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An example of a topology with two Long-power Wide-area Networks (LPWAN) installations with sub-networks formed in the 802.15.4 network.
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A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating ove...
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... this way, a hierarchy is created within the network where (i) the vast majority of the devices are positioned at the lowest level of the hierarchy, (ii) the controllers form the intermediate level of the hiercarchy, and finally (iii) the LPWAN gateways form the top layer of the hierarchy that connect the IoT deployments with the Internet. Figure 1 provides an example of such a hierarchical topology. ...
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... is designed to benefit from the merits of the different networking interfaces and achieve the best mixture in terms of power consumption and data exchange rates. For example, Figure 1 provides a graphical representation of an installation that spans over two physical locations (LPW1 and LPW2). Each location contains an LPWAN gateway, and a set of devices, capable of communicating with the LPWAN gateway. ...
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... set of experiments demonstrates that duty cycling can considerably improve the network lifetime without affecting the performance of the sub-network detection mechanism. Figure 10 indicates the network lifetime as recorded by the gateway device. ...
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... happens mainly because interruption-like timer events are executed even when the device is set to sleep mode. Figure 11 shows exactly how, for extremely low duty cycling rates (<50%), the actual rate is higher than requested. This fact explains how the operation of higher protocols is adversely affected in such conditions. ...
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... fact explains how the operation of higher protocols is adversely affected in such conditions. Figure 11 shows how the duty cycling service actually operates and achieves the requested timings. This is a requirement as the timers scheduled by our protocols and any external interruptions can cause the device to wake-up and continue its operation while the timer routine is served. ...
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... experiments are conducted for 30 min using short beacon interval periods of 500 ms/2500 ms and long beacon interval periods of 3000 ms/150,000 ms. As observed in Figure 12, for the case of 500 ms period, the network requires more time to stabilize. The sub-network discovery module wrongfully reports changes in the topology for such sort beacon interval and this leads the sub-network formation module to constantly attempt to adapt to the new state. ...
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... evaluation is based on a predefined mobility path that is followed by a member of the team that is carrying an IoT device while moving with different walking speeds. The path followed as well as the positions of the sensors are depicted in Figure 13. To better evaluate the operation of the communication scheme in the presence of mobile devices, the used two different speeds: slow, 1 step every 10 s and fast, 1 step every 5 s. ...
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... better evaluate the operation of the communication scheme in the presence of mobile devices, the used two different speeds: slow, 1 step every 10 s and fast, 1 step every 5 s. The results of the experiment are included in Figure 14, which indicate that the mobile devices transmits over 80% more messages during the walk while all devices transmit more messages than they would without movement. This is because mobile devices are always expected to be inconsistent and transmit regularly, while other devices operate on longer intervals for the largest part of the experiment. ...
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... the Jammer device was activated for 10 min and finally the Jammer device was deactivated and the network was allowed to stabilize once again. Observing the function of the Jammer in Figure 15, it is evident that it heavily disrupts the smooth operation of both modules. During the channel disruption, the sub-network detection module continuously produces events and so does the sub-network formation module. ...
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The software-defined networking (SDN) standard decouples the data and control planes. SDN is used in the Internet of Things (IoT) due to its programmability, central view and deployment of innovative protocols, and is known as SD-IoT. However, in SD-IoT, controller selection has never been studied. Controllers control the network and react to dynam...
Citations
... For example, the STM32F103REY is used within the Apple TV 4 remote control. The selected set allows evaluating the performance over different combinations of resource constraints in terms of computational power (e.g., 16 MHz of the nRF51422), limited memory (e.g., 20 KB of RAM by the STM32L072CZ), the availability of hardware support for 128-bits AES or the availability of hardware support for Pseudo-Random Number Generation and the secp256r1 ECC curve. The specifications of the selected microcontrollers are summarized in Table 7. ...
