Smart Sensors and Sensor Networks

1. Smart Sensors and Sensor Networks Lecture3 Time synchronization and communication protocols

2. Smart Sensors and Sensor Networks The time synchronization problem • Time synchronization is a standard problem in distributed systems; • Nodes measure time using local clocks, driven by oscillators; because of random phase shifts and drift rates of oscillators, the local time would start to differ, meaning loose of synchronization; correction becomes necessary; • Factors influencing time synchronization in large systems: • Temperature: the temperature variation throughout the day may cause the clock to speed up or slow down; • Phase noise: caused by: access fluctuation at the hardware interface, response variation of the operating system to interrupts and jitter in the network delay (it can be caused by medium access and queuing delays); • Frequency noise: is due to the instability of the clock crystal; • Asymmetric delay: the delay of the path from one node to another may differ from the return path; • Clock glitches: short undesired pulses on the clock line;

3. Smart Sensors and Sensor Networks • Additional constraints specific to WSNs: • Energy consumption of the time synchronization algorithms: must be minimized; • The time synchronization algorithms must address the mapping between the sensor network time and the Internet (or universal coordinated) time; • Usually large number of nodes to be synchronized; • Varying precision requirements: they are determined by the application; • For short distance multihop broadcast, data processing time and variation of data processing time may contribute the most in time fluctuations and differences in time delays; • Instead of a sender and a receiver, which must be synchronized, in WSNs a lot of tiny processing elements must maintain a similar time within a certain tolerance throughout the lifetime of the network; • Low cost, meaning oscillators with low performances (high fluctuations due to the power supply and to the temperature, large drift); • Time synchronization algorithms must coexist with other algorithms;

4. Smart Sensors and Sensor Networks • The need for time synchronization: the need for accurate timing: • It has to be estimated the angle of arrival of an acoustic wavefront which impinges onto a group of acoustic sensors; • Each of the sensors knows its own position exactly and records the time of arrival of the sound event; • x = d * sin θ and θ = arcsin (x/d); d is known and x can be derived from x = c * Δt, where c ≈ 330 m/s and Δt = time difference between the sensor readings; assuming d = 1 and Δt = 0.001 gives θ≈ 0.336 radians; • If the clocks of the sensors are only within 500 μs accurate, the real time difference is between 500 and 1500 μs, leading to θ from 0.166 to 0.518 radians; • A more reliable estimate can be obtained by time synchronization algorithms or by averaging the errors from multiple sensors;

5. Smart Sensors and Sensor Networks • Time synchronization is necessary also because: • Nodes are switched on at different random times, and therefore, the initial phases of their oscillators are random too; • Oscillators often have a priori a slight random deviation from their nominal frequency (clock drift or clock skew); it is caused by impure crystals and by environmental conditions like pressure, temperature, humidity, which differ on the terrains from the laboratory; • the clock drift is expressed in parts per million (ppm) and gives the number of additional or missing oscillations a clock makes in the amount of time needed for one million oscillations at the nominal rate; cheaper oscillators have larger drifts with higher probability; a deviation of 1 ppm gives 1 s error every ≈ 11.6 days and 100 ppm gives 1 s error every ≈ 2.78 h; for wireless sensors, the drift is assumed to be less than 100 ppm; the higher the deviation the higher the resynchronization rate must be; • The oscillator frequency is time variable; short term variations are caused by temperature, pressure, variations of power supply etc. and long term variations are caused by the age of the crystal; it is safe to assume that the frequency is stable in the range of minutes to tens of minutes; it implies that the time synchronization is made at the same rate;

6. Smart Sensors and Sensor Networks • Simple but inadequate solutions: • Attaching a GPS receiver to each node: • A GPS receiver costs some few dollars, need a separate antenna, need energy continuously to keep in synch (initial synchronization takes minutes) and is not physically compatible with small sensor nodes; • A GPS receiver needs line of sight to at least 4 of the GPS satellites, which is difficult in hilly terrains, forests or in indoor applications; • Let some nodes at the edge of the sensor network send strong timing signals: • Restricted situations: indoors and flat terrains; • Requires a separate frequency and thus a separate transceiver on each node; • Keeping the nodes synchronized all the time: energy must be spent for running continuously time synchronization protocols; unnecessary in many sensor network applications, for example when rare events are monitored (e.g. forest fires);

