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

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QoS-BASED COMMUNICATION PROTOCOLS IN WIRELESS SENSOR NETWORKS
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not considered in wired interconnected networks of processors. The resulting communication schedules determine channel access in sensor clusters, directly affecting the MAC layer in WSNs. 13.5.1 Collaborative Resource Allocation Algorithm [28] The Collaborative Resource Allocation (CoRAl) algorithm [28] aims to dynamically allocate resources such as bandwidth and CPU time for multiple periodic applications in WSNs. Subject to resource availability, CoRAl aims to adjust application sampling frequencies to meet the temporal constraints and maximize network utility. CoRAl is assumed to be executed in fully-connected single-hop WSNs, where all nodes are synchronized and use Earliest Deadline First (EDF) as the scheduling algorithm. Nodes also implement the implicit EDF algorithm as the underlying wireless network MAC protocol. End-to-end applications are considered in reference [28] that are composed of a chain of tasks already assigned to sensors and sequentially executed in a pipelined manner. CoRAl achieves its goals by iteratively executing the following steps until the schedule converges: First, the task execution frequencies on each sensor are locally optimized subject to application execution frequency upper bounds, whose initial values are set to be in nite. Then the execution frequency upper bound of each application is reevaluated based on the updated task frequencies and bandwidth allocation. In CoRAl, the wireless channel is modeled as a dummy node on which only communication can be executed, and the network bandwidth is allocated in the same manner as sensor CPU time allocation. The CoRAl algorithm is presented in Figure 13.11. In each node, an extended version of the SLSS algorithm [29] is implemented to
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CoRAl: Ti : f imax : L: mk: f ild : f ibn : Application i Maximum upper-bound frequency of application Ti Number of Applications Sensor node k Frequency of leader task of application Ti Frequency of bottleneck task of application Ti
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1. Initialize maximum upper-bound frequency of each application Ti : 2. f imax = + , i {1, ..., L } 3. WHILE schedule not converge 4. FOR each sensor m k 5. FOR each task of application Ti assigned on m k , i {1, ..., L } 6. Locally optimize the task subject to f imax using the extended SLSS 7. FOR each application Ti 8. Reevaluate f imax with updated f ild and f ibn
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Figure 13.11. The CoRAl algorithm.
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compute locally optimal frequencies subject to node utility constraints. Different from the original SLSS algorithm, the extended SLSS algorithm in reference [28] takes each task s application execution frequency upper-bound into consideration. After each iteration of local optimization, the upper-bound frequency of each application is calculated. Let the leader task ldi and bottleneck task bni of an application Ti be tasks whose frequency fild and fibn are highest and lowest among all tasks of Ti , respectively. The frequency upper bound of Ti is updated as fimax = fibn + (fild fibn ) , where is the factor that controls frequency convergence speed. The optimization procedure terminates when the weighted difference between leader and bottleneck frequencies converges. CoRAl addresses online resource allocation among multiple applications. According to the simulation results, CoRAl provides performances comparable to the optimal solutions obtained by the nonlinear optimization tool of Matlab at a much higher execution speed. However, in CoRAl, tasks of applications are assumed to be already assigned on sensors, and task mapping remains an open problem. Furthermore, energy consumption is not explicitly considered in reference [28], which is a fundamental problem in WSNs. 13.5.2 EcoMapS Algorithm [30] A task mapping and scheduling solution, EcoMapS, is presented in reference [30] for energy-constrained applications in single-hop WSNs. It is assumed that networks are composed by homogeneous sensors that can calculate and communicate simultaneously. EcoMapS aims to assign computation tasks and schedule communication events with minimum application execution lengths subject to energy consumption constraints. EcoMapS is composed of two phases: the Initialization Phase and the Quick Recovery Phase. The Initialization Phase algorithm aims to minimize schedule lengths subject to energy consumption constraints, while the Quick Recovery Phase algorithm handles runtime sensor failures. In the Initialization Phase, EcoMapS iteratively searches for the schedule with an optimal number of computing sensors involved in computation that results in the minimum schedule length under the energy consumption constraint. To exploit the broadcast nature of wireless communication, a hypergraph representation of the Directed Acyclic Graph (Hyper-DAG) is introduced. The Hyper-DAG representation of task dependency explicitly represents communication as well as computation events: The edges between a task and its immediate successors in a DAG is replaced with a net, which represents the communication task to send the result of a task to all of its immediate successors in the DAG. The Hyper-DAG extension of the DAG in Figure 13.12a is presented in Figure 13.12b, where ri s are the introduced nets. Similar to CoRAl [28], EcoMapS also models the single-hop wireless channel as a virtual node where only communication tasks can be executed. Based on the virtual node model and Hyper-DAG, a communication scheduling algorithm is developed and embedded into the schedule search algorithm, E-CNPT. E-CNPT is a low-complexity algorithm that rst enqueues tasks according to the critical path of a Hyper-DAG, then assigns the enqueued tasks to the node with minimum execution start time. In case
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