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Geographically distributed applications hosted on cloud are becoming prevalent. They run on cross-datacenter network that consists of multiple data center networks (DCNs) connected by a wide area network (WAN). Such a cross-DC network imposes significant challenges in transport design because the DCN and WAN segments have vastly distinct characteristics (e.g., butter depths, RTTs). In this paper, we find that existing DCN or WAN transports reacting to ECN or delay alone do not (and cannot be extended to) work well for such an environment. The key reason is that neither of the signals, by itself, can simultaneously capture the location and degree of congestion. This is due to the discrepancies between DCN and WAN. Motivated by this, we present the design and implementation of GEMINI that strategically integrates both ECN and delay signals for cross-DC congestion control. To achieve low latency, GEMINI bounds the inter-DC latency with delay signal and prevents the intra-DC packet loss with ECN. To maintain high throughput, GEMINI modulates the window dynamics and maintains low butter occupancy utilizing both congestion signals. GEMINI is implemented in Linux kernel and evaluated by extensive testbed experiments. Results show that GEMINI achieves up to 53%, 31% and 76% reduction of small flow average completion times compared to TCP Cubic, DCTCP and BBR; and up to 58% reduction of large flow average completion times compared to TCP Vegas.

At present, while the Internet of Things (IoT) devices bring convenience to people, security issues have led to an increasing number of threats to IoT security. Since IoT devices have a Web application system for device managers to operate, the system can view device information, control and configure device status, and its security is of great significance. Among the various authentication methods provided by IoT devices, the password information authentication mechanism is still a critical method for Web login. If the IoT device has a weak Web password, once a hacker discovers the device, it is straightforward to be attacked and implanted with malicious code to control the device and attack other devices in the network. In response to this problem, this paper designs a set of automatic detection frameworks for weak passwords for web application systems of IoT devices. Based on this framework, an automated weak password detection system was developed to detect weak Web passwords on IoT devices on the wide-area networks of Beijing, Shandong Province, and Zhejiang Province. A total of 12,179 devices with weak Web passwords were found, accounting for all discovered IoT devices of 7.58%, verifying the effectiveness of the proposed framework.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolution approaches do not exploit the coding properties of LoRa and thus cannot work well for low SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We theoretically show that NScale introduces

Various wireless local area network technologies are developed for Internet of Things of which Long-Range Wide Area Network is preferred for low power long range communication. In the LoRa network, multiple LoRa nodes can simultaneously communicate with a LoRa gateway which causes the interference problem. This article presents an approach for estimating the association time duration between each LoRa node and the LoRa gateways for transmitting the data of end users to the LoRa gateways with high packet delivery ratio. The approach uses a Beta distribution based reputation model for estimating the association time duration between each LoRa node and LoRa gateways and Bayesian Game strategy which accommodates unknown private information of the LoRa nodes. The approach is validated by simulating the LoRa network using network simulator-3. We also demonstrate an on-campus traffic monitoring system to detect the reckless driving action and estimate the vehicle speed using sensors embedded nodes deployed along both sides of the road.

Phasor measurement units (PMUs) are widely used to monitor the operating conditions of a smart grid. The PMU communication network is, generally, a public wide area network, hence it is vulnerable to cyberattacks. At present, IEEE C37.118.2 is widely used as the communication standard for PMU communication. However, the IEEE C37.118.2 standard does not specify any security mechanism to protect the PMU data from cyberattacks. Deviation in the transmitted data of PMUs may lead to devastating consequences, such as blackouts in the grid. To mitigate cyberattacks on a PMU, this article proposes a security mechanism based on key distribution scheme that ensures integrity along with confidentiality. This mechanism involves addition of new security fields to IEEE C37.118.2 message structure. The introduction of security mechanism imposes additional computational complexity on resource constraint PMU devices and addition of new security fields in PMU message structure increases overall message size which in turn increases communication delays. In order to assess the additional computational complexity and increased communication delays, an experimental set up consisting of PMU simulators and network emulator is developed in this article. The developed PMU simulator generates IEEE C37.118.2 messages with proposed security mechanisms. Results validate the applicability of the proposed security mechanism in PMU networks without any repercussions.

All-to-all data transmission is a typical data transmission pattern in both consensus protocols and blockchain systems. Developing an optimization scheme that provides high throughput and low latency data transmission can significantly benefit the performance of those systems. This paper investigates the problem of optimizing all-to-all data transmission in a wide area network (WAN) using overlay multicast. We prove that in a hose network model, using shallow tree overlays with height up to two is sufficient for all-to-all data transmission to achieve the optimal throughput allowed by the available network resources. Upon this foundation, we build ShallowForest, a data plane optimization for consensus protocols and blockchain systems. The goal of ShallowForest is to improve consensus protocols’ resilience to skewed client load distribution. Experiments with skewed client load across replicas in the Amazon cloud demonstrate that ShallowForest can improve the commit throughput of the EPaxos consensus protocol by up to 100% with up to 60% reduction in commit latency.

This chapter focuses on the most relevant wireless Internet of Things (IoT) technologies for the Smart Grid. Multiple sets of wireless IoT technologies coexist today because of the broad range and diversity of communications requirements associated with current and emerging IoT applications. Three dimensions are commonly used to compare the scope of wireless IoT technologies: communications range, achievable data rate, and type of spectrum used. IEEE 802.15.4 is a technical standard for Low‐Rate Wireless Personal Area Networks. Low‐Power Wide Area Network technologies in unlicensed spectrum primarily target IoT use cases that are delay tolerant, do not need high data rates, and typically require very low power consumption and cost on the device side. Several industrial alliances and standardization organizations are promoting the use of IP‐based frameworks and open protocols to connect and managing smart objects and devices. On the management side, there are also several protocols suitable for the management of heterogeneous, resource‐constrained devices.