7. Smart Sensors and Sensor Networks • Classifications of synchronization protocols: according to different criteria: • Physical versus logical time: in WSNs, applications and protocols require, in general, physical time; • External versus internal synchronization: algorithms may or may not require time synchronization with external sources like GPS receivers; • Global versus local algorithms: • A global algorithm tries to keep all nodes of a WSN (or of a partition) synchronized; • Local algorithms are often focused on some geographical neighborhood of an interesting event; • In global algorithms, nodes are required to keep synchronized with close single-hop and distant multihop neighbors; • A global synchronization algorithms achieves also local synchronization; • Absolute versus relative time: many applications require only relative synchronization, they need only time differences;

8. Smart Sensors and Sensor Networks • Hardware versus software based algorithms: • Some algorithms require dedicated hardware (GPS receiver, communication equipment); • Software based algorithms use the same channels as for normal data packets for sending messages; • A priori versus a posteriori synchronization: • In a priori algorithms, the time synchronization protocol runs continuously, even where there is no external event to be reported; • In a posteriori synchronization (post-facto synchronization) the synchronization process is triggered by an external event; • Deterministic versus stochastic precision bounds: • Some algorithms can (under certain conditions) guarantee absolute upper bounds on the synchronization error between nodes or with respect to external time; • Other algorithms can only give stochastic bounds in the sense that the synchronization error is with some probability smaller than a prescribed bound;

9. Smart Sensors and Sensor Networks • Performance metrics for time synchronization algorithms are: • Precision: • For deterministic algorithms, the maximum synchronization error between a node and real time or between two nodes is of interest; • For stochastic algorithms, the mean error and the error variance are relevant; • Energy costs: • Depend on several factors: the number of packets exchanged in one round of the algorithm, the amount of computation needed to process the packets and the required resynchronization frequency; • Memory requirements: • To estimate drift rates, a history of previous time synchronization packets is needed; • A longer history allows for more accurate estimates but needs more memory; • Fault tolerance: • The algorithms must cope with failing nodes, error – prone and time – variable communication links; • The algorithms must handle mobility;

10. Smart Sensors and Sensor Networks • Structure of time synchronization algorithms: can be decomposed in 4 conceptual building blocks: • The resynchronization event detection block: • Identifies the moments where resynchronization is triggered; • In most protocols, resynchronization are triggered periodically with a period depending on the maximum drift rate; a single resynchronization process is called a round; • The remote clock estimation block: • Acquires clock values from remote nodes; • In the time transmission technique, a node sends its local clock to a neighboring node which adjusts its local clock; • In the remote clock reading technique, a node sends a request message to another node which answers; the first node estimates the round – trip time; • The clock correction block: • Computes adjustments of the local clock based on the results of the remote clock estimation block; • The synchronization mesh setup block: • Determines which nodes synchronize with each other in a multihop network.

11. Smart Sensors and Sensor Networks • Resynchronization rate: • Simplest case: the node adjusts only its phase drift as a result of a resynchronization round: • If the node’s oscillator drift rate is constant and known to be x ppm and if the desired accuracy is y, then after at most y/ (x * 10-6) s the accuracy constraint is violated; • For x = 20 ppm and y = 1 ms, the resynchronization period is 50 s; • More complex case: try to estimate and correct not only the phase shift but also the current drift, hopefully prolonging the periods before resynchronization is required; • The accuracy is defined for 2 cases: • External synchronization: the nodes 1, 2, …, n are said to be accurate at time t within a bound y if: |Li(t) – t| < y holds for all nodes i Є {1, 2, …, n}; • Internal synchronization: the nodes 1, 2, …, n are said to agree on the time with a bound of y if: |Li(t) – Lj(t)| < y holds for all i, j Є {1, 2, …, n}; • Li(t) is the local time of node i at time t;

12. Smart Sensors and Sensor Networks • Time synchronization protocols can be categorized in different ways: • According to the time reference: • According to the synchronization reference: • Based on sender/ receiver synchronization; • Based on receiver/ receiver synchronization;

