1.2. Author’s Contributions
1.3. Organization of the Paper
2. Overview of WSNs
2.1. Hardware Architecture of a Wireless Sensor
- Acquisition unit. This consists of two subunits: a sensor and an analog-to-digital converter (ADC). The sensor collects digital measurements on environmental parameters and transforms them into analog signals. The ADC converts analog signals into digital signals.
- Processing unit. This is also composed of two units: a data-storage unit, and a processor responsible for data-processing and control procedures allowing the collaboration of the sensor with the others in order to carry out acquisition tasks.
- Communication unit. Its function is information transmission and reception. It is equipped with a transmitter/receiver pair. It allows communication within the network, in our case, by radio frequency (radio waves). However, there are other transmission possibilities (e.g., optical and infra red)
- Power unit. Power supply is a critical element of sensor architecture. It is responsible for supplying energy to all other units. It most often corresponds to a battery, feeding the sensor, whose limited resources make it a problem specific to this type of network. However the recent realization of solar-panel power unit is trying to provide a solution for extending sensor life .
- Mobilizer. Optionally, this is used to move the node to complete the task to be processed.
- Location-Finding System. Optionally, this provides information on the location required by the application and/or routing.
2.2. Types of WSNs
- Terrestrial. This kind of WSNs is intended to be deployed in a terrestrial space. In these networks, hundreds of thousands of sensors are deployed randomly or deterministically over a given area. This type of WSN is much more useful in the field of environmental monitoring and natural phenomena; however, it represents a challenge for the sustainability of the network in terms of energy management .
- Underground. These networks consist of very special sensor nodes known for their high cost and the logistics necessary for their implementation and maintenance. In this type of network, the sensor nodes are installed in the ground. They find their application in agriculture or in minefields, where they are used to monitor conditions in the soil. However, in this type of network, there is a ground node that has a role in transmitting the information detected by the underground nodes to the base station .
- Underwater. This particular type of WSNs is networks hidden under the sea. Because of the hostility represented by the environment of their deployment, this type of network remains an interesting research challenge. These nodes are more expensive than terrestrial sensors, wireless communication is acoustic, bandwidth is limited, signal loss is recurrent, and propagation delays and synchronization problems are frequent [36,37].
- Multimedia. These WSNs are designed to monitor and trace data such as images, videos, and sound. These sensors are equipped with cameras and microphones. This type of network requires good bandwidth and good quality of service, which implies high power consumption for data processing and compression. Preplanning is required for the deployment of these sensors .
- Mobile. This is the most recent type of WSN, and it uses mobile nodes that can autonomously reposition and reorganize the network. After initial deployment, the nodes disperse to gather information. There is also a hybrid network consisting of a combination of mobile and fixed sensors [12,25,26,37,39,40,41,42].
2.3. Topologies of WSNs
2.3.1. Star Topology
2.3.2. Mesh Topology
2.3.3. Hybrid Topology
2.4. WSN Communication
2.4.1. One-Hop Communication
2.4.2. Multihop Communication
2.5. Specific Characteristics of Sensor Networks
2.5.1. Sensor-Node Features
- Energy consumption. Sensors have energy autonomy, and they usually use tiny batteries as energy resources. In most cases, WSNs are deployed in hard-to-reach areas. This makes it difficult or almost impossible to recharge or replace the batteries. This difficulty leads us to deduce that the life of a sensor is essentially dependent on the life of the battery. Therefore, energy-consumption management is a major constraint in this type of network.
- Transmission scope. This is an important criterion for the deployment of a WSN. It is limited by the radiation capacity of the antennas used and the signal strength involved. For example, the communication between two sensor nodes can only take place if the distance between them is not too great, i.e., a few tens of meters in practice. The longer the distance is, the higher the energy cost. This is especially true if there are physical obstacles between the two nodes that may prevent communication.
- Storage and processing power. An intrinsic characteristic of these sensors is their low storage capacity. Although they also have a processor, the sensors cannot perform very large operations due to their relatively low processing power. For example, “mote”-type sensor nodes are composed of an 8-bit 4 MHz microcontroller, 40 KB of memory, and a radio with a bit rate of about 10 Kbps. This remains true even for midrange nodes, such as “UCLA/ROCKWELL’S WINS”, which have a strong ARM 1100 processor with 1 MB flash memory, 128 KB of RAM, and a 100 Kbps radio.
