Wireless sensor network (WSN) is one of the most suitable candidate areas where cognitive radio (CR) technology can be used for opportunistic spectrum access, in the purpose of decreasing significant amount of energy consumption for the whole system. Research in this area is still in its infancy, but it is rapidly progressing. In this project, the basics overview of conventional WSN and CR technology will be provided, and then I investigate the use of CR in WSN in order to show the advantage of CRWSN in saving energy consumption. Some certain prospects and challenges are also illustrated for further development of CRWSN.
Contents Abstract I Introduction II Conventional wireless sensor network i Sensor nodes ii Common WSNs standards and topologies III Cognitive radio technology 10 IV Cognitive radio-based wireless sensor network 13 i CRSN architecture 14 ii CRSN hardware 15 iii CRSN topologies 16 iv Potential application areas of CR-WSNs 18 V CR-WSNs for energy saving 20 i System model 20 ii Result and discussion 22 iii Conclusion 22 VI Challenges and prospects of CRWSNs 22 VII Conclusion and future work 23 References 23 1|P ag e Abstract Wireless sensor network (WSN) is one of the most suitable candidate areas where cognitive radio (CR) technology can be used for opportunistic spectrum access, in the purpose of decreasing significant amount of energy consumption for the whole system Research in this area is still in its infancy, but it is rapidly progressing In this project, the basics overview of conventional WSN and CR technology will be provided, and then I investigate the use of CR in WSN in order to show the advantage of CRWSN in saving energy consumption Some certain prospects and challenges are also illustrated for further development of CRWSN I Introduction Nowadays, communication networks are indispensable part of human life in modern world They have huge applications, ranging from social networking [1], security networks [2], trade and commerce to educational research and development networks [3] Among the leading area of research and developments in wireless communications are techniques and mechanisms to implement the most cost effective and efficient utilization of radio frequency spectrum and energy Radio frequency is considered as the most vital and scarce resource among all wireless network resources, and it is closely followed by energy consumption, especially in low energy, battery powered sensor network devices [4] However, it has been observed that the scarcity of the frequency spectrum is mainly due to the adoption of a static spectrum assignment policy which gives exclusive right-of-use (RoU) to a licensed user of a licensed particular spectrum This exclusive right has led to scarcity of spectrum in licensed spectrum band, while in the unlicensed bands where WSNs operate, there will be overcrowding due to increase in the number of users in this band Recently, Wireless sensor networks (WSNs) have proved itself as one of the most promising technologies for the future [5, 6] A wireless sensor network is a network formed by a large number of sensor nodes, embedded CPUs, working together to monitor a region in order to obtain data about the environment Each node is equipped with a sensor to detect physical phenomena such as light, heat, pressure, etc and uses one or more specific wireless communication technologies to form a network [7, 8] WSNs have gained worldwide attention and were much improved in recent years Today, smart grid [9-11], smart homes [12, 13], smart water networks [14], intelligent transportation [15-17], are infrastructure systems that connect our world more than we ever thought possible, the 2|P ag e common vision of such systems is usually associated with one single concept, the internet of things (IoT) [18, 19], where through the use of sensors, the entire physical infrastructure is closely coupled with information and communication technologies WSNs are regarded as a revolutionary information gathering method to build the information and communication system which will greatly improve the reliability and efficiency of infrastructure systems Compared with the wired solution, WSNs feature easier deployment and better flexibility of devices With the rapid technological development of sensors as well as wireless communication technologies, WSNs will become the key technology for IoT Increasing usage of wireless communications triggered the development of dynamic spectrum access schemes The key enabling technology providing dynamic, i.e opportunistic, spectrum access is the cognitive radio CR is defined as a radio capable of being aware of its surroundings, learning and adaptively changing its operating parameters in real time with the objective of providing reliable ubiquitous spectrally efficient communication With these capabilities, CRs can operate in licensed bands as well as in unlicensed band CRs have three main features; self-awareness, re-configurability and intelligent adaptive behaviour which help static spectrum allocation and utilization give way to a dynamic spectrum access and efficient utilization Dynamic spectrum access, allows the unlicensed user (regarded as secondary user-SU) opportunistic use the licensed band belonging to another user (regarded as primary user-PU) while PU is not currently available As