13. Smart Sensors and Sensor Networks • Protocols based on sender/ receiver synchronization: • One node, the receiver, exchanges data packets with another node, the sender, to let the receiver synchronize to the sender’s clock; • Require bidirectional links between neighboring nodes; • Lightweight time synchronization protocol (LTS): • Attempts to synchronize the clocks of a sensor network to the clocks held by certain reference nodes, which may have GPS receivers; • LTS divides time synchronization into two building blocks: • A pair – wise synchronization protocol which synchronizes two neighboring nodes; • To keep all or the interesting nodes synchronized to a common reference, a spanning tree from the reference node to all nodes is constructed; first, the reference node synchronizes with its children, then the children with their children, and so forth; if the single – hop synchronization errors are independent and identically distributed and have mean zero, the leaf nodes of the tree also have an expected synchronization error of zero but the variance is the sum of variances along the path from the reference node to the leaf node; it can be minimized by finding a minimum – height spanning tree;

14. Smart Sensors and Sensor Networks • The synchronization of all nodes can be obtained also with the distributed multihop LTS protocol: • No spanning tree is constructed, but each node knows the identities of a number of reference nodes along with suitable routes to them; it is the responsibility of the nodes to initiate resynchronization periodically; • Assume that node 1 wants to synchronize with the reference node R; • Node 1 issues a synchronization request toward R, which results in a sequence of pair – wise synchronizations: node 4 synchronizes with node R, node 3 with node 4, and so forth until node 1 is reached; • Intermediate nodes are also synchronized with R and, if node 1 requests synchronization periodically, intermediate nodes never have to request resynchronization by themselves;

15. Smart Sensors and Sensor Networks • A node should choose the closest reference node to minimize its synchronization error; • A minimum weight tree is not constructed explicitly, but it is the responsibility of the routing protocol to find good paths; • In certain conditions, the issue of routing/ synchronization cycles may arise, which have to be avoided; • It is beneficial if a node can take advantage of ongoing synchronizations; • For example, instead of synchronizing node 5 independently with the reference node R, which would cause nodes 3 and 4 to handle two synchronization requests simultaneously, node 5 can ask all nodes in its neighborhood about ongoing synchronization requests; if there is any, node 5 can wait for some time and then attempt to synchronize with the responder; • Another optimization is underlined in fig. using dashed lines: suppose node 5 wants to synchronize; one option would be to let node 5 join an ongoing synchronization request at node 3; on the other hand, it might be necessary to keep other nodes, e.g. 7 and 8, also synchronized with R; to achieve this, node 5 will issue a request through nodes 7 and 8 and synchronize them as intermediate nodes; this is called path diversification; • The distributed multihop LTS has the advantage that it is capable of post – facto synchronization; node i can decide by itself when to synchronize.

16. Smart Sensors and Sensor Networks • Protocols based on receiver/ receiver synchronization: • Multiple receivers of the same timestamped packet synchronize with each other, but not with the sender; • Hierarchy referencing time synchronization (HRTS): • Synchronizes all nodes in a broadcast domain and constructs a synchronization tree to synchronize the whole network; • The synchronization is triggered periodically by the root of the tree; • Synchronization in a broadcast domain: • A dedicated node, called a root node or base station (R), triggers time synchronization at time t1 corresponding to local time LR(t1), by broadcasting a sync_begin announcement packet; • This packet includes 2 parameters: a level value and the identification of one R’s one – hop neighbors, say node i; • The selected node i timestamps the received packet at time t2 with its local time Li(t2); • Another node j timestamps the same packet at time t2’ with Lj(t2’); • t2 and t2’ can differ slightly because of random receiver uncertainties;

17. Smart Sensors and Sensor Networks

18. Smart Sensors and Sensor Networks • Since node i has found its identification in the packet, it formats an answer packet and timestamps it for transmission at time t3 with its local time Li(t3); • Both timestamps Li(t2) and Li(t3) are included in the answer packet; • The root node R receives the packet and timestamps it at time t4 with LR(t4); • R can now estimate the offset OR,I between its local clock and the local clock of node i as: OR,I = [(Li(t2) – LR(t1)) – (LR(t4) – Li(t3))]/ 2 • In the next step, the root node R broadcast the value O and Li(t2) to all nodes; • To adjust its local clock, the node i subtracts from it the offset OR,I; • For node j, it is assumed that this node is in the same broadcast domain and has phase offset Oj,I to node i; • Considering that i and j receive the sync_begin at the same time, we have t2 = t2’ and Li(t2) = Lj(t2’) + Oj,I; upon receiving the clock value Li(t2) from the root node’s final broadcast, node j can compute Oj,I directly as Oj,I = Li(t2) – Lj(t2’); • On the other hand, since: Li(t2) = LR(t2) + OR,I Lj(t2) = LR(t2) + OR,I it results LR(t2) + OR,I = Lj(t2’) + Oj,I