2.5.2. WSN Features
- Sensor autoconfiguration. In a WSN, sensors are deployed either in a random manner by a missile, drone, or airplane, or manually deployed by a human or a robot, inside or around an observed phenomenon such as a field of war, volcanic surface, or sick patient . Thus, each sensor node has the ability, on the one hand, to self-configure in the network, and, on the other hand, to collaborate with other nodes in order to dynamically reconfigure the network in case of network-topology changes . As a reminder, each sensor in a WSN has a transmitting/receiving unit that allows it to communicate with the sensors that are close to it. By exchanging information with them, the sensor can then discover its neighbors and thus know the routing method that it will adopt according to the needs of the application. Autoconfiguration appears to be a necessary feature in the case of WSNs since, on the one hand, their deployment is done randomly in the majority of applications, and, on the other hand, the number of sensors is very high.
- Scalability. Unlike traditional wireless networks (personal, local, or extended), a WSN consists of a very large number of sensors (hundreds or thousands) . The sensor network is scalable in that it has the ability to accept a large number of sensors that work together to achieve a common goal.
- Fault tolerance. In case of a sensor malfunction (lack of energy, interference with observation environment) or if new sensors are added to the network, the existing sensor nodes in the WSN must continue to normally operate without interruption . This explains the fact that a WSN does not adopt a fixed but rather dynamic topology.
- Communication ability. It can take one of two aspects, multihop or one-hop. Multihop communication is less energy-intensive, and it remains the most sought-after type of communication for WSN applications that require low energy consumption. Finally, it should be noted that WSNs use a nonstandardized protocol stack. However, the majority of scientific work that deals with the WSN issue is based on the protocol stack proposed in 2002 by Akyildiz et al. , which is presented in Section 2.6.
2.6. Protocol Architecture
- Application layer. Depending on the functionality of the sensors, different applications can be used and built on this layer.
- Transport layer. It is used to maintain the flow of data when needed in the applications used, especially when connected to the Internet.
- Network layer. This deals with the routing of data provided by the transport layer.
- Data-link layer. As the sensor network environment is noisy and nodes can be mobile, the MAC layer must ensure low power consumption in the data broadcast of neighboring nodes.
- Physical layer. This deals with data-transmission, -reception, -modulation, and -encryption techniques.
- Energy-, mobility-, and task-management levels are responsible for controlling the consumed energy, node movements, and task distribution across the entire protocol stacks. These levels allow sensors to co-ordinate their tasks and minimize energy consumption.
2.7. Dedicated Applications
- A high density of sensor nodes is required to ensure high probability of event detection. This implies that the network must have very good coverage.
- Good coverage of sensor nodes that usually depends on the type of event to be detected.
- An exact location of all sensor nodes in the network is required. This is to locate the event through the sensor node in which the event occurs. Note that distributed location algorithms could be used for this purpose.
- Good network connectivity linked to transmission coverage so that communication protocols allow the alarm sent by sensor nodes to arrive at the sink with high probability and in the shortest time.
2.8. WSN Application Areas
2.8.1. Environmental Applications
2.8.2. Medical Applications
2.8.3. Security Applications
2.8.4. Other Applications
2.9. Challenges of WSN Design and Deployment
- Energy. A sensor node, by its size, is limited in energy resources, storage capacity, and processing. One of the most serious concerns is the lifespan of the sensor node, which is entirely determined by the amount of energy available and the rate of energy consumption (that is, the average energy consumption). Since improving the energy capacity of small batteries still requires a lot of effort, the goal of a long service life is difficult to achieve by increasing the amount of available energy. As a result, the design of energy-efficient MAC protocols, communication strategies, operating systems, and energy-efficient routing mechanisms provides effective ways to extend the life of the node . The use of energy-recovery technologies for sensor-node power supplies has also been an alternative in recent years.
- Quality of Service (QoS). Quality is defined by the ability to interpret the information collected by the sink. In spite of the fact that the QoS requirements vary according to the different WSN applications, the two main measures of QoS are data reliability and latency . Usually, a successful data-exchange rate between the sensor nodes and the sink must be above a certain threshold to ensure network reliability and functionality. Reliability can be further maximized, but this could be at the cost of increased energy consumption. Therefore, a compromise is necessary. In case of latency, for certain types of applications, the reception of the data detected on the sink is strictly timed, so that latency data can cause delay problems and lead to bad decisions, particularly in industrial-surveillance applications.
- Security and privacy. WSNs find their applications in various areas of life. Some applications manipulate private data. Moreover, WSNs are networks whose topology is very frequently subject to change. The deployment of sensor nodes is sometimes done in accessible areas; they may encounter technical interference or human intrusion, which can lead to confidentiality issues. Thus, the network should allow the detection of intrusions. It is therefore necessary to design robust and lightweight algorithms for data encryption, authentication mechanisms for privacy protection, and secure routing for data relays to protect the entire network against passive and active attacks, and denials of external service providers [55,56,57].