Typical cloud systems are often prone to inherent wide area network (WAN) latency. To address this issue fog computing is proposed that enables resource-constrained Internet-of-Things (IoT) devices, to execute deadline-sensitive tasks at the edge of the network. These devices can extend their battery lifespan by intelligently offloading computations as tasks to fog nodes (FNs) in their vicinity. However, finding an optimal offloading plan in a densely connected IoT-fog network is proven to be $\mathcal {NP}$ -Hard. Hence, in this article, we propose a matching theory-based efficient task offloading strategy called METO that aims to reduce the total system energy and number of outages (number of tasks exceeding the deadline) in an IoT-fog interconnection network. As resource allocation involves multiple criteria, their weights are derived using criteria importance though inter criteria correlation (CRITIC). Furthermore, to rank the alternatives we use the technique for order of preference by similarity to ideal solution (TOPSIS). Based on this ranking, we formulate the overall offloading problem as a one-to-many matching game and utilize the deferred acceptance algorithm (DAA) to produce a stable assignment. Simulation is performed in two different settings comprising offloading of homogeneous and heterogeneous tasks. Extensive simulations across both environments confirm that the proposed algorithm outperforms the existing schemes with respect to improved energy consumption, completion time, and execution time. Moreover, METO also shows the reduced number of outages across baselines used for comparison.

We evaluated long range antennae and associated solar-powered global positioning system (GPS) ear tags designed for use with domestic cattle, as a novel system for monitoring ranging behaviour of the koala (Phascolarctos cinereus). The mean location error of our GPS tags was 33.9 m (s.e. = 0.46). The tags were relatively light (30 g), reported eight locations per day when attached to koala radio-collars and had an operating life that exceeded our study period (8 months). Deployed as a stand-alone, solar powered, remote system, this technology can provide a viable option for wildlife tracking projects.

Small-scale commercial rollouts of Cellular-IoT (C-IoT) networks have started globally since last year. However, among the plethora of low power wide area network (LPWAN) technologies, the cost-effectiveness of C-IoT is not certain for IoT service providers, small and greenfield operators. Today, there is no known public framework for the feasibility analysis of IoT communication technologies. Hence, this paper first presents a generic framework to assess the cost structure of cellular and non-cellular LPWAN technologies. Then, we applied the framework in eight deployment scenarios to analyze the prospect of LPWAN technologies like Sigfox, LoRaWAN, NB-IoT, LTE-M, and EC-GSM. We consider the inter-technology interference impact on LoRaWAN and Sigfox scalability. Our results validate that a large rollout with a single technology is not cost-efficient. Also, our analysis suggests the rollout possibility of an IoT communication Technology may not be linear to cost-efficiency.

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

Analyzing blockchain protocols is a notoriously difficult task due to the underlying large scale distributed networks. To address this problem, stochastic model-based approaches are often utilized. However, the abstract models in prior work turn out not to be adoptable to consortium blockchains as the consensus of such a blockchain often consists of multiple processes. To address the lack of efficient analysis tools, we propose a queueing network-based method for analyzing consistency properties of consortium blockchain protocols in this article. Our method provides a way to evaluate the performance of the main stages in blockchain consensus. We apply our framework to the Hyperledger Fabric system and recover key properties of the blockchain network. Using our method, we analyze the security properties of the ordering mechanism and the impact of delaying endorsement messages in consortium blockchain protocols. Then an upper bound is derived of the damage an attacker could cause who is capable of delaying the honest players’ messages. Based on the proposed method, we employ analytical derivations to investigate both the security and performance features, and corroborate close agreement with measurements on a wide-area network testbed running the Hyperledger Fabric blockchain. With the proposed method, designers of future blockchains can provide a more rigorous analysis of their consortium blockchain schemes.

International audience; Internet of things (IoT) technology is based on connecting each real object to the internet. Every single object is uniquely recognized and reachable over the network. IoT last mile connectivity is based on different communication technologies and protocols, where the majority is categorized as short-range networks that operate in ISM bands like Zigbee, Wi-Fi, and Bluetooth. Short-range technologies were successfully tested and deployed in different industrial sectors. However, in the energy sectors, the deployment is challenging in certain hard to reach areas where reliable last-mile connectivity is required between the home area network (HAN) smart meters and the meter data management system (MDMS). Therefore recently, Low Power Wide Area Network (LPWAN) technology implementations, which offer long-range connectivity, has emerged as a promising alternative for IoT in Energy applications. Within LPWAN, a variety of technology implementations exist and operate in licensed and unlicensed spectrum respectively like NB-IoT, LoRaWAN, and Sigfox. In this article, we discuss both the performance of LoRaWAN in a real-world environment and its deployment as a low-cost, long-range, and reliable last-mile solution for energy smart metering in urban area scenario where a short-range solution may not be the optimum one. Furthermore, a prototype that is adapted to the existing Lebanese traditional energy sector was developed to test LoRaWAN's usefulness in Lebanon.

Existing advanced metering infrastructures (AMI) use three levels of bi-directional communication between the customer and utility. Given the increase of the available amount of data provided by smart meters in smart grid, existing AMI network may not have the required bandwidth to perform the monitoring and control in real time. The increase of the AMI network bandwidth is very expensive. On the other hand, the internet employs a set of universal communication protocols and provides a wide communication bandwidth. A new internet-based advanced metering and control infrastructure (IBAMCI) is provided in this paper. It uses only two level of communication: the local area network (LAN) and the internet. Both LAN and internet platforms already exist in most consumer’s premise. The internet infrastructure when utilized eliminates the need for expensive bandwidth upgrades to the utility wide area network (WAN). The proposed IBAMCI is equipped with an integrated internet/cloud-based data management and control server, a user interface and display module. The consumer power consumption can be displayed from anywhere using the internet. Because the proposed IBAMCI offloads the utility WAN, real-time smart grid load monitoring and control becomes possible without delays. Simulations were carried out to illustrate the benefit of the proposed IBAMCI in monitoring large number of customer loads in real time. For existing AMIs, whenever the communication bandwidth becomes insufficient, the only option is to increase the utility network bandwidth, which can be prohibitively expensive.