posited by [20], cognitive radio utilizes the underutilized spectrum resources along time and frequency and provides efficient dynamic spectrum access Leveraging on the advantages of the opportunistic spectrum access provided by CR technology, wireless sensor networks have the potential of operating at lower licensed spectrum band, for example the TV band with efficient spectrum usage and higher energy efficiency due to range extension [21] A CR-based WSN (CRWSN) of CR-based sensor network (CRSN) is multichannel wireless network in which sensor nodes dynamically adapt themselves to the available communication channel [22] The objective of this project report is to provide a clear picture of potentials of CRSNs, the current state-of-the-art and the research about the energy efficiency in integrating WSN with CR technology The remained content of the project report is organized as followed In section II and III, the basic concept and specifications of conventional WSN is provided and CR technology are provided CR-based WSN and CRSN architecture including CR sensor node structure and 3|P ag e possible architecture topologies of CRSN will be presented in section IV The follow section will be the challenges and prospects of CRWSN and the last section I state the concluding and further works in the future II Conventional wireless sensor network Due to recent technological advances, the manufacturing of small and low-cost sensors has become technically and economically feasible These sensors measure ambient conditions in the environment surrounding them and then transform these measurements into signals that can be processed to reveal some characteristics about phenomena located in the area around these sensors (Figure 1) A large number of these sensors can be networked in many applications that require unattended operations, hence producing a wireless sensor network (WSN).Typically, WSNs contain hundreds or thousands of these sensor nodes and these sensors have the ability to communicate either among each other or directly to an external base station (BS) A greater number of sensors allows for sensing over larger geographical regions with greater accuracy Figure 1: Conventional wireless sensor network i Sensor nodes 4|P ag e The sensor node is one of the main parts of a WSN The hardware of a sensor node generally includes four parts: the power and power management module, a sensor, a microcontroller, and a wireless transceiver The power module offers the reliable power needed for the system The sensor is the bond of a WSN node which can obtain the environmental and equipment status A sensor is in charge of collecting and transforming the signals, such as light, vibration and chemical signal, in to electrical signals and then transferring them to the microcontroller, and in each application we use many different kinds of sensors which depend on the property of each project to make sure the system or network working on the highest efficiency and optimal power consumption In fact, I took a research on sensor field, about many kind of sensor but more detailed about temperature, humidity, light, soil moisture sensors…which are available in the workshop such as temperature sensor LM 35, temperature and humidity sensor DHT11 and soil moisture sensor etc i ii iii iv v vi LM 35 Soil moisture sensor DHT11 These sensors are used commonly in simple projects or applications that not required high accuracy and they are very simple to synchronize with MCU After the sensor sensing the environment, it’ll receive a package of the data, and then it transfers data to the microcontroller processing the data accordingly The Wireless Transceiver (RF module) then transfers the data, so that the physical realization of communication can be achieved It’s important that the design of the all parts of a WSN node consider the WSN node features of tiny size and limited power ii Common WSNs standards and topologies 5|P ag e The access network, whose length ranges from a few hundred meters to several miles, includes all the devices between the backbone network and the user terminals It is thus aptly call “the last mile” Because the backbone network usually uses optical fiber structure with a high transmission rate, the access network has become the bottleneck of the entire network system Due to the open property of wireless channels, conflicts will happen in time, space or frequency dimension when the channel is shared among multiple users The function of access network technologies is to manage and coordinate the use of channels resources to ensure the interconnection and communication of multiple users on the shared channel According to the distance and speed of access, existing access technologies can be classified into four categories in Figure 2: wireless local area network (WLAN), wireless metropolitan area network (WMAN), wireless personal area network (WPAN) and wireless wide area network (WWAN) Figure 2: Existing access technologies However, the overall developing trend of high transmission rates is not suitable for the application requirements of WSNs The representative access technologies that are more systematic and noteworthy are Bluetooth 4.0 oriented towards medical WSN; IEEE 802.15.4 oriented towards industrial WSN and WLAN IEEE 802.11 TM in view of the IoT In some basic, simple