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Wireless Sensor Networks Research issues and Addressing approaches BY Eman Shaaban, PhD Associate Professor Computer Systems Dept. Faculty of computer.

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Presentation on theme: "Wireless Sensor Networks Research issues and Addressing approaches BY Eman Shaaban, PhD Associate Professor Computer Systems Dept. Faculty of computer."— Presentation transcript:

1 Wireless Sensor Networks Research issues and Addressing approaches BY Eman Shaaban, PhD Associate Professor Computer Systems Dept. Faculty of computer and information science Ain-Shams university, Cairo, Egypt

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 security for 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 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 Wireless Sensor Networks

4 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 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 Crossbow’s Focus

11 MoteWorks WSN Research Lab at FCIS

12 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 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 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 Transport Layer

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 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. Clustering

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, nonconvex optimization 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 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. Connectivity Based Topology Control

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 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. Topology Control for Tolerating node failures: Forming k-connected WSN

38 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. Topology Control for Tolerating node failures: Designate backups for critical nodes

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

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 security for 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

51 Offline phase Each training point has n(n-1)/2 rules where n is number of valid detected cells. Rule pattern for GSM: RSS1 (LAC1, CID1) {>, <, =} RSS2 (LAC2, CID2) (323, 51103) < (329, 51103)

52 Online phase At matching phase, a query is submitted to system containing set of rules generated by mobile user. Server will search within DB to find most correlated position with maximum number of matched rules. The location of the device is estimated as an average of the latitude and longitude coordinates of the best k matches using Haversine formula

53 Data Collection Data Collection Data is collected using Motorola Milestone smart phone running on android 2.1 with 700 MHz processor, equipped with GPS (act as ground truth) for two different testbeds: ◦ Urban area: “Eastern Cairo, Egypt” ◦ Rural area: “Cairo-El Suez Road”

54 Results Evaluation

55 Effect of changing DS on positioning error a. Long Distances

56 Effect of changing DS on positioning error b. Short Distances

57 Cumulative Distribution Function of Mean Localization Error using different DS

58 Comparing with other Positioning Techniques [1] [2] [3 ] [2]

59 Results Evaluation Results Evaluation DS value acts as balancing factor between size of fingerprint DB and accuracy. We chose 160 m as optimized DS value in order to reduce size of fingerprint DB. In order to improve accuracy, we tried to minimize DS as possible. Mean accuracy was about 29 m in urban when DS = 15 m, and 55 m in rural when DS = 35 m.


61 Enhancing S-LEACH security for WSNs Developing effective security solutions for wireless sensor networks (WSN) are not easy due to limited resources of WSNs and the hazardous nature of wireless medium. A secure clustering protocol that achieves the desired security goals while keeping an acceptable level of energy consumption is a challenging problem in WSN.

62 Enhancing S-LEACH security for WSNs The network activity is organized into rounds, For each round, new nodes act as cluster heads in order to evenly distribute the energy consumption among network nodes, so that nodes will die randomly at approximately the same rate. LEACH incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. S-LEACH is the first modified version of LEACH with cryptographic protection against outside attacks,

63 Enhancing S-LEACH security for WSNs LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol is a basic clustering-based routing protocol for WSNs. This research MS-LEACH to enhance the security of LEACH by providing data confidentiality and node to cluster head (CH) authentication using pairwise keys shared between CHs and their cluster members. The security analysis of proposed MS-LEACH shows that it has efficient security properties and achieves all WSN security goals compared to the existing secured solutions of LEACH protocol. A simulation based performance evaluation of MS-LEACH shows that the protocol outperforms other protocols in terms of energy consumption, network lifetime, network throughput and normalized routing load.


65 Mobility-Aware MAC Protocol for Delay-Sensitive WSNs - Proposed MAC protocol in this research works efficiently with the mobile delay-sensitive applications and provide efficient mechanisms for reducing the delay and handling the mobility while keeping an acceptable energy consumption level.

