5. Application-oriented Adaptation 89
5.5. Performance Evaluation
An experimental study on the office monitoring scenario has been performed to evaluate the proposed LP. The goal of LP is to improve the QoS of the network with adequate network lifetime. Therefore, a proactive adaptation mechanism based on the MAPE framework has been adopted. To fulfill this goal, we focus on answering the following three research questions: (i) Does LP improve the QoS metrics compared to static heuristics and unplanned adaptation? (ii) Does the actual network lifetime meet the application requirement? and (iii) Are the QoS metrics confined within boundaries throughout the entire lifetime?
As aforementioned, a scenario of office monitoring is engineered for evaluation pur-poses. The inherent dynamics in such a scenario are to be exploited to show the effect of planning the QoS levels throughout the entire lifetime. The simulator runs on a virtual machine with a 2.5 GHz processor and 8 GB RAM using an Ubuntu OS. Figure 5.6 shows the layout of the proposed office monitoring scenario. A mobile node broadcast its current coordinates to the neighboring nodes while moving from the right side to the left. As long as a cluster child node receives a packet, it forwards the packet to a so-call cluster head. The cluster head then delivers the packet to the sink via multiple hops. In this case, the simulation provides an application that tracks a moving object in an office monitoring scenario.
For a comparative analysis, we contrast LP to two different strategies, namely lifetime maximization and unplanned adaptation. The former strategy represents a fixed
strat-1
2
4 3
5 8 7
6 9 12
10
11
10m
13
SinkUNode ClusterUHead ClusterUChild MobileUNode UnicastUPacket Multi-hopUPacket
Figure 5.6.: An office monitoring scenario implemented in the Cooja simulator.
egy in which the controllable parameters P are assigned to the minimum values. The latter strategy — inspired from [SVNSO+11] — exploits the environmental dynamics in a proactive manner in order to optimize system performance. Next, we discuss the implementation details of LP and of the unplanned adaptation strategy.
5.5.1. Unplanned Adaptation
Steine et al. [SVNSO+11] introduce an adaptation method by exploiting design-time knowledge of the application scenario dynamics. At design-time, operation modes are defined, as well as the controllable parameters of the network stack. In this case, the parameters are adapted in response to the expected events. Such an approach is only dealing with a limited set of events. Thus, we refine the possible events and their corresponding conditions. The adaptation framework, designed in [SVNSO+11], is not planned in terms of relevant QoS metrics. Hence, invoking the unplanned adaptation for the comparative study is able to clarify the advantages of our proposed approach.
5.5.2. Lifetime Planning
In this section, we detail LP in the simulated office monitoring scenario.
Algorithm 3 introduces the major details of applying LP in a sensor node. At design-time, lower and upper boundaries of QoS are estimated in the light of the expected task lifetime Ltask and the initial energy budget E0. Besides, the lower boundary is adjusted to the user requirements, if existing. Otherwise, it is assigned to the average QoS of the lifetime maximization. For the upper boundary, a set of mapping functions
— extrapolated from the analytical model and simulations — determines the boundary, as stated in line 3. Considering only the analytical model is not practical due to the run-time data loss in the upper layers such as transmission collisions and failures to acknowledge packet reception. Moreover, the probabilities of a busy medium (busy CCAs lead to back-offs in the time domain) have to be used during the performance evaluation. We simulate the network in Cooja for each sensor node, depicted in lines 1-4 in Algorithm 3.
During run-time, ECA rules are continuously evaluated based on the environmental changes. Specifically, four rules have been designed in the light of the criteria listed in
Algorithm 3 Lifetime planning algorithm
Require: task lifetime Ltask, energy budgetE0, user requirement {Rmin,Dmax,Pmax} /*Design-time estimation of the upper QoS boundary */
1: for0≤i <(M −1) do
2: for0≤j <((N/M)−1)do
3: P ←f(E0, Ltask) whereP = {rs, f, Ptx}
4: determine R(sij),D(sij),P(sij)
5: end for
6: end for
/*Run-time processing */
7: monitorQoSinstantenous= R,D,P ∀ sij ∈ S
8: if an ECA rule is firedthen
9: updatethe parametersP .mathematical model
10: end if
11: if (headChi== 0) then . Chi rejects the plan
12: go to line 8
13: else
14: execute the update
15: go to line 7
16: end if
Table 5.3. Two of them monitor the environmental events. The other rules confine the QoS metrics in their boundaries. Finally, the algorithm introduces a simple protocol between a child node and its cluster head for approving the system updates.