LTE Machine Type Communication Category M1 [LTE CAT M (or M1)] is a 3GPP Low Power Wide Area Network most suitable for IoT devices that need low data rates, deep coverage, low-cost, and, most importantly, power-efficient devices. As the number of IoT system devices (called things) is growing massively, there is a shift needed from the traditional LTE network to these low power networks. Power consumption should be minimum for all stages of device functionality to keep the devices working for longest possible time. We performed a power consumption analysis of CAT-M technology under various control path scenarios using a standard CAT-M modem. Due to the absence of a live CAT-M network in India, we use a network simulator to emulate live network scenario by varying network parameters. We measured the power consumed by the CAT-M modem using a standard tool and present simulation results of a variety of network configurable parameters allowed by 3GPP. These parameters are configurable both in scope of user equipment and network deployment. This paper covers some important power measurements related to Power Saving Mode (PSM), extended Discontinuous Reception Mode (eDRX), Uplink Transmission Power, Channel Repetition, Bandwidth and Sub-carrier Spacing. Data path (user payload) considerations are not scope of this document. By the end of the paper, we identified the impact of each parameter on the performance of the CAT-M modem.

Demands for wide-area connectivity between enterprise site-edge networks and central office core networks/cloud data centers have grown rapidly. Various software defined wide area network (SD-WAN) solutions have been developed with the primary aim of improving WAN link utilization. However, mechanisms used by existing SD-WAN solutions fail to provide high reliability and performance required by today’s edge to cloud applications. In this article, we present WAN-aware MPTCP which seamlessly aggregates multiple WAN links into a “big pipe” for better WAN resilience thus minimizing application performance degradation under WAN link failures. We leverage the congestion control of MPTCP to balance traffic across multiple WAN links. The key innovation is to combine LAN virtualization at end systems with WAN virtualization at SD-WAN gateways. Through evaluation in both emulated testbeds and real-world deployment, we demonstrate the performance gain of WAN-aware MPTCP in terms of resilience and throughput over existing SD-WAN solutions.

Ionospheric anomalies, like large ionospheric gradients, might produce a difference between the ionospheric error experienced by the Ground Based Augmentation System (GBAS) reference station and the aircraft on approach. This ionospheric delay difference could lead to hazardous position errors if undetected. For that reason, the GBAS Approach Service Types (GAST) C and D provide solutions against this threat, but the methods employed still face challenges by limiting the availability in certain cases, especially in regions with severe ionospheric conditions. This issue is caused by the use of very conservative ionospheric threat models derived based on the worst-ever-experienced ionospheric gradients in the relevant region. However, these worst-case gradients occur very rarely. Therefore, this paper proposes a methodology capable of detecting ionospheric gradients in real-time and estimating their parameters in near real-time by using a wide area network of dual-frequency and multi-constellation GNSS monitoring stations. Hence, the GBAS stations could use this information to update the threat model currently applied in their algorithms, which would result in an improvement of the GBAS availability in regions where it is degraded. The detection and estimation algorithm is initially theoretically explained. Then, the performance of this algorithm is evaluated with simulated gradients and with a real gradient, utilizing for both the real measurements recorded by a reference network in Alaska. The synthetic gradients are simulated over the nominal real measurements from this network and all the gradient parameters are modified within their ranges in the already existing threat models. In this way, we assess the performance of our algorithm by comparing the differences between the known simulated gradient parameters and the parameters estimated by our algorithm. Additionally, we also evaluate our algorithm with one real ionospheric gradient measured by the same network in Alaska to study the differences between using simulated gradients and real gradients. Results with both simulated gradients and the real gradient show the potential of our methodology to support GBAS stations by detecting and estimating the ionospheric gradients instead of using worst case models all the time.

Since transmission towers are generally located in areas with complex geological environments, such as mountains and rivers, relying only on conduct regular personnel inspections wastes considerable human and material resources. At present, the existing monitoring system has also the problems of data transmission, parameter accuracy and transmission distance. Based on this premise, a novel transmission tower tilt monistoring system was proposed in this paper. This research adopted the latest Low-Power Wide-Area Network (LPWAN) technology. Among tehm, LoRa (Long Range) is a modulation technology that provides a longer communication distance. NB-IoT (Narrow Band-Internet of Things) technology is an emerging technology in the IoT field that supports the cellular data connection of low-power devices in a wide area network. The proposed system through the data transmission of a LoRa multinode network, the tilt parameters of tower were uploaded to an online real-time monitoring system through NB-IoT. Each subnode included MPU6050 tilt sensors and a wireless communication LoRa module. The main node contained an NB-IoT module. It can upload the tilt information to the cloud platform. After uploading the data, administrators can arrange timely targeted maintenance for transmission towers with faults according to the analysis results. The proposed approach covers a wide range, has high service quality, and supports many connections.

Deep neural networks (DNNs) achieve higher accuracy as the amount of training data increases. However, training data such as personal medical data are often privacy sensitive and cannot be collected. Methods have been proposed for training with distributed data that remain in a wide area network. Due to heterogeneity in a wide area network, methods based on synchronous communication, such as all-reduce stochastic gradient descent (SGD), are not suitable, and gossip SGD is promising because it is based on asynchronous communication. Communication time is a problem in a wide area network. Gossip SGD cannot use double buffering that is a technique for hiding the communication time, since gossip SGD uses an asynchronous communication method. In this paper, we propose a type of gossip SGD in which computation and communication overlap to accelerate learning. The proposed method shares newer models by scheduling communication. To schedule the communication, the nodes share the information of the estimated communication time and communication-enabled nodes. This method is effective in both homogeneous and heterogeneous networks. The experimental results using the CIFAR-100 and Fashion-MNIST datasets demonstrate the faster convergence of the proposed method.

