Abstract

     One of the main challenges in the design of wireless ad-hoc and sensor networks is to reduce energy consumption and radio interference and collision, which are strictly and strongly correlated to the transmission range of the nodes. In this article, the transmission ranges of sensors in sparse Wireless Sensor Networks (WSN) is theoretically analyzed and is shown that the transmission ranges of sensors can be significantly reduced in this type of WSNs without losing any connection. Next, a method is presented to calculate reduced transmission ranges. In this method, an iterative transmission range adjustment mechanism is used to select an almost transmission range while no information is needed on the location of nodes or the distance between nodes. This method is very efficient and its communication overhead is low. It can be used as a pre-step of any routing algorithm in order to decrease energy consumption and radio interference in targeted scenarios. The simulation results show the efficiency of the proposed method. We got at least 50% improvement in energy consumption in comparison with full power network, in sparse WSNs.

Introduction

     Wireless Sensor Networks (WSNs) have become increasingly popular in recent years, as they offer a powerful and cost-effective solution for monitoring and collecting data from a variety of environments. A Wireless Sensor Network (WSN) consists of a large number of distributed sensor nodes that cooperatively monitor the physical world [1]. Each node in these networks is typically equipped with a wireless communication device, a small microcontroller, a memory unit and a power source. The nodes in a WSN are typically battery-powered and have limited processing power, memory, and communication bandwidth. Therefore, the design of a WSN requires careful consideration of these constraints, such as the choice of communication protocol, data routing algorithms, and energy management strategies [2].

     The complexity of the WSN domain as well as the presence of many sensor nodes unavoidably introduces a large amount of data in these networks that must be processed, transmitted and received. The sheer amount of data generated by WSNs can pose a significant challenge for the network design and operation. The sensors in a WSN can generate data continuously or periodically, depending on the application requirements. This data must be processed, stored, and transmitted to the base station in a timely and efficient manner. So the complexity of the WSN domain and the presence of many sensor nodes can introduce a large amount of data that must be processed, transmitted, and received [3]. Despite their profound advantages, the utilization of WSNs is often battery-powered and strictly limited due to energy constraints. In fact, most of the energy expenditure of a sensor node occurs during wireless communication, and the remaining energy is consumed during sensing and data processing. The radio transceiver of a node consumes a significant amount of energy during data transmission and reception. Therefore, the energy consumption of a node must be carefully managed to ensure the longevity of the network [4]. Transmission power adjustment for a special transmitter-receiver pair depends on several environmental conditions. The transmission power required to reach the receiver is affected by two main factors including distance and wireless connection quality. Distance affects transmission power. As the distance between the transmitter and receiver increases, the signal strength decreases, resulting in a weaker connection.

Conclusions and future works

     We studied the transmission range of sensors in wireless sensor networks in this study, and proposed a simple yet effective distributed transmission power control algorithm for sparse wireless sensor networks. We showed that this algorithm has advantages in many cases. The main idea of algorithm is simple, but performance of algorithm in targeted scenarios is excellent. We analyzed the performance of this method theoretically and show that using this method is worthwhile in some scenarios, especially in sparse wireless sensor networks with high amount of communication traffic. In these scenarios, our method can be used as a pre-step of any routing algorithm and decrease energy consumption and radio interference. As a future work, we intend to design methods to overcome the collisions problem. Designing methods for resisting the algorithm against the sensor failures is another future work. Furthermore we intend to work on using learning methods like Reinforcement learning for power control. Designing a good energy efficient reward mechanism is one of the main challenges of these methods.

Abstract

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Introduction

At present, wireless sensor networks have been widely applied in various fields such as environmental monitoring, agriculture, and the Internet of Things. In these applications [1,2], wireless communication between nodes in wireless sensor networks plays a crucial role. The Internet of Things has characteristics such as connectivity, intelligence, and real-time. It connects devices through wireless communication technology and endows them with perception, computing, and decision-making capabilities. This enables the Internet of Things to obtain and transmit data in real-time, automate collaboration and data sharing, improve production and operational efficiency, and reduce costs. At the same time, the Internet of Things utilizes big data analysis and predictive maintenance to provide insight into market trends and user needs, helping enterprises make more accurate decisions. In addition, the application of the Internet of Things in fields such as smart homes and health monitoring is improving people’s quality of life and promoting the process of sustainable development. Therefore, the Internet of Things has significant advantages in improving efficiency, reducing costs, enhancing insight, improving quality of life, and promoting sustainable development. The routing optimization of wireless sensor networks has become an important and challenging issue due to the limited energy of sensor nodes, wide and random distribution of nodes, and other characteristics. The goal of wireless sensor network routing optimization is to improve network performance and energy utilization efficiency by selecting appropriate paths, minimizing energy consumption, maintaining low latency, and maximizing network lifespan [3]. It involves techniques and mechanisms such as path selection and transmission scheduling to ensure efficient and accurate data transmission between nodes. The research on routing optimization in wireless sensor networks is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. By adopting effective routing optimization algorithms and mechanisms, more reliable, intelligent, and efficient wireless sensor network systems can be achieved. Wireless sensor network routing optimization plays an important role in the Internet of Things, as it can improve network performance and energy utilization efficiency, extend network lifespan, provide reliable data transmission and support real-time applications for various application fields. Therefore, routing optimization in wireless sensor networks is of great significance in promoting the development and application of animal networking [4,5].