LoRaWAN (Long Range Wide Area Network) is a Low-Power Wide Area Networks (LPWAN) technology with very rapid uptake during the previous years, developed by the LoRa (Long Range) Alliance as an open standard operating over the unlicensed band. Current LoRaWAN architecture foresees specific techniques for bootstrapping end-to-end encryption during network initialization. In particular, this work focuses on the Over-The-Air Activation (OTAA) method, which uses two keys (Network key (NwkKey) and Application key (AppKey)) that are hard-coded into the device and do not change throughout the entire lifetime of the deployment. The inability to refresh these two keys is as a weak point in terms of the overall security of the network especially when considering deployments that are expected to operate for at least 10–15 years. In this paper, the security issues of OTAA are presented in detail highlighting the vulnerabilities against the specific type of attacks. A new scheme for network activation is proposed that builds upon the current LoRaWAN architecture in a way that maintains backwards compatibility while resolving certain vulnerabilities. Under the new mechanism, the devices periodically negotiate new keys securely based on elliptic-curve cryptography. The security properties of the proposed mechanism are analyzed against a specific type of attacks. The analysis indicates that the new secure rejoin mechanism guarantees (i) computational key secrecy, (ii) decisional key secrecy, and (iii) key independence, forward and backward, for both root keys thus properly addressing the considered security vulnerabilities of LoRaWAN. Moreover, the method is implemented in software using the RIOT-OS, a hardware-independent operating system that supports many different architectures for 8 bit, 16 bit, 32 bit and 64 bit processors. The resulting software is evaluated on the FIT IoT-Lab real-world experimentation facility under a diverse set of ARM Cortex-M* devices targeting a broad range of IoT applications, ranging from advanced wearable devices to interactive entertainment devices, home automation and industrial cyber-physical systems. The experiments indicate that the overall overhead incurred in terms of energy and time by the proposed rejoin mechanism is acceptable given the low frequency of execution and the improvements to the overall security of the LoRaWAN1.1 OTAA method.
... The recent developments of the upcoming Fifth-Generation communication network backbones (5G) are expected to influence the development of fog computing with huge benefits with regards to response times, transmission delays, and energy management costs in delay-sensitive applications [24]. The hierarchical structures of cloud-fog computing can provide different types of computing services that improve resource management in smart grids [25,26]. Evidence from real-world deployments indicates the benefits of middleware technologies of storing and processing smart meter data within the smart devices and the network hierarchy [27][28][29][30]. ...
... In LPWAN, concentrators (also called a collector) play an important role by collecting information from all embedded devices located several kilometres away. LPWANs provide huge benefits for IoT deployments, including higher autonomy due to lower energy requirements and deployment costs as only a few concentrators can cover large geographical areas [26,78]. ...
... Moreover, since the low-power operating mode is essential for IoT nodes, being constantly attached to a communication channel for down-link messages is not an option and is carried out only occasionally. In order to accommodate all the needs of the future, very dense, LPWAN deployments specifically designed as extensions need to be proposed and implemented [26,81,82]. ...
In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system’s design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system.
... These connected objects generate a lot data, which can be analyzed to identify trends and information for various purposes. This is where clustering for IoT [5][6][7][8][9][10][11][12][13][14][15][16][17][18] becomes highly demanded. The advantages that clustering provides are numerous, such as the fact that it enables the scalability of the IoT network and reduces the routing overhead by managing the routing decisions on the elected cluster-heads (CHs) [19,20]. ...
The use of wireless sensor networks, which are the key ingredient in the growing Internet of Things (IoT), has surged over the past few years with a widening range of applications in the industry, healthcare, agriculture, with a special attention to monitoring and tracking, often tied with security issues. In some applications, sensors can be deployed in remote, large unpopulated areas, whereas in others, they serve to monitor confined busy spaces. In either case, clustering the sensor network’s nodes into several clusters is of fundamental benefit for obvious scalability reasons, and also for helping to devise maintenance or usage schedules that might greatly improve the network’s lifetime. In the present paper, we survey and compare popular and advanced clustering schemes and provide a detailed analysis of their performance as a function of scale, type of collected data or their heterogeneity, and noise level. The testing is performed on real sensor data provided by the UCI Machine Learning Repository, using various external validation metrics.
... The major challenge in the evolving landscape of wearable devices is to increase the accuracy of the biosignals collected [2], broaden the capabilities for interpretation of data [11], whilst at the same time improving the long-term operativity [5] and guaranteeing the privacy of the users [12]. With the advent of wearable-sensing technologies [? ] and smart textiles [14,15], the miniaturization of embedded systems and energy generators [16], and the introduction of new communication protocols [17] and access technologies [18][19][20], the development of wearable devices that can provide accurate and precise measurements is becoming a realistic possibility. ...
Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.
