Contaminated Areas Monitoring via Distributed Rateless Coding with Constrained Data Gathering Cedomir Stefanovic, Vladimir Crnojevic, Dejan Vukobratovic, Lorenzo Niccolai, Francesco Chiti and Romano Fantacci ACM IWCMC(International Wireless Communications and Mobile Computing Conference)

Outlines Introduction Distributed rateless coding for contaminated area monitoring Simulation results Conclusions 2

Introduction Wireless Sensor Networks (WSNs) represent a key technology for environmental monitoring, emergency detection…. This paper presents a data gathering protocol specifically designed for an emergency monitoring WSN. It is based on distributed implementation of rateless codes [5] being suitable to devices with low computational capabilities. The encoded data is dispersed to the perimeter of the monitored area (boundary zone), where it is collected by a Mobile Collector(MC) The encoding and data dispersion is a modification of the scheme introduced in [6], well-suited for decentralized WSNs deployed in inaccessible locations with no infrastructure support and where SNs are prone to failures due to harsh operating conditions. The proposed scheme ensures data persistence and allows for efficient distributed data storage and data gathering. 3

4 Figure 1: Reference scenario.

5 Rateless packet Rateless packets are generated from sensor measurements stored as equal-length sensor data packets in each of N sensor nodes. – Generation ID : the period when the data were measured, – Sensor IDs : the sensors whose data packets are encoded in the data field, – Degree Counter and Mixing-Time Counter fields control the encoding process.

6 Rateless packet The process of creating rateless packets consists of 3 phases: initialization, encoding and dispersion of the rateless packets. 1) the initialization phase – bN rateless packets are initialized across N sensor nodes of the WSN. – Every sensor node generates b rateless packets, copies its current sensor data packet in the rateless packet data field and puts its ID in the sensor IDs header field. – To each of b rateless packets, sensor independently associates a degree d drawn randomly from a selected degree distribution Ω(d). – As the rateless packet content is initialized with the local sensor data packet, the degree counter is set to value d 1, which is the remaining degree to be collected. – Finally, the mixing-time counter is set to the chosen (global) mixing-time value τ.

7 Rateless packet 2) the encoding phase – the task of each rateless packet is to add to its content the remaining d1 sensor datapackets selected uniformly at random by performing random walk across the WSN. – The probabilities p ij of selecting sensor node j from the set N(i) are obtained locally by each sensor node i. – While performing a random walk, every rateless packet is processed by every sensor node on the path using the following simple rule. If the mixing-time counter 0 – the sensor node only updates the rateless packet header – the mixing-time counter - 1 – forward the rateless packet to the next random hop If the mixing-time counter = 0 – the sensor node adds its sensor data packet to the rateless packet content, – degree counter - 1 – puts its Sensor ID in the list of Sensor IDs, – resets the mixing-time counter to its initial value τ – forwards the rateless packet to the next random hop. Finally, upon collecting d sensor data packets the rateless packet completes its encoding phase.

8 Rateless packet 3) the dispersion phase – The goal of the dispersion phase is to place the rateless packet in its final random position in the network. – To prevent any correlation between the content of the rateless packet and the node where it is finally stored, each rateless packet continues its random walk for another τ hops.

9 Fig. 3. Example of rateless packet initialization, encoding and dispersion phase.

Boundary Detection Identify all the SNs that are within certain distance from the outer network bound; in particular, those that are within the communication range of the Mobile Collector(MC) that is moving around the network perimeter (see Fig. 1) There are 3 classes of boundary detection algorithms, depending on the type of information used for node separation[9]: – 1) geometric algorithms Geometric algorithms exploit the geographical information of node locations – 2) topological algorithms Topological algorithms make use of the node connectivity information, provide low-complexity distributed solutions [10], but are tailored for detection of the nodes that form network borders. – 3) statistical algorithms Statistical algorithms use the statistical properties of the underlying network graph with an aim to derive rule for node classification. The algorithm presented in [11] assumes a uniform random node distribution and exploits the fact that nodes on boundaries have lower average degrees than interior nodes, thus determines a threshold that is used for node separation. 10

Boundary-Zone Detection We assume a uniform random SN distribution over the monitored area and employ a statistical approach. – For the sake of simplicity, we assume that the network is dense enough to have no interior bounds (i.e., there are no holes within the network) we employed a statistical node separation method, but instead of node degree, we used 2-hop neighbor count as the separation criterion. The former requires a very dense network (average node degree of 100 or more), The drawback of the method is that the threshold has to be determined experimentally, i.e., the knowledge of the shape of the monitored area has to be known in advance. Figure 2: Boundary-zone detection, N = 1000, r = 0.1. where the threshold is set to 54.

