Abstract

Keywords

References

Abstract

In this paper, the traditional ant colony algorithm has a slow convergence rate for routing optimization of Mesh networks. A multi-path routing protocol based on improved ant colony algorithm, Fortified Ant protocol, is proposed. The protocol first adds a sorting algorithm based on the ant colony algorithm, and introduces the concept of elite ants to improve the speed of routing optimization. Secondly, this paper also studies the multipath transmission of self-organizing networks. The simulation results show that compared with ADOV, DSR and AOC routing algorithms, the algorithm can quickly find multiple paths with better quality, with fast convergence and overhead. Less advantage.

1. Introduction

Wireless Mesh network is a kind of self-organizing network, mainly composed of two parts, respectively is wireless router (Mesh routers) and wireless client (Mesh clients), Mesh router constitute the backbone network, Mesh clients through the Internet backbone network connection.Under the communication environment of wireless mesh network, the topology of mesh network is relatively stable, so it has great research value in the temporary arrangement of communication network.

As the future of wireless Mesh networks has gradually been recognized by many scholars, many experts have studied the multipath routing methods of wireless Mesh networks, and many have achieved certain results. In [1], a multi-path routing discovery method based on particle swarm optimization for wireless mesh networks is proposed. The congestion prediction degree and node forwarding goodness function are defined as the fitness function of the particle swarm algorithm model to ensure the validity of the calculated path. High efficiency. Literature [2] mainly studies the feasibility of ant colony algorithm applied to route optimization in Mesh networks. Simulation experiments show that ant colony algorithm can be used for route optimization of Mesh networks, and its optimization speed is fast, and it can achieve global optimization.

In this paper, on the basis of above research, in view of the traditional Ant colony algorithm optimization is slow and single path routing is easy to lose data grouping problem, put forward a kind of Ant colony algorithm based on sorting multipath routing protocols – Fortified Ant protocol.

Abstract

Keywords

References

Abstract

At present, wireless sensor networks are developing rapidly with the support of the Internet of things. Wireless sensor networks can deliver the information people need at any time, free from the constraints of time and space. Wireless sensor network is widely used, which lays a solid foundation for the development of Internet of things. As the node deployment environment of wireless sensor networks is usually very complex, it is necessary to study the security of wireless sensor networks so as to reduce security threats and network attacks. In this paper, the security and application of the wireless sensor network are emphasized.

1. Introduction

Wireless Sensor Network (WSN) is perception, and the main tool is sensor. The use of sensors can effectively sense the external environment, and then with the help of wireless network for information transmission, to meet user needs. The security of wireless sensor network must be paid attention to, because it is supported by high technical content and complex structure, once there is a problem, the consequences are unimaginable. The development of the Internet of things is inseparable from wireless sensor networks, and the ability of people to communicate quickly is also closely related to it [1].

WSN can be used to collect all kinds of data and information and deal with all kinds of complex environments, the application of WSN covers all aspects. In the military field, it can be used to detect the deployment of troops in enemy terrain and other situations, and it has the function of detecting biological and chemical pollution and nuclear radiation. In terms of environmental monitoring and protection, it can be used for data collection in the field environment, tracking animal footprints, analyzing pollution situation, and predicting the explosion of forest fire and debris flow. In the field of industry and agriculture, the monitoring of crop growth and product flow intelligent production. In the field of medical care and health, it can realize the function of obtaining physiological data of patients, analyzing their conditions, and timely receiving medical treatment. In addition, wireless sensor network plays a crucial role in the field of smart home, smart transportation, smart city, cultural relic protection and business, etc. Wireless sensor network is changing people’s life [2].

The information security of WSN is concerned in many contexts, such as military medical disaster prevention and other fields. Wireless sensor networks use open wireless communication channel technology to transmit data, but without security protection means, data is very vulnerable to internal and external attacks [3]. However, the amount of computation based on the cryptographic defence method is not suitable for WSN network. Therefore, it is also a big problem to choose an appropriate encryption method to ensure the security of WSN information [4] .

