A survey of wireless sensor technologies applied ... - Semantic Scholar

9th ECPA conference, 7-11/07/13, Lleida, Spain

A survey of wireless sensor technologies applied to precision agriculture J.M. Barcelo-Ordinas1, J.P. Chanet2, K.-M. Hou3, J. García-Vidal1 1 Universitat Politècnica de Catalunya-Barcelona-TECH, Barcelona, Spain 2 UR TSCF, Irstea, Aubière, France 3 Blaise Pascal University, Clermont-Ferrand II, France Abstract This paper gives a state-of-art of wireless sensor network (WSN) technologies and solutions applied to precision agriculture (PA). The paper first considers applications and existing experiences that show how WSN technologies have been introduced in to agricultural applications. Then, a survey in hardware and software solutions is related with special emphasis on technological aspects. Finally, the paper shows how five networking and technological solutions may impact the next generation of sensors. These are (i) scalar wireless sensor networks, (ii) wireless multimedia sensor networks, (iii) Mobility of nodes, (iv) tag-based systems, and (v) smart-phone applications. Keywords Wireless sensor networks, smart-phones, tag-based systems, mobility networks. Introduction Over recent years, there have been important advances in several technologies related to wireless communications and in-network processing, a steady increase of processing capacity, the appearance of mature wireless sensor network (WSN) hardware and software platforms, and the widespread adoption of smart-phones. All these innovations offer a wide set of novel alternatives which could potentially address unsolved problems in PA and offer more convenient alternatives to existing solutions. Currently, a wide generation of commercial WSN platforms exists such as Mica2, iLive2 or Waspmote. In general, for PA application, a daughter board is needed to interface with the soil moisture sensors such as Watermark and Decagon. Aqeel-urRehman et al, (2011) reviewed hardware and sensor technologies concluding the lack of a generalized solution to different services and problems and the need of complete frameworks to develop systems from acquisition to the modeling and the decision support. The potential applications of WSN in PA cover a large set of scenarios and applications. We classify these following 5 different general schemes, where each of them present a particular set of challenges and opportunities. Networks of scalar (e.g., temperature, humidity, etc) sensors allow data acquisition for the control of greenhouses in order to optimize the consumption of resources (energy, water, etc). WSN could also bring solutions to develop smart building for agriculture with regulatory policies (e.g. silo). Field monitoring stations are now currently used by farmers. Wireless multimedia (e.g., images, video, etc) sensor networks (WMSN), in the context of PA, allow performing tasks such as detection of certain types of insect pests and diseases, tracking of cattle and monitoring of large areas of agricultural land. WMSN requires increasing capabilities of WSN nodes in terms of processing, bandwidth and memory.


Wireless links hardly resemble the typical P2P wired links, as they are prone to deep variations in its quality, due to phenomena such as interference and environment change. Moreover due to mobility, the network may suffer from momentary or permanent disconnections. In situations where the sensing nodes are mobile (e.g. unmanned vehicles), wireless links are broken and the network becomes opportunistic in nature, meaning that the existence of a routing path between source and destination cannot be guaranteed and the nodes will exchange data when they opportunistically meet in the field. Tag-based systems such as RFID (Radio Frequency IDentification) or NFC (Near Field Communication) can impact on different agricultural processes. The history of the produced goods can be monitored and stored on smart tags. Food and agricultural animal traceability impact on early detection of health problems and control of infectious outbreaks. The logistic of any agriculture exploitation can be deeply impacted by the use of RFID technology. Current adoption of NFC in smart-phones may also bring interesting opportunities for interaction between the user and the sensing systems. Smart-phones allow farmers to be permanently connected with the farm information system. They could use one as a connected sensor to, for instance, detect diseases with the camera, or as a crowd-sensing system to get data for insect pest detections and alerting systems or as a modern interface to monitor farm equipment and sensors for irrigation. Applications and existing experiences In recent years, wireless technologies have been introduced in to agriculture (Wang, et al, 2006). Sectors with high added value have initially taken precedence: greenhouse crops (Liu et al, 2007; Ahonen et al, 2008; Matijevics, 2009), horticulture (LópezRiquelme et al., 2009), vineyards (Galmes 2006; Morais et al., 2008b; Matese et al., 2009). Such crops also present characteristics that favor the deployment of wireless sensors: short range indoor application for greenhouses, small parcels of perennial crop with infrastructure (post) for vineyards, etc. Measurements have mainly focused on soil moisture (Cardell-Oliver et al., 2005; Valente et al., 2007) to optimize crop irrigation (Chanet et al., 2006). Wireless sensors are also widely used as advanced weather stations, measuring many parameters (temperature, humidity, radiation, etc) necessary for crop management (Fukatsu&Masayuki, 2005). The WSN are also included in control loops and are also used as actuators as in irrigation applications (Sikka et al. 2006). Beyond field applications, WSN are also used for animal management (Bishop-Hurley et al., 2007; Nadimi et al., 2008), raw materials survey (Green et al., 2009), and they should play an important role in the deployment of agricultural robotics (Cartade et al., 2012; Noguchi et al. 2004). However, the main challenges for WSN in agriculture remain issues of radio propagation signals in conjunction with changing environment during the season (Larsen et al., 2011; Vougioukas et al., 2012), the harvesting and the management of energy (Morais et al., 2008a) and the reliability of equipment (Mahapatroa and Khilar, 2012). Finally, Panchard et al, (2008) described the difficulties in deploying real platforms in PA and the obstacles put by farmers to adopt WSN, mainly due to the uncertainty about benefit-to-cost ratio of the technology. Scenarios and general cases. Networks of scalar WSN connected.


