Wireless Sensor Networks Research issues and Addressing approaches BY Eman Shaaban , PhD

1. Wireless Sensor Networks Research issues and Addressing approaches BYEmanShaaban, PhD Associate Professor Computer Systems Dept.Faculty of computer and information scienceAin-Shams university, Cairo, EgyptEman.shaaban@cis.asu.edu.eg

2. Outline Part 1: Introduction - Mote research lab at FCIS - Common design issues in WSNs Part 2: Related Researches - Relative RSS-Based GSM Localization - Enhancing S-LEACH securityfor WSNs - Mobility-Aware MAC Protocol for Delay-Sensitive WSN - Efficient routing protocol for VANET Part 3: Related Works In Progress - A scalable sensor localization scheme for WSN - Connectivity Restoration in WSNs Through Node Repositioning

3. Wireless Sensor Networks A sensor network is composed of a large number of sensor nodes that are densely deployed inside or very close to the phenomenon: • Deterministic or Random deployment • Self-organizing capabilities

4. Wireless Sensor Networks Sensor readings are transmitted over a wireless channel to a running application that makes decisions.

5. Main Constraints • Limited processing and power capabilities. • Scarce network resources • Weak security • Intermittent connectivity and frequent node failures • Heterogeneous systems (Hardware, O/S, Applications) • Densely deployment • Frequent topology changes • Broadcast communication paradigm • Possible absence of unique global ID

6. Classification of WSN Applications • Event Detection and Reporting: The significant design problem is that of routing the event report to the sink, once the event is detected

7. Classification of WSN Applications • Data Gathering and Periodic Reporting: each sensor is expected to constantly produce some amount of data which has to be sent to the sink. The sink might not be directly interested in the individual measurements, but could require a distributed computation of some function of the sensor readings.

8. Classification of WSN Applications • Sink-initiated Querying: This enables the sink to extract information from different regions in space. For the underlying communication protocols, we need effective means to address and route data to and from dynamic sets of sensors.

9. Classification of WSN Applications • Tracking-based Applications: when the target is detected, the sink needs to be notified promptly. Then, the sink may initiate queries to receive time-stamped location estimates of the target, so that it can calculate the trajectory and keep querying the appropriate sets of sensors.

10. Crossbow’s Focus MoteWorks WSN Research Lab at FCIS • End-to-End enabling platform for the creation of WSN Processor-Radio-Data Logger “Mote” Sensor cluster or interface card

11. MoteWorks WSN Research Lab at FCIS

12. MoteWorks WSN Research Lab at FCIS • Hardware Platform: • 30 mote of type “MICAz 2.4 GHz: • Microprocessor: ATmega128L • Radio: CC2420 IEEE 802.15.4 compliant, ZigBee ready radio frequency transceiver integrated with an Atmega128L • External Serial Flash: AT45DB041 512 KB • 51-Pin Expansion connector

13. MoteWorks WSN Research Lab at FCIS • 20 MDA100 ( Mote Data Acquisition board) • 51-Pin Expansion connector • 10-bit analog input • Light sensor • Temperature sensor • Prototyping area supports connection to eight channels of the Mote’s analog to digital converter (ADC0–7). USART and the I2C digital communications bus. The prototyping area has 45 unconnected holes used for breadboard of circuitry

14. MoteWorks WSN Research Lab at FCIS • 10 MIB520 (Mote Interface Boards) for Programming, gateway, and base station. • Protocol characteristics and performance can be validated through experimentations using a real sensor network environment.

15. Research Classification

16. Research Classification • Component level: improving the sensing, communication, storage, and computation capabilities of an individual sensor device. • Communication level: mechanism of networking and coordinating several sensor devices in an energy-efficient and scalable fashion. • Service level: are developed to enhance the application, system performance and network efficiency

17. Research at Communication Level • Optimize the communication protocols, to best satisfy the application level objectives as each WSN application imposes a unique set of goals and produces a different type of data traffic. For each application class, different problems associated with designing the communication protocols. application specific design are recommended. • Cross-layer Collaboration between all the layers to make the protocols lightweight and energy-efficient.

