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International audience; This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.

International audience; Wireless sensor networks (WSNs) have become pervasive and are used for a plethora of applications and services. They are usually deployed with specific applications and services; thereby precluding their re-use when other applications and services are contemplated. This can inevitably lead to the proliferation of redundant WSN deployments. Virtualization is a technology that can aid in tackling this issue. It enables the sharing of resources/infrastructures by multiple independent entities. This position paper proposes a novel multi-layer architecture for WSN virtualization and identifies the research challenges. Related work is also discussed. We illustrate the potential of the architecture by applying it to a scenario in which WSNs are shared for fire monitoring.

The development of Wireless Sensor Networks (WSNs) has been attained in the past few years due to its important using in wide range of application. The readings of data derived from WSN nodes are not always accurate and may contain abnormal data. This paper proposed an anomaly detection and classification algorithm in WSNs. At first, an integration of Contourlet Transform (CT) algorithm and One Class Support Vector Machine (OCSVM) algorithm (CT-OCSVM) is utilized to detect outliers then Fuzzy Inference System (FIS) is used to identify the source of these outliers. The underlying aim of this paper focuses on treating the collected streams of data as raw datum of an image, which is then passed through some filters using CT to get compressed size of directional subbands coefficients. The coefficients of CT are examined by OCSVM algorithm to detect anomalies. Finally the source of anomalies is identified based on using FIS and by exploiting the spatial temporal correlation existing between the sensed data. The integrated algorithm is tested using different types of filters. Real datasets collected from a small WSN constructed in a local lab are used for testing the integrated algorithms. The simulation results have shown a high rate of accurate classification with high detection rate and low false alarm rate.

Fine dust refers to harmful substances floating in the air. It is divided into PM 2.5 and PM 10, and has the characteristic that the particles are small enough to be invisible to the naked eye. When fine dust enters a room, it can enter the human body through the bronchi and cause lung or respiratory diseases. To solve the health problems caused by fine dust, research and development about various air purification systems are progressing. In this paper, we introduce a Wireless Sensor Networks (WSNs)-based Internet of Things (IoT) air purification system. This WSNs-based IoT air purification system refers to a system in which an IoT air purifier and a window are automatically controlled based on fine dust values detected by sensor nodes. Therefore, because it is important to maintain the integrity of the fine dust values, SSL/TLS, an encryption protocol, is applied to this system. However, the existing SSL/TLS has a problem in which, if an attacker attempts a false data injection attack, the symmetric key itself used to encrypt and decrypt the data is stolen, so it cannot be detected. To solve this problem, in this paper we propose a Discrete Event System Specification (DEVS) model based on Data Calibration that verifies whether the fine dust values detected by sensor nodes and an IoT air purifier is within a preset error range. If the fine dust value is not within the preset error range, it is detected as false data, filtered, and not stored in the database. Because this proposed scheme verifies the integrity of the fine dust values, it not only raises the accuracy of collected sensing data, but also prevents abnormal operation of an IoT air purifier and a window in advance. Therefore, the security of the WSNs-based IoT air purification system is improved.

Sensor node location information is critical in many Wireless Sensor Network (WSN) applications with random sensor node deployment. In such applications, node localization using a faster area exploration mechanism is needed to initiate precise sensing and communication. In this paper, faster area exploration approach for mobility-assisted localization scheme is proposed for mission-critical WSN applications. Each Mobile Anchor (MA) node embedded with a localization module moves in the sensor field in a coordinated manner while localizing the deployed static nodes. The proposed area exploration scheme is implemented using a hybrid of max-gain approach and cost-utility based frontier (HMF) approach. The paper addresses the issue of sparse anchor node condition during the localization process. The simulation results obtained using Cooja simulator shows that the proposed mobility-assisted localization scheme results in accurate localization with minimum delay. The proof-of-concept of the proposed scheme is demonstrated using Berkeley static nodes and custom designed MA nodes.