Abstract

Keywords

1. Introduction

2. Cost-effective domain generalization

3. An implementation of cost-effective domain generalization

4. Experiments and results

5. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgment

Research Data

References

Vitae

Abstract

Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited. In this work, a Cost-Effective Domain Generalization (CEDG) algorithm has been proposed to train an efficient network with minimum labor requirements. CEDG transfers networks from a publicly available source domain to an application-specific target domain through an automatically allocated synthetic domain. The target domain is isolated from parameters tuning and used for model selection and testing only. The target domain is significantly different from the source domain because it has new target categories and is consisted of low-quality images that are out of focus, low in resolution, low in illumination, low in photographing angle. The trained network has about 7 M (ResNet-20 is about 41 M) multiplications per prediction that is small enough to allow a digital signal processor chip to do real-time recognitions in our WSN. The category-level averaged error on the unseen and unbalanced target domain has been decreased by 41.12%.

1. Introduction

Wireless sensor networks (WSNs) typically are designed to detect and identify neighboring objects in wild [1, 2, 3, 4] with sound or vibration sensors in the form of single [5] or microarrays [6]. The sound or vibration sensor has many advantages [7, 8, 5], such as low cost, low energy consumption, and relatively low in algorithm complexity. However, they are unsuitable for mixed objects detection because their spatial resolutions are usually too low to distinguish each person in a group of pedestrians. To overcome this shortage, we have employed cameras in our WSNs which has been proved to be effective for dense targets identification [9]. Unfortunately, images captured by WSNs are noisy, such as low in illumination, resolution and photographing angle, which are different from most publicly available datasets. Because the severe limitation in data and resources, despite the rapid development in deep learning [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], WSN-applicable deep-learning-based image classification algorithms evolve slowly. So, cost-effective dataset construction methods are needed urgently to build datasets that corresponding to specific WSN applications. Several random images of the target application (target domain) that was captured during our field experiments have been shown in Fig. 1, where targets like persons and cars are hard to identify.

Because of limited communication bandwidth, WSNs cannot run deep neural networks (DNNs) in a remote cloud (or fog) which is a common strategy for embedded devices [21, 22, 23, 24, 25]. To run DNNs in such devices locally [26, 27], a training strategy is wanted to cut computation costs without decreasing identification accuracy significantly. Fortunately, Han et al. [28, 29] have pointed out that only parts of weight parameters in neural networks are playing essential roles during predictions. Therefore, it is possible to train an efficient DNN for WSNs with fewer parameters if we can fully utilize key weight parameters.

1- Introduction

2- Ethanol architecture

3- Ethanol implementation

4- Case studies

5- Other uses of Ethanol

6- Related work

7- Conclusions

References

Abstract

Wireless Networks have become ubiquitous to support the growing demand from mobile users, and other devices, like in the Internet of Things (IoT). This article proposes a Software-Defined Wireless Networking architecture specialized in 802.11 Wireless LANs, called Ethanol, which provides a more fine-grained control. Ethanol is the first wireless SDN architecture that extends the control to the user devices. Further, Ethanol allows intelligent white-box control with finer grain than the state of the art, since it is optimized for WiFi. The proposed architecture is evaluated on a prototype over three use cases. Ethanol can be deployed in any Access Point (AP) running embedded Linux, since it has a negligible overhead — up to 1% in memory and 0.1% in CPU usage. Our results show that Ethanol dynamically alter the throughput of an application up to 3x during a prioritization period, returning bandwidth to other applications outside this period. Only by controlling the best time to perform the handover, our results show about 45% improvement over the traditional signal-based handover process.