Conclusion

Wireless sensor network routing optimization is one of the key issues in the Internet of Things, which is of great significance for improving network performance, extending network lifespan, providing reliable data transmission, and supporting real-time applications. The current routing optimization methods have problems such as high energy consumption, high transmission delay, high number of dead nodes, and low network throughput. To address these issues, this article proposes a wireless sensor network routing optimization method based on improved ant colony algorithm in the Internet of Things. Through experimental testing, it has been proven that the proposed method has absolute advantages over the current method in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. This research achievement can not only improve the energy efficiency of wireless sensor networks, but also improve the quality of data transmission and provide better support for real-time applications. At the same time, this method can also optimize network capacity and meet the network performance requirements of different application scenarios. These contributions provide important support for the reliability, intelligence, and efficiency of wireless sensor networks, and promote the further development of wireless sensor network technology in various application fields. However, there are still some challenges in the research of routing optimization in wireless sensor networks. For example, when optimizing routing in a dynamic environment, it is necessary to consider issues such as changes in network topology and the balance between energy consumption and data transmission quality. These challenges require further in-depth research to propose more comprehensive and adaptable routing optimization methods for wireless sensor networks, and to promote the development and application of animal networking technology.

Abstract

In WSN sensor nodes are connected to their neighbor nodes by a wireless channel and these all nodes are operated on a battery to perform any operations. So improving the lifetime of WSN is a major research challenge by effective utilization of each and every node’s energy. The lifetime of WSNs can be improved in three steps: path creation, path selection, and power saving. In existing works, path selection has the major role to utilize and enhance lifetime. In the proposed PStack algorithm, path generation is a major concern, and routing is applied. It takes advantage of and solves the limitations of existing algorithms using the stack concept in WSN. The proposed algorithm shows a better result than the improved LEACH protocol because it uses the data structure with graph theory.

Introduction 

Basic foundation of any kind of network communication is path generation from source to destination. Especially in the WSN path generation has a major role. A better path generating between source and destination leads to optimized load balancing and achieves high lifetime. Most of the existing models have single backbone meaning that it creates a single path in network with single backbone, may not be ideal to prolong the lifetime[1]. A simple WSN model is considered with six sensor nodes and a sink as depicted in figure1. It is understood that all sensor nodes are kept active 24X7 irrespective of whether is it in use or not. This leads to unnecessary usage of battery power leading to drain down quickly. Therefore the node automatically disconnected itself from its neighbours[5].

In this work node’s energy means all node’s energy not only the selected nodes energy. Because improving the node’s energy may not able to prolong the lifetime, it can be happen only when all nodes energy will be utilize effectively without wasting any nodes energy. According to the review of literature, there are various techniques introduced and implemented to improve the lifetime of WSN. All these techniques were applied either in routing algorithms or region (area) of WSN as cluster. These techniques may be categories as: 1. WSN with single sink and 2.WSN with more than one sink.

CONCLUSION

PStack algorithm in WSN eliminate the drawbacks of all existing protocol, which is used for the improvement of lifetime of WSN such as AODV, CDS, KMAT, DSR, NBS, VBS and cluster. PStack algorithm results will be in form of the number of stacks with different sizes. Number of stacks represents the number of paths from source to sink and size of stack represents the number of sensor nodes in particular path which having the significant roles for path selection technique. This is the great way to improve the lifetime of any kinds of WSN. This algorithm works more effectively when density of nodes are high. It also supports for static as well as dynamic nodes in WSN.

Limitation of this work is that this assumption may have chances to become wrong if sensor nodes participated in any communication without knowledge of sink, or sink didn’t able to update the energy level of nodes properly

Abstract

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node’s lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one.