Network monitoring has been traditionally conducted using Simple Network Management Protocol (SNMP): a network monitoring protocol that allows network administrators to keep track of every node in the network and ensure that it behaves correctly. This paper presents the Constrained Monitored Protocol (CoMP): a lightweight resource-efficient alternative to SNMP that targets the low-end devices of the Internet of Things (IoT). These devices are characterised by severe resource constraints in terms of memory, processing power, and bandwidth. Moreover, they are often energy-constrained as well, powered either by small batteries or energy harvesting. While SNMP does work with these devices, it has an unnecessary overhead resulting in a waste of resources that could otherwise be used for some other task, or to save energy. Furthermore, this paper proposes a cross-protocol CoMP-SNMP proxy that operates at the border router of the resource-constrained network and enables the efficient monitoring of resource-constrained IoT devices using CoMP from existing SNMP-based network monitoring infrastructures.

This paper considers the novel problem of deriving a Time Division Multiple Access (TDMA) link schedule for rechargeable wireless sensor networks (rWSNs). Unlike past works, it considers: (i) the energy harvesting time of nodes, (ii) a battery cycle constraint that is used to overcome so called memory effects , and (iii) battery imperfections, i.e., leakage. This paper shows analytically that the battery cycle constraint and leaking batteries lead to unscheduled links. Further, it presents a greedy heuristic that schedules links according to when their corresponding nodes have sufficient energy. Our simulations show that enforcing the battery cycle constraint increases the link schedule by up to 1.71 (0.31) times for nodes equipped with a leaking (leak-free) battery. When nodes have a leaking battery, the derived schedules are on average 1.05 times longer than the case where nodes have a leak-free battery. Finally, the battery cycle constraint reduces the number of charge/discharge cycles by up to 47.41% (45.67)% when nodes have a leak (leak-free) battery. Between leak-free and leak battery scenarios, using the former produces up to 51.46% fewer cycles than the latter.

Green communications is a challenging issue for communications and networking. In order to save energy, nodes of the wireless sensor networks (WSNs) usually adopt the low duty cycle mode which brings a large delay to the event detection and data transmission to the sink and deteriorates the network’s timely processing of the event. In this paper, bi-adjusting duty cycle schedule (BADCS) scheme is proposed to reduce event detection latency as well as data routing delay for low duty cycle wireless sensor networks. BADCS scheme consists mainly of two duty cycle adjustment algorithms: (a) active slot asynchronous adjustment algorithm for nodes in the same sensing area; (b) the continuous adjustment algorithm for two adjacent nodes on the routing path with one active slot interval. Specifically, the operations are 2-fold. First, perform asynchronous operations on the active slots of the nodes in the same sensing area, so that the active slots of the nodes in the same sensing area are distributed as evenly as possible without overlapping. In this way, it is possible to reduce the latency by the time the event is perceived after its occurrence. Secondly, active slots of the nodes on the routing path are adjusted to be with pipeline style, so that when the nodes receive the data packet, they can route through the continuous active slots, thus greatly reducing the delay of data routing. Two adjustment algorithms of active slot are given in detail in this paper. The performance of BADCS mode is discussed in detail, and its performance is better than the previous strategy. Comprehensive experiments are conducted, and the results demonstrate that the proposed BADCS scheme significantly improves event detection performance in terms of detection latency, detection probability, and routing delay. Detection delay and routing delay are reduced as high as 3.91% and 56.22% respectively.