projects and applications such as monitoring temperature, humidity, smoke, light, pressure which not required far distance between nodes or high speed 6|P ag e transmission … we use radio frequency (RF) module, technique which included in IEEE 802.15.4 standard for communicating among nodes or among nodes and BS, with a little bit higher requirement we can use GPRS, 3G technology to upload the data to the base data then the client can access the data from computer or smartphone with internet connected IEEE 802.15.4 is a standard which specifies the physical layer and media access control for low-rate wireless personal area network (LR-WPANs) It’s main standardized technology for low cost, low power, low date rate, short range wireless networks It operates in ISM bands, 868MHz, 915 MHz, 2.4GHz and was seen as the main wireless communication technology for automation and control applications The over-the-air data rates in this standard are 250kb/s, 40kb/s, 20kb/s and use star or peer-to-peer topologies with 16bits or 64bits addressing schema Every wireless communication devices in a network need to assemble as a “team”, WSNs are the same way In WSNs network, nodes or devices are connected together in several different layouts or topologies to give the network its structure, these topologies define the way the devices are logically connected to each other but their physical arrangement may be different There are some common topologies used in WSNs network such as: Pair, Star, Mesh or Cluster Tree, each of them is used in specific applications and projects to give the best efficiency [23, 24] Pair topology This is the simplest network topology with only two nodes or devices One node must be a FFD or PAN coordinator to form the network and the other can be configured as an end device or a router [23] Star topology The star topology network is formed by a FFD - PAN coordinator placed in the centre of the network and surrounded by several RFDs – End devices Any data packet exchange between ends devices must pass through the coordinator, which routes them as needed between devices The advantage of star topology is simple and packets go through at most two hops to the destination but it also has some drawbacks such as; there is no alternative path from the source to the destination and the network totally depends too much on the coordinator so that the coordinator may become bottlenecked 7|P ag e Figure 3: Pair, star, mesh, cluster tree topologies Mesh topology Mesh topology, known as a peer-to-peer network, consists of a PAN coordinator, several router and end device nodes There is no communications restriction in this topology that means data packets can pass through multiple hops to reach their destination In addition, a mesh topology is well-known for its self-healing ability, meaning during transmission, if a path fails, the node will find an alternative path to the destination so that it can eliminate dead zones The other mesh topology advantage is that devices can be close to each other which help to decrease the transmission power in the network Beyond its advantages, there are also some disadvantages such as: low performance for long-thin networks, larger routing tables that implicitly use more memory, incorporates a low number of nodes and produces a low throughput and compared with star topology, mesh topology requires greater overhead and more complex routing [23, 25, 26] Cluster tree topology In cluster tree topology, the PAN coordinator of the clusters forms a tree structure, and acts as intermediate aggregators and routers of data between different devices Routers in this topology form a backbone of softs, with end devices clustered around each router and it’s nearly the same as a mesh configuration [23, 27] Furthermore, several hybrid topologies are being developed and researched for maximizing efficiency of power consumption, routing, and gathering data Depending on the purpose of each project, a WSN uses one or combined wireless communication technologies to exchange data within the network to achieve the highest efficiency of transmitting and energy consumption Table depicts the specifications of some common wireless communication technologies which are usually used in WSN 8|P ag e Specifications Working Frequency Range Network Topology ZigBee 868/915 MHz, 2.4 GHz Up to 1500m Star, Mesh, Tree Bluetooth 2.4 GHz 10m Piconet, Scatternet BLE 2.4 GHz 77m WirelessHART 2.4 GHz ISA100.11a 2.4 GHz 50250m 50250m 6LoWPAN 868/915 MHz, 2.4 GHz IPv6 Power Consumption Self Healing Latency Entries Max data rate Application References Monitoring Smart girds, home [23 ,24] Standards Low - Low - Ultra Low - Star, Mesh Small Low 65536 250 Kbps - Medium 720 Kbps - Low 7+ Mbps Low Low ~30000 250 Kbps Low Low 30000+ 250 Kbps Star, Mesh, Tree Low Low Depend 250 Kbps - Low - Low 110 Mbps Up to 480 Mbps High - Medium Depend ~150 Mbps Piconet, Mesh Scattermet Star, Mesh UWB 3.1 - 10.6 GHz 10-30m Piconet, Peer-to-Peer Wi-Fi 2.4 GHz, GHz 100m BSS, ESS ANT/ANT+ 2.4 GHz 30m Star, Mesh, Tree - Ultra Low - Low 65536 per shared channel (8 shared channel) Z Wave 15 ISM bands 30m40m Mesh - Low - Low 232 Smart phone, IoT, Health, Sport & Fitness Industrial automation Industrial automation Automation, Entertainment Applications Navigation systems, WMSN, military Multimedia, Voice data