Introduction

WSNs are very organized at this point [1]. Ten to fifteen years from now, universal Internet access to WSNs with built-in security is a reasonable expectation. It is noticed when the Internet is converted into a real-world network [2]. The potential uses for this technology are many, spanning fields as diverse as medicine, ecology, transportation, the armed forces, the entertainment industry, homeland security, crisis management, and smart cities [3].

ZigBee is the backbone of many modern wireless networks. Expansions in these areas, along with others such as product development, human security requirements, resource management, and the practical application of product-material values, are all part of the present advances. As the technology behind semiconductors has improved, new theoretical and practical questions about the use of emotional networks in commercial and community settings have emerged [4].- [6].

Conclusion

Mechanically inspired energy gadgets have gained much popularity in recent years. The main reason for this is how many crucial tasks are completed in factories. As a result of its settings, other devices placed on top of it may conduct input and transmission tasks. The suggested solution allows the gadget to use almost little power while protecting sensitive information. In this way, the machinery may function better while using less power. Because they use less electricity, these gadgets can store more electricity. As a result, the surplus heat dissipated. So, there needs to be more information on what exactly such a temperature-lowering gadget would need to function. Therefore, energy efficiency is rather good. Since surplus electricity is not leaking out of the plants, there is zero energy waste. Considering these factors, the new approach is much more efficient. Direct communication between nodes is planned as a future update. When two nodes are close to one another, the sensor node may make use of the other node’s short-range communication. Measures of time and safety are taken at breakneck speeds.

Abstract

Modern wireless technology demands the implementation of preset Sensor nodes for a structured wireless network. The network has sensor nodes for surveillance or environmental sensing, which wirelessly transmit data to a collection point. Therefore, data transfer must be protected by preventing external intrusion attacks. This will be handled by designing an effective intrusion detection system proposed as a Composite Intrusion detection system (CIDS). It is suitable for a network in heterogeneous network structure with a capable of identifying externals attacks like flooding of data’s, sending unwanted data packets and changing the destination node. For routing of data packets between the nodes, minimum power utilization with changeable cluster heading method is used. The activities of sensor nodes will be monitored and a dataset is formed on the basis of the node’s activity. It is known as Network Databases (NDB). Using this dataset, the intrusion attacks will be identified by using Artificial Neural Network (ANN). ANN will be trained with a predefined dataset for the effective identification of external attacks. The proposed CIDS methodology shows the high accuracy of identifying the external attacks on the sensor networks when comparing to the previous designed system in all the types of attacks.

INTRODUCTION

Wireless based sensors networks is a revolutionary invention in the modern era used for all types of application like surveillance in military application, collection of healthrelated data in medical field [1]. It has set of distribution sensor nodes in an application area for sensing the data with a predefined geographic location. The location of nodes will be decided on the basis of XY co-ordinates. The sensor nodes have to collect the sensing data from their corresponding location and passed to a cluster head. The head nodes will make effective data aggregation method and passed the concern data to base station [2]. The data transmission will be done by using any of the routing protocols and it should be secured from the external threat. Also, the power backup of the sensor is a tradeoff between the data transmission and securing the data from the threats [4]. The data packets routing is depicted in figure 1.

As the sensor nodes are in open area and the data transmission will be carried over in an open platform with limited network resources, the chances on injection of threats will be high. In spite the position of nodes is a heterogeneous network, chances on threats will be high [5].

CONCLUSION

The fundamental purpose of the CIDS is to identify external threats and their nature before they penetrate the network’s structure. As a result of the excellent implementation of data packet routing, detection is performed efficiently with the aid of a changing cluster heading node and low power consumption of sensor nodes. As the CIDS identify risks utilizing artificial neural networks, its efficiency is superior to that of existing specified research networks. The sort of external attacks recognized by the planned CIDS flooding of Data’s unscheduled data transmission is a significant factor. This is because data flooding reduces network efficiency and causes a significant amount of network resources to be squandered. By providing the correct data set during the training phase of neural networks, this threat detection can be modified. This is done on a periodic basis to acquire the node activity dataset. Therefore, the suggested system is capable of identifying external threats and their categories in the shortest time possible, with advance notification to all other networks on the sorts of threats found.

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.