- Adaptability. The design of a WSN must be sufficiently flexible and adaptable to be deployable across a wide range of application scenarios. The entire network should be kept functional regardless if the number of sensor nodes is hundreds, thousands, or very small. Due to the mobility of sensor nodes and observed events, as well as the possibility that a sensor node can fail in the network, the overall topology of the network may be subject to frequent change. Therefore, the design of a WSN must be sufficiently intelligent and robust to deal with these dynamic topology scenarios [10,31].
- Localization. WSNs play an important role in the problem of locating elements in a given deployment area. Certainly, GPS is a well-known tracking system and the most used location system. However, it is not satisfactory for this use, because it accumulates handicaps. It is only available outdoors and if no obstacle obstructs the field of view of the receivers. In addition, an operation under dense foliage or in cities with narrow streets is not possible, which are very bad conditions. Moreover, it is particularly expensive, both in terms of hardware, multiplied to many copies in a network with high sensor density. Moreover, signal reception is very energy-greedy, which is not compatible with management problems of battery service life. Location is done in two steps: distance estimation to the other nodes, and triangulation. The development of new localization techniques has become a major concern for WSNs [33,58].
3. Overview of Big Data
3.1. Characterization of Big Data
3.2. Toward Smart Data
3.3. Big Data Generation
3.3.1. Medical and Biomedical Data
3.3.2. Internet of Things
3.3.3. Social Media
3.3.4. Scientific Data
3.4. Areas of Utility of Big Data
3.4.1. Scientific Research
3.4.3. Socioeconomic and Political Domain
3.4.4. Transport and Energy Domain
3.5. Big Data Challenges
3.5.1. Data Acquisition and Storage
- Heterogeneity. In general, data-mining algorithms and machine learning expect homogeneous data, and cannot understand the fine distinction between them. IT systems work the fastest if they can store multiple items that are the same size and structure. Thus, Big Data must be carefully structured as a first step in their acquisition process .
- Scale. The first thing everyone thinks about Big Data is size. The management of large volumes of data and high growth has been a major problem for many decades. The volume of data is increasing faster than IT resources. Unfortunately, parallel data-processing techniques that were useful in the past for processing data between nodes do not apply directly to intranode parallelism, since the architecture is very different.
- Delay. The larger the dataset to be processed is, the longer it takes to analyze it. Designing a system that effectively treats size is also likely to result in a system capable to more quickly process a given dataset size. However, it is not only this speed that is generally heard when we talk about Velocity in the context of Big Data . There are many situations in which the result of the analysis is immediately required. Given a broad dataset, it is often essential to find elements that meet a specific criterion. The analysis of all the data to find the appropriate elements is obviously impracticable .
- Privacy and Security. One of the key value propositions of Big Data is access to data from multiple and diverse domains. Security and privacy play a very important role in Big Data research and technology. In areas such as social media and health information, more data are collected on individuals. It is therefore feared that some organizations will know too much about individuals, developing algorithms that randomize personal data into a large dataset problem .
3.5.2. Data Processing
4. Big Data Collection in LS-WSNs
4.1. Static Architectures
4.1.1. Static-Sink and Static-Sensor Architecture
4.1.2. Architecture with Sensor Nodes and Several Static Sinks
4.2. Architecture Based on Mobility
4.2.1. Static Sink Architecture and Moving Sensor Nodes
4.2.2. Architecture with a Mobile Sink and Static Sensor Nodes
4.2.3. Architecture with a Mobile Sink and Mobile Sensor Nodes
4.2.4. Architecture with Sensors and Multiple Static Sinks
4.2.5. Architecture with Sensors and Multiple Mobile Sinks
4.2.6. Hybrid Architectures
4.3. Comparison of Data-Collection Architectures in the Context of LS-WSNs
5. Data Transferring in LS-WSNs
5.1. Network Structure-Based Routing Protocols
- Flat routing. Called ‘data centric’ as well, all sensor nodes here have the same tasks to perform. This is the first approach used in the routing of data in the WSN. It is based on the collaboration of all the nodes of the network. Data properties are specified by a naming system by attribute (attribute, value) because of the difficulty of assigning a global identifier to each node given their large number. Among their advantages is their simplicity, hence the possibility of establishing communications at no additional cost where each node would only need information from its direct neighbors. The disadvantage is the depletion of the energy resources of the nodes close to the base station because all traffic toward the latter obligatorily passes by them.