As the development of modern science and technology, LBS and location-aware computing are increasingly important in the practical applications. Currently, GPS positioning system is a mature positioning technology used widely, but signals are easily absorbed, reflected by buildings, and attenuate seriously. In such situation, GPS positioning is not suitable for using in the indoor environment. Wireless sensor networks, such as ZigBee technology, can provide RSSI (received signal strength indicator) which can be used for positioning, especially indoor positioning, and therefore for location based services (LBS).The authors are focused on the fingerprint database method which is suitable for calculating the coordinates of a pedestrian location. This positioning method can use the signal strength indication between the reference nodes and positioning nodes, and design algorithms for positioning. In the wireless sensor networks, according to whether measuring the distance between the nodes in the positioning process, the positioning modes are divided into two categories which are range-based and range-free positioning modes. This paper describes newly improved indoor positioning method based on RSSI fingerprint database, which is range-free. Presented fingerprint database positioning can provide more accurate positioning results, and the accuracy of establishing fingerprint database will affect the accuracy of indoor positioning. In this paper, we propose a new method about the average threshold and the effective data domain filtering method to optimize the fingerprint database of ZigBee technology. Indoor experiment, which was conducted at the University of Warmia and Mazury, proved that the distance achieved by this system has been extended over 30 meters without decreasing the positioning accuracy. The weighted nearest algorithm was chosen and used to calculate user’s location, and then the results were compared and analyzed. As a result, the positioning accuracy was improved and error did not exceed 0.69 m. Therefore, such system can be easily applied in a bigger space inside the buildings, underground mines or in the other location based services.

In wireless sensor networks (WSNs), target coverage is an important issue which aims at finding a set of sensors to monitor the targets for maximizing both the surveillance quality and network lifetime. However, most of them assumed that each sensor is battery powered and the Boolean Sensing Model (BSM) is applied. Sensors powered by battery have a limited lifetime while the BSM is difficult to reflect the physical features of sensing. This paper proposes target coverage mechanisms, called C-MMQT and D-MMQT , which consider the solar-powered sensors and allows the battery to be recharged for maintaining the perpetual lifetime of sensor networks. The proposed mechanisms apply the Probabilistic Sensing Model (PSM) and consider that different targets have different importance. Two challenges have been overcome in this paper. First, each sensor is well scheduled for switching between recharging and working states for maintaining its perpetual lifetime since its battery is solar powered. Second, the sensors that are able to monitor the common target are well scheduled for cooperative sensing to maximize the surveillance quality since PSM is applied. Two efficient sensor schedules are proposed to maximize the surveillance quality of the bottleneck target which has the lowest surveillance quality. Performance study shows that the proposed mechanisms outperform the existing mechanisms in terms of Quality of Monitoring, Average utility, Fairness and Efficiency index.

Due to the flexibility and mobility, unmanned aerial vehicle (UAV) can work as a movable sink to receive the data collected by sensors in wireless sensor networks (WSNs). This paper analyzes the capacity of UAV assisted data collection in WSNs, which provides a guideline for the parameters optimization of data collection in the presence of UAVs. In this paper, the service area of UAVs covers the area where sensors are distributed. The charging points for UAVs are placed around the service area, which provides energy supply for UAVs. The charging point is the starting and ending point of a UAV's trajectory. The service area is partitioned into multiple service cells. UAVs traverse these service cells to receive the data collected by the sensors in the service cells. The per-node capacity and average execution time of UAVs are used as two metrics to measure the performance of data collection in WSN. The upper and lower bounds of per-node capacity are derived respectively. It is discovered that the number of UAVs, the number of service cells and the trajectories of UAVs affect the per-node capacity of WSN. The per-node capacity can be optimized by adjusting the numbers of UAVs and service cells. Two path planning algorithms of UAVs are designed. With path planning, the per-node capacity is optimized to be closer to the upper bound, which achieves highly efficient data collection. The simulation results verify the correctness of the derived results.

High-density seismic array method has attracted enormous interest in geophysical methods, and it is an advanced seismic observation technology which is of great significance in oil and gas resource detection and underground space exploration. However, designing a portable sensor node that prolongs the lifetime and equips wireless monitoring system to improve construction efficiency has been still a challenge for the high-density seismic array method. In this paper, a portable energy-efficient wireless sensor node and a hybrid communication system that content themselves with complex terrain, large-scale and reliable transmission are proposed to tackle the restrictions of high-density seismic survey. A Wi-Fi communication system based on the star network structure at the core network layer and a ZigBee communication system based on wireless multi-hop structure in the extended network layer form the hybrid communication system. In order to balance the energy of nodes in the extended network and prolong the network lifetime, this paper proposes a multi-hop variable weight routing method (MVWRM). The method selects the cluster-head node and the multi-hop route by changing the weight of the residual energy of the node and the weight of the distance at different periods. Then, a series of simulations and experiments are carried out for verification. The experiments show that the proposed sensor node suggest good performance in equivalent noise level (0.8μV@500Hz, PGA = 1), total harmonic distortion (124dB). Simulation results and performance analysis validate that the effectiveness of the proposed MVWRM can extend about 10% of the node lifetime.

Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACO-based transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement.

Wireless Sensor Networks (WSNs) is the base for many critical applications where a large number of nodes are deployed in the monitored field. Those nodes suffer from limited processing capabilities as well as energy. They are also required to live for a long time; sensors send their data to the sink node through multi-hop routing. The sink node is responsible for making the appropriate decision based on the received data. This shows how critical the information fusion process at the sink node. Such fusion is affected by the quality of the collected information from the deployed sensors. In addition, information handling in the WSN is also affected by environmental conditions, sensors energy, and data reliability, which play important roles in the eminence of information fusion. This paper introduces an efficient environment-aware fusion-based reliable routing algorithm named E3AF. The algorithm takes different parameters into consideration, including environmental metrics, energy consumption balance among sensor nodes, and network and data reliability. The algorithm models the environment to allow the routing algorithm to avoid going through areas marked dangerous. The paper formulates the problem into an optimization problem, which is structured as an Integer Linear programming (ILP) to help grasp and obtain the optimum solution. Furthermore, the paper presents swarm intelligence as a heuristic algorithm when the optimal solution is not possible. In comparison to EFMRP, some of the used equations were corrected, and more realistic conditions and constraints were added. With extensive experiments, the proposed algorithm is evaluated and proved its efficiency.

Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications. Localization offers a variety of services for the customers, for example, in the field of WSN, its importance is unlimited, in the field of logistics, robotics, and IT services. Particularly localization is coupled with the case of human-machine interaction, autonomous systems, and the applications of augmented reality. Also, the collaboration of WSNs and distributed robotics has led to the creation of Mobile Sensor Networks (MSNs). Nowadays there has been an increasing interest in the creation of MSNs and they are the preferred aspect of WSNs in which mobility plays an important role while an application is going to execute. To overcome the issues regarding localization, the authors developed a framework of three algorithms named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF) Localization algorithms. In our previous study, the authors only focused on EKF-based localization. In this paper, the authors present a modified Kalman Filter (KF) for localization based on UKF and PF Localization. In the paper, all these algorithms are compared in very detail and evaluated based on their performance. The proposed localization algorithms can be applied to any type of localization approach, especially in the case of robot localization. Despite the harsh physical environment and several issues during localization, the result shows an outstanding localization performance within a limited time. The robustness of the proposed algorithms is verified through numerical simulations. The simulation results show that proposed localization algorithms can be used for various purposes such as target tracking, robot localization, and can improve the performance of localization.

Wireless Sensor Network (WSN) and Internet of Things (IoT) together have the potential to change the whole world into a smart planet. IoT technology has been a huge boon for a clean, green, and sustainable environment. This technology benefits numerous industries by improving connectivity and reducing energy wastage. IoT has the potential to make our environment more sustainable and help us to reduce pollution all across the globe. But due to limited resources in both these networks, it is very challenging to form a complete secure system. This survey paper examines the various security requirements and attacks possible in WSN and IoT. The paper surveys existing approaches like blockchain, fog/edge computing and machine learning to ensure security of IoT systems. The paper also evaluates the performance of common machine learning algorithms using IoT datasets.

The paper addresses the impact of peak-to-average power ratio (PAPR) and spectrum of the waveform, as well as load resistance on the performance of low-power harvesting device in a real-life wireless power transfer (WPT) scenario. In the current study, a combination of the classic voltage doubler circuit for RFDC conversion and premanufactured device for DC-DC conversion is used. For the investigation of conversion efficiency and harvesting device performance, three types of waveforms are used: single tone, multicarrier signals with low PAPR and multicarrier signal with high PAPR. In order to generate high-PAPR signal, subcarriers with the same amplitude and phase are summed, whereas for generation of low PAPR signal the phases of the subcarriers are chosen pseudo-randomly. Over-the-air transmission in 865 MHz ISM band is made using directional antennas and all multicarrier waveforms have equal 5 MHz bandwidth. To evaluate the performance of harvesting device and conversion efficiency, the average voltages at the input and output of the RF-DC converter as well as at the output of the DC-DC converter with corresponding input and load impedance are measured. The experiments have shown that the employed multicarrier signals can greatly improve the performance of harvesting device during WPT under certain conditions, which are discussed in the paper.