Up to 60 Kbit/s Fitness monitoring 100 Kbit/s Control and sensor applications Table 1: Comparison of common wireless communication technologies used in WSN 9|P ag e Mobile phone, Wearables [28,29,30] [31,32,33] [34,35] [36,37,38] [39,40] [41] [42,43,44] [45,46] [47] III Cognitive radio technology CRs are borne out of a software radio, which is a transceiver whose communication functions are realised as programs running on a suitable processor It comprises all layers to the application layer [48] A software-defined radio (SDR) is a practical implementation of a software radio in which received signals are sampled after a suitable band selection filter instead of directly sampling antenna output If in addition, a SDR can sense its environment, track changes, and react upon its findings, then it is referred to as a CR CRNs can provide high bandwidth wireless communication to users through dynamic spectrum access (DSA) techniques and heterogeneous architectures In CR terminology, primary users (PUs), also known as incumbent users, are licensed users with legacy rights or higher priority to utilise a particular part of the spectrum Secondary users (SUs), also referred to as cognitive users, are unlicensed users with a lower priority, and exploit the spectrum opportunistically such that PUs not suffer harmful interference from them SUs as a result must possess CR capacity, such as dynamic spectrum access techniques, that will allow them to function in the most favourable channel Only users with a tangible legal or regulatory right to spectrum are considered PUs In unlicensed bands, e.g in ISM frequency bands where most of the industrial wireless network technology operates, there are no PUs Despite the perceived importance of some applications, SUs compete equally for the same resource A CRN can be multiband, multichannel, multiservice and multi-standard [48] CR shall give SUs the ability to (1) detect licensed PUs and evaluate which parts of the wireless spectrum are available for use (spectrum sensing), (2) select the best available spectrum channel (spectrum decision), (3) coordinate access to this channel with other SUs (spectrum sharing) and (4) vacate the channel when a licensed user is detected (spectrum mobility) [49] The dynamic spectrum access operation where CRs use temporarily unused spectrum, also known as white space or a spectrum hole is illustrated in Fig 10 | P a g e IV Cognitive radio-based wireless sensor network CR-wireless sensor networks (CR-WSNs) are a specialized ad hoc network of distributed wireless sensors that are equipped with cognitive radio capabilities CR-WSN is different in many aspects with a conventional WSN and conventional distributed cognitive radio networks (CRNs) The following section details the differences in the aspects among ad hoc CRNs, WSNs, and CR-WSNs CR-WSNs normally involve a large number of spatially distributed energy-constrained, self-configuring, self-aware WS nodes with cognitive capabilities They require cognition capacity for a high degree of cooperation and adaptation to perform the desired coordinated tasks They have not only to transfer data packets, but also to protect incumbent license users More explicitly, this is a system that employs most of the capabilities required for a CR system, as defined by International Telecommunication Union (ITU) [54] and also for WSNs According to Akan et al [55], a CR-WSN is defined as a distributed network of wireless cognitive radio wireless sensor (CRWS) nodes, which sense an event signal and collaboratively communicate their readings dynamically over the available spectrum bands in a multi-hop manner, ultimately to satisfy the application-specific requirements Figure 6: CR-WSN model In CR-WSNs, a wireless sensor node selects the most appropriate channel once an idle channel is identified and vacates the channel when the arrival of a licensed user on the channel is detected The cognitive radio technique is probably one of the most 13 | P a g e promising techniques for improving efficiency of the WSNs CR-WSNs increase spectrum utilization, and fulfills the end-to-end goal, increase network efficiency and extend the lifetime of WSNs Figure presents a CR-WSNs model i CRSN architecture Figure 7: A typical cognitive radio sensor network (CRSN) architecture Cognitive radio sensor nodes form wireless communication architecture of CRSN as shown Fig over which the information obtained from the field is conveyed to the sink in multiple hops The main duty of the sensor nodes is to perform sensing on the environment In addition to this conventional sensing duty, CRSN nodes also perform sensing on the spectrum Depending on the spectrum availability, sensor nodes transmit their readings in an opportunistic manner to their next hop cognitive radio sensor nodes, and ultimately, to the sink The sink may be also equipped with cognitive radio capability, i.e., cognitive radio sink In addition to the event readings, sensors may exchange additional information with the sink including control data for group formation, spectrum allocation, spectrum handoffaware route determination depending on the specific topology A typical sensor field contains resource-constrained CRSN nodes and CRSN sink However, in certain application 14 | P a g e scenarios, special nodes with high power