In this work, we investigate a cost-efficient strategy to implement robust online overhead transmission line monitoring for stable energy transfer in smart power grid. As a first step, we first present an analytical framework to evaluate communication performances of wireless sensor networks installed for the real-time monitoring of overhead power transmission line, wherein each tower performs fountain code (FC)-based transmission for guaranteeing transmission reliability between each tower and a substation. Then, we evaluate communication performances given in terms of successful FC-based transmission probability and its end-to-end delay for each tower. Further, our analysis helps formulate an optimization problem that aims at minimizing the total number of activated wide area network (WAN) communication modules deployed on towers(i.e., minimizing the total monetary cost required to use WAN communications), while simultaneously supporting three different types of communication quality of service (QoS) requirements (e.g., reliability, delay, robustness).To find the solution of the problem, we propose the Path Decision in the order of the Farthest Towers (PDFP) algorithm, which enables to find a suboptimal-solution of the problem in real-time. The key idea of PDFT algorithm is that paths (i.e., whether to use a WAN communication module) are sequentially determined in the order of towers farthest from the substation. To identify the validity of the proposed algorithm, we conduct performance evaluations with using public topological and geometrical information of an actual national power grid in Korea. Performance evaluation results show that the proposed PDFT algorithm can find towers that need to activate their WAN communication modules for guaranteeing diverse QoS requirements in real-time, as compared to an optimal-solution algorithm without significant difference in total number of activated WAN communication modules.

Air pollution poses significant risk to environment and health. Air quality monitoring stations are often confined to a small number of locations due to the high cost of the monitoring equipment. They provide a low fidelity picture of the air quality in the city; local variations are overlooked. However, recent developments in low-cost sensor technology and wireless communication systems like Internet of Things (IoT) provide an opportunity to use arrayed sensor networks to measure air pollution, in real time, at a large number of locations. This article reports the development of a novel low-cost sensor node that utilizes cost-effective electrochemical sensors to measure carbon monoxide (CO) and nitrogen dioxide (NO2) concentrations and an infrared sensor to measure particulate matter (PM) levels. The node can be powered by either solar-recharged battery or mains supply. It is capable of long-range, low power communication over public or private long-range wide area network (LoRaWAN) IoT network and short-range high data rate communication over Wi-Fi. The developed sensor nodes were co-located with an accurate reference CO sensor for field calibration. The low-cost sensors’ data, with offset and gain calibration, show good correlation with the data collected from the reference sensor. Multiple linear regression (MLR)-based temperature and humidity correction results in mean absolute percentage error (MAPE) of 48.71% and $R^{2}$ of 0.607 relative to the reference sensor’s data. Artificial neural network (ANN)-based calibration shows the potential for significant further improvement with MAPE of 38.89% and $R^{2}$ of 0.78 for leave-one-out cross-validation.

LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This article presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of Things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, this article takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoT-based applications are also presented, and this article closes with a few research challenges and future research directions in this regard. This article aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications.

The Internet of Things (IoT) is a network of devices for communicating machine to machine (M2M) based on wired and wireless Internet. IoT in agriculture is a revolutionary technology that can be applied to agricultural production year-round. The aim of this study is to summarize cases of IoT being applied to agricultural automation in the agricultural sector and to discuss the limitations and prospects for expanding the application of IoT technology in Korea. The application of IoT in agriculture was classified and analyzed based on previous data, and the sensors and communication technologies used were compared. Based on the analysis results, the limitations of and prospects for IoT in agriculture were discussed. IoT was widely used in agriculture, such as management systems, monitoring systems, control systems, and unmanned machinery. In addition, the various wireless communication technologies used in agriculture, such as Wi-Fi, long-range wide area network (LoRaWAN), mobile communication (e.g., 2G, 3G, and 4G), ZigBee, and Bluetooth, were also used in IoT-based agriculture. With the development of various communication technologies, such as 5G, it is expected that faster and broader IoT technologies will be applied to various agricultural processes in the future. IoT-based agriculture equipped with a communication system suitable for each agricultural environment can contribute to agricultural automation by increasing crop quality and production and reducing labor.

The Internet of Things (IoT) denotes a concept of connected devices that communicate seamlessly over the Internet. The wireless connectivity between IoT nodes and the Internet gateways can be realized in unlicensed frequency bands upon the low-power wide-area network (LPWAN). It is foreseen that due to a massive introduction of IoT devices the unlicensed bands will experience an increased interference level which will negatively affect the communication performance. Therefore, adaptive solutions should be considered to obtain robustness. This letter presents a time-modulated antenna array (TMAA) for LPWAN receivers and demonstrates its interoperability with a commercial of-the-shelf LoRa device. According to the conducted experimental investigation, whereby the beam-steering is obtained over the sidebands inherently generated by the TMAA, both the received signal strength and the signal-to-noise ratio (SNR) are improved by means of the progressive delay applied to the time modulation functions.

Mobile Augmented Reality (MAR) applications usually contain computation-intensive tasks which far outstrip the capability of mobile devices. One way to overcome this is offloading computation-intensive MAR tasks to remote clouds. However, the wide area network delay is hard to reduce. Thanks to edge computing, we can offload MAR tasks to nearby servers. Prior studies focus on either single-task MAR applications offloading or dependent tasks offloading for a single user. In this article, we study the offloading decision of MAR applications from multiple users, each of which is comprised of a chain of dependent tasks, over a generic cloud-edge system consisting of a group of heterogeneous edge servers and remote clouds. We formulate the Multi-user Multi-task MAR Application Scheduling (M3AS) problem, which is NP-hard. We present Mutas, an efficient scheduling algorithm that jointly optimizes server assignment and resource management. We also consider the online version of M3AS and present OnMutas. Extensive evaluations demonstrate that both Mutas and OnMutas can significantly reduce the service delays of MAR applications when compared to three other heuristics.