In this paper, actual research of the multilayered networks will be presented. The multilayered networks will become an important phenomenon in an upcoming new generation of 5G networks. In the near future, new network technologies, Internet of Things (IoT) and Smart Cities will reveal new possibilities of 5G networks. These networks along with IoT are considered a promising technology that interconnects different types of networks into one fully functional network. Part of those networks will be also Mobile Ad-Hoc Networks (MANET), Wireless Sensor Networks (WSN) and Fly Ad-Hoc Networks (FANET). The aim of this paper is to present the concept of a multilayered network model consisted of networks mentioned above. The simulations prove, that such a model is able to provide better data rates, delivery time and save some energy due to the lower number of hops.

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings, (2) the detection of anomalies from data reported by sensors, and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

Wireless sensor networks (WSNs) have good performance for data transmission, and the data transmission of sensor nodes has the function of symmetry. However, the wireless sensor nodes are facing great pressure in data transmission due to the increasing amount and types of data that easily cause premature energy consumption of some nodes and, thus, affects data transmission. Clustering algorithm is a common method to balance energy consumption, but the existing algorithms fail to balance the network oad effectively for big data transmission. Therefore, an optimal data transmission with data-location integration (ODTD-LI) is proposed for WSNs in this paper. For optimal data transmission, we update the network topology once for one round. In the proposed algorithm, we perform calculations of the optimal cluster heads, clustering and data transmission routing through three steps. We first deploy N homogeneous and symmetry nodes in a square area randomly and calculate the optimal number of cluster heads according to the node ocations. then, the optimal number of cluster heads, energy consumption, the distances and degrees of the nodes are taken into consideration during the clustering phase. Direct communication is carried out within a cluster, and the member nodes of the cluster pass the information directly to the cluster head. Lastly, an optimal hybrid routing from each cluster node to Sink is constructed for data transmission after clustering. The simulations verify the good performance of the proposed algorithm in view of the ifetime, average delay, coverage rate (CR) and oad balance of the network compared with the existing algorithms. Through the research conducted in this paper, we find that our work has good performance for selecting the hybrid routing in the network with the nodes randomly arranged.

Smart building equipment and automation systems often become a target of attacks and are used for attacking other targets located out of the Home Area Network. Attacks are often related to changes in traffic volume, disturbed packet flow or excessive energy consumption. Their symptoms can be recognized and interpreted locally, using software agent at Home Gateway. Although anomalies are detected locally at the Home Gateway, they can be exploited globally. Thus, it is significantly important to detect global attack attempts through anomalies correlation. Our proposal in this paper is the involvement of the Network Operator in Home Area Network security. Our paper describes a novel strategy for anomaly detection that consists of shared responsibilities between user and network provider. The proposed two-tier Intrusion Detection System uses a machine learning method for classifying the monitoring records and searching suspicious anomalies across the network at the service provider's data center. Result show that local anomaly detection combined with anomaly correlation at the service providers level can provide reliable information on the most frequent IoT devices misbehavior which may be caused by infection. National Centre for Research and Development (NCBiR) in Poland

Wireless sensor networks are featured by restricted network resources, which is quite possible to result in low positioning precision and serious time delay in positioning, accordingly, the overall network positioning quality may be reduced; to improve the positioning precision of WSN, based on the DV-HOP positioning algorithm, two aspects of the node positioning were improved from the error precision and least square estimation; thus, a WSN positioning algorithm based on 3D discrete chaotic mapping was proposed: first, a 3D discrete chaotic mapping was constructed, the Chaos Optimization Algorithm was introduced into the positioning error precision calculation, and the unknown nodes were positioned by introducing the least square estimation; second, a simulation experiment of new algorithm was performed from the aspects of communication radius and topological structure. The experimental results showed that the algorithm proposed in this paper can effectively reduce the positioning error caused by calculation and improved the positioning precision. Further, based on the algorithm of this paper, the moving mechanism could be introduced to make dynamic planning for overall network resources, so that the energy cost of the algorithm of this paper in the confirmation process of WSN network terminal could be further reduced to make the algorithm more valuable in engineering field.