A first decision in the deployment of a wireless sensor network is the network system architecture. This includes (i) the hardware and operating system to be used, (ii) the place and topology that the network will have (sparse network, random network, clustered network, grid network, etc), (iii) the power management of the different types of nodes (batteries, solar panels), (iv) the communication protocols involved in the transfer of data including energy-efficient mechanisms and (v) the application-specific requirements (in the case of PA, are related to the agricultural process, for example the cycle of life of cropping season). Several wireless technologies are available for PA applications. The technology defines issues like data rate, communications range or complexity in building the communication stack and the application. Moreover, it defines the sensor capabilities in terms of buffering, computation, bandwidth and battery consumption. The placement and density of the wireless nodes also impacts the performance. There is a clear trade-off between node positioning and density with reliability, efficient data acquisition and energy-efficient data forwarding mechanisms. Wireless node energy depletion is a challenge: Morais et al, (2008a) analyzed solar radiation, wind and water flow as feasible energy sources that can be explored to meet the energy needs of a wireless sensor network. From the physical layer point of view, deploying a wireless sensor network in harsh environments is challenging, Dapper et al, (2003) discussed the influence that foliage has in the radio channel conditions; Vougioukas et al, (2012) proposed modeling vegetation-attenuation with a parametric and exponential decays (PED); Vine& Karam, (1996) analyzed the impact of maize and soybeans in losses and Thelen et al, (2005) studied radio wave propagation effects in potato fields. Huebner et al, (2011) proposed for environmental monitoring and agricultural applications, the use of VHF and UHF with radio software components to build a long-range wireless sensor with low energy consumption at low data rates. Energy efficient mechanisms and cross-layer design (Akyildiz et al, 2002) are a requirement in any efficient design of protocol stack in wireless sensor networks. Wireless Multimedia Sensor Networks (WMSN). Images, (Zhang&Kovacs, 2012), have widely been applied in PA for remote sensing applications such as crop monitoring, soil properties monitoring and mapping, crop pest management, leaf chemical content analysis, or weed control monitoring using satellite, airplanes, helicopters or unmanned vehicles with high resolution cameras. On the other hand, wireless sensor devices are increasing their computing and communication resources to include multimedia data processing that can be applied to many PA applications. These devices will be low-cost and low-power and able to perform object detection, object monitoring and object tracking. Examples of applications are video surveillance of invasive insects, detection and monitoring of plagues, crop monitoring data acquisition or monitoring of cattle. Multimedia over wireless sensor networks is challenging due to the low computation and low communication capabilities of wireless sensor devices, (Akyildiz et al, 2007). Tavli et al, (2012) surveyed the hardware wireless sensor architectures developed with the aim of integrating multimedia data to wireless sensor applications, while Almalkawi, et al, (2010) surveyed the challenges to develop networking protocols and mechanisms at any layer of the communication protocol stack: MAC, network and transport layer together with cross-layering energy-efficient mechanisms to transport multimedia data to the sink. The research and applicability of computer vision algorithms for constraint wireless sensors still is in its infancy. The major challenges are (i) how to process the image or video frames at the sensor side in order to detect,