Introduction

Wireless networks have become ubiquitous and dense. More devices will become connected to the network because of IoT. Cisco predicts that 50 billion things will connect to the Internet by 2020.1 Emerging high-speed network standards (e.g. 802.11ad) are migrating to higher frequencies, which are absorbed by walls, thus requiring the densification of APs. Thus, a more refined control of all the network devices is needed to increase future wireless networks scalability in these environments. Current management architectures for Wireless LANs (WLANs) employ proprietary controllers. These controllers perform network-wide optimizations such as adjustment of transmission power at each AP, selection of best operational channels, faster client mobility, and enhanced traceability, as well as Quality of Service (QoS) policy (rate limiting) enforcement, and security. However, those controllers only manage compatible devices, usually from a single manufacturer, since they rely on proprietary interfaces and Management Information Bases (MIBs). Furthermore, current wireless network controllers for WLAN remain (mostly) closed [1] and the industry has little incentive to change. This article presents Ethanol, an Software Defined Networking (SDN) architecture for IEEE 802.11 WLANs. Ethanol refactors the control plane functionalities between the APs, and the controller, creating hooks in the AP implementation that trigger events to be treated by the controller. Also, the controller can use getter/setter methods to change the AP behavior, controlling features such as client mobility, association and disassociation, QoS, link-level parameters and current state, and virtual APs. The key benefit of Ethanol is its flexibility, and the use cases it enables, for example those shown in Sections 4 and 5.

Software Defined Networking (SDN) has focused enormous attractiveness in changing conventional network by means of offering flexible and dynamic network management. It has drawn important concentration of the researchers from together academia and industries. Mainly, integrating SDN in Wireless Body Area Network (WBAN) applications specifies capable results in terms of handling with the issues like traffic management, security, energy efficiency etc. Recent improvements in miniaturization and energy efficient physiological sensor designs in SDN based Wireless Body Area Networks (WBANs) paved the way for health monitoring systems for collection and processing the real-time physiological data. The collection of signals from different sensor allows reliable diagnosis in heterogeneous than in homogeneous WBANs. Inspired by the evolutions of heterogeneous WBANs, a study on Wireless Electroencephalography Sensor Networks (WESNs) is carried out under distributed signal processing. The distributed WESNs are designed under two different hierarchy i.e. Hierarchical Fully-Connected Topology (HFCT) and Ad-Hoc Nearest-Neighbor Topology (ANNT) to improve the energy-efficiency using distributed Multi-channel Weighted Weiner Filter design (MW2F). Here, each module transmits linear combination of local channels with other modules. The power efficiency is improved Journal Pre-proof in MW2F signal processing algorithm by avoiding centralization of EEG data. A case study is carried out to test the reduced energy consumption after the removal of eye blink artifacts and it is tested with centralized counterparts. The MW2F is evaluated in both topologies against centralized environments and significant reduction of eye blink artifacts improves the energy efficiency in HFCT than other topologies.

Introduction

The energy efficient and miniaturization is the most hot research area in health monitoring systems, where the body is placed with several sensor nodes. These nodes can collect the EEG data and process it in real time and measures are considered to improve the energy efficiency. This system is called as Wireless Body Area Networks (WBANs) [1], and it offers higher demand in the area of physiological monitoring in hospital environment. The WBAN system is composed of several nodes with physiological sensors, wireless communication devices and a signal processing unit. These nodes can communicate with other sensor nodes placed over the body or it can be connected to a fusion center or a storage device. The signals collected from various resources are analyzed jointly in heterogeneous WBANs in real time environment for medical diagnosis.

Wireless devices have a plethora of technologies at their disposal to connect to the Internet and other services. Management and control of each technology are traditionally isolated, and coordination between technologies is nearly non-existent. This isolation leads to poor resource usage, which in turn reduces performance and service guarantees. To satisfy growing user demands, we need to leverage the different service guarantees offered by each technology. Additionally, we need to improve orchestration between technologies to increase performance and flexibility while offering a more extensive range of service guarantees and maximizing resource utilization across networks and users. In this work, we present the general challenges one encounters when managing heterogeneous wireless networks. We argue that the primary challenge is the heterogeneity itself, the number of different devices and technologies, the different service requirements, and the increasing complexity as a consequence. However, technology abstraction can overcome these challenges. We provide an overview of state of the art commercial and scientific solutions and show their strengths and weaknesses. Based on this, we discuss the current status and what future challenges still await to provide full seamless heterogeneous wireless network management.