Providing ACID transactions under conflicts across globally distributed data is the Everest of transaction processing protocols. Transaction processing in this scenario is particularly costly due to the high latency of cross-continent network links, which inflates concurrency control and data replication overheads. To mitigate the problem, we introduce Ocean Vista—a novel distributed protocol that guarantees strict serializability. We observe that concurrency control and replication address different aspects of resolving the visibility of transactions, and we address both concerns using a multi-version protocol that tracks visibility using version watermarks and arrives at correct visibility decisions using efficient gossip. Gossiping the watermarks enables asynchronous transaction processing and acknowledging transaction visibility in batches in the concurrency control and replication protocols, which improves efficiency under high cross-data center network delays. In particular, Ocean Vista can access conflicting transactions in parallel and supports efficient write-quorum/read-one access using one round trip in the common case. We demonstrate experimentally in a multi-data center cloud environment that our design outperforms a leading distributed transaction processing engine (TAPIR) more than tenfold in terms of peak throughput, albeit at the cost of additional latency for gossip and a more restricted transaction model. The latency penalty is generally bounded by one wide area network (WAN) round trip time (RTT), and in the best case (i.e., under light load) our system nearly breaks even with TAPIR by committing transactions in around one WAN RTT.

This paper discusses how to address and overcome some of the problems related to smart data generation and visualization, such as the poor autonomy of wireless sensor devices and the flexibility of the data management platform. We described the implementation and field experiment of a modular IoT application for Smart-Farming, in which the sensor devices are powered by an on-board battery and the data management system is based on a highly flexible software stack, capable of displaying time series graphs and processing millions of data per second. The experiment shown that the power consumption of the sensor devices depends on many factors and that the lifecycle of the devices can reach years using ultra-low power processors, low power wide area network (LPWAN) such as LoRaWAN and a mix of energy saving techniques. The data management and visualization platform shown to be able to display many types of time-series graphs, deal with a wide variety of data-sources and effectively manage a large amount of data.

Software-defined networking (SDN) has evolved as an approach that allows network administrators to program and initialize, control, change and manage networking components (mostly at L2-L3 layers) of the OSI model. SDN is designed to address the programmability shortcomings of traditional networking architectures commonly used in cloud datacenters (CDC). Deployment of SDN solutions have demonstrated significant improvements in areas such as flow optimization and bandwidth allocation in a CDC. However, the benefits are significantly less explored when considering Software-Defined Wide Area Networks (SD-WAN) architectures in the context of delivering solutions by networking multiple CDCs. To support the testing and bench-marking of data-driven applications that rely on data ingestion and processing (e.g., Smart Energy Cloud, Content Delivery Networks) across multiple cloud datacenters, this paper presents the simulator, IoTSim-SDWAN. To the best of our knowledge, IoTSim-SDWAN is the first simulator that facilitates the modeling, simulating, and evaluating of new algorithms, policies, and designs in the context of SD-WAN ecosystems and SDN-enabled multiple cloud datacenters. Finally, IoTSim-SDWAN simulator is evaluated for network performance and energy to illustrate the difference between classical WAN and SD-WAN environments. The obtained results show that SD-WAN surpasses the classical WAN in terms of accelerating traffic flows and reducing power consumption. Refereed/Peer-reviewed

The ability of low-power wide area network (LPWANs) to provide low-cost wireless access to geographically spread-out devices while at the same time catering to long battery life running for several years continues to meet a vital need in the IoT/smart cities market. LoRaWAN shot to prominence as off-the-shelf availability of equipment democratized the technology for private and public use. Its reliance on the unlicensed spectrum that is shared by other competing technologies limits performance and scalability. Operation in the licensed spectrum is a promising direction. However, the techno-economic aspects surrounding this is an open question addressed for the very first time in this article. We show that even with as small a chunk as 1 MHz, significant boost in performance is possible as networks scale. We examine the impact of different parameters such as network scale, subscription models, and subscriber mix for different tariffs (based on publicly available cellular IoT pricing plans) on the economic feasibility and highlight the implications this has on spectrum pricing. This broadens the appeal of this article to cellular and non-cellular network operators of IoT/smart city applications and spectrum regulators who are keen to re-farm/allocate spectrum for different IoT applications.

WSN can be a tech in-full development with top-notch, outstanding foreseeable future. The utilization of wireless sensor network (WSN) to get a good quality observation is consists using some range of detector nodes having a network capacity which might be set up for productive effects or to get constant monitoring function. Water is critical within our own lives because of our wellbeing insurance and in different cases such as farming, marketplace, and for the animal’s presence. Diseases linked to inadequate sanitation and water states have over 200 million cases reported causing 5–10 million deaths throughout the world. Water tracking procedures are indispensable to restrain exactly the challenge is gently linked to inadequate household allocation, wasteful usage and too little decent. Wireless sensor networks also have gained fame over the study area only because they supply a promising infrastructure for both multiple monitoring and control software. For fixing the dilemma, various drinking water caliber processes were grown previously in the last ten years. The parameters included with the drinking water quality tracking would be the PH degree, turbidity, dissolved oxygen and temperature has been quantified at the true period from the detectors that ship the info into the bottom channel or control/monitoring space. This document suggests the way the tracking platform might be set up highlighting the elements of low-cost, simple setup, and effortless maintenance and handling. Using this radio platform for tracking purpose is not only going to lower the total observation procedure expenditure in the duration of centers installation and labor price tag but can even offer versatility in the duration of location or distance. In this paper, the essential layout and execution of WSN comprising Lora (wide area network) established technological innovation is suggested. The developed system is cheap and makes it possible for customization. A few preliminary links between dimension to rate the efficiency and efficiency of the machine can also be shown.