- Hierarchical routing. This approach is based on the formation of clusters (common areas) [36,85,100]. The principle is to route the data collected by each node of the cluster to its head of area, the Cluster Head (CH), which, after processing their common parts, forwards them to the next destination (If the CH cannot directly reach the station, basic information is routed to the next zone leader). The advantage is the reduction of communication and energy costs by minimizing the number of messages circulating on the network, since CHs apply aggregate functions on the data of the cluster, which makes it possible to combine them. The disadvantage concerns the size of the network. In addition, as network size increases, the CH election process becomes critical and greedy in resources.
- Location-Based Routing. The identification of the geographic locations of the sensor nodes on the gathering area is of paramount importance for the data-routing mechanisms in the WSN. This location information allows the calculation of sensor positions and the distances between them to build the shortest paths between a source node and its destination. This routing approach is more energy-efficient because it dispenses sensor nodes from using random or probabilistic methods to search for routes [109,110]. In addition, the location of the nodes (and consequently of their regions) makes it possible to only broadcast requests to these regions and to avoid their diffusion in broadcast mode (global diffusion to all the nodes), thus significantly reducing the number of transmissions. The disadvantage is the need to equip sensor nodes with a satellite tracking system, such as GPS, which consumes a lot of energy.
5.2. Feature-Based Routing Protocols
- Routing protocols based on data routing. In this kind of protocol, each sensor node must choose a node of its neighborhood to deliver it the task of conveying these data. This decision is made using different techniques, including table-based routing, broadcasting, and filtering by neighbors, or flooding.
- Routing protocols based on route selection. Route selection is the process of choosing the best route to a sink from the potential routes depending on the constraints of the network and the sensor nodes. Although most protocols only choose a better route (single-path routing), multipath protocols select and save more than one path for different purposes, such as energy, traffic balance, reliability, and robustness. The entities involved in the selection process may include the source sensor node, the intermediate nodes, or the sink node.
- Routing protocols based on route discovery. Each sensor node of a WSN acts as a router and participates in the routing of data to the sink. Many debates and works have discussed when and how many routes each node must maintain in its routing table. Indeed, a node must memorize the roads to reach a sink, or only search and keep track of roads when it has an interest in reaching the sink. As a result, three types of routing protocols are distinguished depending on how routes are maintained in the routing tables. Route discovery is categorized into proactive, reactive, and hybrid routing protocols.
- Routing protocols based on alternating routes. In a WSN, from a sensor node, a routing protocol can maintain a single path that allows it to reach a sink. It can also maintain multiple paths to reach a single destination. We distinguish two classes of protocols: single path and multiple paths.
5.3. Data-Collection Steps in LS-WSNs
6. Challenges of Big Data Collection in LS-WSNs
Conflicts of Interest
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|Architecture||Reliability||Number of Sinks||Energy Consumption||Latency||Lifespan||Scalability|
|Static sensors and sinks|
|Li et al. (2018) ||high||many||low||medium||low||yes|
|Ari et al. (2018) ||high||1||low||medium||medium||yes|
|Wang et al. (2018) ||medium||1||medium||high||medium||yes|
|Ari et al. (2016) ||high||many||low||medium||low||yes|
|Zhang et al. (2012) ||low||1||strong||high||low||difficult|
|Di Francesco et al. (2011) ||medium||many||medium||low||medium||yes|
|Chen et al. (2009) ||high||many||medium||low||high||yes|
|Werner et al. (2006) ||low||1||strong||high||low||difficult|
|Mobile sensors and static sink|
|Irish et al. (2019) ||medium||many||low||medium||no|
|Handcock et al. (2009) ||medium||many||low||medium||no|
|Huang et al. (2005) ||medium||1||medium||high||medium||no|
|Mobile sink and static sensor nodes|
|Sabor et al. (2018) ||medium||many||medium||medium||high||yes|
|Kumar et al. (2018) ||medium||1||medium||high||medium||yes|
|Zhong and Ruan (2018) ||low||many||medium||medium||medium||no|
|Ari et al. (2017) ||high||1||medium||medium||high||yes|
|Khan et al. (2013) ||high||1||medium||medium||medium||yes|
|Zungeru et al. (2012) ||high||many||medium||low||medium||yes|
|Jea et al. (2005) ||low||1||medium||medium||high||no|
|Juang et al. (2002) ||low||1||medium||low||medium||no|
|Juang et al. (2002) ||depends||weak||no|
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