sources, i.e., actors, which act upon the sensed event, may be part of the architecture as well [68] These nodes perform additional tasks like local spectrum bargaining, or acting as a spectrum broker Therefore, they may be actively part of the network topology It is assumed that the sink has unlimited power and a number of cognitive transceivers, enabling it to transmit and receive multiple data flows concurrently ii CRSN hardware CR wireless sensors have hardware constraints in terms of computational power, storage and energy Unlike conventional wireless sensors, they have a responsibility to sense channels, analyze, decide, and act CR wireless sensors should be capable of changing the parameter or transmitters based on an interaction with its environment As shown in Figure 8, a CRWS consists of six basic units: (i) a sensing unit; (ii) a processing and storage unit; (iii) a CR unit; (iv) a transceiver unit; (v) a power unit; and (vi) a miscellaneous unit Sensing units contain sensors and analog to digital converters (ADCs) The analog signal observed by the sensor is converted to a digital signal and sent to the processing unit The CRWS should have cognition capability using a state-of-the art artificial intelligence technology This capability is accommodated in the CR unit The CR unit needs to adapt the communication parameters dynamically, such as carrier frequency, transmission power, and modulation The unit needs to select the best available channel, share the spectrum with other users, and manage the spectrum mobility, i.e., vacate the currently using channel in the case the PU wants to use that channel A transceiver unit is responsible for receiving and sending data Because the energy harvesting techniques in wireless sensor nodes have developed rapidly, the energy harvesting or recharging units are optional and sensor specific A miscellaneous unit is an application- specific additional unit, such as a location-finding unit, energy harvesting unit, and mobilizing unit, etc Akan et al [55] proposed a similar hardware structure of a CR wireless sensor Designing intelligent hardware for CR-WSNs is a very challenging issue Many artificial intelligence techniques have been proposed to fulfill the basic principle of CR, i.e., observation, reconfiguration and cognition Some examples include artificial neural 15 | P a g e networks (ANNs), metaheuristic algorithms; hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs) The factors that affect the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed in reference [56] Nevertheless, it is unclear how much intelligent hardware for WS-CRNs is intelligent enough and no threshold has been defined for it Figure 8: Hardware structure of CR wireless sensor iii CRSN topologies According to the application requirements, cognitive radio sensor networks may exhibit different network topologies as explored in the following 1) Ad Hoc CRSN: Without any infrastructural element, inherent network deployment of sensor networks yields an ad hoc cognitive radio sensor network as shown in Fig Nodes send their readings to the sink in multiple hops, in an ad-hoc manner In ad hoc CRSN, spectrum sensing may be performed by each node individually or collaboratively in a distributed way Similarly, spectrum allocation can also be based on the individual decision of sensor nodes This topology imposes almost no communication overhead in terms of control data However, due to hidden terminal problem, spectrum sensing results may be inaccurate, causing performance degradation in the primary user network 2) Clustered CRSN: In general, it is essential to designate a common channel to exchange various control data, such as spectrum sensing results, spectrum allocation data, neighbour discovery and maintenance information Most of the time, it may not be possible to find such common channel available throughout the entire network However, it has been shown in 16 | P a g e [69] that finding a common channel in a certain restricted locality are highly possible due to the spatial correlation of channel availability Therefore, a cluster-based network architecture as in Fig (a) is an appropriate choice for effective operation of dynamic spectrum management in CRSN In this case, cluster-heads may also be assigned to handle additional tasks such as the collection and dissemination of spectrum availability information, and the local bargaining of spectrum To this end, new cluster-head selection and cluster formation algorithms may be developed for CRSN which jointly consider the inherent resource constraints as well as the challenges and requirements of opportunistic access in CRSN 3) Heterogeneous and Hierarchical CRSN: In some cases, CRSN architecture may incorporate special nodes equipped with more or renewable power sources such as the actor nodes in wireless sensor and actor networks (WSAN) [68] These nodes may have longer transmission ranges, and hence, be used as relay nodes much like the mesh network case This forms a heterogeneous and multi-layer hierarchical topology consisting of ordinary CRSN nodes, high power relay nodes, e.g., cognitive radio actor nodes, and the sink as shown in Fig (b) While the presence of capable actor nodes may be exploited for effective opportunistic access over the CRSN, the associated heterogeneity brings additional challenges