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Energy efficient Topologies for Wireless Sensor Networks

Energy efficient Topologies for Wireless Sensor Networks

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  • 1. Chapter 1Energy-Efficient Topologiesand Routing for WirelessSensor Networks1.1 IntroductionThe right kind of topology is must to get optimum performance from wirelesssensor networks (WSNs). WSNs with patterned topologies can efficientlysave energy and achieve long networking lifetime. We have a look at dif-ferent patterned topologies for constructing WSN’s which can provide therequired coverage area and the connectivity of all sensor nodes and also arehighlighted the performance measures of each kind of topology. Then wediscuss several routing protocols for WSNs in patterned topologies, whichare based on different parameters when selecting next-hop neighbor.1.2 Modelling Connected-Coverage WSN’sIn this model some important parameters that effect the performance ofWSN’s are featured : Connected-Coverage WSN’s is defined as network thatcan ensure the coverage and connectivity within specified and described areabetween the sensor nodes.We assume the region to be 2-dimensional. Assume the area of the region tobe ’A’ and sensor nodes ’N’ and one base station placed in the region. Eachnode participating can sense the event and collect data within a circularregion with radius rs , centre point being the node itself. No two nodes cancommunicate with each other if the Euclidean distance (distance calculatedby Pythagorean formula) between them is maximum rt , that is, nodes s1and s2 can communicate with each other if their Euclidean distance Ed(s1 ,s2 ) ≤ r t .The sensing ability of each sensor node diminishes as distance increases. 1
  • 2. The sensing ability at point ’y’ of sensor node si is assumed to be inverselyproportional to Ed(si , y)k where ’k’ is a sensor technology-dependent param-eter. This characteristic of sensor nodes introduces an important parameter,we call it sensing strength factor dmm , stating how well region A is coveredand sensed. If we define mini Ed(si , y) as the distance of point y to itsclosest sensor node, y ∈ A, then all points in A have a distance at mostmaxy A mini Ed(si , y). We use dmm to denote this distance : dmm = maxy Amini Ed(si , y)A .Thus dmm is the maximum distance from any point to its closest sensornode. The power consumption is another important parameter to measurehow much energy different topology patterns can save for WSNs. Eachsensor node includes : • Sensing unit. • Processing unit. • Transceiver unit. • Power unit.Based on these ’power consumption’ can be divided into three domains : • Sensing. • Communication. • Data Processing.Out of these three our area of concern is with the maximum energy spent bya sensor node in data communication which involves both power consumedin data transmission, denoted by Pt , and in data reception, denoted by Pr .That is, the power consumed by a sensor node is Ps = P t + Pr .1.2.1 Patterned Topologies for Connected-Coverage WSNs :In this section we discuss various topology patterns with a fixeed criteriaof having 25 sensor nodes and then compare performance of WSN’s underthose patterns :1.2.2 WSN’s with Hexagon-Based Topology :In hexagon-based WSNs, each sensor node has three neighbour nodes locateduniquely around the node and connection to the neighbour nodes are madein such a way that it obtains the minimum unit in the shape of hexagon andthat is why it is called as Hexagon-Based WSN. The distance between twoneighbouring nodes is set to be rt so that direct communication is availablebetween them and each neighbor provides maximal additional sensing area. 2
  • 3. A figure is given below and in that node 06 is assumed to be aggregationnode which plays the role of aggregating the sensed information in the WSNand reporting to the base station. Figure 1.1: A WSN with hexagon-based topology1.2.3 WSN’s with Square-Based Topology :In square based node each sensor node has four neighbour nodes locatedaround it and connection is made in such a way that it obtains the minimumunit in shape of a square.The distances of the node to its neighbor nodesare set to rt and node 04 is assumed to be the aggregation node.1.2.4 WSNs with Triangle-Based Topology :In triangle-based WSNs each sensor node has six neighbour nodes locatedaround it and connection is made in such a way that it obtains the minimumunit in shape of triangle.The distances of the node to its neighbor nodes areset to rt and node 04 is assumed to be the aggregation node. In this topologywe find that every point within the area is covered by at least two sensornodes and the reliability provided by such kind of node placement is called2-reliability. A 2-reliability WSN can maintain its connected-coverage forany single sensor node failure. When every point is covered by at least ksensor nodes, the sensor network is called k-reliability. The WSNs withother patterned topologies are 1-reliability.1.2.5 WSNs with Strip-Based Topology :We know that to maintain the connectivity, the distance between two nodesshould not be more than rt . So in order to maximise the coverage area withsame no of nodes, strip-based topology may be used.The strip-based WSNbelow given figure clearly shows that sensor nodes 40, 41 and 42 connect 4 3