The LoRaWAN technology, an example of low power wide area network, is considered a possible IoT-derived solution for realizing private cellular (single-hop) communications in the industrial domain. However, despite LoRaWAN promises long range and dense environments with many obstacles (as the industrial ones), it may suffer from coverage issue. Additionally, the inverse relationship between data rates and range may be unacceptable for many industrial applications. In this article, an innovative LoRaWAN range extender (based on an enhanced LoRaWAN node) is described, and its integration in the infrastructure of industrial Internet of Things (IIoT)-enabled industrial wireless networks is presented. A frame relay strategy is suggested, thus avoiding to tradeoff the highest data rates against an increased sensitivity. The feasibility of the solution is formally verified and the features of the realized proof-of-concept prototype, based on commercially available hardware, are discussed. The experimental results, obtained with a purposely designed test bench, show the effectiveness of the proposed approach. In particular, the range extender is transparent for legacy LoRaWAN networks as confirmed by correct relaying of uplink and downlink for different types of LoRaWAN message.

Cloud computing, despite its inherent advantages (e.g., resource efficiency), still faces several challenges. The wide area network used to connect the cloud to end users could cause high latency, which may not be tolerable for some applications, especially Internet-of-Things (IoT) applications. Fog computing can reduce this latency by extending the traditional cloud architecture to the edge of the network and by enabling the deployment of some application components on fog nodes. Application providers use Platform-as-a-Service (PaaS) to provision (i.e., develop, deploy, manage, and orchestrate) applications in cloud. However, existing PaaS solutions (including IoT PaaS) usually focus on cloud and do not enable provisioning of applications with components spanning cloud and fog. Provisioning such applications requires novel functions, such as application graph generation, that are absent from existing PaaS. Furthermore, several functions offered by existing PaaS (e.g., publication/discovery) need to be significantly extended in order to fit in a hybrid cloud/fog environment. In this article, we propose a novel architecture for PaaS for hybrid cloud/fog system. It is IoT use case driven, and its applications’ components are implemented as virtual network functions (VNFs) with execution sequences modeled as graphs with substructures, such as selection and loops. It automates the provisioning of applications with components spanning cloud and fog. In addition, it enables the discovery of existing cloud and fog nodes and generates application graphs. A proof of concept is built based on Cloudify open source. Feasibility is demonstrated by evaluating its performance when the PaaS modules and application components are placed in clouds and fogs in different geographical locations.

Advanced persistent threat, network intrusions and anomalies, and invasions of location and data privacy are constantly violating edge networks, disturbing guaranteed services, and further destroying critical end devices and cyberinfrastructure. Edge gateway defense against malicious cyber threats and privacy leakage for a multitude of connected physical devices remains a significant problem. Edge computing empowered network intelligence will become a practical solution to tackle this challenge by building gateway-focused protection mechanisms, dynamically extending the edge network, and establishing a flexible and intelligent platform to perform rapid responses with the cloud and wide area network. In this article, we present the research in edge intelligence assisted gateway defense in cyber security. Specifically, we first provide the overview of edge computing, and then discuss the motivations of developing and deploying a robust and effective edge intelligence. We also present the details of edge intelligence assisted gateway defense, including major cyber threats and defense mechanisms at gateways. We further offer detailed descriptions of the emerging data-driven approaches for gateway defense. Finally, we perform an experimental study, and we demonstrate that robust edge intelligence achieves high detection accuracy and effective computational performance.

In this letter, a metal-frame mobile antenna for Long-Term Evolution (LTE)/wireless wide area network (WWAN) smartphone application using a novel matching network is proposed. The proposed antenna consists of two parts, a typical inverted-F antenna for the low band, and an asymmetric T-slot for the high band. By means of multibranch technology based on matching circuit, the proposed design occupies a small part of the space surrounding the ground with only 1 mm ground clearance, which makes it a very competitive solution for full-screen phones. Finally, it completes the LTE 824–960 MHz and 1710–2690 MHz. The designed procedure and working mechanism of the antenna are discussed in detail. A prototype of the proposed antenna is fabricated and measured, and both simulated and experimental results are presented and discussed.

LoRa, a Low Power Wide Area Network (LPWAN) technology, demands reduced network infrastructure to cover a large area, with low power consumption. In the context of vehicular communications, LoRa has the potential to support monitoring and cooperative navigation applications, where small amounts of data can be transmitted asynchronously through the network. Nevertheless, the literature lacks evaluations of LoRa in the context of vehicular communications. Thus, in this paper we analyze the performance of this technology operating in an urban mobility environment, using LoRa terminal devices embarked on vehicles and a LoRa receiver unit, acting as infrastructure. Moreover, we evaluate the equivalence between experimental and simulated results, obtained from the communication link between the LoRa module inside a vehicle and a LoRa receiver comparing to those produced by NS-3 simulations. Three metrics of interest are evaluated: Packet Delivery Ratio (PDR), Packet Inter-Reception (PIR) time, and Received Signal Strength Indicator (RSSI). Field experiments are performed at the campus of the Federal University of Rio de Janeiro (UFRJ), Brazil. Results revealed that all the metrics evaluated in the simulated experiments are consistent with the results of the real experiments, however, we concluded that the model can be improved by looking for a stronger correlation with real experiments.