Introduction

Today’s devices, such as phones, wireless access points (APs), sensors, and other machines, are often equipped with multiple networking technologies to enable them always to stay connected. This connectivity allows users to use a plethora of Internet and other services through technologies like Digital Subscriber Line (DSL), Long-Term Evolution (LTE), or IEEE 802.11 with even more technologies in the future such as IEEE 802.11ax/ay or 5G solutions [1–3]. We expect this trend to continue, with a further increase in available technologies. From this follow two scenarios: (i) technologies cover similar scenarios but do not share spectrum, such as LTE and IEEE 802.11 bring Internet access to users, (ii) technologies cover different scenarios but share the spectrum, such as IEEE 802.11 and Bluetooth. Currently, technologies are isolated, and applications or the operating system takes care of technology selection. This isolation leads to inefficient use of each technology; for example, one technology is congested while another has plenty of free resources. To truly achieve the high bandwidth and low latency requirements of today’s services, orchestration across technologies needs to be in place. Only a holistic approach allows optimizing the performance of services in these heterogeneous networks.

1- Introduction

2- Layouts

3- Sampling techniques

4- Communication technology

5- Prediction models

6- Decision support systems

7- Conclusion and future work

References

Abstract

Integration of Wireless Sensor Networks (WSN) in the field of agriculture has provided a new direction for the production of crops. The same can be applied particularly to greenhouses. Greenhouses provide a protected environment for plants and crops. This paper reviews the role of WSN in monitoring of greenhouses. The paper presents an end-to-end survey; starting from the layout of crops in greenhouses, wireless technology used for communication of sensors, techniques to choose transmission interval or rate. Layouts and sampling techniques are also classified and compared. Other than these, the paper also studies various prediction models and decision supporting techniques adopted in greenhouses for efficient integration and management of WSN. These prediction models are also comprehensively analyzed in this work.

Introduction

Modern day scenario of agriculture has changed. Over the years, population has increased. According to UN’s world population prospect 2017 report, population is estimated to reach 9.8 billion by 2050 (United Nations, 2017). Along with population, demand for food is also multiplying. According to Food and Agriculture Organization of the United Nations (FAO), although there are 30,000 types of edible plants available, humans cultivate only 4% of it (FAO, 1999). The agricultural land has considerably reduced due to various factors like urbanization and industrialization. To cope up with these situations, modern-day technological solutions are required. Other than this, water scarcity, increase in fertilization and dynamic climate changes have also strained to integrate technology to achieve required production in agriculture with minimum wastage of resources. WSN is one prominent technology that has developed over past few years and has found its path in many applications like military, defense, healthcare, agriculture, etc. In agriculture, WSN is particularly used to achieve a state called Precision Agriculture (PA). PA works on the phenomenon of observing (using sensors) and responding (using actuators) and tries to achieve parametric values and conditions required for optimum health and yield of crop. PA also focuses on optimizing resources used for production. Fig. 1 shows the complete process of monitoring and control in a field. Sensors are placed at required stations in suitable and optimal deployment. Sensed information is sent to central repository through available wireless channel.

1- Introduction

2- Network model

3- Transmission probability

4- Successful content delivery probability

5- Numerical results

6- Conclusion

References

Abstract

We investigate a novel file-level carrier sensing based content delivery (FL-CSBCD) protocol to accommodate the transmission of multimedia files in fog-aided wireless networks. Particularly, the proposed FL-CSBCD protocol contains two phases, i.e., the file contention phase and the communication contention phase. The file contention phase aims to resolve the contentions among the requested multimedia files at each fog user equipments (F-UEs), such that the cache-hit performance is maximized. The communication contention phase is designed to resolve the collisions among concurrent content deliveries of the same file, such that the mutual interference can be suppressed. By modeling the fog-aided wireless networks with Poisson point process, we capture the density of active F-UEs, and evaluate the successful content delivery probability (SCDP) with the FL-CSBCD protocol. We show by simulations that the FL-CSBCD protocol can enhance the cache-hit performance while guarantee the coverage probability of content delivery.