  • 4. Figure 1.2: A WSN with square-based topologyFigure 1.3: A WSN with triangle-based topology 4
  • 5. self-connected strips 00 - 05, 10 - 14, 20 - 25 and 30 - 34. By this way of nodeplacement, these sensor nodes construct a connected WSN with strip-basedtopology. The total number of sensor nodes is 25 as before and here weassume node 05 to be the aggregation node. Figure 1.4: A WSN with strip-based topology1.3 Performance Comparison :Now we compare the above four types of patterned topologies on basis of : • Coverage area. • Sensing strength factor. • Reliability. • Energy consumption.We assume rt = rs = r in all WSN’s.For coverage area comparison we do not consider the marginal places cov-ered by edge sensor nodes because it occupies negligible portion of wholecoverage area. 5
  • 6. For energy consumption comparison, we fix the destination to be the ag-gregation node as designated above. The source is fixed to be a node withdistance ’4r’ from the destination. In this case, the less the energy is con-sumed, the better the node placement pattern is.The following table gives the results of these performances comparison.Figure 1.5: Performance comparison among WSNs with different patternedtopologies Looking at the table we can see that strip-based topology provides maxi-mal connected-coverage with the same number of sensor nodes and consumesleast energy by the routing protocol of flooding. WSNs in triangle-basedtopology provide the best reliability and the best sensing strength whiletrading off total coverage area and energy consumption. These conclusionshold when comparison is performed in general cases of large-scale WSNs.1.4 Routing Protocols in Patterned WSNs :Several routing protocols which only require local information and this waythey are different from Directional Source-Aware Protocol (DSAP) whereeach node must have the knowledge of global information of topology arehighlighted.We define a routing selection function f(h, s) for a sensor node to chooseneighbor nodes when routing the message back to the aggregation node.The function is determined by the hop count value ’h’ of neighbor nodesand stream unit ’s’ which has been sent by neighbor nodes. We denote thebattery life of sensor node i by bi .There are three approaches to route back the message for different aims. Allof them are based on the routing selection function f(h, s) = α h + β s. • Maximize the total energy saving for WSNs, i.e., B = i bi : This can be obtained by minimizing first the hop count value ’h’ when choosing 6
  • 7. next-hop neighbor and then minimize ’s’. In this case, α = 1, andβ =σ, where σ is a small number which approximates to 0. • Maximize the minimal energy maintained by all sensor nodes, i.e., mini bi : This can be obtained by minimizing first the stream units ’s’ of next-hop neighbour and then ’h’. In this case, α = σ, andβ = 1. • Maximize both the total energy and minimal energy of all sensor nodes. This can be obtained by minimizing both ’h’ and ’s’. In this case, α =β = 1.We name the protocols as routing selection function-based protocols. Itworks as follows : • Distance identification : The aggregation node floods the discovery message in the WSN with a determined TTL value. Each sensor node records its distance from the aggregation node by hop count. If a sensor node receives several broadcast messages, it records the least value of hop count. • Data collection: When a sensor node senses any abnormal event and needs to report the event, it chooses a neighbor with minimized f(h, s) to route back the message. From Figure given below, we can see that Approach 1 provides least network lifetime. Approach 2 gets a longer lifetime than approach 1 and, however, trades off much more energy consumption by choosing a longer path to the aggregation node in the WSN. Approach 3 can provide the longest lifetime, which is almost as twice as that provided by Approach 1 because it tries to find a shorter path and a next-hop neighbor with more energy in every step. 7
  • 8. Figure 1.6: Energy consumption and lifetime comparison among three ap-proaches 8