Among the others, sensor and actor deployment, increased communication overhead due to hierarchical coordination, and the need for cognitive radio capability over the actor nodes need to be addressed Figure 9: Possible network topologies for CRSN (a) Clustered (b) heterogeneous hierarchical 17 | P a g e iv Potential application areas of CR-WSNs CR-WSNs may have a wide range of application domains Indeed, CR-WSN can be deployed anywhere in place of WSNs Some examples of prospective areas where CR-WSNs can be deployed are as follows: facility management, machine surveillance and preventive maintenance, precision agriculture, medicine and health, logistics, object tracking, telemetries, intelligent roadside, security, actuation and maintenance of complex systems, monitoring of indoor and outdoor environments This section discusses some of the potential areas where CR-WSNs can be deployed with examples Military and Public Security Applications Conventional WSNs are used in many military and public security applications, such as: (a) chemical biological radiological and nuclear (CBRN) attack detection and investigation; (b) command control; (c) Gather the information of battle damage evaluation; (d) battlefield surveillance; (e) intelligence assistant (f) targeting, etc In the battlefield or in disputed regions, an adversary may send jamming signals to disturb radio communication channels [57, 58] In such situations, because CR-WSs can handoff frequencies over a wide range, CR-WSNs can use different frequency bands, thereby avoiding the frequency band with a jamming signal In addition, some military applications require a large bandwidth, minimum channel access and communication delays For such applications, CR-WSNs can be a better option Health Care In a health care system, such as telemedicine, wearable body sensors are being used increasingly Numerous wireless sensor nodes are placed on patients and acquire critical data for remote monitoring by health care providers In 2011, the IEEE 802.15 Task Group (BAN) [59] approved a draft of a standard for body area network (BAN) technology Wireless BAN-assisted health care systems have already been in practice in some remote areas of developing countries, such as in Nepal and India [59,60] Wireless BAN for healthcare systems is suitable for areas, where the number of health specialists is relatively low Medical data is critical, delay and error sensitive Therefore, the limitation of traditional WSN, as discussed in the previous section confines the potentiality of telemedicine The 18 | P a g e QoS may not be achieved at a satisfactory level if the operating spectrum band is crowded in convenient ‘telemedicine with BAN’ The use of ‘CR wearable body wireless sensors’ can mitigate these problems due to bandwidth, jamming and global operability, hence improve reliability Figure 10 presents a model for wireless BAN with CR wireless sensors A significant amount of research has been carried out in the area of WBASN [61] The requirements of cognitive radio implementation in wireless medical networks are discussed in reference [62] Figure 10: Wireless body area network with CRWS Home Appliances and Indoor Applications Many potential and emerging indoor applications require a dense WSNs environment to achieve an adequate QoS Conventional WSNs experience significant challenges in achieving reliable communication because ISM bands in indoor areas are extremely crowded Some examples of the indoor applications of WSNs are intelligent buildings, home monitoring systems, factory automation, personal entertainment, etc CR-WSNs can mitigate the challenges faced by conventional indoor WSNs applications Transportation and Vehicular Networks The IEEE 1609.4 standard proposes multi-channel operations in wireless access for vehicular environments (WAVE) The WAVE system operates on the 75 MHz spectrum in the 5.9 GHz band with one control channel and six service channels All vehicular users will have to contend for channel access and use it to transmit the information in the 5.9 GHz band However, it still suffers from spectrum insufficiency problems This spectrum scarcity issue and the requirements of cognitive radio in WAVE have been studied [63-65] 19 | P a g e Some preliminary works in CR-enabled vehicular communications have already been done [64] Vehicular wireless sensor networks are emerging as a new network paradigm for proactively gathering monitoring information in urban environments CR-WSNs are likely to be more relevant in this field Although this area still needs to be examined, some protocols for highway safety using CR-WSNs have been proposed [66, 67] V CR-WSNs for energy saving In cognitive radio networks, unlicensed users (primary users) are allowed to use idle frequency channels CR enabled nodes are capable to adaptively tune their transmission parameters based on the operating environment In the present work, we assume that wireless sensor network consists of smart radio nodes with CR capability These smart radio nodes can sense the spectrum, identify the white space and decide to use the best possible available channel for its communication Smart radios can adapt their operating parameters dynamically including transmitter power, operating frequency, modulation scheme and coding Communication range is an important system