The rapid advancement of network, communication and Internet technology resulted with always-on, always-connected, device-independent and remote online working, business, education and entertainment environment. Consequently, users are searching for solutions and technologies that enable fast and reliable wide area network connection and the typical solution is through using personal computers connected with ethernet cable to network equipment and infrastructure that supports gigabit ethernet connection. Besides the complex network infrastructure that can influence performance, the bottleneck can also be caused by insufficient hardware, operating system and software resources on clients’ machines. Therefore, in this paper a networking performance evaluation of three globally most common and most used versions of Windows operating systems; namely Windows 7TM, Windows 8.1TM and Windows 10TM, on two identical computer systems, is conducted. Networking performance measurements are performed with three different benchmarks: namely iPerf, D-ITG and NetStress. Performance evaluation results showed that a newer versions of an operating system bring certain networking performance improvements but by sacrificing other performances.

Internet of Things (IoT) is a very promising technology in forest engineering, especially for the environment and plant growth monitoring. LoRa Wide Area Network (LoRaWAN) is a prevailing choice for the Forestry IoT owing to its low-power and long-range ability. Real-world deployment and network optimization require accurate path-loss modeling, so the LoRaWAN radio channel in the forest is needed to be intensively studied. However, most of the subsistent propagation models do not involve specific forestry environmental parameters. In this paper, two parameters related with the trees are considered: the leaf area index and the tree trunk diameter. Due to the time-changing characteristics of these two items (from spring to winter), an empirical model has been developed through extensive measurement campaigns: Firstly, the channel measurement platform is designed based on a real scene of mixed forest. Secondly, the fading characteristics of the channel transmission for LoRa nodes are tested, and the corresponding model is presented and evaluated. Lastly, an energy harvesting LoRaWAN is deployed and operated in a sampled forest region of Eastern China for environment monitoring based on our propagation model. The results show that 433 MHz LoRa path loss in the mingled forest could be precisely predicted by our proposed model. Moreover, network coverage and energy consumption optimization of the LoRa nodes could be performed, which enables the perpetual development of reliable forestry evolution monitoring system.

The distributed control plane is the alternate solution to reliability and scalability like potential issues in the software-defined wide area network (SDWAN), but the static mapping between controller and switches might cause an uneven load distribution among controllers. Migration of switches from one controller to another under-loaded controller is a solution to handle the peak traffic. However, the switch migration process is a complex process that may affect the end-user quality of service (QoS), as a result, the response time of the control messages are also affected. To address this issue, the authors present RTSM, a novel strategy, that optimises the response time of the control messages during switch migration. Further, an SDWAN architecture was proposed, in which the introduction of the layer-2 controller reduces the dependency on WAN communication of the forwarding devices. The Karush–Kuhn–Tucker conditions have been applied for target controller selection, which ensures improved response time. A switch selection method was also introduced, which minimally affects the end-user QoS during device migration in SDWAN scenario. To evaluate the performance of the RTSM, Mininet simulator with the Floodlight controller has been used, and the result shows that the proposed algorithm outperforms the other existing works during load balancing.

The Internet of Things (IoT) undergoes a rapid transformation this last decade, thanks to the appearance of low-power wide area network technologies, such as LoRa/LoRaWAN, SigFox, and narrowband IoT, which allow reducing the deployment cost of sensors and other IoT devices. Many emerging services such as smart city, Industry 4.0, and autonomous driving are based on IoT devices and applications to collect and analyze data and control end devices (i.e., actuators). Among these services, several IoT applications, such as data analytics, need to be deployed at the edge to either reduce the latency to access data or treat the high amount of generated data locally. However, in the context of LoRa/LoRaWAN, most of the current IoT service deployments run the applications at a central cloud to ease the integration with existing software as a service (SaaS) platforms, without exploiting the benefits of edge computing. In this article, we propose a new framework that leverages the ETSI multi-access edge computing (MEC) model to deploy LoRabased IoT applications at the edge. In particular, the proposed model takes advantage of the ETSI MEC features, such as dynamic deployment of an IoT application at the edge and application life cycle management. In addition, the proposed framework allows running an IoT application as a 5G network slice at the edge.

IEEE Software Defined Networks (SDN) has brought a new form of network architecture that simplifies network management through innovations and programmability. But, the distributed control plane of SD-Wide Area Network is challenged by load imbalance problem due to the dynamic change of the traffic pattern. The packet_in messages are one of the major contributors of the control’s load. When such packet rate exceeds a certain threshold limit, the response time for control request increases non-linearly. In order to achieve better end-user experience, most of the previous works considered the optimal switch to controller association with an objective to minimize the response time on LAN environment but ignores the consequence of large scale network. In this regard, the proposed work realizes the necessity of layer-2 and layer-3 controller in LAN and WAN environment separately. A load prediction based alertness approach has been introduced to reduce the burden of the controllers. This approach may create an additional delay for the initial packets of the flow entry that lead to more prediction error. However, the proposed method reduces the error by selecting an optimal timeout value of the flow. Further, minimization of the response time between router to the controller has been taken care of. An extensive simulation shows the efficacy of the proposed scheme.

A low power wide area network (LPWAN) is becoming a popular technology since more and more industrial Internet of things (IoT) applications rely on it. It is able to provide long distance wireless communication with great power saving. Given the fact that an LPWAN covers a wide area where all end nodes communicate directly to a few gateways, a large number of devices have to share the gateway. In this situation, chances are many collisions could occur, leading to waste of limited wireless resources. However, many factors affecting the number of collisions that cannot be solved by traditional time series analysis algorithms. Therefore, deep learning methods can be applied here to predict collisions by analyzing these factors in an LPWAN system. In this paper, we propose long short-term memory extended Kalman filter (LSTMEKF) model for collision prediction in the LPWAN in terms of the temporal correlation which can improve the LSTM performance. The efficacies of our models are demonstrated on the data set simulated by LoRaSim.