18. Transport Layer • Event-to-Sink reliability (reliable event detection at sink) rather than end-to-end reliability (reliable delivery of individual packets from a source to destination) • Sink is only interested in the collective information of sensor nodes within the event radius and not their individual data Event radius sink

19. Common Design Issues in WSNs Service Level • Compression and aggregation • Localization • Synchronization • Topology control • Security

20. Compression and Aggregation • Data-compression: Compressing data before transmission to base station • Data aggregation: Data is collected from multiple sensors and Combined together to transmit to base station • Reduces the energy consumption of packet transmissions, lowers the traffic load and therefore reduces the contentions and collisions

21. Compression and Aggregation • Data fusion can also be integrated with data-centric routing aims to locate routes that lead to the largest degree of data aggregation. • Power efficient in-network distributed data processing algorithms to implement efficient query processing and data management. • Most of data-gathering and fusion mechanisms reside in or below the network layer

22. Address-centric routing vs Data-centric routing

23. Clustering • The network is divided into clusters, and each cluster has associated a cluster head node. • Each node in the cluster sends its sensed data to the cluster head where it belongs. • The cluster head aggregates the data packets received into a single packet, which is transmitted to the sink • Clustering improves resource utilization and prolong network lifetime and also provide load balancing if appropriately configured • The salient advantage of using clusters in a sensor network comes from in-network data aggregation.

24. Clustering

25. Localization • Localization has become the hot research spot of wireless sensor network. • Localization is critical for data stamping, clustering, topology control, location-based information querying and geographical routing. • Localization algorithms: secure, minimum cost and localization errors. • Efficient and accurate localization for sensors in WSNs of arbitrary size—is an essential requirement for tomorrow’s wireless sensor networks to provide their intended services

26. Localization • Range based schemes: use absolute point to point distance estimates (range) or angle estimates in location calculation. Range-based methods require additional equipment. • Range free schemes: use only connectivity information to locate the entire sensor network. For example hop-counting techniques in which each anchor computes an average size for one hop.

27. Localization Techniques • The coordinates of nodes in a network can be calculated using: • Geometrical techniques: triangulation, trilateration and multitrilateration. • Multidimensional scaling: convert distance information into the coordinate vector. • Algorithms for convex and nonconvex optimization: formulating the localization problem as a nonlinear, nonconvexoptimization task solved by global optimization solvers. • Hybrid schemes that use two different techniques.

28. Hierarchical cluster-based location systems • Hierarchical cluster-based solutions are often proposed to improve scalability and efficiency of the location system. For position estimation of cluster heads usually more complex but accurate protocols are used. The remaining nodes can use a simpler but less accurate method with cluster heads as reference nodes.

29. Localization: boundary detection • Localized edge detection • Centralized edge determination • More Challenges to estimate a boundary using mobile sensing nodes. • Recent research effort to achieve promising accuracy of boundary estimation in the future.

30. Synchronization • Time synchronization: adjusting sensors local clocks to a common time scale for: • Data global time-stamp • Cooperation • Scheduling sleep-wake patterns of the nodes in power-saving algorithms. • Both localization and synchronization have communication overheads, and these tradeoff issues have been addressed in several works

31. Topology Control • Coverage topology control: maximize a reliable sensing area while consuming less power. sensing coverage of the entire region of interest is assured. Degree of coverage is application dependent. Impacts on energy conservation • Connectivity topology control: concern more about network connectivity and emphasizes the message retrieve and delivery in the network.

32. Coverage topology control: Given a fixed number of static and mobile nodes how should they be deployed in a monitoring region so that area coverage is maximum? Coverage increased

33. Connectivity Based Topology Control • Nodes transmit at max power levels • Nodes transmit at min power levels • High energy consumption • High interference • Low throughput • Network may partition

34. Connectivity Based Topology Control • Global connectivity • Low energy consumption • Low interference • High throughput Mechanisms to maintain an efficient sensor connectivity topology: controlling the radio power level to achieve optimized connectivity topology. Power Management that maintains a good wake/sleep schedule . Mobile relays to link disjoint batches of nodes.