2- Review methodology

3- IoT implementation in arable farming

4- Current and potential applications

5- Challenges and solutions

6- Conclusions and future perspectives

References

Introduction

The global population and its food consumption are growing alarmingly quickly, while climate change effects are simultaneously complicating the challenge of ensuring food security in a sustainable manner (Godfray et al., 2010; Tilman, Balzer, Hill, & Befort, 2011). Data-driven agriculture is one of the main strategies and concepts proposed to increase production efficiently while decreasing its environmental impact (Foley et al., 2011). Data-driven technologies in general are quickly advancing with the development of the Internet of Things (IoT), and may become an important part of the future of farming (Brewster, Roussaki, Kalatzis, Doolin, & Ellis, 2017; Jayaraman, Yavari, Georgakopoulos, Morshed, & Zaslavsky, 2016; Verdouw, 2016; Wolfert, Ge, Verdouw, & Bogaardt, 2017). Smart Farming, also called Agriculture 4.0 or digital farming (CEMA, 2017), is developing beyond the modern concept of precision agriculture, which bases its management practices on spatial measurements largely thanks to Global Positioning System (GPS) signals. Smart farming bases its management tasks also on spatial data but is enhanced with context-awareness and is activated by real-time events, improving the performance of hitherto precision agriculture solutions (Sundmaeker, Verdouw, Wolfert, & Perez Freire,
2016; Wolfert et al., 2017). Additionally, Smart Farming usually incorporates intelligent services for applying and managing Information and Communication Technologies (ICT) in farming, and allows transverse integration throughout the whole agri-food chain in regards to food safety and traceability (Sundmaeker et al., 2016). IoT is therefore a key technology in smart farming since it ensures data flow between sensors and other devices, making it possible to add value to the obtained data by automatic processing, analysis and access, and this leads to more timely and cost-effective production and management effort on farms. Simultaneously, IoT enables the reduction of the inherent environmental impact by real-time reaction to alert events such as weed, pest or disease detection, weather or soil monitoring warnings, which allow for a reduction and adequate use of inputs such as agrochemicals or water. IoT eases documentation and supervision of different activities as well as the traceability of products, improving the environmental surveying and control in farms by the appropriate authorities. The IoT concept was introduced by Kevin Ashton in 1999 in relation to linking Radio-Frequency Identification (RFID) for supply chains to the internet (Ashton, 2009), but has no official definition. It implies, however, the connection of a network of “things” to or through the internet without direct human intervention. “Things” can be any object with sensors and/or actuators that is uniquely addressable, interconnected and accessible through the world-wide computer network, i.e. the Internet. The application of IoT in agriculture is advantageous because of the possibility to monitor and control many different parameters in an interoperable, scalable and open context with an increasing use of heterogeneous automated components (Kamilaris, Gao, Prenafeta-Boldu, & Ali, 2016), in addition to the inevitable requirement for traceability. As a result of IoT, agriculture is becoming data-driven, i.e.

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.

Opportunistic underwater sensor networks (OUSNs) are deployed for various underwater applications, such as underwater creatures tracking and tactical surveillance. However, the storage capacity of the sensor nodes in such networks may be insufficient, especially when a wealth of data messages are generated rapidly in some emergency response applications. The message dissemination in OUSNs therefore may differ significantly from those in wireless sensor networks or delay-tolerant networks, where network throughput should be taken as one of the primary objectives of network performance. To this end, the strategies for message storing, disseminating and discarding are investigated, and a Message Dissemination Approach for Storage-Limited (MDA-SL) OUSNs is proposed. In MDA-SL, the messages are preferred to be disseminated to the nodes with higher speed or larger residual storage. In addition, the copies of newer messages are inclined to be discarded when their message holders’ storage is full. Simulation results demonstrate the excellent performance of MDA-SL, showing that it can achieve satisfactory network throughput with propagation delay being restricted according to the diverse application requirements.

Introduction

With the broad deployments of mobile sensor nodes, opportunistic mobile sensor networks (OMSNs) [1,2] are introduced to conduct large-scale sensing at a lower cost compared to that of a ubiquitous static infrastructure of sensing devices. Because of the node mobility, however, the available contacts between nodes may be scarce and short, leading to some unstable communication paths. Opportunistic message dissemination techniques enable the network nodes to communicate in an environment where the contemporaneous end-to-end paths are unavailable or unstable, by allowing a data message to be transferred from source to destination in discrete hops even when an end-to-end communication path never emerges. As a derivative form of OMSNs, the opportunistic underwater sensor networks (OUSNs) [3] technology enables various underwater applications, such as underwater creatures tracking [4] and tactical surveillance [5]. However, compared with massive collected data, the storage capacities of nodes are usually insufficient [6,7]. What is worse, the network throughput (which is defined as the total number of data messages received per unit time by the des- ∗ Corresponding author. tinations of all the multi-hop flows in the network) is significantly restricted because the capacity of nodal storage modules is extremely limited (the space memory is even measured in KB [8], e.g., the chip CC2430 has a flash memory of 128 KB [9]).