66 WSN nodes use the neighboring nodes as relays. These relays do not know the exact time at which they are going to receive a packet for forwarding, they have to keep the receiver circuitry of their radios on (idle listening). Designing power-saving algorithms to meet the delay and energy requirements of a given application is an important design problem. Mobility-Aware MAC Protocol for Delay-Sensitive WSNs

67 - Adjust its sleep-wakeup cycle time dynamically according to the current energy utilization efficiency and average latency experienced by the sensor. - For a receiver sensor node, if it notices that the latency becomes intolerable, it unilaterally doubles the original duty cycle. Therefore, the node which increases duty cycle is able to get more chances to receive packets. - Doubling duty cycles is allowed only when the current power consumption level is below the TE value. Mobility-Aware MAC Protocol for Delay-Sensitive WSNs

68 -Provides a mechanism for mobile nodes to quickly follow the schedule of cluster it is moving to. - Border nodes are aware of their cluster schedule and their neighbors schedule on different clusters. If a mobile node starts crossing the border of two adjacent clusters, border nodes will detect its mobility. -Once the mobility is detected, the proposed protocol will trigger the nodes close to the border nodes to do neighbor discovery more aggressively depending on the speed of the mobile node Mobility-Aware MAC Protocol for Delay-Sensitive WSNs


70 Simulation and results A network of 16 nodes forming two virtual clusters is set up for simulations. Node 0 is the source and node 15 is the sink.

71 Packet size: 30 Bytes. Traffic pattern : UDP/ CBR Routing protocol: DSR. MAC Protocols: S-MAC, DSMAC, MS-MAC, and MD- SMAC Dmin: 1 sec. Dmax: 4 sec. TE : 0.4J. Simulation and results

72 Disconnectivity Duration -Amount of time the mobile node has been disconnected from the network after moving from its original cluster to another cluster. In this duration, the mobile node receives no packets. The mobile node remains disconnected till it receives the schedule of the new cluster.


74 For the protocols that don’t support mobility- handling, the mobile node remains disconnected for a long time as they have to wait for the next neighbor discovery period to get the new schedule. The proposed protocol MD-SMAC is quite better. because of its dynamic duty cycle which means faster neighbor discovery frequency. Disconnectivity Duration

75 Queue delay Time difference between a packet gets into the queue and it is sent out. Each node calculates the average queue delay for the packets received in each synchronization period. After the average queue delay becomes intolerable, MD-SMAC protocols fires the delay handling mechanism by doubling the duty cycle leading to decreasing the queue delay.


77 End-to-end delay The amount of time from the packet being sent from the source to the time being received at the destination. Also mobility-handling mechanism Slightly improves queue delay, where the nodes increase the frequency of the neighbor discovery which means that the nodes will be awake for a longer time which in turn decreases the sleep latency.


79 Energy consumption The amount of the energy consumed to the amount of the initial energy. MD-SMAC provides both mobility-handling and delay-handling mechanisms with the least amount of energy.


81 Efficient routing protocol for VANET A stable, reliable and robust to frequent path failures caused by vehicles’ mobility routing mechanism over VANETs is an important step toward the realization of effective vehicular communications.

82 AOMDV Multipath extensions to AODV protocol to cope effectively with high mobility-induced route failures. Discovers multiple loop-free disjoint paths between the source and the destination in a single route discovery. So, a new route discovery is needed only when all these paths fail. Causes fewer interruptions to the application data traffic when routes fail. They also have the potential to lower the routing overhead because of fewer route discovery operations.


84 Proposed SD-AOMDV When a source node requires to send a packet to destination node: SD-AOMDV gets direction and speed of both source and destination nodes. Based on direction and speed of both source and destination, intermediate nodes that can be participating in route between source and destination are specified.

85 Proposed SD-AOMDV A node can be selected as next hop in route between source and destination under two conditions: – Intermediate node that moves in same direction with source and/or destination. – Intermediate node that have minimum difference between its speed and average speed of source and destination.


87 Proposed SD-AOMDV – For each intermediate node in a disjoint path, the difference between its speed and average speed of source and destination is calculated. – For each disjoint path, speed metric is the maximum of the differences calculated in step 1. – For all disjoint paths, the forward path is the path with the minimum speed metric. With equal speed metrics values, the path with minimum hop count is selected.