Wireless sensor networks (WSNs) have many applications in climate monitoring, fire detection, smart homes, and smart cities, as well as detecting and monitoring dynamic, spatio-temporal events such as storms, traffic, and animal movement. WSNs are an important component of the Internet of Things (IoT), and they play an important role in monitoring and collecting surrounding context and reporting the sensed data. In this paper, we consider a WSN deployed to monitor spatio-temporal events and a mobile sink which acts as the gateway between the IoT and the WSN. Data gathering is a challenging problem in WSNs and IoT since the algorithms have to be energy-efficient and the information has to be available to the end user in a timely manner and without redundancies. In this paper, we propose an improvement of the reactive, anchor-based routing protocol with constrained flooding and dynamic clustering from Aranzazu-Suescun and Cardei (Spatio-temporal event detection and reporting in mobile-sink wireless sensors networks, 2017). We propose a new event-based clustering mechanism and a new dynamic clustering technique. This improvement results in reduced energy consumption and a higher number of packets processed successfully by the sink compared to Aranzazu-Suescun and Cardei (Spatio-temporal event detection and reporting in mobile-sink wireless sensors networks, 2017). Data collected by the mobile sink are shared to the end users through the IoT infrastructure. We conducted simulations using WSNet, an event-driven simulator for WSNs. We measured various performance metrics, such as the average residual energy, the number of active clusters, and the percentage of events processed successfully by the sink.

This paper presents the development of propagation models for wireless sensor networks for landslide management systems. Measurements of path loss in potential areas of landslide occurrence in Thailand were set up. The effect of the vegetation and mountain terrain in the particular area was therefore taken into account regarding the measured path loss. The measurement was carried out with short-range transmission/reception at 2400 MHz corresponding to IEEE 802.15.4 wireless sensor networks. The measurement setup was divided into two main cases, namely, the transmitting and receiving antennas installed on the ground and 1-m high above the ground. The measurement results are shown in this paper and used to develop propagation models suitable for operation of short-range wireless sensor networks of landslide management systems. The propagation model developed for the first case was achieved by fitting the averaged experimental data by the log-normal model plus the standard deviation. For the second case, the model was derived from the ray tracing theory. The mountain-side reflection path was added into the model which contained the reflection coefficient defined for the soil property. Furthermore, the resulting propagation models were employed in order to realistically evaluate the performance of wireless sensor networks via simulations which were conducted by using Castalia. In the simulations, the sensor nodes were placed as deterministic and random distributions within square simulated networks. The comparison between the results obtained from the deterministic and random distributions are discussed.

Many recent wireless sensor network (WSN) routing protocols are enhancements to address specific issues with the “low-energy adaptive clustering hierarchy” (LEACH) protocol. Since the performance of LEACH deteriorates sharply with increasing network size, the challenge for new WSN protocols is to extend the network lifespan while maintaining high scalability. This paper introduces an energy-efficient clustering and hierarchical routing algorithm named energy-efficient scalable routing algorithm (EESRA). The goal of the proposed algorithm is to extend the network lifespan despite an increase in network size. The algorithm adopts a three-layer hierarchy to minimize the cluster heads' load and randomize the selection of cluster heads. Moreover, EESRA uses multi-hop transmissions for intra-cluster communications to implement a hybrid WSN MAC protocol. This paper compares EESRA against other WSN routing protocols in terms of network performance with respect to changes in the network size. The simulation results show that EESRA outperforms the benchmarked protocols in terms of load balancing and energy efficiency on large-scale WSNs.