The massive increase in the Internet of Things (IoT) has brought a diverse long-range, low-power, and low bit-rate wireless network technologies. The LoRa a low-power wide area network (LPWAN) gained popularity as radio technology for the realization of many IoT applications. LoRa is typically employed together with LoRaWAN MAC protocol and operates in the license-free ISM bands. LoRa networks have an issue with scalability when the number of end nodes connected to one network is larger than the shared number of channels, that causes a collision and packets loss through receiving a wide range of different message sizes from various application. In this paper, we describe an accurate and efficient way confirmed by simulation to calculate the probability of collision rate and packet loss in LPWANs under various circumstances. Moreover, based on the LoRaWAN specification, we consider a dense network deployment of IoT devices. In the event of collisions, our proposed algorithms is classified to two approaches. Firstly a time scheduling algorithm is proposed for LoRaWAN networks that consist of devices supporting LoRaWAN class C mode for synchronization in the beginning between the gateway and the end nodes. Afterwards these devices switch to class A to significantly decrease the collision and to enhance scalability by assigning a Guard Time to each end node. Gateway acknowledgment (ACK) messages to the end nodes are used through class C. Secondly, we also propose a distance spreading factor algorithm according to the distance of the end nodes from the gateway to reduce the probability of collision. Furthermore, many of these devices are battery powered, therefore low power consumption is required.

Due to the development of the Internet and the smart end systems such as smartphones, portable laptop, and other smart mobile devices, as well as the emergence of concepts such as cloud computing, social networks, and Internet of Things, 4G and 5G have changed network requirements. A software-defined network (SDN) is a new architecture to support new network requirements. This architecture is composed of three layers, data, control, and application plane. A lot of papers have been published in well-known journals to improve network performance, so we classify the solutions for performance improvement based on three SDN layers and three SDN-based spheres which are (a) SDN-data center, (b) wireless networks, and (c) software-defined wide area network. This paper surveys the articles which have been published by Springer, IEEE, Elsevier, and ACM. We extract the performance improvement solutions and resources which are required for performance control and the metrics to evaluate the network performance. This article can help network enthusiasts to better understand, investigate, and improve the performance of SDN-based networks.

We conducted a qualitative study to get a preliminary understanding of indigents needs. The results helped us identify system requirements that led to the design of the HELPi system, a digital bracelet to enable local agencies to provide support to indigents who used roam the city. The paper aims to present the studies conducted to inform the design of our technological approach to addressing challenges related to acceptance, coverage of users, and battery lifespan. We built a prototype using LoRa™, which is a Low Power Wide Area Network (LPWAN) technology that offers several features that make it suitable for our application domain; e.g., it enables Machine-to-Machine (M2M) communications. Thus, we have taken advantage of this feature and propose a communication protocol to allow a dynamic coverage of users. We conducted assessments to determine users’ perception about its usefulness, and to measure the performance of the LoRa’s signal propagation and the battery energy consumption for the deployment of the HELPi system. According to our results, the proposed communication protocol should work with a LoRa™ Gateway in the outside setting condition and with an SNR threshold higher than − 2 dB to guarantee the successful reception of messages transmitted.

The long-range wide area network (LoRaWAN) has recently emerged as one of the most potential technologies for the mobile Internet of Things (IoT) applications. In a LoRaWAN, the network-managed adaptive data rate (ADR) method is responsible for managing resource allocation (i.e., spreading factor (SF) and transmit power (TP)) to end-devices (EDs) through ADR commands. When an ED is mobile and receives a new configuration (i.e., SF and TP) from the network server, the propagation scenario could have been dramatically changed. Hence, the SF and the link budget will no longer be valid, which results in packet loss and massive retransmissions. Therefore, we propose a mobility-aware resource (SF) assignment scheme (M-ASFA), which aims to allocate the best SF to IoT-enabled mobile EDs at each uplink transmission by considering the strength of the signals that a gateway receives from the EDs. The simulation results demonstrate that, in our proposed M-ASFA solution, the SF is assigned to mobile EDs by proactively responding to the mobility of the EDs. This proactive behavior of the proposed scheme enhances the packet success ratio by significantly reducing the impact of interference, retransmissions, and packet loss when compared with the LoRaWAN-based ADR.

Many fire situations have represented the loss of lives and material costs due to the lack of early fire detection through smoke or gas sensing, which can become complex and critical. Meanwhile, engineers worldwide develop and test multiple systems for smoke and gas detection, commonly based on sensor networks, digital image processing, or computer vision. Furthermore, the detection system must work thoroughly with alarms and warnings that aware of a risk situation for prompt evacuation of the population in the surroundings based on a reliable data network topology with adequate device deployments that will let us know the moment a fire outbreak. This paper presents a low-cost Internet of Things (IoT) prototype for fire detection in outdoor environments based on sensors and Low Power Wide Area Network (LPWAN), focused on the accuracy in the temperature and gas measurement at the moment a fire starts. For its achievement, we integrated wireless components, development boards, and electronic devices, following the management of information updates through a database schema for the alarm settings based on the data gathered from the sensors.

Rapid data streams are generated continuously from diverse sources including users, devices, and sensors located around the globe. This results in the need for efficient geo-distributed streaming analytics to extract timely information. A typical geo-distributed analytics service uses a hub-and-spoke model, comprising multiple edges connected by a wide-area-network (WAN) to a central data warehouse. In this paper, we focus on the widely used primitive of windowed grouped aggregation , and examine the question of how much computation should be performed at the edges versus the center . We develop algorithms to optimize two key metrics: WAN traffic and staleness (delay in getting results). We present a family of optimal offline algorithms that jointly minimize these metrics, and we use these to guide our design of practical online algorithms based on the insight that windowed grouped aggregation can be modeled as a caching problem where the cache size varies over time. We evaluate our algorithms through an implementation in Apache Storm deployed on PlanetLab. Using workloads derived from anonymized traces of a popular analytics service from a large commercial CDN, our experiments show that our online algorithms achieve near-optimal traffic and staleness for a variety of system configurations, stream arrival rates, and queries.