Besides, Table 5.3 summarizes the operational mode and all possible scenarios for lifetime maximization, unplanned adaptation, and LP, respectively. In fact, adapting general criteria — such as the traffic size and the speed of mobile nodes — mostly covers all possible events in office monitoring scenario. The settings are classified in the light of a mobile node’s state i.e., mobile or stationary. The former has been classified in accordance with the speed and the number of mobile nodes. Thus, four cases emerge by considering only two linguistic variables low and high, as expressed in the table. Each strategy has different values of the transmission powerPtxand the channel check raterc, an indirect indicator of the duty cycle. For unplanned adaptation, the values indicated in the table are selected to reduce the power consumption, as proposed in [SVNSO+11].
Alternatively, the values for LP are derived based on the required lifetime Ltask via the mapping functions. Below, we discuss the obtained results in the context of the aforementioned research questions.
5.5.2.1. Evaluating the QoS metrics
In this section, we examine the impact of applying LP, unplanned adaptation, and life-time maximization on the QoS metrics. Figure 5.7 and Figure 5.8 show a comparison between the three strategies in terms of the average PDR — representing a realistic measure of the reliability R — and the average delay D in several milliseconds. The horizontal axes gives the ID number of children according to Figure 5.6. In these
exper-Table 5.3.: Mode selection for office monitoring scenario.
Parameters Configurations
Mode of MSNs Mobile Stationary
Mode of SSNs Stationary Stationary
Scenario Settings Traffic Low Low High High – –
Speed Low High Low High Low High
Number of MSNs 1 1 4 4 1 4
Moving Speed (m/s) 0.5 1 0.5 1 – –
Lifetime Maximization Ptx (dBm) -7 -7 -7 -7 -7 -7
rc (Hz) 8 8 8 8 8 8
Unplanned Adaptation Ptx (dBm) -7 -7 -7 -3 -7 -7
rc (Hz) 8 16 16 64 8 16
Lifetime Planning Ptx (dBm) -7 -3 -3 0 -7 -3
rc (Hz) 8 32 64 64 32 32
iments, we focus on the communication link between cluster heads and their children.
Accordingly, QoS values of the sink and the cluster heads (node 1, 4, 8, and 12) have been eliminated from the figures.
As expected, LP achieves a higher reliability and a shorter latency than the other approaches, as can be seen in Figure 5.7 and Figure 5.8, respectively.
2 3 5 6 7 9 10 11
Node index 0.70
0.75 0.80 0.85 0.90 0.95 1.00
Average PDR (%)
Lifetime Planning Unplanned Adaptation Lifetime Maximization
Figure 5.7.: Impacts of three strategies on the average PDRs for the office monitoring scenario.
Particularly, LP achieves an approximately 9.6% higher reliability than unplanned adaptation and a 20% higher reliability than lifetime maximization. Similarly, LP gets about53% less delay than unplanned adaptation and78% less delay than lifetime maxi-mization. This excel is reasonable due to spending more energy in case of LP. However, we still need to double-check the impact of such improvements on the lifetime.
Figure 5.9 delineates the lifetime of cluster heads and children for all strategies. The
2 3 5 6 7 9 10 11 Node index
0 100 200 300 400 500
Average latency (ms)
Lifetime Planning Unplanned Adaptation Lifetime Maximization
Figure 5.8.: Impacts of three strategies on the average delay for the office monitoring scenario.
average actual lifetime in case of LP is about 40% less than the one of unplanned adap-tation, and 50% less than the one of lifetime maximization. Nevertheless, the obtained network lifetime (approximately100days) achieves the task lifetime used for estimating the QoS boundaries. Hence, we can conclude that LP (i) manages to improve the QoS metrics (i.e., reliability and latency), (ii) avoids any adaptation conflicts, and (iii) meets the expected task lifetime.
2 3 5 6 7 9 10 11
Node index 0
50 100 150 200 250
Average lifetime (days)
Lifetime PlanningUnplanned Adaptation Lifetime Maximization
Figure 5.9.: Impacts on the lifetime for the office monitoring scenario.
5.5.2.2. Evaluating the QoS boundaries
Finally, we need to indicate how the expected lifetime is met. In this section, the average reliability and the average delay are examined for node 6 during several runs over the various scenarios. As it can be seen in Figures 5.10(a) and 5.10(b), the QoS boundaries are colored in gray and marked with triangles. Obviously, both strategies have the same behavior, but they reside at different levels. For the PDR, the LP (in blue) values are confined between the two gray thresholds, as shown in Figure 5.10(a). Alternatively, unplanned adaptation (in red) is reduced without any restrictions to reduce the energy consumption. Figure 5.10(b) shows a similar behavior for the delay metrics.
1 2 3 4 5 6 Scenario index 0.75
0.80 0.85 0.90 0.95 1.00
Average PDR Lifetime Planning
Unplanned Adaptation
(a) PDR
2 4 6 8 10
Scenario index 0
50 100 150 200 250
Average latency (ms)
Lifetime Planning Unplanned Adaptation
(b) Latency
Figure 5.10.: Average end-to-end metrics in cases of LP and unplanned adaptation in terms of various scenarios.