QoS-BASED COMMUNICATION PROTOCOLS IN WIRELESS SENSOR NETWORKS in .NET framework

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Figure 13.12. (a) DAG and (b) hyper-DAG examples.
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communication between sensors is necessary, the proposed communication scheduling algorithm is executed. The Quick Recovery Phase algorithm handles sensor failures by adaptively adjusting the previous schedule. If idle sensors exist, the tasks of the failing sensor are migrated to an idle sensor. Otherwise, they are merged to a sensor that has the most idle time. EcoMapS is a task mapping and scheduling solution for WSNs that provides application energy consumption guarantee with minimum schedule lengths. According to the simulation results, EcoMapS has superior performance over existing mechanisms in terms of minimizing schedule lengths. Also, the alternative schedules generated after sensor failures are shown to have satisfying performance with small recovery latency. However, EcoMapS has no guarantee of application deadline constraints. 13.5.3 Energy-Balanced Task Allocation Algorithm [31] An energy-balanced task allocation (EBTA) solution is presented in reference [31]. EbTA assumes single-hop clustered homogeneous WSNs with multiple wireless channels, where sensors are equipped with dynamic voltage scaling (DVS)-enabled processors. EBTA considers real-time applications composed by interdependent tasks. The design objective of EBTA is to map and schedule application tasks to sensors such that balanced energy consumption is minimized subject to deadline constraints. In reference [31], applications are represented with directed acyclic graphs (DAGs) and the scheduling problem is formulated as an integer linear programming (ILP) problem. The exclusive wireless channel access feature is incorporated as additional constraints in the ILP problem. Because the formulated ILP problem is computationally costly, a three-phase heuristic is proposed in reference [31] to provide a practical solution. In Phase 1, tasks are grouped into clusters to minimize overall application execution time assuming in nite number of sensors. Each task rst constitutes a cluster by itself. Then all communication tasks are examined in a nonincreasing order of their data volume. For each communication event e(i, j) between computation task Ti and Tj , the
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clusters containing Ti and Tj are merged if it leads to shorter application execution time. When evaluating application execution time, communication events are scheduled to the channel with smallest available time using the First-Come FirstServe (FCFS) policy. In Phase 2, the task clusters from Phase 1 are assigned to sensor nodes with the objective of minimizing the maximum energy expenditure among all sensors. The task clusters from Phase 1 are rst sorted in a nondecreasing order of energy consumption and are stored in a queue . The clusters in are then assigned to the sensor with the minimum normalized energy consumption (task execution energy consumption normalized by sensor residue energy, norm energy for short). Each time after a task cluster is assigned to a sensor, the norm-energy of the sensor is updated. This procedure repeats until all task clusters are assigned. Finally, a DVS heuristic is presented for Phase 3 to decrease energy consumption by iteratively adjusting the CPU voltage level of each task. In each iteration, a critical node that has the highest norm energy is selected. Among the tasks assigned on the critical node, a task is selected such that, by decreasing its CPU supply voltage to the next level, is decreased the most. Each time when a task is adjusted, the application schedule is iteratively adjusted accordingly to meet inter-task dependency constraints. EbTA is one of the rst proposals that addresses task allocation in WSNs, where both communication and computation tasks are considered. It is shown through simulations that the three-phase heuristic achieves longer lifetime compared with the baseline without DVS. The performance of the three-phase heuristic is also found to be comparable to that of the ILP-based approach via simulations. 13.5.4 RT-MapS Algorithm [32] The RT-MapS algorithm [32] is proposed for single-hop clustered WSNs, which are composed of homogeneous DVS sensors with nite number of voltage levels. The design objective of RT-MapS is to provide application deadline guarantees with the minimum energy consumption for WSNs applications. The RT-MapS algorithm contains two phases, namely, Task Mapping and Scheduling (TMS) Phase and DVS Phase. The owchart of RT-MapS is shown in Figure 13.13. In the TMS phase, computation and communication events are simultaneously assigned and scheduled with the objective of minimizing energy consumption subject to deadline constraints. To guarantee deadline constraints, sensors are scheduled with highest CPU speed in the TMS phase. Schedules generated in the TMS phase are then further optimized in the DVS phase by reducing CPU speed to decrease energy consumption. Similar to EcoMapS [30], RT-MapS employs Hyper-DAGs to represent applications and utilizes the virtual node model of wireless channels. The RT-MapS solution is outlined with the pseudocode in Figure 13.14. Here, the communication scheduling algorithm sequentially schedules communication tasks on the virtual channel node to avoid packet collision. Broadcasting is also realized in the communication scheduling algorithm to conserve energy. The communication scheduling algorithm is embedded in the execution of the task mapping and scheduling algorithms, H-CNPT and H-MinMin. H-CNPT is different from
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