parameter in wireless sensor networks as it directly affects the energy consumption [4] In this work, I compare the radio propagation loss in terms of the communication range available to conventional sensor nodes with that available to sensor nodes with CR capability Numerical results are presented to show the advantage of CR in terms of increased communication range and hence the energy saving i System model We consider a wireless sensor network comprised of smart radio sensor nodes equipped with cognitive radio capability Link level simulation is performed in MatLab 7.0.4 for computing difference in the communication range available in ISM band that is 2.412 GHz and white space that is 980 MHz Using channel gain at the two different channels, energy saving is computed Sensor nodes enabled with CR capability can dynamically adapt their operating frequency The equation defined by H.T Friis which describes this wave behaviour in “free space*”, called the Friis Transmission, the equation, is: 𝑃𝑅 = 𝑃𝑇 𝐺𝑇 𝐺𝑅 ( 20 | P a g e 𝑛 ) ×( ) 4𝜋 𝑑 Where PR = power received (watts) PT = power transmitted (watts) GT = gain of transmit antenna (scalar) GR = gain of receive antenna (scalar) = wavelength (metric or English) d = distance separating transmitter and receiver (metric or English) n = exponent for environmental conditions (n = defines “free space”) Some related formulas can be deduced from basic Friis transmission equation: Path loss in scalar form (where GT = GR = 1): 𝐿𝑃𝐴𝑇𝐻 = 𝑃𝑅 𝑛 =[ ] [ ] 𝑃𝑇 4𝜋 𝑑 Path loss in decibel form (where GT = GR = 1): 𝐿𝑃𝐴𝑇𝐻 = 𝑃𝑅 − 𝑃𝑇 = 20 log ( ) + 10𝑛 log ( ) 4𝜋 𝑑 The generic Range Calculator spread-sheet equation solved for distance “d”: 𝑑= 𝑎𝑟 4𝜋 × 1020 With ar = path loss + fade margin Transmission range is computed at 2.412 MHz for primary (licensed) users and at 980 MHz for secondary (unlicensed) users It is assumed that white space is available at this frequency, which is made available to the secondary users in opportunistic manner With a cognitive radio, secondary user (mobile station) can dynamically switch its radio to available channel in opportunistic manner Relative energy saving as a function of transmission power is computed as given by 21 | P a g e ii Result and discussion iii Conclusion VI Challenges and prospects of CRWSNs With spectrum handoff capability, tactical surveillance CRSNs may be less susceptible to interception and jamming threats still CRSN having limitation as follows: Node development: For realization, development of efficient and practical CR-WSNs is one of the major issues for in CRWSN Considering the design principles and operation objective of the sensor network, the limitations of the nodes, hardware and software requirements for sensor nodes with cognitive radio capabilities, there is the need for extensive study in order to come up with such efficient and practical nodes Node deployment: There is the need for proper mathematical analysis for optimum node deployment for various topologies for the purpose of developing efficient and practical node deployment mechanisms Where there exists information about the primary user activities, spectrum characteristics may provide improvement of the network lifetime and transmission quality Optimal network coverage: As a result of the primary user activity couple with node failure, the spatial location of sensor nodes may vary Under this condition, to maintain maximum network coverage, it is certain some nodes may have to transmit with more power, which results in power and energy consumption But on the other hand, connectivity may be achieved at longer ranges with lower frequencies which help to save transmission energy It then becomes necessary to consider dynamic spectrum management while analysing optimum network coverage Also a new topology schemes which addresses trade-off between network lifetime and network coverage should be introduced Coordinated and uncoordinated operation: Operations such as spectrum sensing, spectrum detection, spectrum allocation, spectrum sharing, and spectrum handoff may be performed individually by sensor nodes or cooperatively among sensor nodes It therefore becomes necessary to carry out detailed comparison between the coordinated and uncoordinated network operation for efficient communication in a resource-constrained CRSN 22 | P a g e Clustering issue: For a cluster-based CR-WSN, clustering and hierarchy formation increases communication overhead which may be increased due to node mobility and spectrum handoff Therefore, for applications using cluster-based and hierarchical topologies, dynamic spectrum aware cluster formation and maintenance techniques must be investigated VII Conclusion and future work Cognitive radio can improve spectrum utilization and communication quality with opportunistic spectrum access capability and adaptability to the channel conditions Dynamic spectrum management provides multiple channel access which helps to solve the problems caused by the dense deployment and burst communication nature of sensor networks Even though cognitive radio could have lots of advantage like dynamic spectrum access, adaptability it comes with shortcoming There exist significant challenges for the realization of CRSN If we able to solve this challenges then CRSN will 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