19. Smart Sensors and Sensor Networks • This gives: OR,j = Lj(t2) – LR(t2) ≈ Lj(t2’) – LR(t2) = LR(t2) + OR,I – Oj,I – LR(t2) = OR,I – Oj,I = OR,I – (Li(t2) – Lj(t2’)) • Therefore, node j can compute the phase offset to node R directly from its own observation Lj(t2’) and the values from the final broadcast, without exchanging any packets with other nodes; • The important property of this scheme is that three packets suffice to synchronize all of R’s neighbors to R’s clock, no matter what their number is and whether they are in mutual range or not; • In contrast to other protocols, the receivers of sync_begin packet do not have to exchange their observations in a pair – wise fashion and the protocol is therefore insensitive to the network density; • Although not necessary to work, it was proposed to run this protocol on a separate MAC channel; the goal is to separate synchronization – related traffic from user data traffic and thus reduce the probability of collision and the medium access delays when the selected node i answers to the root node;

20. Smart Sensors and Sensor Networks • Network synchronization over multiple hops: • We consider that the whole network is static and connected and there is at least one reference node having access to an external time source and, thus, to UTC; there can be multiple reference nodes; • These reference nodes are assigned level 0 and become root nodes for their one – hop neighbors; they trigger resynchronization periodically, according to the described protocol; • The level is included in the sync_begin packet; • All nodes behave according to the following rule: they maintain a local level variable, initialized with a large value; if the level value received in the sync_begin packet is smaller, the node accepts the packet, sets its own level to the received value + 1 and becomes a root node of its own, starting synchronization with its neighbors, after the triggering round has finished; if the value received is larger than or equal to the own level variable, the sync_begin packet is ignored; the process continues in a recursive fashion until the leaves of the network are reached; • Nodes always synchronize over multiple hops with the closest reference node and their level variable indicates the hop distance; • The depth of the tree can be restricted by a depth parameter contained in the sync_begin packet; it is decremented in every level of the tree;

21. Smart Sensors and Sensor Networks Communication protocols in WSNs • Communication protocols for WSNs have to: • Be adapted to the collective effort of the sensors; • Fuse, extract or aggregate data from the sensors; • Self – organize the sensor nodes into clusters to complete a task or overcome certain obstacles (e.g. geographical obstacles); • Protocols and algorithms for traditional wireless ad hoc networks may not be suited for WSNs, some of the main causes being: • Sensor networks are application specific; • Sensors nodes work collaboratively together; • Sensor nodes are very energy constrained compared to traditional wireless ad hoc devices; • Protocols are developed at different layers: • They are not isolated but in an integrated and hierarchical framework; • Control and status messages will be exchanged among different layers in order for the system to take advantage of the interdependencies;

22. Smart Sensors and Sensor Networks • Application layer protocols: • Although many application areas for WSNs were defined, only a few application layer protocols were developed; • Sensor management protocol (SMP): • A SMP can be used to access sensor networks through other networks, such as the Internet; • A SMP protocol makes the hardware and software of the lower layers transparent to the sensor network management applications; • System administrators interact with sensor networks by using SMP; unlike other networks, SNs consists of nodes which do not have global ID and they are usually infrastructureless; therefore, SMP needs to access the nodes by using attribute – based naming and location – based addressing; • Some of the following tasks are performed through a SMP: • Introducing rules for data aggregation, attribute – based naming and clustering; • Time synchronization of the sensor nodes; • Exchanging data related to location – finding algorithms; • Moving sensor nodes; • Turning sensor nodes on and off; • Authentication, key distribution and security in data communication; • Reconfiguring the sensor network, querying the network configuration and the status of nodes;

23. Smart Sensors and Sensor Networks • Task assignment and data advertisement protocol (TADAP): • An important operation in WSNs is interest dissemination: users sent their interest to a sensor node, a subset of the nodes or the whole network; • The interest may be about a phenomenon or a triggering event; • Another approach is the advertisement of available data: the sensor nodes advertise the available data to the users and they query the data in which they are interested; • A TADAP may be useful for lower layer operations, such as routing; • Sensor query and data dissemination protocol (SQDDP): • Provides user applications with interfaces to issue queries, respond to queries and collect incoming replies; • These queries are not issued to a particular node; instead, attribute – based or location – based naming is preferred; • Examples: • Attribute – based query: “the locations of the nodes that sense temperature higher than 400C”; • Location – based naming: “temperatures read by the nodes in region A”; • A sensor query and tasking language (SQTL) was proposed;