monitor or track objects with the minimum energy costs given the small computability and memory resources of a wireless sensor, and (ii) how to compress the image or video frames with such resources and knowing that the current bandwidth in sensors is lower than Mb/s. There is a clear trade-off between the battery consumption needed to perform these tasks at the node side with low CPU regime and low buffering capabilities and the battery consumption in the communications subsystem to transmit the high demanding traffic produced by multimedia data, (Akyildiz et al, 2007), (Almalkawi, et al, 2010) or (Cesana et al, 2012). Examples of the use of WMSN in PA are Wark et al, (2007) who presented Fleck-1, a sensor platform that includes a camera for sensing the state of pasture fields. Garcia-Sanchez et al, (2011) integrated a network of scalar sensors (salinity, temperature, pH and soil-moisture) with a video surveillance wireless sensor node in a broccoli crop farm. Finally, Zhang&Xiao, (2012) presented a low-complexity image compression scheme for a WMSN in a crop monitoring application. Mobility of Nodes and Disruption of the connectivity. Some applications require the use of mobile nodes that connect to a remote server; e.g. (Wark et al, 2006; Billingsley et al, 2009; Ramos, 2010). Cellular networks provide adequate connectivity in many cases, but for other scenarios, cellular networks have obvious limitations due to: (i) limitations in power consumption of devices, (ii) connection costs or (iii) lack of network coverage. In these non-cellular scenarios, the network is often opportunistic, meaning that there is not an operator who engineers the network to achieve a continuous coverage. These networks can be built either following a centralized access paradigm (e.g. WiFi Access Points of a community network or a Mesh network), or a peer-to-peer paradigm (e.g. using short-range technologies for establishing node-to-node links). In opportunistic networks, it is a normal condition that wireless links have an intermittent connectivity and that nodes can stay disconnected for long periods of time, and source and destination nodes might never be connected to the same network, at the same time. As is well known, (Farrel et al, 2006), TCP/IP does not work well in spotty and disconnection-prone environments and the use of alternative architectures is often necessary. One important example of alternative architecture is the Delay Tolerant Networking (DTN) architecture, proposed as a general solution for disconnected networks; (Fall, 2003). IETF RFC 4838 (Cerf et al, 2007) defines the basic functionalities, such as the “bundle layer”, for interconnection of DTN. Examples of transport protocols for DTNs are the Licklider Transmission Protocol (LTP), IETF RFC 5325 (Burleigh et al, 2008), or Saratoga, (Wood et al, 2007). However, these transport protocols are focused on scenarios of large latencies and scheduled disconnections, meaning that it is not clear how well they would perform in scenarios such as the ones appearing in Smart Agriculture. Another important example is the opportunistic network architecture; (Pelusi et al, 2009). Routing in opportunistic networks (Cao&Sun, 2012) is generally complicated due to the absence of knowledge about the topology of the network, and innovative routing schemes based on contacts and opportunities have been proposed. The DTN paradigm is useful for PA applications that present mobility of nodes. Examples are mobile nodes that collect data from static nodes such as sensors or tagbased systems and monitoring of cattle that have an attached sensor device. Tag-based Systems. RFID is based on a wireless non-contact system that uses radio-frequency electromagnetic fields to transfer data from a tag attached to an object, and having a


range lower than some tenths of meter. There are current RFID devices, (RuizGarcia&Loredana, (2011)), able to measure temperature, humidity, vibration, concentration of gases, etc that can fit a wide range of applications such as greenhouse, precision irrigation, food chain control, viticulture, or electronic identification of cattle among others. Examples of use are: the matching of bins with their corresponding tree during harvesting (Ampatzidis&Vougioukas, 2009), building smart sensor networks for soil moisture measurements (Vellidis et al, 2008) or secure identification of plants in the potted flowers industry (Barge et al, 2010). Smart-phone based applications. Smart-phones are going to impact profoundly the Farm Management Information System (FMIS). These new devices may become the unique wireless gateway for farmers to their FMIS, to their decision support tools, to their sensors, to their equipment, to the Internet. Indeed, thanks to his smart-phone, the farmer can interact with the WSN deployed in his fields. This equipment allows, for example, to have access to field monitoring systems from anywhere (Peres et al., 2011). For this, they use different embedded wireless technology (GSM, Bluetooth, WiFi, etc). Smart-phones can also manage the deployed WSN (Xiong et al., 2013). Smart-phones are also important tools for decision making in the field: the farmer has a wireless tool for decision support always connected (Arroqui et al., 2012; Hwang&Hyun, 2011). These terminals also ensure recording of location-based practice for traceability across many applications marketed (Ballandonne, 2012). Beyond being connected terminals, smartphones are also connected sensors (GPS, photo, accelerometer, etc) and thus allow users to localize measurement points and transmit them to the FMIS (Guizard, 2012; MolinaMartínez et al., 2011; Molina-Martínez&Ruiz-Canales, 2009). The farmer can make decisions based on field measurements while connected in real time with stakeholders. One may also consider applications such crowd-sensing to track diseases by farming communities (Demirbas et al., 2010; Newell et al, 2012). Conclusions This paper summarizes the main trends in technology that wireless technologies can bring to the field of Precision Agriculture. The paper shows how scalar and multimedia sensor networks will be a key element in the different processes involved in PA. Moreover, tag-based systems and smart-phone technologies will also complete the range of wireless technologies that will offer a wide set of novel alternatives which could potentially address unsolved problems in PA. Acknowledgements This work has been supported by grants TIN2010-21378-C02-01 and SGR2009-1167. References Ahonen, T. and Virrankoski, R. and Elmusrati, M., (2008). Greenhouse monitoring with wireless sensor network. IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, 2008. MESA, Beijing, China, pp. 403-408 Akyildiz, I.F; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. (2002). Wireless sensor networks: a survey. Comput. Netw., 38, pp. 393-422 Akyildiz, I.F., Melodia, t., Chowdhury, K. R., (2007). A survey on wireless multimedia sensor networks. Computer Networks, Vol. 51, pp. 921-960.


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