reduce the number of dropped packets. In this paper, we study the impact of CPU usage on packet loss and battery drain during data transmission. Our study proposes a design for an efficient data transmission platform for smart farms that takes into consideration CPU usage, signal strength and battery life.

Natural disasters are among the main causes of large-scale failure scenarios. The dynamic behaviour of natural disasters and their probabilistic failure pattern indicates a need for a dynamic probabilistic protection approach to address the issue and reduce the number of disrupted connections in the network. Appropriate data protection against a time-varying destructive phenomenon serves to prevent damage before it occurs. In this case, the endangered route should be detected and the data passing through should be rerouted through more reliable paths prior to failure. The high-risk paths can be detected based on the decision parameters in the dynamic preventive approach. In this article, we develop a new method to improve the assignment procedure of decision factors in preventive protection models using network topological properties and links failure probability. The proposed decision parameters are adaptive with the network conditions and updateable while the disaster spreads through the region. The adaptive characteristic of the rerouting decision parameter will enhance network efficiency and facilitate network management.

© 2016 IEEE. Transferring collected physiological data from health facilities to a cloud-based health analytical platform can be seen as an efficient and cost effective approach to provide clinical support to rural and remote healthcare centres from urban based specialists. A cloud-based healthcare platform will reduce the requirement of patient transfer due to lack of clinical experts or providing consultative support through the phone. However transferring physiological data streams through a data path with insufficient quality and unsatisfactory conditions may have negative performance impact on real-time data processing. To address this issue, we study the benefit of using software-defined networking (SDN) technology. SDN as an emerging networking paradigm, can be employed to manage and control network conditions and apply desired policies. This research introduces the significant features in SDN technology to transfer physiological data streams through an alternative path with a better quality rather than the congested predetermined shortest path in order to enhance data transfer reliability and improve real-time data processing quality.

The impact of natural disasters can be catastrophic for communication networks and may cause significant costs for service providers and subscribers. Dynamic spreading of failures in natural disasters follows a time-varying probabilistic pattern, which requires a dynamic probabilistic response to mitigate the effect of failures. In this paper we examine the preventive protection scheme as an effective dynamic probabilistic solution to address large-scale failure scenarios and provide an algorithm to adjust probabilistic decision-making parameters. The proposed scheme can be used by network operators to adjust decision parameters in a preventive protection model to decrease the number of disrupted connections in an effective way. Reducing the number of damaged connections may lead to increased network resiliency level, which is the main concern in large-scale failure scenarios caused by natural disasters.

Large-scale failure resulting from natural disasters or intentional attacks is now considered a serious risk for communication network infrastructure. The impact of large scale network connection disruptions may cause significant costs for service providers and subscribers. In this paper, we develop a novel approach for preventive protection of the network based on a probabilistic model for large-scale failure scenarios. In particular, we aim to develop a scheme to improve uninterrupted delivery of data in the network to provide desired services in case of large-scale failures. We conduct simulations to show the significant reduction in the average number of disrupted connections and network disruption time due to failure.

Communication network infrastructures are always in danger of natural disasters or physical attacks. The destructive effects of these incidents can cause large-scale failures in the network. This paper proposes a model for the impact of failure considering wave energy behaviour to compute probability of failures, and calculates required spare capacities in path restoration mechanism based on this model and compares it with the deterministic model. The proposed model can be employed to identify additional resources to provide the expected level of survivability in the event of large-scale failure scenarios.

In this paper, we study the impact of time-varying large-scale failure scenarios on communication network infrastructure. Natural disasters such as earthquakes or nuclear explosions can cause such failures in the network. We model the impact of failure using deterministic and probabilistic failure models, and use simulations to evaluate the performance of a path restoration scheme in such scenarios. Different probabilistic scenarios are computed and discussed and compared to the deterministic method. Required restoration time in different probabilistic failure models is simulated and total number of restorable demands is calculated. The proposed approach can be used as a framework to evaluate required capacity and restoration time to refine restoration schemes for achieving better survivability levels.

One of the most significant current groups of security endangerments in the Internet is DoS/DDoS attacks. The goal of these kinds of attacks is to completely engage available resources so that legitimate users are not able to access a service. Some traceback approach has been proposed to traceback source of attack. One of these methods is Intention-driven iTrace which is the working base of the ICMP traceback. By this method, it will be possible to increase effective ICMP traceback messages which can provide useful information to the victim in tracing source of attack. Reconstructed path to the source of attack by the victim can be done accurately when more effective ICMP traceback messages are generated in critical routers. In this paper, we proposed a model considering incoming packets routed to the victim and by modifying intention-driven iTrace model, we can generate more effective ICMP traceback packets to locate the source of attack more accurately.