24. Smart Sensors and Sensor Networks • Time synchronization protocols: • Provides sensor nodes with a common view of the time throughout the sensor network; • Localization protocols: • Because sensors may be randomly deployed in any area, they must be aware of their locations in order to provide meaningful data to the users; • Location information may be required also by the transport, data network and data – link layer protocols; • A localization protocol must be: • Robust to node failures; • Less sensitive to measurement noise; • Low error in location estimation; • Flexible in any terrain; • Two types of localization techniques: • Beacon based; • Relative location based;

25. Smart Sensors and Sensor Networks • Both techniques may use range and angle estimation for sensor node localization via received signal strength, time of arrival, time difference of arrival and angle of arrival; • The first type of techniques: • Are based on beacons with known positions: the positions are manually configured or known through GPS receivers; • In the ad hoc localization system, there are a few nodes with known positions and the other nodes discover their locations through a two – phase process: ranging and estimation; • In the ranging phase, each node estimates the range of its neighbors; • In the next phase, the nodes with unknown positions, estimate their locations with respect to the nodes with known positions; • The second type of techniques: • Are appropriate in areas unreachable by beacons or GPS or when sensor nodes are jammed by environmental or manually induced noise; • In addition, low – end sensor nodes may exhibit nonlinear device behavior and non – Gaussian measurement noise; • The location information is relayed hop by hop from the source to the sink; in order to obtain precise relative location information, the sensor nodes must collaboratively work together; • Furthermore, energy may be additionally conserved by enabling sensors to track the locations of their neighbor nodes;

26. Smart Sensors and Sensor Networks • Transport layer protocols: the main objectives of the tr. layer are: • To bridge application and network layers by application multiplexing and demultiplexing; • To provide data delivery service between the source and the sink with an error control mechanism tailored according to the specific reliability requirement of the application layer; • To regulate the amount of traffic injected into the network via flow and congestion control mechanisms; • The specificities of the SNs impose modifications and constraints: • Conventional end – to – end, retransmission based error control mechanism used by TCP is not feasible for SNs; • The specific objective of the sensor network also influences the design requirements of the transport layer protocols; • Different functionalities are required to handle the transport needs with respect to the direction of the flow: • The main data flow takes place in the forward path; • The reverse path carries less data (queries and commands to source nodes, programming/ retasking binaries);

27. Smart Sensors and Sensor Networks • Forward (event – to – sink) transport: • An event in the sensor field needs to be tracked with a certain amount of accuracy at the sink; • Unlike traditional communication networks, the SN necessitates an event – to – sink reliability at the transport layer; this involves a reliable communication of the event features to the sink rather than conventional packet – based reliable delivery of the individual sensing reports or packets generated by individual sensors in the field; • Congestion in the forward path should also be addressed by the transport layer: once the event is sensed by a number of sensor nodes within the coverage of the phenomenon, a significant amount of traffic will be generated by these sensors, leading to congestion;

28. Smart Sensors and Sensor Networks • Although the transport layer solutions in conventional wireless networks are relevant, they are inapplicable for sensor networks: • These solutions are based on the reliable data transport following end – to end – TCP semantics: they are based on acknowledgements and end – to – end retransmissions; • Because of inherent correlation in data flows generated by the sensor nodes, these mechanisms for end – to – end reliability are superfluous and drain significant amounts of energy; • Reverse (sink – to – sensors) transport: • Dissemination of the type of data carried by the reverse path requires 100 % reliability; this strict reliability requirement involves a certain level of retransmission and acknowledgement mechanisms; • These mechanisms should be incorporated into the transport layer protocols cautiously in order not to compromise scarce sensor network resources totally; as a consequence, local retransmissions and negative acknowledgement would be preferable over end – to – end retransmissions and acknowledgements; • The sink is more involved in the sink – to – sensor data transport: • A sink with plentiful energy and communication resources can broadcast data; • This will reduce the amount of traffic forwarded in the multihop sensor network infrastructure and thus will help sensors to conserve energy; • Data flows in the reverse path will generate less congestion compared to the forward path;