Distributed rateless coding Assume – N sensor nodes – each SN initializes b copies of its own information packet and assigns independently and randomly to each initial rateless packet a degree d drawn from Ω(d) – The random walk mixing time τ M – The encoded packets are first let to randomly walk over the network for dispersion time τ D number of hops, after which the walks are constrained to end in the boundary-zone nodes. 12

Distributed rateless coding The average number of hops h that a rateless packet makes during the encoding and the subsequent dispersion – d is average packet degree – h BZ is the average number of hops needed to reach the boundary-zone nodes 13

Distributed rateless coding The total number of hops per original data packet, which is an indicator of energy- expenditure of the scheme 14

Simulation results Setting – Distributed rateless coding is started and b · N rateless packets are created, encoded, and dispersed toward the boundary-zone – Rateless packets degrees are selected either according to RS or Bounded-Degree RS (BDRS) degree distribution with maximum degree 66[8]. – Random walks are designed using Metropolis- Hastings algorithm. 15 [8] A. Shokrollahi. Raptor Codes. IEEE Transation on Information Theory, 52(6):2551–2567, June 2006.

16 Figure 3: Average number of packets N avg needed for successful decoding, N = 500, r = 0.12, b = 3 The graph shows that a scheme using RS degree distribution is better then its corresponding BDRS version; however, this comes at the price of an increased energy-expenditure. An increase in both τ M and τ D decreases N avg, where the former improves the performance of distributed encoding and the latter makes data gathering more efficient. The average degrees of employed degree distributions are d RS 8.37 and d BDRS 4.87, meaning that a rateless packet on average makes approximately 3.5 · τ M hops more in the case of RS degree distribution.

17 Figure 4: Average no. of packets N avg needed for successful decoding, N = 500, r = 0.12 Encoding a larger number of rateless packets when τ M and τ D are small increases the chances of collecting packets that are only partially innovative. This result is advantageous as a smaller number of rateless packets in the network decreases both N avg and the overall energy-expenditure.

18 Figure 5: Average no. of packets N avg needed for successful decoding, N = 1000, r = 0.1 The increase in τ M /τ D improves the performance, and for short τ M /τ D the performance is better for smaller number of encoded packets 2

Conclusions We presented a solution for network perimeter data gathering based on distributed rateless codes. The proposed scheme is suitable for emergency monitoring applications via WSNs deployed in partially inaccessible locations. The use of rateless codes provides low complexity encoding/ decoding and ensures data persistence despite node failures. 19

References [6] D. Vukobratovic, C. Stefanovic, V. Crnojevic, F. Chiti, and R. Fantacci, Rateless Packet Approach for Data Gathering in Wireless Sensor Networks, IEEE Journal on Selected Areas in Communications, vol. 28, no. 7, Sep [8] A. Shokrollahi. Raptor Codes. IEEE Transation on Information Theory, 52(6):2551–2567, June [9] Y. Wang, J. Gao, and J. S. B. Mitchell. Boundary Recognition in Sensor Networks by Topological Methods. In Proc. of ACM MobiCom 2006, Los Angeles, CA, USA, September [10] S. Funke and C. Klein. Hole Detection or: How Much Geometry Hides in Connectivity?. In Proc. of ACM SCG06, Sedona, AZ, USA, Jun [11] S. P. Fekete, A. Kr¨oller, D. Pfisterer, S. Fischer, and C. Bushmann. Neighborhood-Based Topology Recognition in Sensor Networks. In Proc. Of ALGOSENSORS04, Turku, Finland, July [12] D. Vukobratovic, C. Stefanovic, and V. Stankovic, Fireworks: A random linear coding scheme for distributed storage in wireless sensor networks, in Proc. of IEEE ITW 2010, Dublin, Ireland, Sep [13] S. Acedanski, S. Deb, M. Medard, and R. Koetter, How good is random linear coding based distributed networked storage, in Proc. of NETCOD 2005 Workshop, Riva dal Garda, Italy, Apr