35. Topology Control for Tolerating node failures: Forming k-connected WSN Forming k-node disjoint communication paths between pairs of nodes in the network to tolerate the failure of up to k-1 consecutive node failures without suffering partitioning, and achieve that using the least number of redundant nodes. Such optimization is a very challenging problem that has been proven to be NP-hard for most of the formulations of sensor deployment, even for k=1. Several heuristics have been proposed to find suboptimal solutions.

36. 2 Vertex disjoint paths bet. 1&11

37. Topology Control for Tolerating node failures: Forming k-connected WSN Place the minimum number of relay nodes such that each sensor is connected to at least two relays and the inter-relay network is 2-connected.

38. Topology Control for Tolerating node failures: Designate backups for critical nodes The backup nodes can be simply passive spares among redundant nodes, or among the active nodes. When failure of critical nodes is detected, these active spares will have to quit what they are doing and relocate to substitute failed nodes. Assigned k distinct spares will enable the recovery to take place even if k-1 of these spares.

39. Topology Control for Tolerating node failures: Designate backups for critical nodes A backup node that would cause the least coverage degradation is favored. In addition, low node degree would make a node an attractive backup. Other solutions opt to localize the scope of the recovery by picking backups within the 2-hop neighborhood of a failed critical node Af. If not found, the search widens to include more distant nodes. Upon detecting the failure of Af, the designated spare will travel to replace Af or a series of cascaded relocation on the shortest route between Af and the selected backup will be triggered to split the travel load on multiple nodes

40. Topology Control for Tolerating node failures: Reactive Connectivity Restoration Schemes` Real-time connectivity restoration implements a recovery procedure when a node failure is detected. Such a reactive methodology better suits dynamic WSNs. The idea is to utilize existing alive nodes which can move and reposition them to the appropriate locations. Effectively, the network topology is restructured to regain strong connectivity.

41. Topology Control for Tolerating node failures: Reactive Connectivity Restoration Schemes

42. Outline Part1: Introduction - Mote research lab at FCIS - Common design issues in WSNs Part 2: Related Researches - Relative RSS-Based GSM Localization - Enhancing S-LEACH securityfor WSNs - Mobility-Aware MAC Protocol for Delay-Sensitive WSN -Efficient routing protocol for VANET Part 3: Related Works In Progress - A scalable sensor localization scheme for WSN - Connectivity Restoration in WSNs Through Node Repositioning

44. Relative RSS-Based GSM Localization • This study has proposed and implemented a robust technique for localization using Relative Received Signal Strength (RRSS) of GSM. • The study has been tested and analyzed in Egypt roads using realistic data and Android smart phones. The performance evaluation showed good results compared with other similar environment fingerprint positioning techniques.

45. Database Correlation-based localization • Store the location-dependent network parameters seen by MS (fingerprints), for the whole coverage area of the location system in a database that is used by a location estimation algorithm. • When MS needs to be located, the necessary measurements are performed and transmitted to the location server. • The location server calculates the MS location by comparing the transmitted information and the fingerprints of the database.

47. Fingerprinting • Taking RSS at the MS as the fingerprint variable. A single fingerprint in the database consists of: • Location coordinates • RSS from serving base station and other neighboring base stations in that location. • With GSM/GPS it is possible to obtain RSS from the serving cell and maximum of five neighboring cells.

48. Relative RSS-Based GSM Localization • RSSs of the BSs are often influenced by some static factors such as the distances, obstructions and the transmission powers of the Aps. RSS at a particular location would vary time to time due to multi-path propagation and fading . • Although RSSs received from a BS at a location change over time, the relative RSSs which refer to the relations of the RSSs received from the different base stations are more stable than the absolute RSSs. • Hence we define rules on RRSSs for every location in a space and infer a user’s location by matching the rules observed at the user and the rules obtained for the space.

49. Relative RSS based GSM localization • Offline phase: the data base of fingerprint is built where RRSS for each antenna at a particular position/sample. • Online or matching phase: calculate the most probable sample which has maximum correlation between rules.

50. Offline phase • Collecting fingerprint at normal speed of roads is more practical than stopping at each training point. • Building an average RSS table to record the average RSS from all cells for each location over a day. • For each location, rules are generated automatically for each pair of BSs based on the average RSSs of the BSs at that location. • The accuracy of the location estimate is highly dependent on the density of the set of collected fingerprints