29. Smart Sensors and Sensor Networks • Network layer protocols: • The network layer of the sensor networks is usually designed according to the following principles: • Energy efficiency is always an important consideration; • Sensor networks are mostly data – centric; • An ideal sensor network has attribute – based addressing and location awareness; • Data aggregation is useful only when it does not hinder the collaborative effort of the sensor nodes; • The routing protocol is easily integrated with other networks (e.g. Internet); • These design principles serve also as a guideline when designing a routing protocol for sensor networks; • In order to integrate with data, a routing protocol may be designed according to data – centric techniques; • A data centric routing protocol requires attribute – based naming; it is used for carrying out queries using the attributes of the phenomenon; usually the users are more interested in the phenomenon than in an individual sensor node;

30. Smart Sensors and Sensor Networks • A data – centric routing protocol should also utilize the data aggregation: • The sensor network used to gather the information can be perceived as a reverse multicast tree, where the nodes within the area of the phenomenon send the collected data toward the sink; • Data aggregation can be perceived as a set of automated methods of combining data from many sensor nodes into a set of meaningful information; • Care must be taken when aggregating data for not missing the specifics of the data, e.g. the location; they may be of interest for certain applications;

31. Smart Sensors and Sensor Networks • The network layer must allow easy integration with other networks: • The sinks are the basis of a communication backbone that serves as a gateway to other networks; • The users may query the sensor networks through the Internet or the satellite network depending on the purpose of the query or the type of application;

32. Smart Sensors and Sensor Networks • An overview of the state of the art is shown in next fig.: • The schemes presented above implement some of the design principles previously mentioned; • For example the SMECN creates an energy – efficient subgraph of the sensor networks; it tries to minimize the energy consumption while maintaining connectivity of the nodes in the network;

33. Smart Sensors and Sensor Networks • Data link layer protocols: • The data link layer is primarily responsible for multiplexing data streams, data frame detection, medium access and error control; • These tasks must be fulfilled by taking into account the specifics of the SNs: the collaborative and application – oriented nature, the physical constraints (energy and processing limitations) etc. • Data link layer protocols are divided in: MAC and error protocols; • Medium Access Control protocols: • Must achieve two objectives: • Establish data communication links for creating a basic network infrastructure needed for multihop wireless communication in a densely scattered sensor field; • Regulate access to shared media so that communication resources are fairly and efficiently shared among sensor nodes; • MAC protocols for conventional wireless networks are inapplicable to the sensor networks: the primary goal of a MAC protocol in an infrastructure – based cellular system is to provide high QoS and bandwidth efficiency, mainly with dedicated resource assignment strategy; this is impractical for SNs where there is no a base station and power directly influences the network lifetime;

34. Smart Sensors and Sensor Networks • For infrastructureless – based wireless systems (e.g. Bluetooth, MANETs) the situation is similar; • In those systems the power consumption is of secondary importance because they refer to nodes with portable battery – powered devices; • Therefore, none of their existing MAC protocols can be directly used in the SNs because of network time concerns; • The MAC protocol for SNs must have: built – in power conservation, mobility management and failure recovery strategies; • Error control protocols: • Error control is critical, especially in some applications such as mobile tracking and machine monitoring; • In general, the error control mechanisms in communication networks can be categorized into two main approaches: • Forward error correction (FEC); • Automatic repeat request (ARQ); • ARQ-based error control mainly depends on retransmission for the recovery of lost data packets; • It incurs significant additional retransmission cost and overhead; • The use of ARQ in SNs is limited due to the scarcity of the energy and processing resources of the sensor nodes;

35. Smart Sensors and Sensor Networks • FEC schemes have inherent decoding complexity that require considerable processing resources in the sensor nodes; • Simple error control codes with low – complexity encoding and decoding might be the best solutions for error control in SNs; • Channel bit error (BER) is a good indicator of link reliability; a good choose of the error correcting code can result in several orders of magnitude reduction in BER and an overall gain; the coding gain is generally expressed in terms of the additional transmit power needed to obtain the same BER without coding; • The link reliability can be achieved by increasing the output transmit power or the use of suitable FEC scheme; • Using FEC is the most efficient solution, given the constraints of the sensors; • Although the FEC can give significant reduction in BER for any value of the transmit power, the additional processing power for encoding and decoding must be considered when designing an FEC scheme; • If this additional power is greater than the coding gain, the whole process is not energy efficient and the system is better without coding; • If the additional power is less than the coding gain, SNs will have to implement FEC; • FEC schemes should be powerful, energy – efficient and with low – complexity.