Introduction

Recent advancements in smartphone industry and wireless internet technology have triggered a dramatic increase in the demand of bandwidth-hungry multimedia services [12]. Particularly, according to recent industry predictions [3], the annual traffic generated by multimedia content delivery in 2021 will be 4 times of that in 2016 with an increasing rate of 26% per year. To satisfy such unprecedented growth of user demands on multimedia services, it is necessary to investigate novel network architectures and develop advanced transmission techniques for the next generation wireless networks [2,7,10,11,19,23,29]. Fog-aided wireless network is considered to be a promising content-centric network architecture to alleviate the congestion of current cellular networks induced by the massive and redundant downloadings of multimedia contents [15–17,28]. Particularly, by deploying a generic caching, computing, and communication platform across the wireless edges, fog-aided wireless network can effectively offload the data traffic from the remote cloud sever and execute the delay-sensitive tasks in the proximity of the end users, which thereby substantially improves the network performance. To further enhance the spectral efficiency of the fog-aided wireless networks, the innovative technique of cache-enabled D2D communication [4,9,14,26] is utilized for multimedia content delivery. Particularly, by leveraging the cache-enabled D2D communications, a conventional cellular user equipment (C-UE) can directly obtain the multimedia content from a fog user equipment (F-UE) with caching capability in its proximity without the use of infrastructure based edge nodes.

1- Introduction

2- Background

3- Related work

4- Proposed solution

5- Performance evaluation

6- Conclusion and future work

References

Abstract

Internet of Things (IoT) and cyberphysical system (CPS) technologies play huge roles in the context of Industry 4.0. These technologies introduce cognitive automation to implement the concept of intelligent production, leading to smart products and services. One of the technological challenges related to Industry 4.0 is to provide support to big data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange, and processing. In order to deal with this challenge, in this article, a QoS-aware cloud based solution is proposed by adapting a recently proposed seamless resources sharing architecture to the IoT scenario. The resulting solution aims at improving device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0. Results are obtained via simulations of three QoS demanding industrial applications. The outcomes of the simulations show that both delay and jitter QoS metrics are kept below their specific thresholds in the context of VoIP applications used for distributed manipulators fine tuning control. In the case of video-based production control, the jitter was controlled to meet the application demands, and even the throughput for best-effort supervisory systems HTTP access is guaranteed.

Introduction

Industry 4.0 introduces several changes to the original approach of industrial automation. IoT and CPS technologies play huge roles in this context introducing cognitive automation and consequently implementing the concept of intelligent production, leading to smart products and services (Drath and Horch, 2014). Besides the aforementioned technologies, Industry 4.0 typically relies on big-data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange and processing. Flexibility and scalability are important requirements of Industry 4.0, since the physical environment of the factories is in constant change. Implementing these requirements demands dealing with the disposition of the sensors and actuator devices in the factories, and how easily they can be displaced, introduced, and reorganized (Freitas et al., 2013). This flexible environment can be attained by diminishing the needs for cabling to connect the devices, which is possible using wireless technologies. Implementing a QoS-aware cloud based solution is challenging in the context of wireless connections and demands three main issues to be tackled (Wollschlaeger et al., 2017): (I) the coexistence of different wireless protocols and systems, (II) interoperability between communication systems, and (III) seamless engineering to allow adaptive factory operation. In order to deal with these issues, in this article, a recently proposed seamless resources sharing architecture (Kunst et al., 2016a,b; Fernandez et al., 2017) is adapted to the IoT scenario aiming at improving the device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0.