88 Performance evaluation Mobility Model: Manhattan. MAC : 802.11 with transmission range of 250 m. Traffic pattern: Several CBR/UDP connections between randomly chosen source-destination pairs. Two scenarios : City scenario and highway. City scenario: 70 vehicles in 2000 x 2000m area. Speed of vehicles are varying from 10 to 90 km/h for 20 sessions and Pkt size of 512 B. Highway Scenario: 60 vehicles in 2000 x 2000m area. Speeds of vehicles are varying from 60 to 120 km/h for 25 sessions and Pkt size for 512 B.

89 City scenario: Varying Packet Generation rate vs Delay

90 City scenario: Varying Packet Generation rate vs PDF

91 City scenario: Varying Packet Generation rate vs NRL

92 Average delay is decreased by 71.5%. It is reduced specially with high packet rate since more available valid and stable paths exist in SD-AOMDV due to considering directions in routing decision, and also much more data packets will be delivered to destinations without waiting for route discovery latency. PDF is increased by 11.47% and NRL is increased by 76.2 % due to the increasing of RREQ and RREP routing packet sizes especially with low traffic. Simulation results City scenario

93 Simulation results City scenario At low packet rates a new route discovery is needed for almost every data packet that is generated because previously discovered route will likely break by the time next data packet arrives at the source. This is evident from the higher route discovery frequency and routing overhead at the lowest packet rate. So as the packet rate increases gradually, performance improvements also become higher.

94 Simulation results City scenario In such scenarios because there are not enough packets to take advantage of alternate paths before they break. So as the packet rate increases gradually, performance improvements also become higher with AOMDV. With very high packet rates, relative performance gain with AOMDV reduces as it does not have any mechanism to mitigate congestion at high loads

95 Performance of SD-AOMDV is also improved in highway scenario as in city scenario. Delay has been decreased with 63.06%. PDF has been increased with 26.33%. NRL has been increased with 74.88%. SD-AOMDV has a better ability to handle high mobility of vehicles in highway by finding much more stable paths than AOMDV. Simulation results Highway scenario

96 Highway scenario: Varying Packet Generation rate vs delay

97 Highway scenario: Varying Packet Generation rate vs PDF

98 Highway scenario: Varying Packet Generation rate vs NRL

99 SD-AOMDV add the mobility parameters: speed and direction to hop count as new AOMDV routing metric to select next hop during the route discovery phase. Simulation results show that SD- AOMDV has outperformed AOMDV in city and highway with different traffic scenarios. Efficient routing protocol for VANET

100 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 security for 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

101 Scalable sensor localization scheme for WSN Randa mahmoud, Eman Shaaban

102 Scalable sensor localization scheme for WSN ` Propose a localization scheme for large-scale deployment. The anchors in the network are first partitioned into many clusters according to their physical positions, and sensors are assigned to these clusters if they have a direct connection to one of the anchors. Each cluster formulates a subproblem, and the subproblems are solved independently on each cluster using the SOCP relaxation. SOCP relaxation model permits solution for problem sizes up to a few thousand using available SOCP solvers.

103 Scalable sensor localization scheme for WSN ` Dynamic priorities are created for clusters according to the count of anchors in each cluster. Sensors whose distance error value is within a given tolerance are labeled and treated as acting anchors for the next cluster.

104 Connectivity Restoration In WSNs Through Node Repositioning Heba Essam, Eman Shaaban, Mohamed Younis

105 In this research we investigate a centralized optimal solution for connecting multiple partitions after one or multiple node fail, in which the total travel distance of the nodes is minimized. Our approach is based on formulating the cascaded movement of nodes to replace the failed nodes and restore the connectivity as Minimum-cost flow network models. Connectivity Restoration In WSNs Through Node Repositioning

106 Conclusion WSNs become a ubiquitous part of our environment and many technical issues are still to be fully addressed and successful approaches require advanced paradigms, interdisciplinary expertise, and cross-layer solutions. Despite the large amount of existing work, WSNs remain an exciting and open field of research


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